Next Article in Journal
Effect of Whole-Body Vibration Exercise on Pain, Disability, Balance, Proprioception, Functional Performance and Quality of Life in People with Non-Specific Chronic Low Back Pain: A Systematic Review and Meta-Analysis
Next Article in Special Issue
Validation of an Inhaled Therapy Beliefs Questionnaire in Patients with Chronic Obstructive Pulmonary Disease
Previous Article in Journal
Working Memory and Language Relate to Report of Socio-Emotional Functioning in Children with Hearing Loss
Previous Article in Special Issue
Morbidity among Adolescent Hypnotic Drug Users in Norway: An Observational Population-Based Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 13
Issue 6
10.3390/jcm13061638
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Drug Related Problems among Older Inpatients at a Tertiary Care Setting

by
Porrawee Pramotesiri
1,
Krongtong Putthipokin
2 and
Sirasa Ruangritchankul
1,*
1
Division of Geriatric Medicine, Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand
2
Clinical Pharmacy Unit, Pharmacy Division, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2024, 13(6), 1638; https://doi.org/10.3390/jcm13061638
Submission received: 12 February 2024 / Revised: 2 March 2024 / Accepted: 4 March 2024 / Published: 13 March 2024
(This article belongs to the Special Issue Chronicity, Multimorbidity, and Medication Appropriateness)
Browse Figure
Versions Notes

Abstract

:
Background: Older persons are more likely to have multiple chronic diseases, leading to the simultaneous use of many medications. This situation results in increased drug-related problems (DRPs), which are the causes of adverse health outcomes. Therefore, we aimed to evaluate the prevalence of and associated risk factors for exposure to >1 criterion of DRPs among older adults admitted to a tertiary care hospital. Methods: We conducted a cross-sectional study involving 357 participants aged ≥60 years admitted to Ramathibodi Hospital from 1 February 2022 to 30 November 2022. The participants were evaluated for baseline characteristics, medications and DRPs and were classified into two groups, according to their exposure to DRPs: patients with exposure to ≤1 criteria and patients with exposure to >1 criterion of DRPs. Multivariate logistic regression analysis was performed to determine the independent risk factors for exposure to >1 criterion of DRPs. Results: Overall, 205 (57.4%) patients experienced >1 criterion of DRPs. Approximately 67.8%, 71.7% and 7.6% of the participants were exposed to at least one potentially inappropriate medication (PIM), drug–drug interaction (DDI) and adverse drug events (ADE), respectively. The most frequently prescribed PIMs were proton pump inhibitors (PPIs) (17.3%). Antineoplastics (48.1%) were the most frequently drug class related to ADEs. Overall, 37% of the ADEs in the current study were considered preventable ADEs. After adjustment for potential confounders, polypharmacy and the use of proton pump inhibitors, hypoglycemics, diuretics, psycholeptics, psychoanaleptics and cardiac therapy medications were correlated with a higher risk of exposure to > 1 criterion of PIMs, DDIs or ADEs. Conclusions: Therefore, comprehensive medication reviews and careful medication prescriptions are recommended in the geriatric population.

1. Introduction

Worldwide, aging populations have risen markedly during the past decade [1]. Similar to several other countries, Thailand became a “complete aged society” in 2022, with at least 20% of the population being ≥60 years of age [2]. The Thai geriatric population was almost 13.3 million in 2021 and is predicted to increase to 16.7 million by 2040 [2]. On account of a longer life expectancy, many older people tend to require more medical care and hospitalization. According to a preceding study, 65% of hospitalized patients were older persons, and they occupied 70% of the bed days [3]. In addition, older persons are more prone to multiple chronic conditions and diseases, including cardiovascular disease (CVD), diabetes mellitus (DM), hyperlipidemia (HLP), hypertension (HT), chronic obstructive pulmonary disease (COPD), anemia, chronic kidney disease (CKD) and cancer [4,5]. These adverse health problems result in an increased demand for simultaneous medication use. Multiple medication uses for acute medical problems pose a greater risk of drug-related problems (DRPs). A DRP is defined as the undesirable event involving medication therapy that interferes with desired medical outcomes [6]. Many classification systems have been developed to categorize DRPs, including the Helper–Strand classification [7], Cipolle’s classification [8] and the Pharmaceutical Care Network Europe (PCNE) [8]. Three common types of DRPs mentioned in clinical practice represent potentially inappropriate medications (PIMs), drug–drug interactions (DDIs), and adverse drug events (ADEs) [6,9,10,11,12,13]. Additionally, older populations are susceptible to DRPs, owing to age-related changes in pharmacokinetics and pharmacodynamics, cognitive decline and functional impairment [10,14,15]. DRPs increase the chance of unfavorable outcomes, such as poor quality of life, hospitalization, health care expenditure, morbidity and mortality [16,17,18,19,20]. As reported in previous studies, approximately 30% to 70% of hospitalized older patients developed DRPs [12,21,22,23]. Many studies have shown more than one DRP per person [24,25]. Furthermore, recent studies reported the prevalence of PIMs ranging from 28% to 73% [11,26,27,28], DDIs from 8% to 58% [12,29,30] and ADEs from 15% to 58% in the older population [13,31,32]. However, appropriate medicine management could prevent 15% to 70% of DRPs [33,34]. Therefore, healthcare providers should pay attention to comprehensive medication reviews and medication reconciliation to decrease the occurrence of DRPs [35,36,37].
Although many recent studies have explored DRPs in hospitalized patients, no study to date has reported the prevalence of and factors related to DRPs among hospitalized older patients in Thailand. The first aim of this study was to investigate the prevalence of and factors associated with exposure to >1 criterion of DRPs among hospitalized older patients in Thailand. The second aim was to explore the characteristics and prevalence of PIMs, DDIs and ADEs and describe the medications related to these DRPs.

2. Materials and Methods

2.1. Study Design, Setting and Participants

This cross-sectional study was conducted from 1 February 2022 to 30 November 2022. The current study involved 357 hospitalized older adults aged ≥60 years who were admitted to four internal medicine wards at Ramathibodi Hospital, Mahidol University, in the time period mentioned above. The sample size was calculated according to the prevalence of DRPs among older Thai adults (63.3%) in the previous study [38]. The participants were categorized into two groups: exposure to >1 criterion of DRPs or DRPs > 1 group and exposure to ≤1 criterion of DRP or DRP ≤ 1 group. The number of required participants was calculated from the following formula: N = Zα2pq/d2, where p represents the estimated prevalence with DRPs of 0.633, Zα is the level of significance at the 95% confidence interval of 1.96, q is 1-p of 0.367 and d represents the precision of the estimate of 0.05. The calculated sample size for the study was 357. Patients with palliative care needs, hospital stays of <48 h and no response to the questionnaire or communication were excluded from the current study.

2.2. Data Collection and Measurement Tools

Data collection took place in the period between 1 February 2022 and 30 November 2022. Within 24 h of admission, medical staff reviewed and gathered the comprehensive data from the patients’ electronic medical records (EMRs), conducted face-to-face interviews and performed clinical assessments. A total of 357 participants completed the clinical assessment, and the questionnaire was administered through face-to-face interviews. All comprehensive data were gathered in their entirety during the study period. The data collected from the EMRs consisted of information on baseline characteristics, such as age, sex, race; body weight and height; health conditions: comorbidities and Charlson comorbidity index (CCI) scores [39]; psychological problems; current prescribed medications and documented DRPs; healthcare services: history of emergency room (ER) and outpatient department (OPD) visits, as well as hospital admission within the past 12 months; and laboratory results associated with exposure to >1 criterion of DRPs: white blood cell count, hemoglobin level, platelet count, partial thromboplastin time, prothrombin time, thrombin time, blood sugar level, sodium, potassium, calcium, magnesium, phosphorus, uric acid, blood urea nitrogen, creatinine, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, serum albumin level, gamma-glutamyl transferase, total bilirubin, direct bilirubin, thyroid-stimulating hormone, free triiodothyronine and free thyroxine.

2.2.1. Face-to-Face Interview-Administered Questionnaire

Within 24 h of admission, a questionnaire administered through a face-to-face interview was used to gather information on socio-economic variables (educational and marital status, income, medical insurance, career, history of caregivers and drug administrators, living situation and social activity), lifestyle behaviors (herbal use, and smoking and drinking status), cognitive function, depressive moods, activity of daily living (ADL) and clinical symptoms of DRPs. This questionnaire also evaluated geriatric syndromes [40], including falls, fecal and urinary incontinence, pressure ulcer, visual and auditory impairment, pain, weight loss, and immobility. Medical personnel assessed cognitive function using the Montreal Cognitive Assessment (MoCA). The total MoCA score ranges from 0 to 30 [41,42], with lower scores representing more severe decline in cognitive function, and a total score of <25 indicating cognitive impairment. In terms of mental health conditions, depression was evaluated using the Thai Geriatric Depression Scale-15 (TGDS-15) on a scale of 0 to 15, with a score >5 reflecting depression [43,44]. Functional ability was assessed using the Barthel ADL index [45] and the Lawton instrumental ADL (IADL) index [46,47]. The Barthel ADL index sums up the performance ability on the items of bathing, feeding, dressing, self-grooming, mobility, toilet use and incontinence. The index score has a range from 0 to 20, with lower scores reflecting a greater level of dependence. The Lawton IADL index assesses the performance of eight activities: shopping, handling finances, handling medications, food preparation, housework, laundry, telephone use and transportation. The total score ranges from a minimum of “0” to a maximum score of “8”, with lower scores indicating a higher level of functional incapacity. The data on socio-economics, lifestyle behaviors and geriatric syndrome were assessed by using a face-to-face interviewer-administered questionnaire. This questionnaire consists of binary questions, which are closed questions with only “Yes” or “No” answers; for example, “Have you had urinary incontinence in the geriatric condition section?” Participants were informed to answer “Yes” if he or she had urinary incontinence and “No” if he or she did not experience urinary incontinence.
To assess the medication profiles, clinical pharmacists and geriatricians recorded the prescribed medications in the database. All medications were reported in terms of name, route of administration, frequency and dose. The medications were classified using the codes established by the Anatomical Therapeutic Chemical (ATC) classification system, which is the international standard system of medication usage and is recommended by the World Health Organization. Furthermore, the prevalence and characteristics of DRPs, namely, PIMs, DDIs and ADEs, were reported by clinical pharmacists and geriatricians.

2.2.2. Clinical Measurements

Medical practitioners assessed the participants’ body mass index (BMI) and clinical frailty within 24 h of admission as well. The BMI was defined as a person’s body weight in kilograms divided by the square of height in meters [48]. An experienced clinical physician evaluated the patients for frailty using the Clinical Frailty Scale (CFS), a clinical assessment tool for frailty in older populations [49]. The CFS score ranges from 1 (very fit) to 9 (terminally ill) [50].

2.2.3. Definitions

  • Polypharmacy (PP)
PP refers to the simultaneous use of five or more medications [51].
  • Drug-related problem (DRP)
By definition, a DRP is any unpleasant event involving pharmacotherapy that interferes with achieving the desired goals of medical care [6]. The DRP is classified differently based on a specific focus. The classification systems established by Hepler and Strand [7], Cipolle and Morley [8] and the Pharmaceutical Care Network Europe (PCNE) [6] are widely used in research and clinical practice. In the current study, DRPs referred to only PIMs, DDIs and ADEs [52,53,54,55]. The outcome of interest was the exposure to DRPs, captured with the standard definition of this study and classified as a binary outcome: exposure to >1 criterion of DRPs and exposure to ≤1 criterion of DRPs.
  • Potentially inappropriate medication (PIM)
PIM is defined as a medication that should be avoided because of an unfavorable risk ratio greater than the clinical benefit in the geriatric population [56,57]. In this study, PIMs were identified using the 2019 American Geriatrics Society (AGS) Beers Criteria® (AGS Beers Criteria®), USA [58]. Furthermore, PIMs were evaluated and classified into five main categories: PIMs independent of individual’s medical condition, PIMs due to drug–disease and drug–syndrome interactions, drugs that must be used with caution in older adults, PIMs due to DDIs and drugs that must be adjusted in older adults with changes in kidney function [58].
  • Drug–drug interaction (DDI)
The definition of DDI is the alteration of the activity of one drug by another drug, regardless of whether adverse events occur or not [59,60]. To determine DDIs, all drugs were evaluated using the Micromedex Drug Interaction Database [61]. Potential drug interactions were only taken into account for single pairwise drug combinations. All DDIs were reported with regard to severity and interaction effects. The severity of DDIs was categorized into four groups: minor, moderate, major and contraindicated DDIs [61].
  • Adverse drug event (ADE)
An ADE is any detrimental experience resulting from medication exposure [62,63]. In this study, ADEs included adverse drug reactions, medication errors, drug abuse and drug overdose. The Naranjo algorithm [64] was used to determine the likelihood of adverse drug reactions occurring due to medication use. In addition, all participants were evaluated for the severity of ADEs by the Common Terminology Criteria for Adverse Events (CTCAE) [65] and were evaluated for ADE preventability by the Schumock and Thornton criteria [66].

2.3. Statistical Analysis

All statistical analyses in the current study were performed using the SPSS software package for Windows, version 25.0 (IBM Corp., Armonk, NY, USA). To describe baseline clinical characteristics and biochemical and medication profiles, continuous data are presented as mean ± standard deviation (SD) and median ± interquartile range (IQR), while categorical data are presented as percentage. In the comparison of all variables between patients with exposure to ≤1 criteria and >1 criterion of DRPs, Pearson’s chi-squared test or Fisher’s exact test was performed for categorical variables, and the unpaired Student’s t-test or Mann–Whitney U test was performed for continuous variables. Univariate logistic regression analysis was first used to identify potentially significant factors for exposure to >1 criterion of DRPs. A multivariate logistic model was then performed to adjust for significant variables, age, sex, Barthel ADL, Lawton IADL, CFS score, CCI score and cognitive function to explore independent risk factors for exposure to >1 criterion of DRPs. The results were reported as odds ratio (ORs) and 95% confidence intervals (CIs). A p-value of <0.05 was determined as statistical significance.

2.4. Ethical Considerations

The research protocol was assessed and approved by the Committee on Human Rights Related to Research Involving Human Subjects, Faculty of Medicine Ramathibodi Hospital, Mahidol University (Protocol Number: COA. MURA2021/1068). All methods were carried out in accordance with the relevant guidelines and regulations in the Declaration of Helsinki. All participants provided written informed consent based on preference of literacy level before their participation. The copy of informed consent form provided to all participants was approved by the ethics committee. To protect confidentiality and privacy, patient data were coded and replaced with a participant identification number and date of birth. Furthermore, these personal confidential data were electronically stored in password-protected folders and used strictly by the approved research team for the purpose of this research.

3. Results

3.1. Baseline and Clinical Characteristics, and Biochemical Parameters

In total, 357 older inpatients were enrolled during the study period. Of the 357 participants, 205 (57.4%) and 152 (42.6%) experienced >1 criterion and ≤1 criteria of DRPs, respectively. The ages of the participants ranged from 60 to 94 years, and the mean age of the total population was 71.9 (SD 2.7) years. Most of the participants were female (n = 194; 54.3%) and had received ≤12 years of education (n = 237; 66.4%). Less than 10% of the patients were single. The results of the descriptive and comparative analysis of baseline characteristics between the two groups are shown in Table 1. The number of hospitalized patients aged >70 years was significantly higher among patients with >1 criterion compared to those with ≤1 criteria of DRPs (p = 0.009). The BMI was not different between the two groups [23.6 (4.9) kg/m2 in the DRPs >1 group vs. 23.4 (3.9) kg/m2 in the DRP ≤1 group]. With regard to lifestyle patterns, 37.8%, 35%, and 27.5% of the total participants were alcohol drinkers, smokers and herbal medicine users, respectively. However, there was no significant difference in the prevalence of these behaviors between the two groups (p >0.05). Before admission, the number of participants living at a residential age care facility (RACF) was markedly higher among patients exposed to >1 criterion of DRP as compared to those exposed to ≤1 criteria of DRP (p = 0.001). With respect to discharge destination, the number of patients discharged to a RACF was significantly higher in the DRPs >1 group compared with those in the DRP ≤1 group (p = 0.002). In addition, a significant higher proportion of participants in the DRP ≤1 group were able to self-administer their medications compared to those in the DRPs >1 group (78.9% vs. 67.3%, respectively; p = 0.015). In terms of healthcare services, the number of hospitalized older patients with a history of ≥10 OPD visits or ≥5 doctor visits within 1 year was remarkably higher among patients with >1 criterion compared to those with ≤1 criteria of DRPs (p < 0.05). There was no substantial difference in the median length of hospital stay between the two groups (p = 0.846).
In terms of comorbidities, patients with >1 criterion of DRPs had substantially more comorbidities and higher CCI scores than those with ≤1 criteria of DRP (p < 0.001), as shown in Table 2. In both groups, the three most common comorbidities were hypertension (85.4%), dyslipidemia (72.3%) and anemia (49.3%). Participants exposed to >1 criterion of DRPs were more likely to have HLP, HT, CKD, obstructive sleep apnea (OSA), anemia, arrhythmia, coronary artery disease (CAD) and congestive heart failure (CHF) than those with ≤1 criteria of DRP (p < 0.05). Additionally, CHF (9.5%), non-ST elevation, myocardial infarction (9.2%) and acute cholangitis (5.0%) were the three leading causes of hospital admission.
Regarding geriatric syndromes and functional abilities, more than half of the total population had cognitive impairment (79.3%), as indicated by a MoCA score < 25, impaired IADLs (68.1%), sleep disorder (57.7%) and significant weight loss (53.8%) (Table 3). The participants with >1 DRPs tended to have greater frailty and dependence in IADL compared to those with ≤1 DRP (p < 0.001). Furthermore, the number of participants with a history of falls within the last 3 months or those ambulating with assistance were substantially higher in the DRPs >1 group than those in the other group (p < 0.05). Nevertheless, the number of participants with cognitive impairment (MoCA score < 25), depression (TGDS score >5) or impaired Barthel ADLs (Barthel ADLs score < 12) was not significantly different between the two groups.
The mean prothrombin time and median blood urea nitrogen and creatinine levels were significantly higher in patients exposed to >1 criterion compared to those exposed to ≤1 criteria of DRPs (p < 0.05), as presented in Table 4. By contrast, the median alanine aminotransferase concentration, mean hemoglobin and free triiodothyronine concentration were markedly lower in patients with >1 criterion of DRPs compared to those with ≤1 criteria of DRPs. However, there was no apparent difference in the serum albumin, sodium, potassium, calcium, magnesium or phosphate levels between the two groups (p > 0.05).

3.2. Medication Profiles

All prescribed medications classified by ATC codes are revealed in Table 5 and Table 6. The total number of prescribed medications in the current study was 3136, ranging from 1 to 23 per person. The median number of prescribed medications was 11 (IQR 8–13 medications) in patients who experienced >1 criterion of DRPs and 5 (IQR 3–8 medications) in those who experienced ≤1 criteria of DRPs. The number of participants exposed to PP was significantly higher among those with >1 criterion compared to those with ≤1 criteria of DRPs (97.1% vs. 59.9%, respectively; p < 0.001). In both groups, the three most commonly prescribed medications were lipid-modifying agents (62.2%), followed by vitamins (54.3%) and antithrombotic agents (46.2%). Among patients with >1 criterion of DRPs, the prescriptions of drugs for acid-related disorders, drugs used in diabetes, vitamins, mineral supplements, antithrombotic agents, cardiac therapy, antihypertensives, diuretics, beta-blocking agents, lipid-modifying agents, blood substitutes, calcium channel blockers, systemic hormonal preparations, immunostimulants, immunosuppressants, drugs for treatment of bone diseases, antiepileptics, psycholeptics, psychoanaleptics, antihistamines for systemic use and cough and cold preparations were substantially greater compared to those with ≤1 criteria of DRPs (p < 0.05).

3.3. DRPs

The prevalence of DRPs is presented in Table 5 and Table 7. Of all 357 participants, 303 (84.9%) were exposed to at least 1 DRP of PIMs, DDIs and/or ADEs. Additionally, 98 (27.5%) patients were exposed to 1 DRP, 188 (52.7%) to 2 DRPs and 17 (4.8%) to a combination of PIMs, DDIs and ADEs. Approximately 67.8%, 71.7% and 7.6% of the total participants were exposed to at least 1 PIMs, DDIs and ADEs, respectively.
Of the 3136 prescribed medications, 612 PIMs were determined according to the 2019 AGS Beers Criteria® in 357 participants, as shown in Table S1. The most frequently prescribed PIMs were proton pump inhibitors (PPIs) (17.3%) and benzodiazepines (13.8%).
The severity and interactive effects of DDIs are reported in Figure 1 and Table S2. A total of 1210 medication combinations contributed to DDIs, as shown in Table 7. Approximately 50% of the total population was exposed to moderate DDIs. The medication combination often implicated in DDIs was amlodipine with simvastatin, the resulting interaction of which increases the risk of myopathy and rhabdomyolysis. However, these DDIs had no detrimental clinical outcomes.
The ADEs and their preventability are shown in Table 7 and Tables S3–S6. Of the 27 reported ADEs, 15 (55.6%) and 12 (44.4%) were categorized as severe and life-threatening, respectively. More than 50% of the patients developed hematological problems (n = 19; 70.4%), including febrile neutropenia (n = 7; 25.9%). Antineoplastics (48.1%) and antithrombotic agents (22.2%) were the drugs most frequently related to ADEs. Overall, 37% of the ADEs in the current study were considered preventable ADEs. Anticoagulants, including warfarin, enoxaparin and dabigatran, accounted for 60% of the preventable ADEs, contributing to undesirable bleeding.

3.4. Factors Associated with Exposure to >1 Criteria of DRPs

According to the multivariate logistic regression analysis, significant variables were adjusted by age, sex, Barthel ADL, Lawton IADL, CFS score, CCI score and cognitive function (Table 8). After this adjustment, the factors associated with exposure to >1 criterion of DRPs were PP (OR 8.25, 95% CI 3.14–21.70) and increased use of medications for acid-related disorders (OR 3.82, 95% CI 2.08–7.00), drugs used in diabetes (OR 2.33, 95% CI 1.19–4.59), cardiac therapy (OR 3.19, 95% CI 1.29–7.89), diuretics (OR 8.20, 95% CI 3.11–21.65), psycholeptics (OR 4.59, 95% CI 1.80–11.69) and psychoanaleptics (OR 5.97, 95% CI 1.21–29.34). By contrast, age, sex, ADL, clinical frailty, comorbidities, a history of falls within the past 12 months and cognitive function were not associated with exposure to >1 criterion of DRPs.

4. Discussion

The present study revealed factors correlated with exposure to >1 criterion of DRPs and the characteristics of PIMs, DDIs and ADEs, as well as the medication related to these DRPs amongst hospitalized older patients aged ≥60 years during the study period, from 1 February 2022 to 30 November 2022. The prevalence of DRPs among older adults admitted to the medical department of our tertiary care hospital was 84.9%, which corresponds to the finding reported by Ramanath and Nedumballi [67]. Regarding the demographic characteristics, advanced age is a risk factor for DRPs due to its associated alterations in physiology, pharmacokinetics and pharmacodynamics, such as the deterioration of hepatic and kidney function. Furthermore, it has been found that older adults are more likely to be sensitive to the activities of certain drugs, such as anticholinergics and opioids, leading to DRPs [14,68]. Sluggett et al. [69] reported that older adults residing in RACF are liable to develop DRPs because of increased frailty and coexisting chronic diseases, resulting in the need for many drug therapy regimens and a risk of drug-related harm. The increase in OPD visits may also contribute to the rise in prescribed medications and DRPs [70,71].
In the current study, patients with high CCI scores were more prone to DRPs, which is consistent with the results of previous studies [9,72,73]. The most common chronic diseases were HT and HLP [74], whereas CHF was the most common cause of hospitalization in the geriatric population [9]. Similar to the results of preceding studies [75,76,77,78,79,80,81,82,83], hospitalized older persons exposed to many DRPs tended to have HLP, HT, CHF, CKD, OSA, CAD, arrhythmia and anemia. HT may contribute to multiple chronic diseases, including stroke, cognitive impairment, and CVD, which are treated with many medication combinations. This situation may potentially account for DRPs [84,85,86]. Additionally, many patients with CKD are vulnerable to DRPs due to their complex medication regimens and deterioration of drug excretion. Medication dosing mistakes often occur in patients with CKD. Therefore, the dose and dose interval should be adjusted to reduce inappropriate doses and possible toxicity. Pathophysiological changes related DRPs in patients with CHF include venous congestion, arterial hypoperfusion and neurohormonal activation, each of which may affect pharmacokinetics [87]. Hepatic congestion is a common feature in CHF patients and can progress to cirrhosis, leading to decreased drug metabolism and hepatic drug clearance [88]. Apart from hepatic dysfunction, kidney dysfunction is frequently found in CHF patients as a result of reduced renal blood flow and renal venous hypertension [89]. Furthermore, multiple complex medication regimens and several narrow therapeutic medications (e.g., digoxin and lidocaine) are used in CHF to balance water and sodium hemostasis and improve cardiac function [90], leading to a higher risk for DRPs [91,92,93].
With respect to the geriatric syndrome and psycho-cognitive status, we found that patients exposed to >1 criterion of DRPs tended to have more impaired ADL, frailty or a history of falls within the last 3 months than those who experienced ≤1 criteria of DRP, similar to findings from previous studies [94,95]. Frailty is a predictive factor of adverse clinical consequences, such as falls, fractures and functional inability [96]. The increasing number of DRPs in frail older adults may be explained through frailty-related pharmacokinetic and pharmacodynamic changes, such as reductions in the glomerular infiltration rate and hepatic drug clearance, especially in the phase II metabolism, which involves conjugation by coupling the medication to another molecule [97,98,99,100].
By contrast, we found no relationship between cognitive function and the increase in undesirable DRPs, which is in accordance with the findings reported by Paisansirikul et al. [38]. Unlike previous literature reviews, older adults with dementia have greater experience with DRPs, especially ADEs, owing to increased blood–brain permeability and sensitivity to the neurological effects of medications [101,102].
In terms of biochemical profiles, higher serum creatinine and lower hemoglobin levels are substantially associated with a greater incidence of DRPs [72,103,104]. The kidneys eliminate many types of prescribed drugs or their active metabolites. These renally excreted drugs should be prescribed based on the patient’s renal function or serum creatinine level to reduce exaggerated pharmacologic effects [105]. The common use of antithrombotic agents predisposes certain pharmacological therapies to develop ADEs, such as bleeding complications and anemia, especially in the older population [106]. The result of our study contrasts with those in the previous study, indicating that an increased serum albumin concentration is an important risk factor for potential DRPs [107,108]. Albumin accounts for 60% of drug-binding proteins. Medications binding to human serum albumin are usually pharmacologically inactive. On the contrary, the unbound portion of the medication could exert pharmacological activity [109]. Blood protein concentration plays a major role in the therapeutic dose and drug side effects. The free drug concentration is increased in hypoalbuminemia, leading to decreased therapeutic medication safety and increased risks of drug side effects and toxicity.
PP is an outstanding factor in this study population, with individuals taking up to 23 medications per person. As expected, a high proportion of hospitalized older patients (81.2%) experienced PP. Similar to previous studies, most hospitalized older adults were exposed to PP [110,111,112,113].
The three most frequently prescribed medications were lipid-modifying agents, vitamins and antithrombotic agents. Antithrombotic agents are often prescribed to prevent and treat atherothrombotic events, CADs and atrial fibrillation (AF). Furthermore, new medications have been developed and launched, including non-vitamin K antagonist oral anticoagulants (NOACs) [114]. In the previous decade, the NOACs were approved by the Food and Drug Administration for the treatment of AF [115] and were recommended in clinical practice guidelines. With respect to vitamins, many hospitalized older patients develop malnutrition during hospitalization [116]. Hence, multiple macronutrients and micronutrients are required to maintain health status and diminish mortality. Thiamine and vitamin D deficiency are widely documented in the admitted geriatric population because of chronic diseases, age-related physiologic changes and nutritional difficulties [117,118]. Therefore, several vitamins have to be prescribed in clinical practice, based on patients’ individual characteristics and clinical requirements [119].
With regard to PIM use, most hospitalized older patients in the study (67.8%) were likely to experience at least 1 PIM, in agreement with studies by Silva et al. [120] and Varavithya et al. [121]. Nevertheless, the prevalence of PIMs varies according to healthcare settings, population structures and PIM assessment tools, including the AGS Beers Criteria®, the STOPP/START criteria, the PRISCUS list and the Laroche list [56,58,122,123]. In accordance with a recent study [120], the most common PIMs in older population involve medications for treatment diseases of the gastrointestinal (GI) and central nervous systems. According to the 2019 AGS Beers Criteria®, the most common medications implicated in PIMs are PPIs [120,121], followed by benzodiazepines [120]. The 2019 AGS Beers Criteria® also indicate that older adults with long-term PPI use are susceptible to adverse events, such as Clostridium difficile infection, vitamin B12 deficiency, community-acquired pneumonia, fractures, and bone loss [58,124,125,126,127,128]. The inclusion of benzodiazepine as a PIM may be explained by the fact that benzodiazepines are well known to be associated with a risk of cognitive impairment, delirium, falls and fractures [58,129,130,131].
The results of the current study are consistent with those reported by Dagnew et al. [12], who found that the incidence of DDIs was high (71.1%) in the geriatric population. The majority of participants (47.9%) experienced one to five DDIs, and most DDIs (48.7%) were of moderate severity [12]. The most common therapeutic agents involved in DDIs were the combination of simvastatin and amlodipine. This drug interaction may contribute to the clinical risks of myopathy and rhabdomyolysis, similar to Dias et al.’s study, conducted in Brazil [132].
In two previous studies [133,134], approximately 30% of the overall population was exposed to ADEs, most of which were associated with antithrombotic and antineoplastic agents. Febrile neutropenia caused by antineoplastic agents was the most common ADE in our study. On the contrary, many studies have indicated an increased risk of GI-related ADEs, such as GI bleeding, in patients taking nonsteroidal anti-inflammatory drugs [135,136]. The ADEs observed in the study were categorized as severe and life-threatening in this susceptible population [34]. According to a previous study, older adults are more prone to severe adverse drug reactions because of a greater risk of polypharmacy and decline of hepatic function [137]. The prevalence of preventable ADEs in our study was 37% among all participants, which is lower than that reported in previous studies [34,133]. The prescribed medication category most often associated with preventable ADEs was anticoagulants (60%). This result contradicts that reported by Gray et al. [138], who found that half of the preventable ADEs resulted from psycholeptic agents.
Upon multivariate logistic regression analysis, PP, and the historical use of drugs for acid-related disorders, drugs used in diabetes, cardiac therapy, diuretics, psycholeptics, and psychoanaleptics were factors associated with exposure to >1 criterion of DRPs, which is consistent with the results of previous studies [103,107,108,139,140,141,142,143]. A high PP burden could be attributed to a significant experience of potential DRPs linked to increased DDIs and ADEs. Additionally, the number of taken medications is associated with the risk and severity of ADEs [144,145]. The toxicity of many drug combinations may be synergistic and greater than the total toxicity of medications used alone. Psycholeptics and psychoanaleptics contained in drugs classified as ATC code N (nervous system) are often considered “dirty” medications because they interact with many bonded molecular targets [146]. Hence, these medications may cause various ADEs [143]. Furthermore, several drugs classified as psycholeptics and psychoanaleptics are mentioned as PIMs according to the 2019 AGS Beers Criteria® [58]. In addition, most psycholeptics and psychoanaleptics are metabolized through cytochrome P450, resulting in an increased risk of drug interactions [147,148]. Cardiac therapy drugs, classified as ATC code C01, comprise many medications with narrow therapeutic margins, such as digoxin and amiodarone, which usually interfere with other medications and come with potential drug interactions. Likewise, drugs used in diabetes, such as metformin and sulfonylurea, are also associated with DRPs. According to the 2019 AGS Beers Criteria®, long-acting sulfonylurea is notorious for increasing the risk of severe hypoglycemia [58]. Metformin may contribute to drug–disease interactions, which could raise the risk of lactic acidosis, particularly in patients with renal insufficiency [58,131,149]. Diuretics can affect age-related homeostasis, resulting in a greater risk of orthostatic hypotension, falls and electrolyte imbalance [149]. On the other hand, age, sex, ADL, clinical frailty, comorbidities, a history of falls within the past 12 months and cognitive function were not correlated with exposure to >1 criterion of DRPs, which is in contrast with preceding studies [38,150,151,152,153].
To the best of our knowledge, this is the first study to demonstrate the prevalence of and factors associated with exposure to >1 criterion of DRPs among hospitalized older patients in Thailand. Furthermore, this study revealed the characteristics of PIMs, DDIs and ADEs and their related medications. The main strength of the present study is the involvement of medical assessors, who conducted many clinical measurements and gathered comprehensive health data from several domains using a combination of EMR reviews and face-to-face interviews. Additionally, physicians and clinical pharmacists evaluated all DRPs and related medications using various standard assessment tools.
However, several limitations of this study should be mentioned. First, the study focused on older adults aged ≥60 years admitted to a tertiary care hospital. In addition, many older inpatients, such as those with palliative care needs, hospital stays <48 h and no response to the questionnaire or communication were excluded from the current study. Therefore, the results may need to be more generalizable. Second, the cross-sectional nature of this study restricts the determination of a temporal relationship between associations and outcomes. Third, patients with mild dementia who were able to communicate were still included in the study. This might have contributed to inaccurate information retrieval and recall bias. Fourth, the absence of the exact etiology and mechanism of ADEs may have led to difficulty in preventing ADEs. Finally, only DDIs involving a single pairwise medication combination were considered in the current study, but we did not include drug interactions from combinations of three or more medications.
The essential results, including independent risk factors, could be included into a comprehensive measurement tool and geriatric assessment to identify DRPs in high-risk older individuals. Early detection and prevention of DRPs could be achieved through appropriate medication management. Additional prospective cohort studies are needed to assess adverse outcomes of DRPs, such as readmission, revisit to ER and mortality. Further cohort studies would encourage healthcare personnel to be aware of DRPs and their detrimental outcomes and to promote medication management, such as comprehensive medication reviews and medication reconciliations in order to optimize the prescription of medication for this susceptible population. In addition, an electronic prescribing system, which is a computer-based system used to create drug prescriptions and monitor and report drug-related problems, has long been implemented in western countries [154]. The prescribing system could prevent medication errors, prescribing abuse, drug interaction and adverse drug reactions [155,156]. Therefore, the use of this computer-based system should be widely encouraged in clinical practice to alert physicians and help them avoid DRPs. Additionally, an academic training program regarding appropriate medication use should be provided to physicians and clinical pharmacists. These medication management efforts would address potential DRPs and diminish their adverse consequences.

5. Conclusions

This study confirms that a high proportion of hospitalized older patients are susceptible to PIMs, DDIs and ADEs. The independent factors associated with exposure to >1 criterion of DRPs were PP and the use of medications for acid-related disorders, drugs used in diabetes, cardiac therapy, diuretics, psycholeptics and psychoanaleptics. Therefore, the findings from this study should be utilized to develop comprehensive assessment tools for the early detection and prevention of DRPs in this high-risk population. Furthermore, medical staff should be encouraged to perform comprehensive medication reviews to address preventable DRPs.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jcm13061638/s1. Table S1: potentially inappropriate medications in the study population according to the 2019 Beer Criteria; Table S2: lists of the most common drug interactions with their potential effects, according to the Micromedex Drug Interaction Database; Table S3: severity of adverse drug events; Table S4: category of adverse drug events; Table S5: category of medications associated with adverse drug events; Table S6: preventability of adverse drug events.

Author Contributions

Conceptualization: P.P., K.P. and S.R.; methodology: P.P. and S.R.; validation: S.R.; formal analysis: P.P. and S.R.; investigation: P.P. and S.R.; resources: P.P., K.P. and S.R.; data curation: P.P. and S.R.; visualization: S.R.; supervision: S.R.; project administration: S.R.; writing—original draft preparation: P.P., K.P. and S.R.; writing—review and editing: P.P. and S.R. All authors have read and agreed to the published version of the manuscript.

Funding

This study received no external funding.

Institutional Review Board Statement

The study was conducted according to the principles of the Declaration of Helsinki and was approved by the Committee on Human Rights Related to Research Involving Human Subjects, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Thailand (protocol number: COA. MURA2021/1068; date of approval: 29 December 2021).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in the current study are not publicly available due to ethical restrictions. However, data are available from the corresponding author upon reasonable request.

Acknowledgments

The authors would like to thank all medical staff members and clinical pharmacists of the internal medicine wards of Ramathibodi Hospital, Mahidol University, for gathering comprehensive medical data.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. World Health Organization. Ageing and Health. 2022. Available online: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health (accessed on 12 January 2024).
  2. Foundation of Thai Gerontology Research and Development institute (TGRI). Situation of the Thai Older Persons 2021; Institute for Population and Social Research, Mahidol University: Nakhon Pathom, Thailand, 2021; 120p. [Google Scholar]
  3. Cornwell, J.; Levenson, R.; Sonola, L.; Poteliakhoff, E. Continuity of Care for Older Hospital Patients: A Call for Action. 2012. Available online: https://www.kingfund.org.uk/publications/continuity-care-older-hospital-patients (accessed on 10 January 2024).
  4. He, Z.; Bian, J.; Carretta, H.J.; Lee, J.; Hogan, W.R.; Shenkman, E.; Charness, N. Prevalence of Multiple Chronic Conditions Among Older Adults in Florida and the United States: Comparative Analysis of the OneFlorida Data Trust and National Inpatient Sample. J. Med. Internet Res. 2018, 20, e137. [Google Scholar] [CrossRef]
  5. Center for Chronic Disease Prevention. About Chronic Diseases. 2022. Available online: https://www.cdc.gov/chronicdisease/about/index.htm (accessed on 10 January 2024).
  6. Pharmaceutical Care Network Europe Foundation. PCNE Classification for Drug-Related Problems V9.1—Classification for Drug related problems. 2019. Available online: https://www.pcne.org/upload/files/417_PCNE_classification_V9-1_final.pdf (accessed on 10 January 2024).
  7. Hepler, C.D.; Strand, L.M. Opportunities and responsibilities in pharmaceutical care. Am. J. Hosp. Pharm. 1990, 47, 533–543. [Google Scholar] [CrossRef]
  8. Cipolle, R.J.; Strand, L.M.; Morley, P.C. Pharmaceutical Care Practice: The Patient-Centered Approach to Medication Management Services; McGraw Hill: New York, NY, USA, 2012. [Google Scholar]
  9. Hailu, B.Y.; Berhe, D.F.; Gudina, E.K.; Gidey, K.; Getachew, M. Drug related problems in admitted geriatric patients: The impact of clinical pharmacist interventions. BMC Geriatr. 2020, 20, 13. [Google Scholar] [CrossRef] [PubMed]
  10. Pfister, B.; Jonsson, J.; Gustafsson, M. Drug-related problems and medication reviews among old people with dementia. BMC pharmacol. Toxicol 2017, 18, 52. [Google Scholar] [CrossRef] [PubMed]
  11. Chandrappa, S.; Divya, R.; Rajarathna, K. Assessing prescriptions for potentially inappropriate medications using Beer’s criteria in elderly in-patients at a tertiary care hospital. Natl. J. Physiol. Pharm. Pharmacol. 2019, 9, 145. [Google Scholar] [CrossRef]
  12. Dagnew, S.B.; Tadesse, T.Y.; Zeleke, M.M.; Yiblet, T.G.; Addis, G.T.; Mekonnen, G.B.; Negash, E.A. Drug–drug interactions among hospitalized elderly in patients at medical wards of Northwest Ethiopia’s Comprehensive Specialized Hospitals: A multicenter observational study. SAGE Open Med. 2022, 10, 20503121221135874. [Google Scholar] [CrossRef]
  13. de Figueiredo, T.P.; de Souza Groia, R.C.; Barroso, S.C.C.; do Nascimento, M.M.G.; Reis, A.M.M. Factors associated with adverse drug reactions in older inpatients in teaching hospital. Int. J. Clin. Pharm. 2017, 39, 679–685. [Google Scholar] [CrossRef]
  14. Mangoni, A.A.; Jackson, S.H. Age-related changes in pharmacokinetics and pharmacodynamics: Basic principles and practical applications. Br. J. Clin. Pharmacol. 2004, 57, 6–14. [Google Scholar] [CrossRef]
  15. Wooten, J.M. Pharmacotherapy considerations in elderly adults. South. Med. J. 2012, 105, 437–445. [Google Scholar] [CrossRef] [PubMed]
  16. Fernandez-Llimos, F.; Faus, M.J. From “drug-related problems” to “negative clinical outcomes”. Am. J. Health Syst. Pharm. 2005, 62, 2348–2350. [Google Scholar] [CrossRef]
  17. Ahmad, A.; Mast, M.R.; Nijpels, G.; Elders, P.J.; Dekker, J.M.; Hugtenburg, J.G. Identification of drug-related problems of elderly patients discharged from hospital. Patient Prefer. Adherence 2014, 8, 155–165. [Google Scholar] [PubMed]
  18. Watanabe, J.H.; McInnis, T.; Hirsch, J.D. Cost of Prescription Drug-Related Morbidity and Mortality. Ann. Pharmacother. 2018, 52, 829–837. [Google Scholar] [CrossRef] [PubMed]
  19. Naples, J.G.; Hanlon, J.T.; Schmader, K.E.; Semla, T.P. Recent Literature on Medication Errors and Adverse Drug Events in Older Adults. J. Am. Geriatr. Soc. 2016, 64, 401–408. [Google Scholar] [CrossRef] [PubMed]
  20. Salvi, F.; Marchetti, A.; D’Angelo, F.; Boemi, M.; Lattanzio, F.; Cherubini, A. Adverse drug events as a cause of hospitalization in older adults. Drug Saf. 2012, 35 (Suppl. 1), 29–45. [Google Scholar] [CrossRef] [PubMed]
  21. Meng, L.; Qu, C.; Qin, X.; Huang, H.; Hu, Y.; Qiu, F.; Sun, S. Drug-Related Problems among Hospitalized Surgical Elderly Patients in China. BioMed Res. Int. 2021, 2021, 8830606. [Google Scholar] [CrossRef] [PubMed]
  22. Bekele, F.; Fekadu, G.; Bekele, K.; Dugassa, D.; Sori, J. Drug-related problems among patients with infectious disease admitted to medical wards of Wollega University Referral Hospital: Prospective observational study. SAGE Open Med. 2021, 9, 2050312121989625. [Google Scholar] [CrossRef] [PubMed]
  23. Tharanon, V.; Putthipokin, K.; Sakthong, P. Drug-related problems identified during pharmaceutical care interventions in an intensive care unit at a tertiary university hospital. SAGE Open Med. 2022, 10, 20503121221090881. [Google Scholar] [CrossRef]
  24. Lenssen, R.; Heidenreich, A.; Schulz, J.B.; Trautwein, C.; Fitzner, C.; Jaehde, U.; Eisert, A. Analysis of drug-related problems in three departments of a German University hospital. Int. J. Clin. Pharm. 2016, 38, 119–126. [Google Scholar] [CrossRef]
  25. Tigabu, B.M.; Daba, D.; Habte, B. Drug-related problems among medical ward patients in Jimma university specialized hospital, Southwest Ethiopia. J. Pharm. Pract. Res. 2014, 3, 1–5. [Google Scholar] [CrossRef]
  26. Wang, F.; Ma, Z.; Liu, M.; Wu, X. Potentially inappropriate medications at admission and discharge in older adults: A comparison of the Beers 2019 and 2015 criteria. Int. J. Clin. Pharmacol. Ther. 2020, 58, 299–309. [Google Scholar] [CrossRef]
  27. Sharma, R.; Bansal, P.; Garg, R.; Ranjan, R.; Kumar, R.; Arora, M. Prevalence of potentially inappropriate medication and its correlates in elderly hospitalized patients: A cross-sectional study based on Beers criteria. J. Fam. Community. Med. 2020, 27, 200–207. [Google Scholar]
  28. Osei, E.K.; Berry-Cabán, C.S.; Haley, C.L.; Rhodes-Pope, H. Prevalence of Beers Criteria Medications Among Elderly Patients in a Military Hospital. Gerontol. Geriatr. Med. 2016, 2, 2333721416637790. [Google Scholar] [CrossRef]
  29. Kashyap, M.; D’Cruz, S.; Sachdev, A.; Tiwari, P. Drug-drug interactions and their predictors: Results from Indian elderly inpatients. Pharm. Pract. 2013, 11, 191–195. [Google Scholar] [CrossRef]
  30. Očovská, Z.; Maříková, M.; Vlček, J. Potentially clinically significant drug-drug interactions in older patients admitted to the hospital: A cross-sectional study. Front. Pharmacol. 2023, 14, 1088900. [Google Scholar] [CrossRef] [PubMed]
  31. Dequito, A.B.; Mol, P.G.; van Doormaal, J.E.; Zaal, R.J.; van den Bemt, P.M.; Haaijer-Ruskamp, F.M.; Kosterink, J.G. Preventable and non-preventable adverse drug events in hospitalized patients: A prospective chart review in the Netherlands. Drug Saf. 2011, 34, 1089–1100. [Google Scholar] [CrossRef] [PubMed]
  32. Rozenfeld, S.; Giordani, F.; Coelho, S. Adverse drug events in hospital: Pilot study with trigger tool. Rev. Saude Publica 2013, 47, 1102–1111. [Google Scholar] [CrossRef] [PubMed]
  33. Cano, F.G.; Rozenfeld, S. Adverse drug events in hospitals: A systematic review. Cad. Saude Publica 2009, 25 (Suppl. 3), S360–S372. [Google Scholar] [CrossRef] [PubMed]
  34. Klopotowska, J.E.; Wierenga, P.C.; Stuijt, C.C.; Arisz, L.; Dijkgraaf, M.G.; Kuks, P.F.; Asscheman, H.; de Rooij, S.E.; Lie-A-Huen, L.; Smorenburg, S.M.; et al. Adverse drug events in older hospitalized patients: Results and reliability of a comprehensive and structured identification strategy. PLoS ONE 2013, 8, e71045. [Google Scholar] [CrossRef] [PubMed]
  35. Thomsen, L.A.; Winterstein, A.G.; Søndergaard, B.; Haugbølle, L.S.; Melander, A. Systematic review of the incidence and characteristics of preventable adverse drug events in ambulatory care. Ann. Pharmacother. 2007, 41, 1411–1426. [Google Scholar] [CrossRef] [PubMed]
  36. O’Sullivan, D.; O’Mahony, D.; O’Connor, M.N.; Gallagher, P.; Cullinan, S.; O’Sullivan, R.; Gallagher, J.; Eustace, J.; Byrne, S. The impact of a structured pharmacist intervention on the appropriateness of prescribing in older hospitalized patients. Drugs Aging 2014, 31, 471–481. [Google Scholar] [CrossRef] [PubMed]
  37. Chan, D.C.; Chen, J.H.; Kuo, H.K.; We, C.J.; Lu, I.S.; Chiu, L.S.; Wu, S.C. Drug-related problems (DRPs) identified from geriatric medication safety review clinics. Arch. Gerontol. Geriatr. 2012, 54, 168–174. [Google Scholar] [CrossRef] [PubMed]
  38. Paisansirikul, A.; Ketprayoon, A.; Ittiwattanakul, W.; Petchlorlian, A. Prevalence and Associated Factors of Drug-Related Problems Among Older People: A Cross-Sectional Study at King Chulalongkorn Memorial Hospital in Bangkok. Drugs Real World Outcomes 2021, 8, 73–84. [Google Scholar] [CrossRef] [PubMed]
  39. Sundararajan, V.; Henderson, T.; Perry, C.; Muggivan, A.; Quan, H.; Ghali, W.A. New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality. J. Clin. Epidemiol. 2004, 57, 1288–1294. [Google Scholar] [CrossRef] [PubMed]
  40. Inouye, S.K.; Studenski, S.; Tinetti, M.E.; Kuchel, G.A. Geriatric syndromes: Clinical, research, and policy implications of a core geriatric concept. J. Am. Geriatr. Soc. 2007, 55, 780–791. [Google Scholar] [CrossRef] [PubMed]
  41. Hemrungrojn, S.; Tangwongchai, S.; Charoenboon, T.; Panasawat, M.; Supasitthumrong, T.; Chaipresertsud, P.; Maleevach, P.; Likitjaroen, Y.; Phanthumchinda, K.; Maes, M. Use of the Montreal Cognitive Assessment Thai Version to Discriminate Amnestic Mild Cognitive Impairment from Alzheimer’s Disease and Healthy Controls: Machine Learning Results. Dement. Geriatr. Cogn. Disord. 2021, 50, 183–194. [Google Scholar] [CrossRef] [PubMed]
  42. Tangwongchai SP, M.; Charernboon, T.; Akkayagorn, L.; Hemrungrojn, S.; Phanthumchinda, K.; Tangwongchai, S.P.M. The validity of thai version of the montreal cognitive assessment (MoCA-T). Dement Neuropsychol. 2009, 3, 136–178. [Google Scholar]
  43. Wongpakaran, N.; Wongpakaran, T.; Van Reekum, R. The Use of GDS-15 in Detecting MDD: A Comparison Between Residents in a Thai Long-Term Care Home and Geriatric Outpatients. J. Clin. Med. Res. 2013, 5, 101–111. [Google Scholar] [CrossRef]
  44. Wongpakaran, N.; Wongpakaran, T. Prevalence of major depressive disorders and suicide in long-term care facilities: A report from northern Thailand. Psychogeriatrics. 2012, 12, 11–17. [Google Scholar] [CrossRef]
  45. Mahoney, F.I.; Barthel, D.W. Functional Evaluation: The Barthel Index. Md. State Med. J. 1965, 14, 61–65. [Google Scholar]
  46. Lawton, M.P.; Brody, E.M. Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist 1969, 9, 179–186. [Google Scholar] [CrossRef]
  47. Validity and Reliability of Lawton Instrumental Activities of Daily Living Scale (L-IADL) and Older People’s Quality of Life Questionnaire (OPQOL-Brief): Thai version. 2017. Available online: https://www.researchgate.net/publication/342096378_karsu_ksakhwamtrnglaeakhwamtheiyngkhxngbaebsxbtham_Lawton_Instrumental_Activities_of_Daily_Living_Scale_L-IADL_laea_Older_People%27s_Quality_of_Life_Questionnaire_OPQOL-Brief_chba_bphasathiy_Validity_an (accessed on 12 January 2024).
  48. Nuttall, F.Q. Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutr. Today. 2015, 50, 117–128. [Google Scholar] [CrossRef]
  49. Church, S.; Rogers, E.; Rockwood, K.; Theou, O. A scoping review of the Clinical Frailty Scale. BMC Geriatr. 2020, 20, 393. [Google Scholar] [CrossRef] [PubMed]
  50. Rockwood, K.; Song, X.; MacKnight, C.; Bergman, H.; Hogan, D.B.; McDowell, I.; Mitnitski, A. A global clinical measure of fitness and frailty in elderly people. Can. Med. Assoc. J. 2005, 173, 489–495. [Google Scholar] [CrossRef] [PubMed]
  51. Gnjidic, D.; Hilmer, S.N.; Blyth, F.M.; Naganathan, V.; Waite, L.; Seibel, M.J.; McLachlan, A.J.; Cumming, R.G.; Handelsman, D.J.; Le Couteur, D.G. Polypharmacy cutoff and outcomes: Five or more medicines were used to identify community-dwelling older men at risk of different adverse outcomes. J. Clin. Epidemiol. 2012, 65, 989–995. [Google Scholar] [CrossRef] [PubMed]
  52. Fernández, A.; Gómez, F.; Curcio, C.L.; Pineda, E.; de Souza, J.F. Prevalence and impact of potentially inappropriate medication on community-dwelling older adults. Prevalencia e impacto de la medicación potencialmente inapropiada en ancianos que viven en comunidad. Biomedica 2021, 41, 111–122. [Google Scholar] [CrossRef] [PubMed]
  53. Viana, S.S.C.; Souza, N.P.D.S.; Aliberti, M.J.R.; Jacob-Filho, W. Use of potentially inappropriate medications and adverse events in older outpatients with acute conditions. Einstein 2020, 20, eAO8024. [Google Scholar] [CrossRef]
  54. Walter, S.R.; Day, R.O.; Gallego, B.; Westbrook, J.I. The impact of serious adverse drug reactions: A population-based study of a decade of hospital admissions in New South Wales, Australia. Br. J. Clin. Pharmacol. 2017, 83, 416–426. [Google Scholar] [CrossRef]
  55. Rodrigues, M.C.; Oliveira, C.d. Drug-drug interactions and adverse drug reactions in polypharmacy among older adults: An integrative review. Rev. Lat.-Am. Enferm. 2016, 24, e2800. [Google Scholar] [CrossRef]
  56. Holt, S.; Schmiedl, S.; Thürmann, P.A. Potentially inappropriate medications in the elderly: The PRISCUS list. Dtsch. Arztebl. Int. 2010, 107, 543–551. [Google Scholar]
  57. Beers, M.H. Explicit criteria for determining potentially inappropriate medication use by the elderly. An update. Arch. Intern Med. 1997, 157, 1531–1536. [Google Scholar] [CrossRef]
  58. By the 2019 American Geriatrics Society Beers Criteria® Update Expert Panel. American Geriatrics Society 2019 Updated AGS Beers Criteria® for Potentially Inappropriate Medication Use in Older Adults. J. Am. Geriatr. Soc. 2019, 67, 674–694.
  59. Tannenbaum, C.; Sheehan, N.L. Understanding and preventing drug-drug and drug-gene interactions. Expert Rev. Clin. Pharmacol. 2014, 7, 533–544. [Google Scholar] [CrossRef]
  60. Triplitt, C. Drug Interactions of Medications Commonly Used in Diabetes. Diabetes Spectr. 2006, 19, 202–211. [Google Scholar] [CrossRef]
  61. Micromedex® HealthCare Series Version 2.0. Greenwood Village, Colorado: Thomson Healthcare Inc. 2013. Available online: http://www.micromedexsolutions.com (accessed on 17 January 2024).
  62. Morimoto, T.; Gandhi, T.K.; Seger, A.C.; Hsieh, T.C.; Bates, D.W. Adverse drug events and medication errors: Detection and classification methods. BMJ Qual. Saf. 2004, 13, 306–314. [Google Scholar] [CrossRef]
  63. Bates, D.W.; Cullen, D.J.; Laird, N.; Petersen, L.A.; Small, S.D.; Servi, D.; Laffel, G.; Sweitzer, B.J.; Shea, B.F.; Hallisey, R. Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE Prevention Study Group. J. Am. Med. Assoc. 1995, 274, 29–34. [Google Scholar] [CrossRef]
  64. Naranjo, C.A.; Busto, U.; Sellers, E.M.; Sandor, P.; Ruiz, I.; Roberts, E.A.; Janecek, E.; Domecq, C.; Greenblatt, D.J. A method for estimating the probability of adverse drug reactions. Clin. Pharmacol. Ther. 1981, 30, 239–245. [Google Scholar] [CrossRef]
  65. Cancer Therapy Evaluation Program. Common Terminology Criteria for Adverse Events, Version 4.03, DCTD, NCI, NIH, DHHS. 2010. Available online: http://evs.nci.nih.gov/ftp1/CTCAE/About.html (accessed on 17 January 2024).
  66. Schumock, G.T.; Thornton, J.P. Focusing on the preventability of adverse drug reactions. Hosp. Pharm. 1992, 27, 538. [Google Scholar]
  67. Ramanath, K.; Nedumballi, S. Assessment of medication-related problems in geriatric patients of a rural tertiary care hospital. J. Young. Pharm. 2012, 4, 273–278. [Google Scholar] [CrossRef]
  68. Yang, J.; Meng, L.; Liu, Y.; Lv, L.; Sun, S.; Long, R.; Shan, X.; Song, J.; Qiu, F. Drug-related problems among community-dwelling older adults in mainland China. Int. J. Clin. Pharm. 2018, 40, 368–375. [Google Scholar] [CrossRef]
  69. Sluggett, J.K.; Harrison, S.L.; Ritchie, L.A.; Clough, A.J.; Rigby, D.; Caughey, G.E.; Tan, E.C.K. High-Risk Medication Use in Older Residents of Long-Term Care Facilities: Prevalence, Harms, and Strategies to Mitigate Risks and Enhance Use. Sr. Care. Pharm. 2020, 35, 419–433. [Google Scholar] [CrossRef]
  70. Ruangritchankul, S.; Krairit, O.; Putthipokin, K.; Chansirikarnjana, S.; Assavapokee, T.; Sraium, S. Polypharmacy among Older Adults in Outpatient Clinic, Internal Medicine Department, Ramathibodi Hospital. Thai J. Toxicol. 2018, 33, 35–50. [Google Scholar]
  71. Troncoso-Mariño, A.; López-Jiménez, T.; Roso-Llorach, A.; Villén, N.; Amado-Guirado, E.; Guisado-Clavero, M.; Fernández-Bertolin, S.; Pons Vigues, M.; Foguet-Boreu, Q.; Violán, C. Medication-related problems in older people in Catalonia: A real-world data study. Pharmacoepidemiol. Drug. Saf. 2021, 30, 220–228. [Google Scholar] [CrossRef] [PubMed]
  72. Saldanha, V.; Araújo, I.B.; Lima, S.I.V.C.; Martins, R.R.; Oliveira, A.G. Risk factors for drug-related problems in a general hospital: A large prospective cohort. PLoS ONE 2020, 15, e0230215. [Google Scholar] [CrossRef]
  73. Sinvani, L.; Kuriakose, R.; Tariq, S.; Kozikowski, A.; Patel, V.; Smilios, C.; Akerman, M.; Qiu, G.; Makhnevich, A.; Cohen, J.; et al. Using Charlson Comorbidity Index to Predict Short-Term Clinical Outcomes in Hospitalized Older Adults. J. Healthc. Qual. 2019, 41, 146–153. [Google Scholar] [CrossRef]
  74. Nojiri, S.; Itoh, H.; Kasai, T.; Fujibayashi, K.; Saito, T.; Hiratsuka, Y.; Okuzawa, A.; Naito, T.; Yokoyama, K.; Daida, H. Comorbidity status in hospitalized elderly in Japan: Analysis from National Database of Health Insurance Claims and Specific Health Checkups. Sci. Rep. 2019, 9, 20237. [Google Scholar] [CrossRef] [PubMed]
  75. Sefera, B.; Getachew, M.; Babu, Y.; Bekele, F.; Fanta, K. Drug-related problems and its predictors among hospitalized heart failure patients at Jimma Medical Center, South West Ethiopia: Prospective interventional study. BMC. Cardiovasc. Disord. 2022, 22, 418. [Google Scholar] [CrossRef]
  76. Truong, T.T.A.; Phan, N.K.; Vo, Q.V.; Diep, H.G.; Vuong, H.T.K.; Le, T.V.; Nguyen, P.M.; Nguyen, T. Drug-related problems in prescribing for coronary artery diseases in Vietnam: Cross-sectional study. Trop. Med. Int. Health 2019, 24, 1335–1340. [Google Scholar] [CrossRef] [PubMed]
  77. Amankwa Harrison, M.; Marfo, A.F.A.; Buabeng, K.O.; Nkansah, F.A.; Boateng, D.P.; Ankrah, D.N.A. Drug-related problems among hospitalized hypertensive and heart failure patients and physician acceptance of pharmacists’ interventions at a teaching hospital in Ghana. Health Sci. Rep. 2022, 5, e786. [Google Scholar] [CrossRef]
  78. Niriayo, Y.L.; Kumela, K.; Kassa, T.D.; Angamo, M.T. Drug therapy problems and contributing factors in the management of heart failure patients in Jimma University Specialized Hospital, Southwest Ethiopia. PLoS ONE 2018, 13, e0206120. [Google Scholar] [CrossRef]
  79. Bagyawantha, N.M.Y.; Coombes, I.D.; Gawarammana, I.; Fahim, M. Impact of a clinical pharmacist on optimising the quality use of medicines according to the acute coronary syndrome (ACS) secondary prevention guidelines and medication adherence following discharge in patients with ACS in Sri Lanka: A prospective non-randomised controlled trial study protocol. BMJ Open 2023, 13, e059413. [Google Scholar]
  80. Shouqair, T.M.; Rabbani, S.A.; Sridhar, S.B.; Kurian, M.T. Evaluation of Drug-Related Problems in Chronic Kidney Disease Patients. Cureus 2022, 14, e24019. [Google Scholar] [CrossRef]
  81. Subeesh, V.K.; Abraham, R.; Satya Sai, M.V.; Koonisetty, K.S. Evaluation of prescribing practices and drug-related problems in chronic kidney disease patients: A cross-sectional study. Perspect. Clin. Res. 2020, 11, 70–74. [Google Scholar]
  82. Alruqayb, W.S.; Price, M.J.; Paudyal, V.; Cox, A.R. Drug-Related Problems in Hospitalised Patients with Chronic Kidney Disease: A Systematic Review. Drug Saf. 2021, 44, 1041–1058. [Google Scholar] [CrossRef]
  83. Kusumawardani, L.A.; Andrajati, R.; Nusaibah, A. Drug-related Problems in Hypertensive Patients: A Cross-sectional Study from Indonesia. J. Pharm. Pract. Res. 2020, 9, 140–145. [Google Scholar]
  84. Abu Farha, R.; Basheti, I.; Abu Al Ruz, H.; Alsaleh, A.; AbuRuz, S. Assessment of drug-related problems and their impact on blood pressure control in patients with hypertension. Eur. J. Hosp. Pharm. 2016, 23, 126–130. [Google Scholar] [CrossRef]
  85. Williams, B.; Mancia, G.; Spiering, W.; Rosei, E.A.; Azizi, M.; Burnier, M. [2018 ESC/ESH Guidelines for the management of arterial hypertension. The Task Force for the management of arterial hypertension of the European Society of Cardiology (ESC) and the European Society of Hypertension (ESH)]. G. Ital. Cardiol. 2018, 19 (Suppl. 1), 3S–73S. [Google Scholar]
  86. Neves, C.M.; Nascimento, M.M.G.D.; Silva, D.Á.M.; Ramalho-de-Oliveira, D. Clinical Results of Comprehensive Medication Management Services in Primary Care in Belo Horizonte. Pharmacy 2019, 7, 58. [Google Scholar] [CrossRef]
  87. Mangoni, A.A.; Jarmuzewska, E.A. The influence of heart failure on the pharmacokinetics of cardiovascular and non-cardiovascular drugs: A critical appraisal of the evidence. Br. J. Clin. Pharmacol. 2019, 85, 20–36. [Google Scholar] [CrossRef] [PubMed]
  88. Sundaram, V.; Fang, J.C. Gastrointestinal and Liver Issues in Heart Failure. Circulation 2016, 133, 1696–1703. [Google Scholar] [CrossRef] [PubMed]
  89. Carubelli, V.; Lombardi, C.; Gorga, E.; Ravera, A.; Metra, M.; Mentz, R.J. Cardiorenal Interactions. Heart Fail. Clin. 2016, 12, 335–347. [Google Scholar] [CrossRef] [PubMed]
  90. Yancy, C.W.; Jessup, M.; Bozkurt, B.; Butler, J.; Casey, D.E.; Drazner, M.H., Jr.; Fonarow, G.C.; Geraci, S.A.; Horwich, T.; Januzzi, J.L.; et al. 2013 ACCF/AHA guideline for the management of heart failure: Executive summary: A report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. Circulation 2013, 128, 1810–1852. [Google Scholar] [CrossRef] [PubMed]
  91. Mastromarino, V.; Casenghi, M.; Testa, M.; Gabriele, E.; Coluccia, R.; Rubattu, S.; Volpe, M. Polypharmacy in heart failure patients. Curr. Heart. Fail. Rep. 2014, 11, 212–219. [Google Scholar] [CrossRef] [PubMed]
  92. Setoguchi, S.; Choudhry, N.K.; Levin, R.; Shrank, W.H.; Winkelmayer, W.C. Temporal trends in adherence to cardiovascular medications in elderly patients after hospitalization for heart failure. Clin. Pharmacol. Ther. 2010, 88, 548–554. [Google Scholar] [CrossRef] [PubMed]
  93. Wilmer, C.M.; Huiskes, V.J.B.; Natsch, S.; Rennings, A.J.M.; van den Bemt, B.J.F.; Bos, J.M. Drug-related problems in a clinical setting: A literature review and cross-sectional study evaluating factors to identify patients at risk. Eur. J. Hosp. Pharm. 2015, 22, 229–235. [Google Scholar] [CrossRef]
  94. Ye, L.; Yang-Huang, J.; Franse, C.B.; Rukavina, T.; Vasiljev, V.; Mattace-Raso, F.; Verma, A.; Borrás, T.A.; Rentoumis, T.; Raat, H. Factors associated with polypharmacy and the high risk of medication-related problems among older community-dwelling adults in European countries: A longitudinal study. BMC Geriatr. 2022, 22, 841. [Google Scholar] [CrossRef] [PubMed]
  95. Khera, S.; Abbasi, M.; Dabravolskaj, J.; Sadowski, C.A.; Yua, H.; Chevalier, B. Appropriateness of Medications in Older Adults Living with Frailty: Impact of a Pharmacist-Led Structured Medication Review Process in Primary Care. J. Prim. Care Community Health 2019, 10, 2150132719890227. [Google Scholar] [CrossRef]
  96. Clegg, A.; Young, J.; Iliffe, S.; Rikkert, M.O.; Rockwood, K. Frailty in elderly people. Lancet 2013, 381, 752–762. [Google Scholar] [CrossRef]
  97. Bachmann, K. Drug metabolism. In Pharmacology; Academic Press: Cambridge, MA, USA, 2009; pp. 131–173. [Google Scholar]
  98. Hilmer, S.N.; Kirkpatrick, C.M.J. New Horizons in the impact of frailty on pharmacokinetics: Latest developments. Age Ageing 2021, 50, 1054–1063. [Google Scholar] [CrossRef]
  99. Hubbard, R.E.; O’Mahony, M.S.; Calver, B.L.; Woodhouse, K.W. Plasma esterases and inflammation in ageing and frailty. Eur. J. Clin. Pharmacol. 2008, 64, 895–900. [Google Scholar] [CrossRef]
  100. Aitken, A.E.; Richardson, T.A.; Morgan, E.T. Regulation of drug-metabolizing enzymes and transporters in inflammation. Annu. Rev. Pharmacol. Toxicol. 2006, 46, 123–149. [Google Scholar] [CrossRef]
  101. Reeve, E.; Trenaman, S.C.; Rockwood, K.; Hilmer, S.N. Pharmacokinetic and pharmacodynamic alterations in older people with dementia. Expert Opin Drug Metab Toxicol. 2017, 13, 651–668. [Google Scholar] [CrossRef] [PubMed]
  102. Nicolazzo, J.A.; Mehta, D.C. Transport of drugs across the blood-brain barrier in Alzheimer’s disease. Ther. Deliv. 2010, 1, 595–611. [Google Scholar] [CrossRef] [PubMed]
  103. Lima, S.I.V.C.; Martins, R.R.; Saldanha, V.; Silbiger, V.N.; Dos Santos, I.C.C.; Araújo, I.B.; Oliveira, A.G. Development and validation of a clinical instrument to predict risk of an adverse drug reactions in hospitalized patients. PLoS ONE 2020, 15, e0243714. [Google Scholar] [CrossRef] [PubMed]
  104. Sakuma, M.; Bates, D.W.; Morimoto, T. Clinical prediction rule to identify high-risk inpatients for adverse drug events: The JADE Study. Pharmacoepidemiol. Drug Saf. 2012, 21, 1221–1226. [Google Scholar] [CrossRef] [PubMed]
  105. Wehling, M. (Ed.) Drug Therapy for the Elderly; Springer: New York, NY, USA, 2013. [Google Scholar]
  106. Simon, H. Anaemoa Complications. PhD Thesis, University of Maryland’s Medical Centre, Baltimore, MD, USA, 2009. [Google Scholar]
  107. Onder, G.; Petrovic, M.; Tangiisuran, B.; Meinardi, M.C.; Markito-Notenboom, W.P.; Somers, A.; Rajkumar, C.; Bernabei, R.; van der Cammen, T.J. Development and validation of a score to assess risk of adverse drug reactions among in-hospital patients 65 years or older: The GerontoNet ADR risk score. Arch. Intern. Med. 2010, 170, 1142–1148. [Google Scholar] [CrossRef] [PubMed]
  108. Falconer, N.; Barras, M.; Abdel-Hafez, A.; Radburn, S.; Cottrell, N. Development and validation of the Adverse Inpatient Medication Event model (AIME). Br. J. Clin. Pharmacol. 2021, 87, 1512–1524. [Google Scholar] [CrossRef]
  109. Ascenzi, P.; Fanali, G.; Fasano, M.; Pallottini, V.; Trezza, V. Clinical relevance of drug binding to plasma proteins. J. Mol. Struct. 2014, 1077, 4–13. [Google Scholar] [CrossRef]
  110. Agosta, L.; Bo, M.; Bianchi, L.; Abete, P.; Belelli, G.; Cherubini, A.; Corica, F.; Di Bari, M.; Maggio, M.; Manca, G.M.; et al. Polypharmacy and sarcopenia in hospitalized older patients: Results of the GLISTEN study. Aging Clin. Exp. Res. 2019, 31, 557–559. [Google Scholar] [CrossRef]
  111. Harugeri, A.; Joseph, J.; Parthasarathi, G.; Ramesh, M.; Guido, S. Prescribing patterns and predictors of high-level polypharmacy in the elderly population: A prospective surveillance study from two teaching hospitals in India. Am. J. Geriatr. Pharmacother. 2010, 8, 271–280. [Google Scholar] [CrossRef]
  112. Lai, X.; Zhu, H.; Huo, X.; Li, Z. Polypharmacy in the oldest old (≥80 years of age) patients in China: A cross-sectional study. BMC Geriatr. 2018, 18, 64. [Google Scholar] [CrossRef]
  113. Wawruch, M.; Zikavska, M.; Wsolova, L.; Kuzelova, M.; Tisonova, J.; Gajdosik, J.; Urbanek, K.; Kristova, V. Polypharmacy in elderly hospitalised patients in Slovakia. Pharm. World Sci. 2008, 30, 235–242. [Google Scholar] [CrossRef]
  114. Hinojar, R.; Jiménez-Natcher, J.J.; Fernández-Golfín, C.; Zamorano, J.L. New oral anticoagulants: A practical guide for physicians. Eur. Heart J. Cardiovasc. Pharmacother. 2015, 1, 134–135. [Google Scholar] [CrossRef]
  115. How, C.H. Novel oral anticoagulants for atrial fibrillation. Singap. Med. J. 2015, 56, 657–659. [Google Scholar] [CrossRef]
  116. Kirkland, L.L.; Kashiwagi, D.T.; Brantley, S.; Scheurer, D.; Varkey, P. Nutrition in the hospitalized patient. J. Hosp. Med. 2013, 8, 52–58. [Google Scholar] [CrossRef]
  117. Pourhassan, M.; Biesalski, H.K.; Angersbach, B.; Lueg, G.; Klimek, C.; Wirth, R. Prevalence of thiamine deficiency in older hospitalized patients. Clin. Interv. Aging. 2018, 13, 2247–2250. [Google Scholar] [CrossRef]
  118. Beirne, A.; McCarroll, K.; Walsh, J.B.; Casey, M.; Laird, E.; McNulty, H.; Ward, M.; Hoey, L.; Molloy, A.M.; Healy, M.; et al. Vitamin D and Hospital Admission in Older Adults: A Prospective Association. Nutrients 2021, 13, 616. [Google Scholar] [CrossRef]
  119. Berger, M.M.; Shenkin, A.; Schweinlin, A.; Amrein, K.; Augsburger, M.; Biesalski, H.K.; Bischoff, S.C.; Casaer, M.P.; Gundogan, K.; Lepp, H.L.; et al. ESPEN micronutrient guideline. Clin. Nutr. 2022, 41, 1357–1424. [Google Scholar] [CrossRef]
  120. e SILVA, D.C.G.; Linhares, M. Potentially inappropriate medication use among elderly patients from a Brazilian general hospital. Infarma Ciênc Farmac. 2016, 28, 27–32. [Google Scholar] [CrossRef]
  121. Varavithya, V.; Tirapat, C.; Rojpibulstit, P.; Poovichayasumlit, P.; Prasert, V.; Vatcharavongvan, P. Potentially inappropriate medication use and the hospitalization rate among Thai elderly patients: A retrospective cohort study. Eur. J. Clin. Pharmacol. 2022, 78, 847–855. [Google Scholar] [CrossRef] [PubMed]
  122. Gallagher, P.; Ryan, C.; Byrne, S.; Kennedy, J.; O’Mahony, D. STOPP (Screening Tool of Older Person’s Prescriptions) and START (Screening Tool to Alert doctors to Right Treatment). Consensus validation. Int. J. Clin. Pharmacol. Res. 2008, 46, 72–83. [Google Scholar]
  123. Laroche, M.L.; Charmes, J.P.; Merle, L. Potentially inappropriate medications in the elderly: A French consensus panel list. Eur. J. Clin. Pharmacol. 2007, 63, 725–731. [Google Scholar] [CrossRef]
  124. Trifan, A.; Stanciu, C.; Girleanu, I.; Stoica, O.C.; Singeap, A.M.; Maxim, R.; Chiriac, S.A.; Ciobica, A.; Boiculese, L. Proton pump inhibitors therapy and risk of Clostridium difficile infection: Systematic review and meta-analysis. World J. Gastroenterol. 2017, 23, 6500–6515. [Google Scholar] [CrossRef]
  125. Dublin, S.; Walker, R.L.; Jackson, M.L.; Nelson, J.C.; Weiss, N.S.; Jackson, L.A. Use of proton pump inhibitors and H2 blockers and risk of pneumonia in older adults: A population-based case-control study. Pharmacoepidemiol. Drug Saf. 2010, 19, 792–802. [Google Scholar] [CrossRef]
  126. Lam, J.R.; Schneider, J.L.; Zhao, W.; Corley, D.A. Proton pump inhibitor and histamine 2 receptor antagonist use and vitamin B12 deficiency. J. Am. Med. Assoc. 2013, 310, 2435–2442. [Google Scholar] [CrossRef] [PubMed]
  127. Thong, B.K.S.; Ima-Nirwana, S.; Chin, K.Y. Proton Pump Inhibitors and Fracture Risk: A Review of Current Evidence and Mechanisms Involved. Int. J. Environ. Res. Public Health 2019, 16, 1571. [Google Scholar] [CrossRef] [PubMed]
  128. Maes, M.L.; Fixen, D.R.; Linnebur, S.A. Adverse effects of proton-pump inhibitor use in older adults: A review of the evidence. Ther. Adv. Drug Saf. 2017, 8, 273–297. [Google Scholar] [CrossRef]
  129. Pariente, A.; de Gage, S.B.; Moore, N.; Bégaud, B. The Benzodiazepine-Dementia Disorders Link: Current State of Knowledge. CNS Drugs 2016, 30, 1–7. [Google Scholar] [CrossRef] [PubMed]
  130. Markota, M.; Rummans, T.A.; Bostwick, J.M.; Lapid, M.I. Benzodiazepine Use in Older Adults: Dangers, Management, and Alternative Therapies. Mayo Clin. Proc. 2016, 91, 1632–1639. [Google Scholar] [CrossRef] [PubMed]
  131. Park, H.; Satoh, H.; Miki, A.; Urushihara, H.; Sawada, Y. Medications associated with falls in older people: Systematic review of publications from a recent 5-year period. Eur. J. Clin. Pharmacol. 2015, 71, 1429–1440. [Google Scholar] [CrossRef]
  132. Dias, B.M.; Santos, F.S.D.; Reis, A.M.M. Potential drug interactions in drug therapy prescribed for older adults at hospital discharge: Cross-sectional study. Sao Paulo Med. J. 2019, 137, 369–378. [Google Scholar] [CrossRef] [PubMed]
  133. Farcas, A.; Bucsa, C.; Sinpetrean, A.; Leucuta, D.; Mogosan, C.; Dumitrascu, D.; Achimas, A.C.; Bojita, M. Preventability analysis of adverse drug reactions detected in two internal medicine departments in Romania. Intern. Emerg. Med. 2014, 9, 187–193. [Google Scholar] [CrossRef] [PubMed]
  134. Mihajlovic, S.; Gauthier, J.; MacDonald, E. Patient Characteristics Associated with Adverse Drug Events in Hospital: An Overview of Reviews. Can. J. Hosp. Pharm. 2016, 69, 294–300. [Google Scholar] [CrossRef]
  135. Jayanthi, C.R.; Renuka, M.; Panchaksharimath, P. An observational study to analyze the adverse drug reactions among the elderly at a tertiary care hospital. Biomed. Pharmacol. J. 2017, 10, 345–352. [Google Scholar] [CrossRef]
  136. Chang, C.H.; Chen, H.C.; Lin, J.W.; Kuo, C.W.; Shau, W.Y.; Lai, M.S. Risk of hospitalization for upper gastrointestinal adverse events associated with nonsteroidal anti-inflammatory drugs: A nationwide case-crossover study in Taiwan. Pharmacoepidemiol. Drug Saf. 2011, 20, 763–771. [Google Scholar] [CrossRef]
  137. Won, S.H.; Suh, S.Y.; Yim, E.; Ahn, H.Y. Risk Factors Related to Serious Adverse Drug Reactions Reported through Electronic Submission during Hospitalization in Elderly Patients. Patients. Korean J. Fam. Med. 2022, 43, 125–131. [Google Scholar] [CrossRef] [PubMed]
  138. Gray, S.L.; Sager, M.; Lestico, M.R.; Jalaluddin, M. Adverse drug events in hospitalized elderly. J. Gerontol. A Biol. Sci. Med. Sci. 1998, 53, M59–M63. [Google Scholar] [CrossRef]
  139. Geeson, C.; Wei, L.; Franklin, B.D. Development and performance evaluation of the Medicines Optimisation Assessment Tool (MOAT): A prognostic model to target hospital pharmacists’ input to prevent medication-related problems. BMJ Qual. Saf. 2019, 28, 645–656. [Google Scholar] [CrossRef] [PubMed]
  140. Nguyen, T.L.; Leguelinel-Blache, G.; Kinowski, J.M.; Roux-Marson, C.; Rougier, M.; Spence, J.; Le Manach, Y.; Landais, P. Improving medication safety: Development and impact of a multivariate model-based strategy to target high-risk patients. PLoS ONE 2017, 12, e0171995. [Google Scholar] [CrossRef]
  141. Urbina, O.; Ferrández, O.; Grau, S.; Luque, S.; Mojal, S.; Marin-Casino, M.; Mateu-de-Antonio, J.; Carmona, A.; Conde-Estévez, D.; Espona, M.; et al. Design of a score to identify hospitalized patients at risk of drug-related problems. Pharmacoepidemiol. Drug Saf. 2014, 23, 923–932. [Google Scholar] [CrossRef]
  142. Trivalle, C.; Burlaud, A.; Ducimetière, P.; IMEPAG Group. Risk factors for adverse drug events in hospitalized elderly patients: A geriatric score. Eur. Geriatr. Med. 2011, 2, 284–289. [Google Scholar] [CrossRef]
  143. Jung-Poppe, L.; Nicolaus, H.F.; Roggenhofer, A.; Altenbuchner, A.; Dormann, H.; Pfistermeister, B.; Maas, R. Systematic Review of Risk Factors Assessed in Predictive Scoring Tools for Drug-Related Problems in Inpatients. J. Clin. Med. 2022, 11, 5185. [Google Scholar] [CrossRef]
  144. Leipzig, R.M.; Cumming, R.G.; Tinetti, M.E. Drugs and falls in older people: A systematic review and meta-analysis: I. Psychotropic drugs. J. Am. Geriatr. Soc. 1999, 47, 30–39. [Google Scholar] [CrossRef]
  145. Woodward, M.C. Deprescribing: Achieving better health outcomes for older people through reducing medications. J. Pharm. Pract. Res. 2003, 33, 323–328. [Google Scholar] [CrossRef]
  146. Wikipedia Contributors. Dirty Drug. 2023. Available online: https://en.wikipedia.org/w/index.php?title=Dirty_drug&oldid=1184110226 (accessed on 12 January 2024).
  147. Bogetti-Salazar, M.; González-González, C.; Juárez-Cedillo, T.; Sánchez-García, S.; Rosas-Carrasco, O. Severe potential drug-drug interactions in older adults with dementia and associated factors. Clinics 2016, 71, 17–21. [Google Scholar] [CrossRef] [PubMed]
  148. Pasqualetti, G.; Tognini, S.; Calsolaro, V.; Polini, A.; Monzani, F. Potential drug-drug interactions in Alzheimer patients with behavioral symptoms. Clin. Interv. Aging. 2015, 10, 1457–1466. [Google Scholar]
  149. Sica, D.A. Diuretic-related side effects: Development and treatment. J. Clin. Hypertens. 2004, 6, 532–540. [Google Scholar] [CrossRef]
  150. Ye, L.; Nieboer, D.; Yang-Huang, J.; Borrás, T.A.; Garcés-Ferrer, J.; Verma, A.; van Grieken, A.; Raat, H. The association between frailty and the risk of medication-related problems among community-dwelling older adults in Europe. J. Am. Geriatr. Soc. 2023, 71, 2485–2494. [Google Scholar] [CrossRef] [PubMed]
  151. Alwhaibi, M.; Balkhi, B. Gender Differences in Potentially Inappropriate Medication Use among Older Adults. Pharmaceuticals 2023, 16, 869. [Google Scholar] [CrossRef] [PubMed]
  152. Sell, R.; Schaefer, M. Prevalence and risk factors of drug-related problems identified in pharmacy-based medication reviews. Int. J. Clin. Pharm. 2020, 42, 588–597. [Google Scholar] [CrossRef]
  153. Lau, D.T.; Mercaldo, N.D.; Shega, J.W.; Rademaker, A.; Weintraub, S. Functional decline associated with polypharmacy and potentially inappropriate medications in community-dwelling older adults with dementia. Am. J. Alzheimer’s Dis. Other Dement. 2011, 26, 606–615. [Google Scholar] [CrossRef]
  154. Marceglia, S.; Mazzola, L.; Bonacina, S.; Tarquini, P.; Donzelli, P.; Pinciroli, F. A comprehensive e-prescribing model to allow representing, comparing, and analyzing available systems. Methods Inf. Med. 2013, 52, 199–219. [Google Scholar] [PubMed]
  155. Esmaeil Zadeh, P.; Tremblay, M.C. A review of the literature and proposed classification on e-prescribing: Functions, assimilation stages, benefits, concerns, and risks. Res. Soc. Adm. Pharm. 2016, 12, 1–19. [Google Scholar] [CrossRef] [PubMed]
  156. Lanham, A.E.; Cochran, G.L.; Klepser, D.G. Electronic prescriptions: Opportunities and challenges for the patient and pharmacist. Adv. Health Care Technol. 2016, 2, 1–11. [Google Scholar]
Jcm 13 01638 g001
Figure 1. The severity of DDIs.
Figure 1. The severity of DDIs.
Jcm 13 01638 g001
Table 1. Baseline characteristics among hospitalized older patients classified by the exposure to DRPs.
Table 1. Baseline characteristics among hospitalized older patients classified by the exposure to DRPs.
CharacteristicsAll (n= 357)DRPs > 1
(n = 205) 		DRP ≤ 1
(n = 152)		p-Value
N (%)N (%)
Age in years, mean (SD) a71.9 (SD 2.7)72.7 (SD 7.8)70.9 (SD 8.1)0.037 #
≤70 years164 (45.9)82 (40.0)82 (53.9)0.009 *
>70 years 193 (54.1)123 (60.0)70 (46.1)
Female194 (54.3)120 (58.5)74 (48.7)0.065 *
Male 163 (45.7)85 (41.5)78 (51.3)
Educational status
≤12 years237 (66.4)138 (67.3)99 (65.1)0.666 *
>12 years 120 (33.6)67 (32.7)53 (34.9)
Marital status
Single23 (6.4)14 (6.8)9 (5.9)0.098 *
Married, widowed, or separated 334 (93.6)191 (93.2)143 (94.1)
Anthropometry
Body weight (kg), mean (SD) a59.5 (SD 12.6)58.9 (SD 13.1)60.2 (SD 12.0)0.347 #
Body mass index (kg/m2), mean (SD) a23.5 (SD 4.6)23.6 (SD 4.9)23.4 (SD 3.9)0.716 #
Lifestyle
Drinking 135 (37.8)75 (36.6)60 (39.5)0.578 *
Smoking 125 (35.0)70 (34.1)55 (36.2)0.690 *
Herbal medicine use 98 (27.5)56 (27.3)42 (27.6)0.948 *
Socioeconomic status
Living alone 51 (14.3)31 (15.1)20 (13.2)0.600 *
Living at residential age care facility 77 (21.6)57 (27.8)20 (13.2)0.001 *
Discharge to residential age care facility103 (28.9)72 (35.1)31 (20.4)0.002 *
No caregiver 236 (66.1)124 (60.5)112 (73.7)0.009 *
Self-drug administration 258 (72.3)138 (67.3)120 (78.9)0.015 *
Healthcare services
Paying for health care; self-paid 293 (82.1)171 (83.4)122 (80.3)0.443 *
History of ≥10 OPD visits within 1 year239 (66.9)153 (74.6)86 (56.6)<0.001 *
History of >1 hospital admission within 1 year44 (12.3)31 (15.1)13 (8.6)0.062 *
History of >1 ER visit within 1 year44 (12.3)27 (13.2)17 (11.2)0.572 *
Number of doctors ≥5 persons 193 (54.1)127 (62.0)66 (43.4)0.001 *
LOS, median (IQR) b6 (IQR 3.5–10)6 (IQR 3–10)6 (IQR 4–10)0.846 +
Data are presented as mean (standard deviation), n (%) or median (interquartile range). * Chi-square test, # Student’s t-test, + Mann–Whitney U test. a Variable represented as mean (SD). b Variable represented as median (IQR). Abbreviations: DRP—drug-related problem; SD—standard deviation; IQR—interquartile range; kg—kilogram; m—meter; ER—emergency room; OPD—outpatient department; LOS—length of stay.
Table 2. Clinical characteristics among hospitalized older patients classified by their exposure to DRPs.
Table 2. Clinical characteristics among hospitalized older patients classified by their exposure to DRPs.
CharacteristicsAll (n = 357)DRPs > 1
(n = 205) 		DRP ≤ 1
(n = 152)		p-Value
N (%)N (%)
Comorbidities
No. of chronic diseases, median (IQR) a5 (IQR 3–6)5 (IQR 4–7)4 (IQR 3–5)<0.001 +
CCI, median (IQR) a6 (IQR 4–8)6 (IQR 5–8)5 (IQR 3–7) <0.001 +
CCI score ≥5257 (72.0)166 (81.0)91 (59.9)<0.001 *
Dyslipidemia258 (72.3)158 (77.1)100 (65.8)0.019 *
Coronary artery disease 104 (29.1)76 (37.1)28 (18.4)<0.001 *
Congestive heart failure 83 (23.2)65 (31.7)18 (11.8)<0.001 *
Arrythmia 96 (26.9)70 (34.1)26 (17.1)<0.001 *
Hypertension305 (85.4)191 (93.2)114 (75.0)<0.001 *
Cirrhosis 26 (7.3)14 (6.8)12 (7.9)0.702 *
Osteoporosis17 (4.8)12 (5.9)5 (29.4)0.261 *
Osteoarthritis 9 (2.5)5 (2.4)4 (2.6)0.909 *
Diabetes mellitus161 (45.1)101 (49.3)60 (39.5)0.066 *
Chronic kidney disease 150 (42.0)106 (51.7)44 (28.9)<0.001 *
Anemia 176 (49.3)111 (54.1)65 (42.8)0.033 *
Depression13 (3.6)10 (4.9)3 (2.0)0.147 *
Dementia5 (1.4)5 (2.4)0 (0.0)0.075 *
Cerebrovascular disease 59 (16,5)38 (18.5)21 (13.8)0.235 *
Cataract72 (20.2)44 (21.5)28 (18.4)0.479 *
Malignancy70 (19.6)45 (22.0)25 (16.4)0.195 *
Obstructive sleep apnea 14 (3.9)12 (5.9)2 (1.3)0.029 *
Primary diagnosis
Congestive heart failure34 (9.5)26 (12.7)8 (5.3)0.018 *
Acute cholangitis 18 (5.0)9 (4.4)9 (5.9)0.513 *
NSTEMI33 (9.2)20 (9.8)13 (8.6)0.698 *
Data are presented as mean (standard deviation), n (%), or median (interquartile range). * Chi-square test, + Mann–Whitney U test. a Variable represented as median (IQR). Abbreviations: DRP—drug-related problem; CCI—Charlson comorbidity index; IQR—interquartile range; NSTEMI—non-ST elevation myocardial infarction.
Table 3. Geriatric conditions among hospitalized older patients classified by their exposure to DRPs.
Table 3. Geriatric conditions among hospitalized older patients classified by their exposure to DRPs.
CharacteristicsAll (n= 357)DRPs > 1 (n = 205) DRP ≤ 1 (n = 152)p-Value
N (%)N (%)
Geriatric conditions
MoCA score, mean (SD) a17.5 (SD 7.0)17.3 (SD 6.9)17.8 (SD 7.3)0.486 #
MoCA score < 25 283 (79.3)168 (82.0)115 (75.7)0.147 *
TGDS score, median (IQR) b3 (IQR 2–4.5)3 (IQR 2–5)2 (IQR 1–4)0.088 +
TGDS score > 566 (18.5)39 (19.0)27 (17.8)0.761 *
BADLs, mean (SD) a18.0 (SD 3.8)17.8 (SD 3.8)18.4 (SD 3.8)0.734 #
BADL impairment33 (9.2)20 (9.8)13 (8.6)0.162 *
IADLs, median (IQR) b6 (IQR 3–8)5 (IQR 2–7.5)7 (IQR 4–8)<0.001 +
IADL impairment243 (68.1)154 (75.1)89 (58.6)0.001 *
CFS score, median (IQR) b4 (IQR 3–5) 4 (IQR 3–5)3 (IQR 3–4)<0.001+
CFS score ≥5 107 (30.0)72 (35.1)35 (23.0)0.014 *
Sleep disorder 206 (57.7)127 (62.0) 79 (52.0)0.059 *
Urinary incontinence 114 (31.9)71 (34.6)43 (28.3)0.204 *
Fecal incontinence 61 (17.1)39 (19.0)22 (14.5)0.259 *
History of fall within the past 3 months 106 (29.7)74 (36.1)32 (21.1)0.002 *
History of delirium 34 (9.5)16 (7.8)18 (11.8)0.199 *
Pressure ulcer 10 (2.8)8 (3.9)2 (1.3)0.199 *
Visual impairment37 (10.4)21 (10.2)16 (10.5)0.931 *
Hearing impairment1 (0.3)1 (0.5)0 (0.0)1.000 *
Significant weight loss 192 (53.8)112 (54.6)80 (52.6)0.707 *
Artificial enteral nutrition 12 (3.4)7 (3.4)5 (3.3)0.948 *
Pain 116 (32.5)60 (29.3)56 (36.8)0.131 *
Ambulation with assistance 120 (33.6) 81 (39.5)39 (25.7)0.006 *
Data are presented as mean (standard deviation), n (%), or median (interquartile range). * Chi-square test, # Student’s t-test, + Mann–Whitney U test. a Variable represented as mean (SD). b Variable represented as median (IQR). Abbreviations: SD—standard deviation; IQR—interquartile range; MoCA—Montreal Cognitive Assessment; TGDS—Thai Geriatric Depression Scale; BADLs—basic activities of daily living; IADLs—instrumental activities of daily living; CFS—Clinical Frailty Scale; DRP—drug-related problem.
Table 4. Laboratory results among hospitalized older patients classified by their exposure to DRPs.
Table 4. Laboratory results among hospitalized older patients classified by their exposure to DRPs.
CharacteristicsAll (n = 357)DRPs > 1
(n = 205) 		DRP ≤ 1
(n = 152)		p-Value
White blood cell count (cells/mm3), median (IQR)7540 (5405–9720)7560 (5890–9975)7170 (5247–8880)0.351 +
Hemoglobin (g/dL), mean (SD)10.6 (2.1)10.4 (1.9)10.9 (2.2)0.046 #
Platelet (cell/mm3), median (IQR)232,000 (159,500–297,500)224,000 (151,000–293,500)250,000 (205,000–292,000)0.257 +
Blood urea nitrogen (mg/dL),
median (IQR)		18 (12–29)25.0 (16.0–36.5)14.0 (10.0–24.8)<0.001+
Creatinine (mg/dL), median (IQR)1.1 (0.8–1.7)1.2 (0.8–2.1)0.8 (0.6–1.1)<0.001+
Sodium (mmol/L), mean (SD)136.9 (3.7)136.7 (3.7)137.2 (3.8)0.248 #
Potassium (mmol/L), mean (SD)4.1 (0.5)4.1 (0.5)4.1 (0.5)0.742 #
Calcium (mg/dL), mean (SD)8.8 (0.8)8.8 (0.8)8.7 (0.8)0.156 #
Magnesium (mg/dL), mean (SD)1.9 (0.3)1.9 (0.3)1.9 (0.3)0.930 #
Phosphate (mg/dL), mean (SD)3.5 (1.2)3.6 (1.2)3.5 (1.1)0.297 #
Aspartate aminotransferase (U/L),
median (IQR)		34 (23.5–53)37 (24–49)35.5 (25.3–68.3)0.256 +
Alanine aminotransferase (U/L), median (IQR)24 (14–48)23 (12–45.5)25.5 (16–58)0.031 +
Alkaline phosphatase (U/L), median (IQR)91.5 (69,3–126.0)90 (66–122.5)93 (76–109.8)0.950 +
Total bilirubin (mg/dL), median (IQR)0.7 (0.5–1.0)0.6 (0.5–1.1)0.7 (0.5–1.2)0.309 +
Albumin (g/L), mean (SD)30.4 (6.5)30.7 (6.1)30.0 (6.8)0.340 #
Blood sugar (mg/dL), mean (SD)132.5 (45.1)135.7 (46.8)128.2 (42.7)0.121 #
Thyroid stimulating hormone (uIU/mL),
median (IQR)		1.3 (0.7–2.3)1.4 (0.8–2.6)1.2 (0.6–1.9)0.207 +
Free triiodothyronine (pg/mL), mean (SD)1.9 (0.5)1.9 (0.4)2.2 (0.6)0.004 #
Free thyroxine (ng/dL), mean (SD)1.0 (0.3)1.0 (0.3)1.0 (0.3)0.596 #
Partial thromboplastin time (sec), mean (SD)28.0 (6.7)28.6 (7.1)27.2 (5.9)0.070 #
Prothrombin time (sec), mean (SD)13.7 (4.2)14.2 (5.1)13.1 (2.3)0.013 #
Thrombin time (sec), mean (SD) 18.7 (4.7)18.9 (5.6)18.4 (3.0)0.297 #
Data are presented as mean (standard deviation) or median (interquartile range). # Student’s t-test, + Mann–Whitney U test. Abbreviations: mm—millimeter; g—gram; dL—deciliter; mg—milligram; mL—milliliter; mmol—millimole; L—liter; U—unit; uIU—micro international unit; pg—picogram; ng—nanogram; SD—standard deviation; IQR—interquartile range; sec—second; DRP—drug-related problem.
Table 5. Prescribed medication use among the study participants according to the ATC system.
Table 5. Prescribed medication use among the study participants according to the ATC system.
CharacteristicsAll (n = 357)DRPs > 1 (n = 205) DRP ≤1 (n = 152)p-Value
N (%)N (%)
Total number of prescribed medications 3136 (100)2241 (100)895 (100)
Number of prescribed medications per person, median (IQR) a9 (IQR 5–12)11 (IQR 8–13)5 (IQR 3–8)<0.001 +
Polypharmacy 290 (81.2)199 (97.1)91 (59.9)<0.001 *
Exposure to at least 1 ADE27 (7.6)24 (11.7)3 (2.0)0.001 *
Exposure to at least 1 PIM242 (67.8)201 (98.0)41 (27.0)<0.001 *
Exposure to at least 1 DDI256 (71.7)202 (98.5)54 (35.5)<0.001 *
Prescribed medications according to ATC classes and codes
A02 Drugs for acid-related disorders 151 (42.3)115 (56.1)36 (23.7)<0.001 *
A03 Drugs for functional gastrointestinal disorders28 (7.8)20 (9.8)8 (5.3)0.118 *
A06 Drugs for constipation 78 (21.8)50 (24.4)28 (18.4)0.177 *
A10 Drug used in diabetes 100 (28.0)74 (36.1)26 (17.1)<0.001 *
A11 Vitamins 194 (54.3)129 (62.9)65 (42.8)<0.001 *
A12 Mineral supplements 139 (38.9)98 (47.8)41 (27.0)<0.001 *
B01 Antithrombotic agents 165 (46.2)123 (60.0)42 (27.6)<0.001 *
B05 Blood substitutes 60 (16.8)42 (20.5)18 (11.8)0.031 *
C01 Cardiac therapy 76 (18.8)60 (29.3)7 (4.6)<0.001 *
C02 Antihypertensives 63 (17.6)52 (25.4)11 (7.2)<0.001 *
C03 Diuretics 66 (18.5)59 (28.8)7 (4.6)<0.001 *
C07 Beta blocking agents 137 (38.4)98 (47.8)39 (25.7)<0.001 *
C08 Calcium channel blockers 158 (44.3)104 (50.7)54 (35.5)0.004 *
C09 Agents acting on the renin-angiotensin system89 (24.9)54 (26.3)35 (23.0)0.474 *
C10 Lipid modifying agents222 (62.2)148 (72.2)74 (48.7)<0.001 *
D Dermatologicals 13 (3.6)8 (3.9)5 (3.3)0.760 *
H02 Corticosteroids 53 (14.8)31 (15.1)22 (14.5)0.865 *
Data are presented as n (%) or median (interquartile range) * Chi-square test, + Mann–Whitney U test. a Variable represented as median (IQR). Abbreviations: IQR—interquartile range; ATC—Anatomical Therapeutic Chemical; DRP—drug-related problem; PIM—potentially inappropriate medication; DDI—drug–drug interaction; ADE—adverse drug event.
Table 6. Prescribed medication use among the study participants according to the ATC system.
Table 6. Prescribed medication use among the study participants according to the ATC system.
CharacteristicsAll (n= 357)DRPs > 1 (n = 205) DRP ≤ 1 (n = 152)p-Value
N (%)N (%)
Prescribed medications according to ATC classes and codes
H Systemic hormonal preparations 51 (14.3)37 (18.0)14 (9.2)0.018 *
H02 Corticosteroids 53 (14.8)31 (15.1)22 (14.5)0.865 *
H03 Thyroid therapy24 (6.7)18 (8.8)6 (3.9)0.071 *
J01 Antibacterial drugs111 (31.1)62 (30.2)49 (32.2)0.687 *
J05 Antivirals for systemic use 57 (16.0)34 (16.6)23 (15.1)0.711 *
L01 Antineoplastic agents8 (2.2)4 (2.0)4 (2.6)0.727 *
L03 Immunostimulants 7 (2.0)7 (3.4)0 (0.0)0.022 *
L04 Immunosuppressants 40 (11.2)29 (14.1)11 (7.2)0.041 *
M05 Drugs for treatment of bone diseases 6 (1.7)6 (2.9)0 (0.0)0.040 *
N02 Analgesics101 (28.3)64 (31.2)37 (24.3)0.154 *
N03 Antiepileptics 54 (15.1)42 (20.5)12 (7.9)0.001 *
N05 Psycholeptics 60 (16.8)52 (25.4)8 (5.3)<0.001 *
N05A Antipsychotics14 (3.9)14 (6.8)0 (0.0)0.001 *
N05B-N05C Anxiolytics, sedatives and hypnotics 48 (13.4)40 (19.5)8 (5.3)<0.001 *
N06 Psychoanaleptics 21 (5.9)19 (9.3)2 (1.3)0.002 *
N06A Antidepressants 22 (6.2)18 (8.8)4 (2.6)0.017 *
N06D Anti-dementia drugs2 (0.6)2 (1.0)0 (0.0)0.510 *
R03 Drugs for obstructive airway diseases53 (14.8)36 (17.6)17 (11.2)0.094 *
R05 Cough and cold preparations38 (10.6)29 (14.1)9 (5.9)0.013 *
R06 Antihistamines for systemic use 45 (12.6)32 (15.6)13 (8.6)0.047 *
Data are presented as n (%). * Chi-square test. Abbreviations: ATC—Anatomical Therapeutic Chemical; DRP—drug-related problem.
Table 7. Prevalence of each type of drug-related problem in the study population.
Table 7. Prevalence of each type of drug-related problem in the study population.
CharacteristicsN (%)
Number of participants with the different combinations of criteria of PIM, DDI, ADE357 (100)
None54 (15.1)
Only PIM41 (11.5)
Only DDI54 (15.1)
Only ADE3 (0.8)
ADE + PIM3 (0.8)
ADE + DDI4 (1.1)
PIM + DDI181 (50.7)
ADE + PIM + DDI17 (4.8)
Potentially inappropriate medications (PIMs), older person357 (100)
No PIM115 (32.2)
At least 1 PIMs242 (67.8)
180 (22.4)
258 (16.2)
≥3104 (29.1)
Drug–drug interactions (DDIs), older person357 (100)
Without DDI101 (28.3)
At least 1 DDI256 (71.7)
With 1–5 DDIs171 (47.9)
With 6–10 DDIs67 (18.8)
With > 10 DDIs18 (5.0)
Severity of adverse drug events (ADEs)27 (100)
Grade 1 Mild0 (0)
Grade 2 Moderate0 (0)
Grade 3 Severe 15 (55.6)
Grade 4 Life-threatening consequences12 (44.4)
Grade 5 Death related to ADE 0 (0)
Data are presented as n (%). Abbreviations: ADE—adverse drug event; PIM—potentially inappropriate medication; DDI—drug–drug interaction.
Table 8. Multivariate analysis of factors associated with exposure to > 1 criterion of DRPs.
Table 8. Multivariate analysis of factors associated with exposure to > 1 criterion of DRPs.
The Associated FactorsMultivariate ap-Value
OR (95%CI)
Baseline and clinical characteristics
History of previous fall in the past 12 months1.79 (0.89–3.58)0.097
Age > 70 years1.09 (0.59–2.04)0.776
Female1.44 (0.79–2.62)0.235
Barthel ADL score < 120.48 (0.16–1.44)0.192
Lawton IADL score < 81.24 (0.62–2.51)0.552
MoCA score < 250.91 (0.43–1.93)0.805
CCI score ≥ 51.22 (0.59–2.50)0.583
CFS score ≥ 50.95 (0.43–2.11)0.907
Medication profiles
Polypharmacy8.25 (3.14–21.70)<0.001 *
A02 Drugs for acid-related disorders3.82 (2.08–7.00)<0.001 *
A10 Drug used in diabetes2.33 (1.19–4.59)<0.001 *
C01 Cardiac therapy3.19 (1.29–7.89)0.012
C03 Diuretics8.20 (3.11–21.65)<0.001 *
N05 Psycholeptics4.59 (1.80–11.69)0.001 *
N06 Psychoanaleptics5.97 (1.21–29.34)0.028
* p < 0.01. a Adjusted for age, sex, Barthel ADL score, Lawton IADL score, MoCA score, CCI score, CFS score, a history of previous falls in the past 12 months and significant variables from the unadjusted model. Data are presented as odds ratio (95% confidence interval). Abbreviations: DRP—drug-related problems; OR—odds ratio; CI—confidence interval.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Pramotesiri, P.; Putthipokin, K.; Ruangritchankul, S. Drug Related Problems among Older Inpatients at a Tertiary Care Setting. J. Clin. Med. 2024, 13, 1638. https://doi.org/10.3390/jcm13061638

AMA Style

Pramotesiri P, Putthipokin K, Ruangritchankul S. Drug Related Problems among Older Inpatients at a Tertiary Care Setting. Journal of Clinical Medicine. 2024; 13(6):1638. https://doi.org/10.3390/jcm13061638

Chicago/Turabian Style

Pramotesiri, Porrawee, Krongtong Putthipokin, and Sirasa Ruangritchankul. 2024. "Drug Related Problems among Older Inpatients at a Tertiary Care Setting" Journal of Clinical Medicine 13, no. 6: 1638. https://doi.org/10.3390/jcm13061638

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop