Next Article in Journal
Frequency of BRAF Mutations in Dysplastic Nevi, Lentigo Maligna, and Melanoma In Situ
Previous Article in Journal
Exploring the Influence of Contextual Factors and the Caregiving Process on Caregiver Burden and Quality of Life Outcomes of Heart Failure (HF) Dyads after a Hospital Discharge: A Mixed-Methods 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 16
10.3390/jcm13164798
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

Mortality Outcomes and Contributing Risk Factors in Patients with Hospital-Associated Disability

by
Soo-Jeong Jo
1,
So-Hee Lee
1,
Hyo-Jin Min
2,3,
Hee-Ji Kim
4 and
Hyun-Ho Kong
1,5,*
1
Department of Rehabilitation Medicine, Chungbuk National University Hospital, Cheongju 28644, Republic of Korea
2
Department of Clinical Pharmacology and Therapeutics, Chungbuk National University Hospital, Cheongju 28644, Republic of Korea
3
Department of Medicine, Chungbuk National University Graduate School, Cheongju 28644, Republic of Korea
4
Department of Nursing, Chungbuk National University Hospital, Cheongju 28644, Republic of Korea
5
Department of Rehabilitation Medicine, Chungbuk National University College of Medicine, Cheongju 28644, Republic of Korea
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2024, 13(16), 4798; https://doi.org/10.3390/jcm13164798
Submission received: 25 June 2024 / Revised: 29 July 2024 / Accepted: 12 August 2024 / Published: 15 August 2024
(This article belongs to the Section Clinical Rehabilitation)
Browse Figures
Versions Notes

Abstract

:
Background/Objectives: Hospital-associated disability (HAD), a functional decline following acute hospitalization, is a common complication associated with mortality and unfavorable prognoses in patients admitted to acute care hospitals. However, few studies have investigated mortality and associated factors in patients with HAD and have been limited by inconsistent HAD assessment tools and criteria. This study investigated mortality and risk factors in patients with HAD using specific criteria. Methods: This retrospective study evaluated patients referred to the Department of Rehabilitation Medicine with suspected HAD between June 2022 and March 2023. The collected data included medical histories, diagnostic tests for HAD (including muscle strength, balance, and modified Barthel Index), and bioelectrical impedance analysis (BIA). Multivariate logistic regression analysis was conducted to identify factors associated with mortality. Kaplan–Meier survival curves were constructed for mortality at 3 and 7 months. Results: A total of 455 patients were identified, among which 206 patients diagnosed with HAD (73.1 ± 12.5 years) were included in the analysis. The 3-month mortality rate was 27.2%. In the multivariate analysis, male sex (odds ratio (OR), 3.23; p < 0.01), a history of cancer (OR, 2.18; p < 0.05), and a low phase angle (OR, 0.69; p < 0.05) were significantly associated with mortality. A phase angle < 2.9° on BIA was associated with a significant increase in 3-month (hazard ratio (HR), 1.85; 95% confidence interval (CI), 1.06–3.23) and 7-month (HR, 2.80; 95% CI, 1.75–4.98) mortality. Conclusions: Patients with HAD had a high mortality rate, with several factors, particularly low BIA phase angles, associated with increased mortality.

1. Introduction

Extensive research has been conducted on the risk of developing functional decline in hospitalized patients with acute health problems, affecting approximately 30% of patients over the age of 70 years [1]. This condition, commonly known as hospital-associated disability (HAD), is characterized by a loss of independence in activities of daily living (ADLs) following hospitalization owing to acute medical illnesses [2,3]. This decline in ADLs manifests as limitations in performing daily tasks, including bathing, dressing, eating, and urinating, as well as mobility functions such as standing up from a chair or bed and walking [2]. The deterioration in ADLs may be attributed directly to the underlying cause of hospitalization or to secondary effects associated with acute hospitalization, such as immobility, polypharmacy, and undernutrition [2,4].
HAD can adversely affect clinical outcomes and result in an increased utilization of medical resources. When patients experience a decline in ADLs, they become incapable of living independently, resulting in higher admission rates to healthcare facilities, longer hospital stays, increased readmission rates, and elevated short- and long-term mortality rates [5,6,7,8]. Furthermore, an increase in the number of patients with HAD leads to higher medical expenditures in socioeconomic terms and increased utilization of other resources, such as caregiver services [9]. Approximately half of the newly occurring loss in ADLs is attributed to hospital admissions, highlighting the importance of HAD for doctors, patients, and their carers [10].
Previous studies on HAD mainly focused on its prevalence [3,11], risk factors [10,12,13,14], and functional outcomes [6,15]. However, few studies have examined the mortality rate of patients with HAD [2,3], and most of these studies had limitations, as functional assessment was mainly conducted through self-report methods rather than objective and quantitative physical function tests [6,9,12,15,16]. In addition, while there have been reports on the associations between body composition analyses (such as the skeletal muscle index, percentage body fat, bioelectrical impedance analysis (BIA), and phase angle) and outcomes in critically ill patients or those with a history of cancer [17,18,19,20,21], no studies have reported these associations in patients with HAD.
Therefore, this study aimed to investigate the mortality rate of HAD and its associated risk factors using various objective and quantitative assessment tools.

2. Materials and Methods

2.1. Study Population

This single-center retrospective study assessed patients admitted to a tertiary hospital between June 2022 and March 2023. Participants included those referred for consultation to the Department of Rehabilitation Medicine, with suspicion of ‘disuse syndrome’ (Korea Rehabilitation Impairment Category (KRIC) 22). ‘Disuse syndrome’ refers to the condition of functional decline after acute hospitalization, despite patients having been previously independent in their ADLs, and this term is indicated as ‘HAD’ in this study.
The inclusion criteria were as follows: (i) patients referred for a diagnosis of HAD through the screening system of the Hospital Information System (HIS) 14 days after hospitalization (n = 372), and (ii) those referred directly to the Department of Rehabilitation Medicine for a diagnosis of HAD (n = 83). The exclusion criteria were as follows: (i) those who failed to meet the diagnostic criteria of ‘disuse syndrome (KRIC 22)’ (n = 195), (ii) those whose general condition deteriorated or who were discharged before evaluation and failed to undergo tests (n = 39), and (iii) those with insufficient medical records (n = 15). As a result, a total of 206 patients (115 males and 91 females) diagnosed with HAD who met the aforementioned inclusion and exclusion criteria were included in the analysis (Figure 1).
Data on age, sex, anthropometric factors (height, weight, body mass index (BMI)), underlying diseases (coronary artery disease, congestive heart failure, cerebrovascular disease, chronic obstructive pulmonary disease (COPD), diabetes mellitus, chronic kidney disease, hypertension, history of cancer), and smoking history were collected. In addition, information related to the patient’s hospitalization, such as length of stay (LOS), intensive care unit (ICU) admission, length of ICU stay, diagnostic code, and department requesting the consultation, was obtained. Information on whether patients died was investigated at an average of 3 and 7 months after the diagnosis of HAD through hospital records or by contacting patients’ family members. Data collection for the patients was carried out by a specialized nurse. Ethical approval was obtained from the Institutional Review Board (IRB) of the tertiary hospital (IRB number: 2023-06-032).

2.2. Automated Screening System for Hospital-Associated Disability

We developed a system to automatically screen individuals at risk of developing HAD using patient information from the HIS (Figure 2). This system automatically refers patients who have been hospitalized for more than 2 weeks but less than 60 days to the Department of Rehabilitation Medicine. However, patients with conditions such as strokes, lower limb fractures, and spinal cord injuries that do not satisfy the KRIC 22 criteria were automatically excluded from the screening. Once referred, a rehabilitation medicine doctor evaluated their previous independent ADLs and then performed physical function tests. If the diagnostic criteria for HAD were met based on these tests, the patient was diagnosed with HAD.

2.3. Diagnostic Criteria for Hospital-Associated Disability

In this study, functional decline was characterized by weakness in muscle strength as well as deterioration in balance ability or ADLs based on the criteria of ‘disuse syndrome (KRIC 22)’. The diagnostic criteria for ‘disuse syndrome’ followed those established by the Health Insurance Review & Assessment Service, Republic of Korea [22]. As previously mentioned, we defined ‘disuse syndrome’ as equivalent to HAD in our study. A rehabilitation medicine doctor diagnosed HAD through a medical examination and functional evaluation. If the pre-admission ADL function was confirmed to be independent using the Katz ADL score acquired from the patient or caregiver, a functional assessment was conducted [23]. Functional evaluation consisted of two physical function tests, namely Manual Muscle Testing (MMT) and the Berg Balance Scale (BBS), as well as the Modified Barthel Index (MBI). All functional evaluations were conducted by an experienced physical therapist. HAD was diagnosed if the sum of the muscle strength scores was less than 48 points, with a concomitant BBS score of <40 points or an MBI score of <80 points [24,25].

2.4. Quantitative Assessment of Functional Decline

The MMT scores were measured for each of the three muscles of the upper and lower extremities (shoulder joint abductor, elbow joint flexor, wrist joint extensor, hip joint flexor, knee joint extensor, and ankle joint extensor (plantar flexor)). Muscle strength was evaluated using the Medical Research Council (MRC) score, which assesses the strength of each muscle on a scale of 0 to 5 points, resulting in a total score of 0 to 60 points [24].
The BBS is a clinical tool used to evaluate balance ability and the risk of falling. It comprises 14 items, including sitting and standing up, standing without assistance, sitting without assistance, and standing with one’s eyes closed. Each item is measured on a scale from 0 to 4, where 0 represents a state in which the test may not be performed, and 4 represents a state in which the test may be performed independently. The total score of the BBS is 56 points, with higher scores indicating better balance ability closer to the norm [26].
The MBI is a standardized tool used to assess the degree of dependence in ADLs and consists of 10 items. The test was conducted by an experienced physical therapist in the tertiary hospital. Each item is rated with 5 grades according to the degree of assistance needed by the patient, with lower scores indicating greater levels of dependence. The maximum score is 100, which represents complete independence in ADLs [27].
Hand grip strength in the dominant hand was measured using a digital dynamometer (T.K.5710, grip-D Takei, Tokyo, Japan (Takei Scientific Instruments Co., Ltd.)). The patient was positioned in a sitting position with their elbow flexed at 90°, and hand grip strength was measured with the wrist and forearm in a neutral position [28,29]. In instances where the patient was unable to sit, the test was conducted in the supine position. Hand grip strength was measured twice, and the higher measurement value was recorded [30]. Owing to the significant difference in normal handgrip strength values between males and females, patients were divided into the low-handgrip-strength and standard groups according to the criteria of the Asian Working Group for Sarcopenia (AWGS) 2019 (normal handgrip strength values: ≥28.0 kg for males and ≥18.0 kg for females) [30].

2.5. Measurement of Body Composition Using Bioelectrical Impedance Analysis

Body composition was evaluated using a BIA device (the BWA 2.0®, InBody Corp., Seoul, Republic of Korea). The BWA 2.0 body composition analyzer employs clamp-type electrodes, with one electrode on each wrist and one on each ankle. The BIA assessment was conducted by an experienced physical therapist within 3 days following the consultation request for the diagnosis of HAD. The BIA provides measurements for the whole-body muscle and fat weights, the whole-body phase angle (PhA) at 50 kHz, the lean body mass, percent body fat (PBF), and the skeletal muscle mass index (SMI). Owing to the significant difference in normal SMI values between males and females, patients were divided into the low-muscle-mass and standard groups according to the BIA criteria of the Asian Working Group for Sarcopenia (AWGS) 2019 (normal SMI values: ≥7.0 kg/m2 for males and ≥5.7 kg/m2 for females) [30]. BIA measures the body resistance (R) and reactance (Xc) by recording a voltage drop in applied current. Resistance is related to total body tissue fluid, while reactance is associated with conserving cell membranes [17]. Reactance leads to a delay in the current relative to the voltage, resulting in a phase shift. This shift can be geometrically described as the angular transformation derived from the ratio of reactance to resistance, known as the PhA [31]. Low PhAs indicate cell death or reduced cell integrity, whereas high PhAs indicate a greater amount of intact cell membranes [32].

2.6. Statistical Analysis

Continuous variables were expressed as means and standard deviations, and categorical variables are expressed as numbers and percentages. To compare characteristics between 3-month survivors and non-survivors among patients with HAD, Student’s t-test and the chi-squared test were employed for continuous and categorical variables, respectively. Univariate logistic regression analysis was performed to identify risk factors related to mortality, and variables with p-values less than 0.05 were considered statistically significant. Multivariate logistic regression analysis was performed using the stepwise elimination approach, including statistically significant variables from the univariate analysis. In addition, receiver operating characteristic (ROC) curve analysis using the Youden method was conducted to determine the optimal cut-off value for the PhA. The Kaplan–Meier method was used to determine whether the mortality rate according to the PhA cut-off was significantly different at the 3- and 7-month time points. Differences in survival curves were assessed using the log-rank test. All statistical analyses were performed using SPSS version 25.0 (IBM Corporation, Chicago, IL, USA), SAS® analytics Pro Version 9.4 (SAS Institute Inc., Cary, NC, USA), and MedCalc® Statistical Software version 20.009 (MedCalc Software Ltd., Ostend, Belgium).

3. Results

3.1. Patient Characteristics

The characteristics of the patients with HAD are shown in Table 1. Of the 206 patients analyzed, 56 (27.1%) died within the 3-month follow-up period and 79 (38.8%) died within the average 7-month follow-up period after diagnosis of HAD. In a comparison between survivors and non-survivors 3 months after HAD diagnosis, there were statistically significant differences between the two groups in terms of sex (male, p < 0.001), history of cancer (p < 0.01), PhA (3.5 ± 1.2° in survivors vs. 3.1 ± 1.1° in non-survivors; p < 0.05), and PBF (29.1 ± 10.9% in survivors vs. 25.4 ± 11.4% in non-survivors; p < 0.05). However, no statistically significant differences were observed in age, BMI, ICU admission, or underlying diseases, excluding cancer history.

3.2. Risk Factors Associated with 3-Month Mortality

Table 2 presents the risk factors affecting the 3-month mortality in patients with HAD. In the univariate analysis, sex (male, odds ratio (OR), 3.16; 95% confidence interval (CI), 1.60–6.27; p < 0.01), history of cancer (OR, 2.59; 95% CI, 1.38–4.86; p < 0.01), low PhA (OR, 0.74; 95% CI, 0.54–0.99; p < 0.05), and PBF (OR, 0.97; 95% CI, 0.94–0.99; p < 0.05) were associated with the mortality within 3 months after diagnosis of HAD. In the multivariate logistic regression analysis, male sex (OR, 3.23; 95% CI, 1.58–6.61; p < 0.01), a history of cancer (OR, 2.18; 95% CI, 1.13–4.20; p < 0.05), and low PhA (OR, 0.69; 95% CI, 0.50–0.95; p < 0.05), excluding PBF, were found to be significantly associated with the 3-month mortality.

3.3. Phase Angle and Mortality of Patients with HAD

In the ROC curve analysis, the best cut-off value of the PhA for predicting 3-month mortality was 2.9° (sensitivity = 55%, specificity = 64%, area under the curve = 0.60, p < 0.05) (Figure 3). Figure 4 shows a Kaplan–Meier survival curve which confirms mortality within 3 months after HAD diagnosis based on the PhA cut-off value and shows that a PhA of less than 2.9° is associated with a statistically significant increase in 3-month mortality (log-rank test; p < 0.05). In addition, Figure 5 shows a Kaplan–Meier survival curve that displays the mortality at an average of 7 months after HAD diagnosis, using the same PhA cut-off value. The results show that when the PhA is below 2.9°, a significant increase is observed in the mortality at 7 months (log-rank test; p < 0.0001).

4. Discussion

This is the first study of mortality and associated risk factors in patients with HAD following the implementation of an automated screening system for those at risk of developing HAD. Among patients diagnosed with HAD, 27.1% died within 3 months, and 35.9% died within 7 months. The analysis showed that mortality within 3 months after the diagnosis of HAD was associated with male sex, a history of cancer, and a low PhA. There was also a significant difference in the trend of mortality in patients with HAD when the PhA cut-off point was set at 2.9°.
The mortality rate for patients with HAD in this study was higher than in previous studies. In previous studies of patients with HAD, the 3-month mortality rate was lower than in our study (11% in patients aged ≥70 years) [5], and the 6-month and 1-year mortality rates were similar to the 3-month rate in our study, at 29% and 33%, respectively [33,34]. Additionally, the mortality rate of patients with HAD in this study was higher than the 3-month mortality rate of 17.2% for all patient groups discharged from tertiary hospitals, regardless of HAD diagnosis [35], and higher than the mortality rate observed in studies of a non-HAD group [15]. There are two possible reasons for the higher mortality rate. Firstly, this study may have included more severely ill patients than previous studies. Other studies recruited patients from several hospitals, including geriatric care and community teaching hospitals as well as tertiary hospitals, and excluded patients admitted to ICUs or targeted patients at Geriatric Post-Acute Care Units [34,36]. Secondly, the composition of the included patients’ diagnoses differed from that of other studies, which could have contributed to the difference in mortality rates. A previous study of patients admitted to the post-acute care unit divided patients into several disease groups and compared the mortality rate; the deconditioning patient group had significantly higher 1-year mortality than the stroke or orthopedic fracture group [36]. The mortality rate may have been higher in this study because it excluded patients with conditions like fractures or strokes and included only patients who were primarily deconditioned.
In this study, a history of cancer and male sex were significantly associated with 3-month mortality in patients with HAD. First, cancer is the leading cause of death in South Korea and is known to increase the severity of hospitalized patients [37]. Notably, previous studies have reported that older hospitalized patients with a history of cancer have significantly higher mortality rates regardless of the initial diagnosis [38]. It is also known that patients with cancer are more likely to develop new disabilities during hospitalization, and the resulting decline in functional status is associated with mortality in patients with cancer after discharge [16]. Second, in this study’s subgroup analysis on sex, males had a significantly higher rate of sarcopenia than females when classified according to the criteria of the AWGS (p < 0.05) and a higher rate of having COPD as an underlying disease (p < 0.01). Because skeletal muscle mass is associated with immune mechanisms and glucose and protein metabolism [39,40], a decrease in skeletal muscle mass is known to affect physiologic function adversely, and our results are consistent with previous reports that a low SMI is associated with higher mortality in critically ill patients [41,42,43]. Furthermore, COPD is one of the leading causes of mortality worldwide and has been reported to increase mortality in hospitalized patients with COPD due to increased risk of hypertension, diabetes mellitus, and coronary artery disease [44,45,46]. In particular, it is well known that acute exacerbations of COPD can contribute to increased mortality in older adult patients in tertiary care hospitals [47,48,49].
In this study, the PhA was associated with a higher risk of mortality in patients with HAD. PhA assessment via BIA is a cost-effective and straightforward method, making it a valuable prognostic factor in clinical settings [50]. Previous studies have reported that low PhAs, indicative of decreased cellular integrity, are associated with higher mortality across various diseases. For example, PhA thresholds for increased mortality have been reported to range from <4.4° to <5.8° for patients with cancer, <5.4° for cirrhosis, <4.2° for heart disease, and <3.5° for older adults [17,18,19,31,50,51]. However, the PhA has received less attention in hospitalized patients, and research in this area is limited. Notably, the PhA cut-off identified in this study was lower than those documented in other studies involving critically ill patients [21,52,53]. As noted, the PhA cut-offs for elevated mortality risk vary significantly between diseases and even within the same condition, likely due to differences in patient demographics. Although the PhA cut-off in this study was relatively low compared to other studies, the findings are consistent with previous research on critically ill older patients (men ≤3.29°, women ≤3.01°) [20].
This study had some limitations. First, it is challenging to generalize the results of this study because it only included Asian patients admitted to a single tertiary hospital. This limitation is planned to be addressed through future multi-center research. Second, this study focused only on patients diagnosed with HAD among those admitted to a tertiary hospital, and therefore caution should be taken when generalizing the results. Third, as this study is a retrospective study, it was not possible to assess the functional status of patients before and after hospitalization using the same methods, and thus the degree of functional decline could not be quantitatively presented. Lastly, this study assessed HAD in patients referred through an automated screening system rather than including all hospitalized patients. Therefore, some patients may not have been included in the analysis. Nevertheless, this study is valuable because it is the first to use patient information from an HIS to screen patients at risk for HAD. The findings show that body composition factors, such as PhAs, are associated with mortality in patients with HAD. Additionally, we defined patients with HAD as those who were previously independent in their daily activities but experienced a new functional decline after hospitalization. This definition can aid in targeting these individuals for intervention and rehabilitation planning [54].

5. Conclusions

In conclusion, patients with HAD had a high mortality rate, with male sex and a history of cancer identified as the main risk factors. Moreover, low PhAs were associated with increased short-term mortality in patients with HAD. The findings from this study could facilitate the early identification of patients with HAD in clinical settings, thereby enhancing their prognoses.

Author Contributions

Conceptualization, S.-J.J. and H.-H.K.; methodology, S.-J.J. and H.-H.K.; formal analysis, S.-J.J. and H.-J.M.; investigation, S.-J.J. and H.-J.K.; resources, S.-J.J.; data curation, S.-J.J., H.-J.M. and H.-J.K.; writing—original draft preparation, S.-J.J. and H.-H.K.; writing—review and editing, S.-J.J., S.-H.L. and H.-H.K.; visualization, H.-J.M.; supervision, H.-H.K.; project administration, H.-H.K.; funding acquisition, H.-H.K. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the research grant of the Chungbuk National University in 2023 (grant number: 2023102061). In addition, this work was supported by the Medical Device Technology Development Program (grant number: 20014701, Modular quantitative aging assessment and care service based on multiple sensors for aging in-home) funded by the Ministry of Trade, Industry, and Energy (MOTIE, Sejong, Republic of Korea).

Institutional Review Board Statement

Ethical approval was obtained from the Institutional Review Board (IRB) of the tertiary hospital (IRB number: 2023-06-032, date of approval: 24 August 2023).

Informed Consent Statement

Patient consent was waived due to the type of study (retrospective, based on existing data).

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Covinsky, K.E.; Palmer, R.M.; Fortinsky, R.H.; Counsell, S.R.; Stewart, A.L.; Kresevic, D.; Burant, C.J.; Landefeld, C.S. Loss of independence in activities of daily living in older adults hospitalized with medical illnesses: Increased vulnerability with age. J. Am. Geriatr. Soc. 2003, 51, 451–458. [Google Scholar] [CrossRef]
  2. Covinsky, K.E.; Pierluissi, E.; Johnston, C.B. Hospitalization-Associated Disability “She Was Probably Able to Ambulate, but I’m Not Sure”. JAMA 2011, 306, 1782–1793. [Google Scholar] [CrossRef] [PubMed]
  3. Loyd, C.; Markland, A.D.; Zhang, Y.; Fowler, M.; Harper, S.; Wright, N.C.; Carter, C.S.; Buford, T.W.; Smith, C.H.; Kennedy, R.; et al. Prevalence of Hospital-Associated Disability in Older Adults: A Meta-analysis. J. Am. Med. Dir. Assoc. 2020, 21, 455–461. [Google Scholar] [CrossRef]
  4. Gillick, M.R.; Serrell, N.A.; Gillick, L.S. Adverse consequences of hospitalization in the elderly. Soc. Sci. Med. 1982, 16, 1033–1038. [Google Scholar] [CrossRef] [PubMed]
  5. Sager, M.A.; Franke, T.; Inouye, S.K.; Landefeld, C.S.; Morgan, T.M.; Rudberg, M.A.; Sebens, H. Functional outcomes of acute medical illness and hospitalization in older persons. Arch. Intern. Med. 1996, 156, 645–652. [Google Scholar] [CrossRef]
  6. Sager, M.A.; Rudberg, M.A.; Jalaluddin, M.; Franke, T.; Inouye, S.K.; Landefeld, C.S.; Siebens, H.; Winograd, C.H. Hospital admission risk profile (HARP): Identifying older patients at risk for functional decline following acute medical illness and hospitalization. J. Am. Geriatr. Soc. 1996, 44, 251–257. [Google Scholar] [CrossRef]
  7. Gallego-González, E.; Mayordomo-Cava, J.; Vidán, M.T.; Valadés-Malagón, M.I.; Serra-Rexach, J.A.; Ortiz-Alonso, J. Functional trajectories associated with acute illness and hospitalization in oldest old patients: Impact on mortality. Front. Physiol. 2022, 13, 937115. [Google Scholar] [CrossRef] [PubMed]
  8. Zisberg, A.; Shadmi, E.; Gur-Yaish, N.; Tonkikh, O.; Sinoff, G. Hospital-associated functional decline: The role of hospitalization processes beyond individual risk factors. J. Am. Geriatr. Soc. 2015, 63, 55–62. [Google Scholar] [CrossRef]
  9. Inouye, S.K.; Peduzzi, P.N.; Robison, J.T.; Hughes, J.S.; Horwitz, R.I.; Concato, J. Importance of functional measures in predicting mortality among older hospitalized patients. JAMA 1998, 279, 1187–1193. [Google Scholar] [CrossRef]
  10. Gill, T.M.; Allore, H.G.; Holford, T.R.; Guo, Z. Hospitalization, restricted activity, and the development of disability among older persons. JAMA 2004, 292, 2115–2124. [Google Scholar] [CrossRef]
  11. Chodos, A.H.; Kushel, M.B.; Greysen, S.R.; Guzman, D.; Kessell, E.R.; Sakar, U.; Goldman, L.E.; Critchfield, J.M.; Pierluissi, E. Hospitalization-Associated Disability in Adults Admitted to a Safety-Net Hospital. J. Gen. Intern. Med. 2015, 30, 1765–1772. [Google Scholar] [CrossRef] [PubMed]
  12. Volpato, S.; Onder, G.; Cavalieri, M.; Guerra, G.; Sioulis, F.; Maraldi, C.; Zuliani, G.; Fellin, R.; Italian Group of Pharmacoepidemiology in the Elderly Study (GIFA). Characteristics of nondisabled older patients developing new disability associated with medical illnesses and hospitalization. J. Gen. Intern. Med. 2007, 22, 668–674. [Google Scholar] [CrossRef]
  13. McCusker, J.; Kakuma, R.; Abrahamowicz, M. Predictors of functional decline in hospitalized elderly patients: A systematic review. J. Gerontol. A Biol. Sci. Med. Sci. 2002, 57, M569–M577. [Google Scholar] [CrossRef] [PubMed]
  14. Admi, H.; Shadmi, E.; Baruch, H.; Zisberg, A. From research to reality: Minimizing the effects of hospitalization on older adults. Rambam Maimonides Med. J. 2015, 6, e0017. [Google Scholar] [CrossRef]
  15. Boyd, C.M.; Landefeld, C.S.; Counsell, S.R.; Palmer, R.M.; Fortinsky, R.H.; Kresevic, D.; Burant, C.; Covinsky, K.E. Recovery of activities of daily living in older adults after hospitalization for acute medical illness. J. Am. Geriatr. Soc. 2008, 56, 2171–2179. [Google Scholar] [CrossRef] [PubMed]
  16. Mehta, K.M.; Pierluissi, E.; Boscardin, W.J.; Kirby, K.A.; Walter, L.C.; Chren, M.M.; Palmer, R.M.; Counsell, S.R.; Landefeld, C.S. A clinical index to stratify hospitalized older adults according to risk for new-onset disability. J. Am. Geriatr. Soc. 2011, 59, 1206–1216. [Google Scholar] [CrossRef]
  17. Gupta, D.; Lammersfeld, C.A.; Vashi, P.G.; King, J.; Dahlk, S.L.; Grutsch, J.F.; Lis, C.G. Bioelectrical impedance phase angle in clinical practice: Implications for prognosis in stage IIIB and IV non-small cell lung cancer. BMC Cancer 2009, 9, 37. [Google Scholar] [CrossRef]
  18. Sánchez-Lara, K.; Turcott, J.G.; Juárez, E.; Guevara, P.; Núñez-Valencia, C.; Oñate-Ocaña, L.F.; Flores, D.; Arrieta, O. Association of nutrition parameters including bioelectrical impedance and systemic inflammatory response with quality of life and prognosis in patients with advanced non-small-cell lung cancer: A prospective study. Nutr. Cancer 2012, 64, 526–534. [Google Scholar] [CrossRef]
  19. Wirth, R.; Volkert, D.; Rösler, A.; Sieber, C.C.; Bauer, J.M. Bioelectric impedance phase angle is associated with hospital mortality of geriatric patients. Arch. Gerontol. Geriatr. 2010, 51, 290–294. [Google Scholar] [CrossRef] [PubMed]
  20. Rosenfeld, R.S.; Gonzalez, M.C.; Freire, S.M.; Lourenço, R.A. Low phase angle in critically ill older patients is associated with late mortality: A prospective study. Nutrition 2023, 105, 111852. [Google Scholar] [CrossRef]
  21. Thibault, R.; Makhlouf, A.M.; Mulliez, A.; Cristina Gonzalez, M.; Kekstas, G.; Kozjek, N.R.; Preiser, J.C.; Rozalen, I.C.; Dadet, S.; Krznaric, Z.; et al. Fat-free mass at admission predicts 28-day mortality in intensive care unit patients: The international prospective observational study Phase Angle Project. Intensive Care Med. 2016, 42, 1445–1453. [Google Scholar] [CrossRef]
  22. Health Insurance Review & Assessment Service, Republic of Korea. Available online: https://www.hira.or.kr/bbsDummy.do?pgmid=HIRAA020049000100&brdScnBltNo=4&brdBltNo=67#none (accessed on 14 July 2020).
  23. Katz, S.; Ford, A.B.; Moskowitz, R.W.; Jackson, B.A.; Jaffe, M.W. Studies of illness in the aged: The index of ADL: A standardized measure of biological and psychosocial function. JAMA 1963, 185, 914–919. [Google Scholar] [CrossRef]
  24. Kleyweg, R.P.; van der Meché, F.G.; Schmitz, P.I. Interobserver agreement in the assessment of muscle strength and functional abilities in Guillain-Barré syndrome. Muscle Nerve 1991, 14, 1103–1109. [Google Scholar] [CrossRef]
  25. Jeon, Y.J.; Kim, G.M. Comparison of the Berg Balance Scale and Fullerton Advanced Balance Scale to predict falls in community-dwelling adults. J. Phys. Ther. Sci. 2017, 29, 232–234. [Google Scholar] [CrossRef]
  26. Berg, K.; Wood-Dauphine, S.; Williams, J.I.; Gayton, D. Measuring balance in the elderly: Preliminary development of an instrument. Physiother. Can. 1989, 41, 304–311. [Google Scholar] [CrossRef]
  27. Shah, S.; Vanclay, F.; Cooper, B. Improving the sensitivity of the Barthel Index for stroke rehabilitation. J. Clin. Epidemiol. 1989, 42, 703–709. [Google Scholar] [CrossRef]
  28. Roberts, H.C.; Denison, H.J.; Martin, H.J.; Patel, H.P.; Syddall, H.; Cooper, C.; Sayer, A.A. A review of the measurement of grip strength in clinical and epidemiological studies: Towards a standardised approach. Age Ageing 2011, 40, 423–429. [Google Scholar] [CrossRef] [PubMed]
  29. Sousa-Santos, A.R.; Amaral, T.F. Differences in handgrip strength protocols to identify sarcopenia and frailty-a systematic review. BMC Geriatr. 2017, 17, 238. [Google Scholar] [CrossRef] [PubMed]
  30. Chen, L.K.; Woo, J.; Assantachai, P.; Auyeung, T.W.; Chou, M.Y.; Iijima, K.; Jan, H.C.; Kang, L.; Kim, M.; Kim, S.; et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J. Am. Med. Dir. Assoc. 2020, 21, 300–307. [Google Scholar] [CrossRef]
  31. Selberg, O.; Selberg, D. Norms and correlates of bioimpedance phase angle in healthy human subjects, hospitalized patients, and patients with liver cirrhosis. Eur. J. Appl. Physiol. 2002, 86, 509–516. [Google Scholar] [CrossRef]
  32. Barbosa-Silva, M.C.G.; Barros, A.J. Bioelectrical impedance analysis in clinical practice: A new perspective on its use beyond body composition equations. Curr. Opin. Clin. Nutr. Metab. Care 2005, 8, 311–317. [Google Scholar] [CrossRef]
  33. Narain, P.; Rubenstein, L.Z.; Wieland, G.D.; Rosbrook, B.; Strome, L.S.; Pieetruszka, F.; Morley, J.E. Predictors of immediate and 6-month outcomes in hospitalized elderly patients: The importance of functional status. J. Am. Geriatr. Soc. 1988, 36, 775–783. [Google Scholar] [CrossRef]
  34. Walter, L.C.; Brand, R.J.; Counsell, S.R.; Palmer, R.M.; Landefeld, C.S.; Fortinsky, R.H.; Covinsky, K.E. Development and validation of a prognostic index for 1-year mortality in older adults after hospitalization. JAMA 2001, 285, 2987–2994. [Google Scholar] [CrossRef]
  35. Gbeasor-Komlanvi, F.A.; Tchankoni, M.K.; Bakoubayi, A.W.; Lokossou, M.Y.; Sadio, A.; Zida-Compaore, W.I.C.; Djibril, M.; Belo, M.; Agbonon, A.; Ekouevi, D.K. Predictors of three-month mortality among hospitalized older adults in Togo. BMC Geriatr. 2020, 51, 507. [Google Scholar] [CrossRef]
  36. Baztán, J.J.; Gálvez, C.P.; Socorro, A. Recovery of functional impairment after acute illness and mortality: One-year follow-up study. Gerontology 2009, 55, 269–274. [Google Scholar] [CrossRef]
  37. Statistics Korea. 2022 Cause of Death Statistics Results. Available online: https://kostat.go.kr/board.es?mid=a10301060200&bid=218&act=view&list_no=427216 (accessed on 21 September 2023).
  38. Kim, K.W.; Jang, S. Characteristics and mortality risk factors in geriatric hospital patients visiting one region-wide emergency department. J. Korean Acad. Community Health Nurs. 2016, 27, 327–336. [Google Scholar] [CrossRef]
  39. Brandt, C.; Pedersen, B.K. The role of exercise-induced myokines in muscle homeostasis and the defense against chronic diseases. BioMed Res. Int. 2010, 2010, 520258. [Google Scholar] [CrossRef]
  40. Lightfoot, A.; McArdle, A.; Griffiths, R.D. Muscle in defense. Crit. Care Med. 2009, 37, S384–S390. [Google Scholar] [CrossRef]
  41. Weijs, P.J.; Looijaard, W.G.; Dekker, I.M.; Stapel, S.N.; Girbes, A.R.; Oudemans-van Straaten, H.M.; Beishuizen, A. Low skeletal muscle area is a risk factor for mortality in mechanically ventilated critically ill patients. Crit. Care 2014, 18, R12. [Google Scholar] [CrossRef]
  42. Looijaard, W.G.; Stapel, S.N.; Dekker, I.M.; Rusticus, H.; Remmelzwaal, S.; Girbes, A.R.J.; Weijs, P.J.M.; Oudemans-van Straaten, H.M. Identifying critically ill patients with low muscle mass: Agreement between bioelectrical impedance analysis and computed tomography. Clin. Nutr. 2020, 39, 1809–1817. [Google Scholar] [CrossRef]
  43. Toptas, M.; Yalcin, M.; Akkoc, İ.; Demir, E.; Metin, C.; Savas, Y.; Kalyoncuoglu, M.; Can, M.M. The relation between sarcopenia and mortality in patients at intensive care unit. BioMed Res. Int. 2018, 1, 5263208. [Google Scholar] [CrossRef] [PubMed]
  44. Lopez, A.D.; Shibuya, K.; Rao, C.; Mathers, C.D.; Hansell, A.L.; Held, L.S.; Schmid, V.; Buist, S. Chronic obstructive pulmonary disease: Current burden and future projections. Eur. Respir. J. 2006, 27, 397–412. [Google Scholar] [CrossRef]
  45. Mannino, D.M.; Thorn, D.; Swensen, A.; Holguin, F. Prevalence and outcomes of diabetes, hypertension and cardiovascular disease in COPD. Eur. Respir. J. 2008, 32, 962–969. [Google Scholar] [CrossRef] [PubMed]
  46. Argano, C.; Scichilone, N.; Natoli, G.; Nobili, A.; Corazza, G.R.; Mannucci, P.M.; Pericone, F.; Corrao, S.; REPOSI Investigators. Pattern of comorbidities and 1-year mortality in elderly patients with COPD hospitalized in internal medicine wards: Data from the RePoSI Registry. Intern. Emerg. Med. 2021, 16, 389–400. [Google Scholar] [CrossRef]
  47. Hasegawa, W.; Yamauchi, Y.; Yasunaga, H.; Sunohara, M.; Jo, T.; Matsui, H.; Fushimi, K.; Takami, K.; Nagase, T. Factors affecting mortality following emergency admission for chronic obstructive pulmonary disease. BMC Pulm. Med. 2014, 14, 151. [Google Scholar] [CrossRef] [PubMed]
  48. Donaldson, G.C.; Seemungal, T.A.; Bhowmik, A.; Wedzicha, J.A. Relationship between exacerbation frequency and lung function decline in chronic obstructive pulmonary disease. Thorax 2002, 57, 847–852. [Google Scholar] [CrossRef]
  49. Suissa, S.; Dell’Aniello, S.; Ernst, P. Long-term natural history of chronic obstructive pulmonary disease: Severe exacerbations and mortality. Thorax 2012, 67, 957–963. [Google Scholar] [CrossRef]
  50. Lee, S.Y.; Lee, Y.J.; Yang, J.H.; Kim, C.M.; Choi, W.S. The Association between Phase Angle of Bioelectrical Impedance Analysis and Survival Time in Advanced Cancer Patients: Preliminary Study. Korean J. Fam. Med. 2014, 35, 251–256. [Google Scholar] [CrossRef]
  51. Colín-Ramírez, E.; Castillo-Martínez, L.; Orea-Tejeda, A.; Vázquez-Durán, M.; Rodríguez, A.E.; Keirns-Davis, C. Bioelectrical impedance phase angle as a prognostic marker in chronic heart failure. Nutrition 2012, 28, 901–905. [Google Scholar] [CrossRef]
  52. Ko, S.J.; Cho, J.; Choi, S.M.; Park, Y.S.; Lee, C.H.; Lee, S.M.; Yoo, C.G.; Kim, Y.W.; Lee, J. Phase angle and frailty are important prognostic factors in critically ill medical patients: A prospective cohort study. J. Nutr. Health Aging 2021, 25, 218–223. [Google Scholar] [CrossRef]
  53. Stapel, S.N.; Looijaard, W.G.; Dekker, I.M.; Girbes, A.R.; Weijs, P.J.M.; Oudemans-van Straaten, H.M. Bioelectrical impedance analysis-derived phase angle at admission as a predictor of 90-day mortality in intensive care patients. Eur. J. Clin. Nutr. 2018, 72, 1019–1025. [Google Scholar] [CrossRef] [PubMed]
  54. Working Group on Functional Outcome Measures for Clinical Trials. Functional outcomes for clinical trials in frail older persons: Time to be moving. J. Gerontol. Biol. Sci. Med. Sci. 2008, 63, 160. [Google Scholar] [CrossRef]
Jcm 13 04798 g001
Figure 1. Study flow chart. MMT, manual muscle test; ADL, activity of daily living.
Figure 1. Study flow chart. MMT, manual muscle test; ADL, activity of daily living.
Jcm 13 04798 g001
Jcm 13 04798 g002
Figure 2. HAD screening system. HAD, hospital-associated disability.
Figure 2. HAD screening system. HAD, hospital-associated disability.
Jcm 13 04798 g002
Jcm 13 04798 g003
Figure 3. ROC curve of the ability of the phase angle to predict 3-month mortality. The empty circle on the curve indicates the cut-off value. AUC, area under the curve; ROC, receiver operating characteristic.
Figure 3. ROC curve of the ability of the phase angle to predict 3-month mortality. The empty circle on the curve indicates the cut-off value. AUC, area under the curve; ROC, receiver operating characteristic.
Jcm 13 04798 g003
Jcm 13 04798 g004
Figure 4. Kaplan–Meier 3-month survival plot for mortality risk of the two groups using cut-offs based on phase angles obtained using bioelectrical impedance analysis (log-rank test: p < 0.05). PhA, phase angle.
Figure 4. Kaplan–Meier 3-month survival plot for mortality risk of the two groups using cut-offs based on phase angles obtained using bioelectrical impedance analysis (log-rank test: p < 0.05). PhA, phase angle.
Jcm 13 04798 g004
Jcm 13 04798 g005
Figure 5. Kaplan–Meier survival plot for mortality risk of the two groups using cut-offs based on phase angles obtained using bioelectrical impedance analysis (log-rank test: p < 0.0001). PhA, phase angle.
Figure 5. Kaplan–Meier survival plot for mortality risk of the two groups using cut-offs based on phase angles obtained using bioelectrical impedance analysis (log-rank test: p < 0.0001). PhA, phase angle.
Jcm 13 04798 g005
Table 1. Characteristics of patients with HAD.
Table 1. Characteristics of patients with HAD.
VariableTotal Population,
n = 206 (100%)		3 Months
Survivors,
n = 150 (72.8%)		3 Months
Non-Survivors,
n = 56 (27.2%)		p-Value
Age, mean (SD)73.1 (12.5)73.2 (12.6)72.8 (12.4)0.85
Sex (Male, %)115 (55.8)73 (48.7)42 (75.0)<0.001
BMI, mean (SD)21.4 (3.9)21.2 (3.9)21.8 (4.1)0.40
Former/current smoker (%)65 (31.6)42 (28.0)23 (41.1)0.07
Comorbidities
Hypertension (%)108 (52.4)80 (53.3)28 (50.0)0.67
Diabetes mellitus (%)93 (45.2)71 (47.3)22 (39.3)0.30
Chronic kidney disease (%)43 (20.9)31 (20.7)12 (21.4)0.90
Coronary artery disease (%)32 (15.5)24 (16.0)8 (14.3)0.76
Heart failure (%)20 (9.7)15 (10.0)5 (8.9)0.82
Cerebrovascular disease (%)33 (16.0)23 (15.3)10 (17.9)0.66
Asthma (%)9 (4.4)7 (4.7)2 (3.6)1.00
COPD (%)12 (5.8)9 (6.0)3 (5.4)1.00
Cancer (%)73 (35.4)44 (29.3)29 (51.8)<0.01
History of ICU admission (%)44 (21.4)37 (24.7)7 (12.5)0.06
Total ICU days, mean (SD) (a)11.6 (9.3)11.8 (9.7)10.4 (7.0)0.72
Physical function
MMT, mean (SD)39.2 (7.6)39.5 (6.6)38.2 (9.6)0.36
BBS, mean (SD)7.2 (9.9)7.9 (10.4)5.6 (8.5)0.14
Grip (b) (Low, %)200 (97.1)145 (96.7)55 (96.7)1.00
Activities of daily living
MBI, mean (SD)26.7 (24.3)28.6 (23.8)21.5 (25.1)0.07
Body composition
SMI (c) (Low, %)199 (79.3)159 (77.2)40 (71.4)0.23
Phase angle, mean (SD)3.4 (1.2)3.5 (1.2)3.1 (1.1)<0.05
PBF (%), mean (SD)28.1 (11.2)29.1 (10.9)25.4 (11.4)<0.05
Values are presented as mean ± SD or number (%); HAD, hospital-associated disability; SD, standard deviation; BMI, body mass index; COPD, chronic obstructive pulmonary disease; ICU, intensive care unit; MMT, manual muscle test; BBS, Berg Balance Scale; MBI, modified Barthel Index; SMI, skeletal muscle mass index; PBF, percentage body fat. (a) Analysis only included patients admitted to the ICU; (b) analysis was performed by segregating data based on the 2019 AWGS guidelines for normal handgrip strength values (≥28.0 kg for males and ≥18.0 kg for females). Low handgrip strength was coded as 1, and normal as 0. (c) Analysis was performed by segregating data based on the 2019 AWGS guidelines for normal SMI values (≥7.0 kg/m2 for males and ≥5.7 kg/m2 for females). Low muscle mass was coded as 1, and normal as 0.
Table 2. Univariate and multivariate analysis of factors affecting 3-month mortality.
Table 2. Univariate and multivariate analysis of factors affecting 3-month mortality.
UnivariateMultivariate
VariablesOR95% CIp-ValueOR95% CIp-Value
Sex (male)3.161.60–6.27<0.013.231.58–6.61<0.01
Cancer2.591.38–4.86<0.012.181.13–4.20<0.05
Phase angle (50 kHz)0.740.54–0.99<0.050.690.50–0.95<0.05
PBF (%)0.970.94–0.99<0.05
OR, odds ratio; CI, confidence interval; PBF, percentage body fat.
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

Jo, S.-J.; Lee, S.-H.; Min, H.-J.; Kim, H.-J.; Kong, H.-H. Mortality Outcomes and Contributing Risk Factors in Patients with Hospital-Associated Disability. J. Clin. Med. 2024, 13, 4798. https://doi.org/10.3390/jcm13164798

AMA Style

Jo S-J, Lee S-H, Min H-J, Kim H-J, Kong H-H. Mortality Outcomes and Contributing Risk Factors in Patients with Hospital-Associated Disability. Journal of Clinical Medicine. 2024; 13(16):4798. https://doi.org/10.3390/jcm13164798

Chicago/Turabian Style

Jo, Soo-Jeong, So-Hee Lee, Hyo-Jin Min, Hee-Ji Kim, and Hyun-Ho Kong. 2024. "Mortality Outcomes and Contributing Risk Factors in Patients with Hospital-Associated Disability" Journal of Clinical Medicine 13, no. 16: 4798. https://doi.org/10.3390/jcm13164798

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