Screening Tools Used by Clinical Pharmacists to Identify Elderly Patients at Risk of Drug-Related Problems on Hospital Admission: A Systematic Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Resources
2.2. Data Extraction
2.3. Search Terms and Search Strategy
2.4. Inclusion and Exclusion Criteria
2.4.1. Paper Inclusion Criteria
2.4.2. Paper Exclusion Criteria
2.4.3. Critical Appraisal and Data Extraction for Included Studies
3. Results
3.1. Included Papers
3.2. Population Characteristics: Age
3.3. Population Characteristics: Gender
3.4. Population Characteristics: Ethnicity
3.5. Population Characteristics: Number of Study Participants
3.6. Development/Modelling, Utilisation or Validation of Assessment Tools
3.7. Study Setting (Country/Countries)
3.8. Primary Outcomes of the Studies
3.9. Number of Risk Factors Included in Risk Scores/Tools
3.10. Identification of Risk Factors
3.11. Statistical Analysis
3.12. Methodologies for Development of Point Scores
3.13. Performance of Risk Score/Tool/Model: Sensitivity, Specificity and/or Discriminatory Ability
3.14. Sample Size and Power of Study
3.15. Quality of Included Studies
4. Discussion
Limitations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Author | Tool |
---|---|
Tangiisuran B. et al. [25] (note: this research was also reported in PhD thesis [24] but only counted as one piece of research in this SR) | BADRI model |
Onder G. et al. [30] | GerontoNet ADR Risk Score |
Trivalle C. et al. [31] | A geriatric score |
O’Connor M.N. et al. [32] | GerontoNet ADR Risk Score |
Alassaad A. et al. [33] | The 80+ score |
Suggett E. [34] | Risk scores to identify patients who were at high risk of (hospital) pharmacist intervention (no specific name for score stated by authors) |
Hohl C.M. et al. (2017) [35] | Clinical decision rules |
Hohl C.M. et al. (2012) [20] | Clinical decision rules |
Bonnerup D.K. et al. [36] | MERIS score |
Kaufmann C.P. et al. (2018) [37] | Drug-Associated Risk Tool (DART) |
Falconer N. et al. (2014) [38] | Assessment of Risk Tool (ART) |
Falconer N. et al. (2017) [39] | Assessment of Risk Tool (ART), |
McAuliffe L. et al. [40] | MEDCOINS score |
McElnay J.C. et al. [41] | A predictive model for adverse drug events (ADEs) in elderly patients (no specific name for model stated by authors) |
Urbina O. et al. [3] | A score that quantifies the risk of a DRP during hospital admission (no specific name for score stated by authors) |
Hickson R.P. et al. [18] | Pharmaceutical assessment screening tool (PAST) |
Saedder E.A. et al. [42] | MERIS score |
Petrovic M. et al. [43] | GerontoNet ADR Risk Score |
O’Mahony D. et al. [44] | The adverse drug reaction risk in older persons (ADRROP) prediction scale |
Author | Sensitivity | Specificity | Other | |||
---|---|---|---|---|---|---|
Discriminatory Ability: AUROC/AUCROC | Discriminatory Ability: C-statistic | Overall Accuracy | Other | |||
Tangiisuran B. et al. | 80.0% for internal validation; 84% for external validation | 55.0% for internal validation; 43% for external validation | 0.74 (95%CI = 0.68–0.79) for internal validation; 0.73 (95%CI = 0.66–0.80) for external validation | |||
Onder et al. | 68% (development) | 65% (development) | 0.71 (95% CI = 0.68–0.73) (development) 0.70 (95% CI = 0.63–0.78) (validation) | |||
Trivalle et al. | n/r | n/r | 0.70 (95% CI = 0.635–0.74) | |||
O’Connor et al. | n/r | n/r | 0.62 (95% CI = 0.57–0.68) on admission (lower on subsequent days, days 5 & 10 post-admission) | |||
Alassaad A. (&et al) | n/r | n/r | 0.71 | |||
Suggett E. | n/r | n/r | 0.607 for main data; 0.616 for validation data | |||
Hohl et al (2017) | n/r | n/r | n/r | n/r | n/r | |
Hohl et al (2012) | 1) More sensitive rule (ADE rule) 96.7% (95%CI = 91.8–98.6%) | 1) 40.3% | ||||
2) More specific rule (Adverse Drug Reaction Rule) 90.8% (95%CI = 81.4–95.7%) | 2) 59.1% | |||||
Bonnerup et al. | n/r | n/r | n/r | n/r | n/r | |
Kaufmann et al. | 67% average (range = 21–100) | 88% average (range = 27–100) | n/r | |||
Falconer et al. (2014) | n/r | n/r | n/r | n/r | n/r | |
Falconer et al. (2017) | n/r | n/r | 0.72 (95%CI = 0.66–0.78) to predict at least one unintended medication discrepancy; from score from 2 flags (>8 regular admission medicines and readmission within 30 days of discharge) | |||
McAuliffe et al. | n/r | n/r | 0.65 (95%CI = 0.60–0.70) | Hosmer-Lemeshow goodness-of-fit = 0.99 | ||
McElnay et al. | 41% (validation) | 69% (validation) | 63% | |||
Urbina et al. | n/r | n/r | 0.778 (95% CI = 0.768–0.789) (training set); 0.776 (95%CI = 0.759–0.792) (validation set) | Hosmer-Lemeshow goodness-of-fit = non-significant (p = 0.13) (validation set) | ||
Hickson et al. | n/r | n/r | n/r | n/r | n/r | n/r |
Saedder et al. | 0.64 (for final version of MERIS) | 0.75 (for final version of MERIS) | 0.76, 0.87, 0.74, 0.66 for final MERIS score in different populations within study | |||
Petrovic et al. | n/r | n/r | 0.64 (95%CI = 0.55–0.74) to predict ADRs probably or definitely related to drug use; 0.69 (95%CI = 0.60–0.77) for predicting Type A ADRs; subgroup analysis: AUC = 0.70–0.79 and 0.80–0.89 | |||
O’Mahony et al. | n/r | n/r | 0.623 (95% CI = 0.598–0.665) (derivation cohort); 0.592 (95%CI = 0.532–0.652) (vallidation cohort) |
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Brady, A.; Curtis, C.E.; Jalal, Z. Screening Tools Used by Clinical Pharmacists to Identify Elderly Patients at Risk of Drug-Related Problems on Hospital Admission: A Systematic Review. Pharmacy 2020, 8, 64. https://doi.org/10.3390/pharmacy8020064
Brady A, Curtis CE, Jalal Z. Screening Tools Used by Clinical Pharmacists to Identify Elderly Patients at Risk of Drug-Related Problems on Hospital Admission: A Systematic Review. Pharmacy. 2020; 8(2):64. https://doi.org/10.3390/pharmacy8020064
Chicago/Turabian StyleBrady, Amanda, Chris E. Curtis, and Zahraa Jalal. 2020. "Screening Tools Used by Clinical Pharmacists to Identify Elderly Patients at Risk of Drug-Related Problems on Hospital Admission: A Systematic Review" Pharmacy 8, no. 2: 64. https://doi.org/10.3390/pharmacy8020064