Clustering of elderly patient subgroups to identify medication-related readmission risks

Automated identification of patient subgroups: A case-study on mortality of COVID-19 patients admitted to the ICU

BACKGROUND

- Subgroup discovery (SGD) is the automated splitting of the data into complex subgroups. Various SGD methods have been applied to the medical domain, but none have been extensively evaluated. We assess the numerical and clinical quality of SGD methods.

METHOD

- We applied the improved Subgroup Set Discovery (SSD++), Patient Rule Induction Method (PRIM) and APRIORI - Subgroup Discovery (APRIORI-SD) algorithms to obtain patient subgroups on observational data of 14,548 COVID-19 patients admitted to 73 Dutch intensive care units. Hospital mortality was the clinical outcome. Numerical significance of the subgroups was assessed with information-theoretic measures. Clinical significance of the subgroups was assessed by comparing variable importance on population and subgroup levels and by expert evaluation.

RESULTS

- The tested algorithms varied widely in the total number of discovered subgroups (5-62), the number of selected variables, and the predictive value of the subgroups. Qualitative assessment showed that the found subgroups make clinical sense. SSD++ found most subgroups (n = 62), which added predictive value and generally showed high potential for clinical use. APRIORI-SD and PRIM found fewer subgroups (n = 5 and 6), which did not add predictive value and were clinically less relevant.

CONCLUSION

- Automated SGD methods find clinical subgroups that are relevant when assessed quantitatively (yield added predictive value) and qualitatively (intensivists consider the subgroups significant). Different methods yield different subgroups with varying degrees of predictive performance and clinical quality. External validation is needed to generalize the results to other populations and future research should explore which algorithm performs best in other settings.

Identifying older adults at increased risk of medication-related readmission to hospital within 30 days of discharge: development and validation of a risk assessment tool

OBJECTIVE

Developing and validating a risk assessment tool aiming to identify older adults (≥65 years) at increased risk of possibly medication-related readmission to hospital within 30 days of discharge.

DESIGN

Retrospective cohort study.

SETTING

The risk score was developed using data from a hospital in southern Sweden and validated using data from four hospitals in the mid-eastern part of Sweden.

PARTICIPANTS

The development cohort (n=720) was admitted to hospital during 2017, whereas the validation cohort (n=892) was admitted during 2017-2018.

MEASURES

The risk assessment tool aims to predict possibly medication-related readmission to hospital within 30 days of discharge. Variables known at first admission and individually associated with possibly medication-related readmission were used in development. The included variables were assigned points, and Youden's index was used to decide a threshold score. The risk score was calculated for all individuals in both cohorts. Area under the receiver operating characteristic (ROC) curve (c-index) was used to measure the discrimination of the developed risk score. Sensitivity, specificity and positive and negative predictive values were calculated using cross-tabulation.

RESULTS

The developed risk assessment tool, the Hospitalisations, Own home, Medications, and Emergency admission (HOME) Score, had a c-index of 0.69 in the development cohort and 0.65 in the validation cohort. It showed sensitivity 76%, specificity 54%, positive predictive value 29% and negative predictive value 90% at the threshold score in the development cohort.

CONCLUSION

The HOME Score can be used to identify older adults at increased risk of possibly medication-related readmission within 30 days of discharge. The tool is easy to use and includes variables available in electronic health records at admission, thus making it possible to implement risk-reducing activities during the hospital stay as well as at discharge and in transitions of care. Further studies are needed to investigate the clinical usefulness of the HOME Score as well as the benefits of implemented activities.

Implications of the medication regimen complexity index score on hospital readmissions in elderly patients with heart failure: a retrospective cohort study

BACKGROUND

The likelihood of elderly patients with heart failure (HF) being readmitted to the hospital is higher if they have a higher medication regimen complexity index (MRCI) compared to those with a lower MRCI. The objective of this study was to investigate whether there is a correlation between the MRCI score and the frequency of hospital readmissions (30-day, 90-day, and 1-year) among elderly patients with HF.

METHODS

In this single-center retrospective cohort study, MRCI scores were calculated using a well-established tool. Patients were categorized into high (≥ 15) or low (< 15) MRCI score groups. The primary outcome examined the association between MRCI scores and 30-day hospital readmission rates. Secondary outcomes included the relationships between MRCI scores and 90-day readmission, one-year readmission, and mortality rates. Multivariate logistic regression was employed to assess the 30- and 90-day readmission rates, while Kaplan-Meier analysis was utilized to plot mortality.

RESULTS

A total of 150 patients were included. The mean MRCI score for all patients was 33.43. 90% of patients had a high score. There was no link between a high MCRI score and a high 30-day readmission rate (OR 1.02; 95% CI 0.99-1.05; p < 0.13). A high MCRI score was associated with an initial significant increase in the 90-day readmission rate (odd ratio, 1.03; 95% CI, 1.00-1.07; p < 0.022), but not after adjusting for independent factors (odd ratio, 0.99; 95% CI, 0.95-1.03; p < 0.487). There was no significant difference between high and low MRCI scores in their one-year readmission rate.

CONCLUSION

The study's results indicate that there is no correlation between a higher MRCI score and the rates of hospital readmission or mortality among elderly patients with HF. Therefore, it can be concluded that the medication regimen complexity index does not appear to be a significant predictor of hospital readmission or mortality in this population.

Machine learning applied to electronic health record data in home healthcare: A scoping review

OBJECTIVE

Despite recent calls for home healthcare (HHC) to integrate informatics, the application of machine learning in HHC is relatively unknown. Thus, this study aimed to synthesize and appraise the literature describing the application of machine learning to predict adverse outcomes (e.g., hospitalization, mortality) using electronic health record (EHR) data in the HHC setting. Our secondary aim was to evaluate the comprehensiveness of predictors used in the machine learning algorithms guided by the Biopsychosocial Model.

METHODS

During March 2022 we conducted a literature search in four databases: PubMed, Embase, CINAHL, and Scopus. Inclusion criteria were 1) describing services provided in the HHC setting, 2) applying machine learning algorithms to predict adverse outcomes, defined as outcomes related to patient deterioration, 3) using EHR data and 4) focusing on the adult population. Predictors were mapped to the Biopsychosocial Model. A risk of bias analysis was conducted using the Prediction Model Risk Of Bias Assessment Tool.

RESULTS

The final sample included 20 studies. Eighteen studies used predictors from standardized assessments integrated in the EHR. The most common outcome of interest was hospitalization (55%), followed by mortality (25%). Psychological predictors were frequently excluded (35%). Tree based algorithms were most frequently applied (75%). Most studies demonstrated high or unclear risk of bias (75%).

CONCLUSION

Future studies in HHC should consider incorporating machine learning algorithms into clinical decision support systems to identify patients at risk. Based on the Biopsychosocial model, psychological and interpersonal characteristics should be used along with biological characteristics to enhance risk prediction. To facilitate the widespread adoption of machine learning, stakeholders should encourage standardization in the HHC setting.

Prediction of 30-Day Readmission in Hospitalized Older Adults Using Comprehensive Geriatric Assessment and LACE Index and HOSPITAL Score

(1) Background: Elders have higher rates of rehospitalization, especially those with functional decline. We aimed to investigate potential predictors of 30-day readmission risk by comprehensive geriatric assessment (CGA) in hospitalized patients aged 65 years or older and to examine the predictive ability of the LACE index and HOSPITAL score in older patients with a combination of malnutrition and physical dysfunction. (2) Methods: We included patients admitted to a geriatric ward in a tertiary hospital from July 2012 to August 2018. CGA components including cognitive, functional, nutritional, and social parameters were assessed at admission and recorded, as well as clinical information. The association factors with 30-day hospital readmission were analyzed by multivariate logistic regression analysis. The predictive ability of the LACE and HOSPITAL score was assessed using receiver operator characteristic curve analysis. (3) Results: During the study period, 1509 patients admitted to a ward were recorded. Of these patients, 233 (15.4%) were readmitted within 30 days. Those who were readmitted presented with higher comorbidity numbers and poorer performance of CGA, including gait ability, activities of daily living (ADL), and nutritional status. Multivariate regression analysis showed that male gender and moderately impaired gait ability were independently correlated with 30-day hospital readmissions, while other components such as functional impairment (as ADL) and nutritional status were not associated with 30-day rehospitalization. The receiver operating characteristics for the LACE index and HOSPITAL score showed that both predicting scores performed poorly at predicting 30-day hospital readmission (C-statistic = 0.59) and did not perform better in any of the subgroups. (4) Conclusions: Our study showed that only some components of CGA, mobile disability, and gender were independently associated with increased risk of readmission. However, the LACE index and HOSPITAL score had a poor discriminating ability for predicting 30-day hospitalization in all and subgroup patients. Further identifiers are required to better estimate the 30-day readmission rates in this patient population.

Predictive Modeling of Hospital Readmission: Challenges and Solutions

Hospital readmission prediction is a study to learn models from historical medical data to predict probability of a patient returning to hospital in a certain period, e.g. 30 or 90 days, after the discharge. The motivation is to help health providers deliver better treatment and post-discharge strategies, lower the hospital readmission rate, and eventually reduce the medical costs. Due to inherent complexity of diseases and healthcare ecosystems, modeling hospital readmission is facing many challenges. By now, a variety of methods have been developed, but existing literature fails to deliver a complete picture to answer some fundamental questions, such as what are the main challenges and solutions in modeling hospital readmission; what are typical features/models used for readmission prediction; how to achieve meaningful and transparent predictions for decision making; and what are possible conflicts when deploying predictive approaches for real-world usages. In this paper, we systematically review computational models for hospital readmission prediction, and propose a taxonomy of challenges featuring four main categories: (1) data variety and complexity; (2) data imbalance, locality and privacy; (3) model interpretability; and (4) model implementation. The review summarizes methods in each category, and highlights technical solutions proposed to address the challenges. In addition, a review of datasets and resources available for hospital readmission modeling also provides firsthand materials to support researchers and practitioners to design new approaches for effective and efficient hospital readmission prediction.

Medication-related hospital readmissions within 30 days of discharge-A retrospective study of risk factors in older adults

Background

Previous studies have shown that approximately 20% of hospital readmissions can be medication-related and 70% of these readmissions are possibly preventable. This retrospective medical records study aimed to find risk factors associated with medication-related readmissions to hospital within 30 days of discharge in older adults (≥65 years).

Methods

30-day readmissions (n = 360) were assessed as being either possibly or unlikely medication-related after which selected variables were used to individually compare the two groups to a comparison group (n = 360). The aim was to find individual risk factors of possibly medication-related readmissions focusing on living arrangements, polypharmacy, potentially inappropriate medication therapy, and changes made to medication regimens at initial discharge.

Results

A total of 143 of the 360 readmissions (40%) were assessed as being possibly medication-related. Charlson Comorbidity Index (OR 1.15, 95%CI 1.5–1.25), excessive polypharmacy (OR 1.74, 95%CI 1.07–2.81), having adjustments made to medication dosages at initial discharge (OR 1.63, 95%CI 1.03–2.58) and living in your own home, alone, were variables identified as risk factors of such readmissions. Living in your own home, alone, increased the odds of a possibly medication-related readmission 1.69 times compared to living in your own home with someone (p-value 0.025) and 2.22 times compared to living in a nursing home (p-value 0.037).

Conclusion

Possibly medication-related readmissions within 30 days of discharge, in patients 65 years and older, are common. The odds of such readmissions increase in comorbid, highly medicated patients living in their own home, alone, and if having medication dosages adjusted at initial discharge. These results indicate that care planning before discharge and the provision of help with, for example, managing medications after discharge, are factors especially important if aiming to reduce the amount of medication-related readmissions among this population. Further research is needed to confirm this hypothesis.

Readmission within three months after inpatient geriatric care-Incidence, diagnosis and associated factors in a Swedish cohort

INTRODUCTION

Readmissions are very costly, in monetary terms but also for the individual patient's safety and health. Only by understanding the reasons and drivers of readmissions, it is possible to ensure quality of care and improve the situation. The aim of this study was to assess inpatient readmissions during the first three months after discharge from geriatric inpatient care regarding main diagnosis and frequency of readmission. Furthermore, the aim was to analyze association between readmission and patient characteristics including demography and socioeconomics, morbidity, physical function, risk screening and care process respectively.

METHODS

The study includes all individuals admitted for inpatient care at three geriatric departments operated by the Stockholm region during 2016. Readmission after discharge was studied within three different time intervals; readmission within 10 days after discharge, within 11-30 days and within 31-90 days, respectively. Main diagnosis at readmission was assessed.

RESULTS

One fourth of the individuals discharged from inpatient geriatric care was readmitted during the first three months after discharge. The most common main diagnoses for readmission were heart failure, chronic obstructive pulmonary disease and pneumonia. Statistically significant risk factors for readmission included age, sex, number of diagnoses at discharge, and to some extent polypharmacy and destination of discharge.

CONCLUSIONS

Several clinical risk factors relating to physical performance and vulnerability were associated with risk of readmission. Socioeconomic information did not add to the predictability. To enable reductions in readmission rates, proactive monitoring of frail individuals afflicted with chronic conditions is necessary, and an integrated perspective including all stakeholders involved is crucial.

Virtual pharmacist interventions on abuse of over-the-counter medications during COVID-19 versus traditional pharmacist interventions

OBJECTIVES

This study aimed to investigate the frequency, nature, and clinical significance of pharmacist interventions on over-the-counter (OTC) medicines with abuse potential across community pharmacies with and without virtual care.

METHODS

In this prospective observational study, a trained research team observed the dispensary teams of 12 community pharmacies in the United Arab Emirates (UAE), 6 of which were operating virtual pharmacy care. A standardized data collection form was used to include information about dispensing of OTC medicines and pharmacist interventions on those with abuse/misuse potential. The clinical significance of the interventions was evaluated by a multidisciplinary committee.

RESULTS

The frequency of pharmacist interventions on OTC medicines with abuse potential across pharmacies with and without virtual services was 83.2% versus 91.0%, respectively, whereas the frequency of pharmacist interventions on OTC medicines with misuse potential across pharmacies with and without virtual services was 79.8% versus 41.2%, respectively. The proportions of clinically significant interventions across pharmacies with and without virtual services were 19.7% versus 10.5%, respectively. Cough medicines were dispensed significantly more across pharmacies with virtual care than across pharmacies without virtual care (25.6% vs. 9.7%, respectively; P = 0.04). Asking the patient to seek the advice of an addiction specialist (adjusted odds ratio = 4.11; P = 0.001) versus refusing to sell the drug was more likely to be associated with pharmacies with virtual services than with pharmacies operating traditional pharmacy services.

CONCLUSION

Virtual pharmaceutical care is a potential approach to reduce the abuse/misuse of OTC medicines but needs some improvements regarding detection of these cases. The UAE is the first country in the region to implement and regulate virtual pharmacy practice.

K-Means Clustering to Elucidate Vulnerable Subpopulations Among Medicare Patients Undergoing Total Joint Arthroplasty

BACKGROUND

The role of preoperative laboratory values for risk stratification following joint arthroplasty is currently ambiguous. In order to improve upon existing risk stratification within joint arthroplasty, this study sought to define novel phenotypes of total hip or total knee arthroplasty patients based entirely on preoperative laboratory measures. These phenotypes ("clusters") were compared to elucidate statistically and clinically significant differences in outcomes.

METHODS

A total of 134,252 patients were gathered from the National Surgical Quality Improvement Program database between 2005 and 2015. "K-means" with 3 clusters was applied using 9 preoperative laboratory values: sodium, blood urea nitrogen (BUN), creatinine, albumin, bilirubin, white blood cell count, hematocrit, platelet count, and international normalized ratio of prothrombin values (INR). Outcome measures included 30-day readmissions, severe adverse events, and discharge to nonhome.

RESULTS

Cluster 2 was characterized by elevated preoperative BUN, creatinine, and INR and demonstrated almost twice the rate of adverse events (3.52% vs 2.20% and 2.22%), 30-day readmissions (6.39% vs 3.31% and 3.71%), and discharge to nonhome (47.97% vs 30.50% and 35.85%). Cluster 3 was characterized by a slightly higher risk of discharge to nonhome than cluster 1 and was overwhelmingly female (79.5% female, 35.8% discharge to nonhome). Cluster 1 represents the lowest-risk subgroup, experiencing the lowest rates of readmissions, adverse events, and discharge to nonhome.

CONCLUSION

Preoperative laboratory values, namely BUN, creatinine, and INR, are useful in identifying patients at risk of adverse outcomes. This analysis supports the existing surgical literature pushing for preoperative hydration as a targeted intervention to expedite recovery.

An Interpretable Algorithm on Post-injury Health Service Utilization Patterns to Predict Injury Outcomes

Purpose Post-injury health service utilization (HSU) contributes to injury outcomes, but limited studies investigated their relationship. This study aims to group injured patients in transport accidents based on minimal historical information of their HSU so that the groups are meaningfully associated with the outcome of interest. Methods The data include 20,692 injured patients who had compensation claims over 3 years. We propose a hybrid approach, combining unsupervised and supervised machine learning methods. Based on the first week post-injury data, we identify a proper clustering of patients best associated with total cost to recovery, as well as the discovery of HSU patterns. This allows developing models to accurately predict the outcome of interest using the discovered patterns. Furthermore, we propose to use decision tree classifiers to accurately classify future patients into the discovered clusters using their first week post-injury information. Results Our hybrid approach has identified eight patient groups. The compactness of the resulted clusters, assessed by Average Silhouette Width metric, is 0.71 indicating well-defined clusters. The resulted patient groups are highly predictive of injury outcomes. They improve the cost predictability more than twice in comparison with predictors such as gender, age and injury type. These groups also have substantial association with patients' recovery. The transparency and interpretability of decision trees allow integrating the resulting classification rules conveniently in operational processes. Conclusions This study provides a framework to discover knowledge and useful insights for health service providers and policy makers to control injury outcomes, and consequently to reduce the severity of transport accidents.

Medication-related hospital admissions and readmissions in older patients: an overview of literature

Background The number of medication related hospital admissions and readmissions are increasing over the years due to the ageing population. Medication related hospital admissions and readmissions lead to decreased quality of life and high healthcare costs. Aim of the review To assess what is currently known about medication related hospital admissions, medication related hospital readmissions, their risk factors, and possible interventions which reduce medication related hospital readmissions. Method We searched PubMed for articles about the topic medication related hospital admissions and readmissions. Overall 54 studies were selected for the overview of literature. Results Between the different selected studies there was much heterogeneity in definitions for medication related admission and readmissions, in study population and the way studies were performed. Multiple risk factors are found in the studies for example: polypharmacy, comorbidities, therapy non adherence, cognitive impairment, depending living situation, high risk medications and higher age. Different interventions are studied to reduce the number of medication related readmission, some of these interventions may reduce the readmissions like the participation of a pharmacist, education programmes and transition-of-care interventions and the use of digital assistance in the form of Clinical Decision Support Systems. However the methods and the results of these interventions show heterogeneity in the different researches. Conclusion There is much heterogeneity in incidence and definitions for both medication related hospital admissions and readmissions. Some risk factors are known for medication related admissions and readmissions such as polypharmacy, older age and additional diseases. Known interventions that could possibly lead to a decrease in medication related hospital readmissions are spare being the involvement of a pharmacist, education programs and transition-care interventions the most mentioned ones although controversial results have been reported. More research is needed to gather more information on this topic.

Impairment for Medication Management in Older Adults: Validity of a Family Report Measure

Objectives: Medication nonadherence can have significant consequences for the health of older adults. Difficulty managing medications is often a sign of cognitive impairment, and monitoring is an early caregiving task for family members. This study examined a screening tool for independence in medication management.Methods: Reliability and validity of the screening tool were assessed in a sample of 152 female care partners for a relative aged 65+years.Results: The tool showed sound test-retest reliability, convergent and discriminant validity, and test utility, such that medication assistance was not better predicted by a global rating of cognitive impairment.Conclusions: In context of cognitive impairment, detection of medication mismanagement could be improved in both primary care and specialty health encounters through adoption of this single-item screening tool.Clinical Implications: This single-item report can be used to quickly facilitate discussions of medication management and cognitive impairment screening in office visits. The item also shows promise for efficient measurement of impairment in medication management than typical IADL assessment language.

Greater Changes in Drug Burden Index (DBI) during Hospitalization and Increased 30-Day Readmission Rates among Older In-Hospital Fallers

A higher drug burden index (DBI) is known to be associated with pre-admission falls leading to hospitalization. We investigated whether a mean difference in DBI (ΔDBI) between the events of in-hospital falls and hospital admission was associated with 30-day readmission in 113 patients ≥50 years who fell during their hospital stays between 2007 and 2014. A greater ΔDBI (≥0.09) was positively associated with higher 30-day readmission rates (incident rate ratio: 2.02; 95% confidence interval: 1.49-2.74). An effort to keep DBI low may thus decrease 30-day readmissions for older in-hospital fallers.

Are Clinical Outcomes Associated With Medication Regimen Complexity? A Systematic Review and Meta-analysis

Background: Current evidence of the influence of the medication regimen complexity (MRC) on the patients’ clinical outcomes are not conclusive. Objective: To systematically and analytically assess the association between MRC measured by the Medication Regimen Complexity Index (MRCI) and clinical outcomes. Methods: A search was carried out in the databases Cochrane Library, LILACS, PubMed, Scopus, EMBASE, Open Thesis, and Web of Science to identify studies evaluating the association between MRC and clinical outcomes that were published from January 1, 2004, to April 2, 2018. The search terms included outcome assessment, drug therapy, and medication regimen complexity index and their synonyms in different combinations for case-control and cohort studies that used the MRCI to measure MRC and related the MRCI with clinical outcomes. Odds ratios (ORs), hazard ratios (HRs), and mean differences (WMDs) were calculated, and heterogeneity was assessed using the I2 test. Results: A total of 12 studies met the eligibility criteria. The meta-analysis showed that MRC is associated with the following clinical outcomes: hospitalization (HR = 1.20; 95% CI = 1.14 to 1.27; I2 = 0%) in cohort studies, hospital readmissions (WMD = 7.72; 95% CI = 1.19 to 14.25; I2 = 84%) in case-control studies, and medication nonadherence (adjusted OR = 1.05; 95% CI = 1.02 to 1.07; I2 = 0%) in cohort studies. Conclusion and Relevance: This systematic review and meta-analysis gathered relevant scientific evidence and quantified the combined estimates to show the association of MRC with clinical outcomes: hospitalization, hospital readmission, and medication adherence.

Electronic Medication Regimen Complexity Index at admission and complications during hospitalization in medical wards: a tool to improve quality of care?

Objective

Adverse events during hospitalization are a major worry considering their frequency and their burden. Many could be avoided by immediate identification of at-risk patients at admission and adapted prevention. The complexity of a patient's medication regimen immediately available at admission is a good indicator of the complexity of the patient's condition. This study aims to determine whether the electronic Medication Regimen Complexity Index (MRCI) at admission is associated with complications during hospitalization.

Design

We performed a multilevel logistic regression model, adjusted for age and sex.

Setting

Premier Perspective™ database, a clinical and financial information system from 417 US hospitals.

Participants

Adults hospitalized for more than 3 days in a medical ward and included in Premier's Perspective™ database for 2006.

Intervention(s)

Multilevel logistic regression.

Main Outcome Measure

Association of the MRCI and complications during hospitalization, defined as in-hospital death, hospital-acquired infection, pressure ulcers; and need for highly technical healthcare, identified as the secondary introduction of catecholamines.

Results

In total, 1 592 383 admissions were included. The median MRCI at admission was 13 [interquartile range: 9-19]. The higher the MRCI, the higher the adjusted odds ratio of the following: in-hospital mortality, hospital-acquired infections, pressure ulcers and the secondary introduction of catecholamines.

Conclusions

Our results suggested that the MRCI at admission was correlated with patient complexity, independent of age. Considering that patients with complex conditions pose a heavier workload for staff, measuring MRCI at admission could be used to allocate resources in medical wards at an institutional level. The MRCI might be a useful tool to assess the management of care.

Medication Regimen Complexity Measured by MRCI: A Systematic Review to Identify Health Outcomes

Objective: To perform a systematic review to identify health outcomes related to medication regimen complexity as measured by the Medication Regimen Complexity Index (MRCI) instrument. Data Sources: Cochrane Library, LILACS, PubMed, Scopus, EMBASE, Open Thesis, and Web of Science were searched from January 1, 2004, until April 02, 2018, using the following search terms: outcome assessment, drug therapy, and Medication Regimen Complexity Index and their synonyms in different combinations. Study Selection and Data Extraction: Studies that used the MRCI instrument to measure medication regimen complexity and related it to clinical, humanistic, and/or economic outcomes were evaluated. Two reviewers independently carried out the analysis of the titles, abstracts, and complete texts according to the eligibility criteria, performed data extraction, and evaluated study quality. Data Synthesis: A total of 23 studies met the inclusion criteria; 18 health outcomes related to medication regimen complexity were found. The health outcomes most influenced by medication regimen complexity were hospital readmission, medication adherence, hospitalization, adverse drug events, and emergency sector visit. Only one study related medication regimen complexity with humanistic outcomes, and no study related medication regimen complexity to economic outcomes. Most of the studies were of good methodological quality. Relevance to Patient Care and Clinical Practice: Health care professionals should pay attention to medication regimen complexity of the patients because this may influence health outcomes. Conclusion: This study identified some health outcomes that may be influenced by medication regimen complexity: hospitalization, hospital readmission, and medication adherence were more prevalent, showing a significant association between MRCI increase and these health outcomes.

Validation of the Turkish version of medication regimen complexity index among elderly patients

OBJECTIVE

The aim of this study was to validate the Turkish version of the 'Medication Regimen Complexity Index' (MRCI).

METHODS

This validation study has been conducted in prescriptions of the first 100 elderly patients who had visited the pharmacy for their prescription refill to evaluate convergent and divergent validity of the Turkish version. The reliability of the Turkish version was assessed with inter-rater and test-retest analysis after its translation and cultural adaptation.

RESULTS

The mean age of the 100 patients (53 women) was 74.9 years (SD = 7.58, 65-95). The scale showed high inter-rater reliability and test-retest reliability for the total and subscale scores (p < 0.05). A strong and positive correlation between the number of medications in a prescription and the total Medication Regimen Complexity Index scores (r = 0.930, p < 0.001) was determined. There were no statistically significant differences between age, gender and MRCI scores (p > 0.05).

CONCLUSION

These results show that the Turkish version of MRCI is a reliable and valid tool in elderly patients.