A data-driven medication score predicts 10-year mortality among aging adults

Defining, identifying and addressing problematic polypharmacy within multimorbidity in primary care: a scoping review

INTRODUCTION

Polypharmacy and multimorbidity pose escalating challenges. Despite numerous attempts, interventions have yet to show consistent improvements in health outcomes. A key factor may be varied approaches to targeting patients for intervention.

OBJECTIVES

To explore how patients are targeted for intervention by examining the literature with respect to: understanding how polypharmacy is defined; identifying problematic polypharmacy in practice; and addressing problematic polypharmacy through interventions.

DESIGN

We performed a scoping review as defined by the Joanna Briggs Institute.

SETTING

The focus was on primary care settings.

DATA SOURCES

Medline, Embase, Cumulative Index to Nursing and Allied Health Literature and Cochrane along with ClinicalTrials.gov, Science.gov and WorldCat.org were searched from January 2004 to February 2024.

ELIGIBILITY CRITERIA

We included all articles that had a focus on problematic polypharmacy in multimorbidity and primary care, incorporating multiple types of evidence, such as reviews, quantitative trials, qualitative studies and policy documents. Articles focussing on a single index disease or not written in English were excluded.

EXTRACTION AND ANALYSIS

We performed a narrative synthesis, comparing themes and findings across the collective evidence to draw contextualised insights and conclusions.

RESULTS

In total, 157 articles were included. Case-finding methods often rely on basic medication counts (often five or more) without considering medical history or whether individual medications are clinically appropriate. Other approaches highlight specific drug indicators and interactions as potentially inappropriate prescribing, failing to capture a proportion of patients not fitting criteria. Different potentially inappropriate prescribing criteria also show significant inconsistencies in determining the appropriateness of medications, often neglecting to consider multimorbidity and underprescribing. This may hinder the identification of the precise population requiring intervention.

CONCLUSIONS

Improved strategies are needed to target patients with polypharmacy, which should consider patient perspectives, individual factors and clinical appropriateness. The development of a cross-cutting measure of problematic polypharmacy that consistently incorporates adjustment for multimorbidity may be a valuable next step to address frequent confounding.

Higher Mortality Rate in Patients with Vertebral Compression Fractures is due to Deteriorated Medical Status Prior to the Fracture Event

Introduction

Vertebral compression fractures (VCF) are the most common low-energy fractures in older people and are associated with increased mortality. To assess mortality risk in patients suffering from VCF, we conducted a retrospective observational long-term cohort study.

Patients and Methods

The study included 270 patients. 221 patients were treated conservatively, and 49 were treated with vertebroplasty. The study group was compared to a control group of 1641 random individuals age and sex-matched. Electronic healthcare data extracted included monthly chronic medications taken regularly 3 months before hospitalisation, analgesics excluded, and date of death.

Results

Patients who suffer from VCF tend to consume more chronic medications. The mean count of chronic medication prescriptions in the 3 months before hospitalisation was 16.41 (±9.11) in the VCF group and 11.52 (± 7.17) in the control cohort (P < .0001). In univariate analysis, patients with VCF showed decreased long-term survival (P < .00). However, when controlled for age, sex, and chronic medications uptake, no significant difference was observed between the groups in a multivariate model (P = .12).

Conclusions

The study demonstrates that VCF as an independent variable has a marginal effect on mortality. The higher mortality prevalent in these patients is due to the deteriorated health status of the patients before fracture.

Development and validation of questionnaire-based machine learning models for predicting all-cause mortality in a representative population of China

Background

Considering that the previously developed mortality prediction models have limited applications to the Chinese population, a questionnaire-based prediction model is of great importance for its accuracy and convenience in clinical practice.

Methods

Two national cohort, namely, the China Health and Nutrition Survey (8,355 individual older than 18) and the China Health and Retirement Longitudinal Study (12,711 individuals older than 45) were used for model development and validation. One hundred and fifty-nine variables were compiled to generate predictions. The Cox regression model and six machine learning (ML) models were used to predict all-cause mortality. Finally, a simple questionnaire-based ML prediction model was developed using the best algorithm and validated.

Results

In the internal validation set, all the ML models performed better than the traditional Cox model in predicting 6-year mortality and the random survival forest (RSF) model performed best. The questionnaire-based ML model, which only included 20 variables, achieved a C-index of 0.86 (95%CI: 0.80-0.92). On external validation, the simple questionnaire-based model achieved a C-index of 0.82 (95%CI: 0.77-0.87), 0.77 (95%CI: 0.75-0.79), and 0.79 (95%CI: 0.77-0.81), respectively, in predicting 2-, 9-, and 11-year mortality.

Conclusions

In this prospective population-based study, a model based on the RSF analysis performed best among all models. Furthermore, there was no significant difference between the prediction performance of the questionnaire-based ML model, which only included 20 variables, and that of the model with all variables (including laboratory variables). The simple questionnaire-based ML prediction model, which needs to be further explored, is of great importance for its accuracy and suitability to the Chinese general population.

Association between medical androgen deprivation therapy and long-term cardiovascular disease and all-cause mortality in non-metastatic prostate cancer

Studies have suggested that prostate cancer (PCa) patients receiving androgen deprivation therapy (ADT) are at increased risk of developing or exacerbating cardiovascular disease (CVD). We aimed to explore the association between ADT for PCa and subsequent CVD and all‐cause mortality in this nationwide, longitudinal study. We also evaluated the role of cardiovascular risk and ADT duration to determine effect modification. Norwegian registry data were used to identify patients with PCa from 2008‐18 and who received primary ADT in the first year after diagnosis. The associations between ADT and composite cardiovascular events, and the individual components of myocardial infarction, stroke and heart failure, in addition to atrial fibrillation and all‐cause mortality, were explored using time‐varying Cox regression models. We included 30 923 PCa patients, of whom 8449 (27%) received primary ADT. Mean follow‐up was 2.9 and 3.8 years for CVD events and mortality, respectively. We found an association between ADT and composite CVD (adjusted HR 1.13: 95% CI 1.05‐1.21), myocardial infarction (1.18: 1.05‐1.32), stroke (1.21: 1.06‐1.38), heart failure (1.23: 1.13‐1.35) and all‐cause mortality (1.49: 1.39‐1.61). These associations persisted in those with low and moderate CVD risk and ADT longer than 7 months. A relationship between ADT and composite CVD and all‐cause mortality was observed, especially in those with moderate CVD risk and longer treatment duration. Future studies with more detailed cancer data are needed to verify the clinical relevance of these results, especially when considering all‐cause mortality within the context of treatment guidelines and benefits of ADT.