Dataset Information

Prescription-based prediction of baseline mortality risk among older men.

ABSTRACT: BACKGROUND:Understanding the association between patients' history of prescribed medications and mortality rate could optimize characterization of baseline risk when the Charlson Comorbidity Index is insufficient. METHODS:Using a Swedish cohort of men selected randomly as controls to men with prostate cancer diagnosed 2007-2013, we estimated the association between medications prescribed during the previous year and mortality rates, using Cox regression stratified for age. RESULTS:Among the 326,450 older men with median age of 69 years included in this study, 73% were categorized as free of comorbidity according to the Charlson Comorbidity Index; however, 84% had received at least one prescription during the year preceding the follow-up. This was associated with a 60% overall increase in mortality rate (hazard ratio [HR] = 1.60, 95% confidence interval [CI] 1.56 to 1.64). Some drugs that were unexpectedly associated with mortality included locally acting antacids (HR = 4.7, 95% CI 4.4 to 5.1), propulsives (HR = 4.7, 95% CI 4.4 to 5.0), vitamin A and D (HR = 4.6, 95% CI 4.3 to 4.9), and loop diuretics, for example furosemide (HR = 3.7; 95% CI 3.6 to 3.8). Thiazide diuretics, however, were only weakly associated with a mortality risk (HR = 1.5; 95% CI 1.4 to 1.5). Surprisingly, only weak associations with mortality were seen for major cardiovascular drug classes. CONCLUSIONS:A majority of older men had a history of prescribed medications and many drug classes were associated with mortality rate, including drug classes not directly indicated for a specific comorbidity represented in commonly used comorbidity measures. Prescription history can improve baseline risk assessment but some associations might be context-sensitive.

SUBMITTER: Gedeborg R

PROVIDER: S-EPMC7595371 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Prescription-based prediction of baseline mortality risk among older men.

Gedeborg Rolf R Garmo Hans H Robinson David D Stattin Pär P

PloS one 20201029 10

<h4>Background</h4>Understanding the association between patients' history of prescribed medications and mortality rate could optimize characterization of baseline risk when the Charlson Comorbidity Index is insufficient.<h4>Methods</h4>Using a Swedish cohort of men selected randomly as controls to men with prostate cancer diagnosed 2007-2013, we estimated the association between medications prescribed during the previous year and mortality rates, using Cox regression stratified for age.<h4>Resu ...[more]

PMID: 33119680

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Project description:OBJECTIVE:To test the hypothesis that non-frail older men with poorer sleep at baseline are at increased risk of frailty and death at follow-up. METHODS:In this prospective cohort study, subjective (questionnaires) and objective sleep parameters (actigraphy, in-home overnight polysomnography) were measured at baseline in 2505 non-frail men aged ?67years. Repeat frailty status assessment performed an average of 3.4 years later; vital status assessed every four months. Sleep parameters expressed as dichotomized predictors using clinical cut-points. Status at follow-up exam classified as robust, intermediate (pre-frail) stage, frail, or died in interim. RESULTS:None of the sleep disturbances were associated with the odds of being intermediate/frail/dead (vs. robust) at follow-up. Poor subjective sleep quality (multivariable odds ratio [MOR] 1.26, 95% CI 1.01-1.58), greater nighttime wakefulness (MOR 1.31, 95% CI 1.04-1.66), and greater nocturnal hypoxemia (MOR 1.47, 95% CI 1.02-2.10) were associated with a higher odds of frailty/death at follow-up (vs. robust/intermediate). Excessive daytime sleepiness (MOR 1.60, 95% CI 1.03-2.47), greater nighttime wakefulness (MOR 1.57, 95% CI 1.12-2.20), severe sleep apnea (MOR 1.74, 95% CI 1.04-2.89), and nocturnal hypoxemia (MOR 2.28, 95% CI 1.45-3.58) were associated with higher odds of death (vs. robust/intermediate/frail at follow-up). The association between poor sleep efficiency and mortality nearly reached significance (MOR 1.48, 95% CI 0.99-2.22). Short sleep duration and prolonged sleep latency were not associated with frailty/death or death at follow-up. CONCLUSIONS:Among non-frail older men, poor subjective sleep quality, greater nighttime wakefulness, and greater nocturnal hypoxemia were independently associated with higher odds of frailty or death at follow-up, while excessive daytime sleepiness, greater nighttime wakefulness, severe sleep apnea and greater nocturnal hypoxemia were independently associated with an increased risk of mortality.

Project description:BackgroundMultimorbidity presents an increasingly common problem in older population, and is tightly related to polypharmacy, i.e., concurrent use of multiple medications by one individual. Detecting polypharmacy from drug prescription records is not only related to multimorbidity, but can also point at incorrect use of medicines. In this work, we build models for predicting polypharmacy from drug prescription records for newly diagnosed chronic patients. We evaluate the models' performance with a strong focus on interpretability of the results.MethodsA centrally collected nationwide dataset of prescription records was used to perform electronic phenotyping of patients for the following two chronic conditions: type 2 diabetes mellitus (T2D) and cardiovascular disease (CVD). In addition, a hospital discharge dataset was linked to the prescription records. A regularized regression model was built for 11 different experimental scenarios on two datasets, and complexity of the model was controlled with a maximum number of dimensions (MND) parameter. Performance and interpretability of the model were evaluated with AUC, AUPRC, calibration plots, and interpretation by a medical doctor.ResultsFor the CVD model, AUC and AUPRC values of 0.900 (95% [0.898-0.901]) and 0.640 (0.635-0.645) were reached, respectively, while for the T2D model the values were 0.808 (0.803-0.812) and 0.732 (0.725-0.739). Reducing complexity of the model by 65% and 48% for CVD and T2D, resulted in 3% and 4% lower AUC, and 4% and 5% lower AUPRC values, respectively. Calibration plots for our models showed that we can achieve moderate calibration with reducing the models' complexity without significant loss of predictive performance.DiscussionIn this study, we found that it is possible to use drug prescription data to build a model for polypharmacy prediction in older population. In addition, the study showed that it is possible to find a balance between good performance and interpretability of the model, and achieve acceptable calibration at the same time.

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Prescription-based prediction of baseline mortality risk among older men.

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Prescription-based prediction of baseline mortality risk among older men.

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