ArticleBMC health services research2016
Effects of longitudinal changes in Charlson comorbidity on prognostic survival model performance among newly diagnosed patients with hypertension.
Article in BMC health services research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The relationship between comorbidities, physical inactivity, kinesiophobia and physical performance in hypertensive individuals: a cross-sectional study.BMC cardiovascular disorders · 2025Article
- Early-onset alcohol, tobacco, and illicit drug use with age at onset of hypertension: a survival analysis.Social psychiatry and psychiatric epidemiology · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundTo assess the use of updated comorbidity information over time on ability to predict mortality among adults with newly diagnosed hypertension.
methodsWe studied adults 18 years and older with an incident diagnosis of hypertension from Alberta, Canada. We compared the prognostic performance of Cox regression models using Charlson comorbidities as time-invariant covariates at baseline (TIC) versus models including Charlson comorbidities as time-varying covariates (TVC) using Akaike Information Criterion (AIC) for testing goodness of fit.
resultsThe strength of the association between important prognostic clinical variables and mortality varied by modeling technique; for example, myocardial infarction was less strongly associated with mortality in the TIC model (Hazard Ratio 1.07; 95% Confidence Interval (CI): 1.05 to 1.1) than in the TVC model (HR 1.20; 95% CI: 1.18 to 1.22). All TVC models slightly outperformed TIC models, regardless of the method used to adjust for comorbid conditions (individual Charlson Comorbidities, count of comorbidities or indices). The TVC model including all 17 Charlson comorbidities as individual independent variables showed the best fit and performance.
conclusionAccounting for changes in patient comorbidity status over time more accurately captures a patient's health risk and slightly improves predictive model fit and performance than traditional methods using TIC assessment.
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