Effects of longitudinal changes in Charlson comorbidity on prognostic survival model performance among newly diagnosed patients with hypertension. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Rymkiewicz, Peter; Ravani, Pietro; Hemmelgarn, Brenda; McAlister, Finlay; Southern, Danielle; Walker, Robin; Chen, Guanmin; Quan, Hude
• Is Part Of: BMC health services research. Volume 16:Issue 1(2016)
• Page Start: 1
• Page End: 7
• Publication Date: 2016-12
• Subjects: Charlson -- Comorbidity -- Model performance -- Hypertension -- Longitudinal change; Public health -- Research -- Periodicals; Medical care -- Research -- Periodicals; 362.1072
• DOI: 10.1186/s12913-016-1910-8
• Languages: English
• ISSNs: 1472-6963

Abstract

Abstract Background To assess the use of updated comorbidity information over time on ability to predict mortality among adults with newly diagnosed hypertension. Methods We 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. Results The 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. Conclusion Accounting 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.