Modified-Chronic Disease Score (M-CDS): Predicting the individual risk of death using drug prescriptions

Abstract

Background Estimating the morbidity of a population is strategic for health systems to improve healthcare. In recent years administrative databases have been increasingly used to predict health outcomes. In 1992, Von Korff proposed a Chronic Disease Score (CDS) to predict 1-year mortality by only using drug prescription data. Because pharmacotherapy underwent many changes over the last 3 decades, the original version of the CDS has limitations. The aim of this paper is to report on the development of the modified version of the CDS. Methods The modified CDS (M-CDS) was developed using 33 variables (from drug prescriptions within two-year before 01/01/2018) to predict one-year mortality in Bologna residents aged ≥50 years. The population was split into training and testing sets for internal validation. Score weights were estimated in the training set using Cox regression model with LASSO procedure for variables selection. The external validation was carried out on the Imola population. The predictive ability of M-CDS was assessed using ROC analysis and compared with that of the Charlson Comorbidity Index (CCI), that is based on hospital data only, and of the Multisource Comorbidity Score (MCS), which uses hospital and pharmaceutical data. Results The predictive ability of M-CDS was similar in the training and testing sets (AUC 95% CI: Training [0.760-0.770] vs. testing [0.750-0.772]) and in the external population (Imola AUC 95% CI [0.756-0.781]). M-CDS was significantly better than CCI (M-CDS AUC = 0.761, 95% CI [0.750-0.772] vs. CCI-AUC = 0.696, 95% CI [0.681-0.711]). No significant difference was found between M-CDS and MCS (MCS AUC = 0.762, 95% CI [0.749-0.775]). Conclusions M-CDS, using only drug prescriptions, has a better performance than the CCI score in predicting 1-year mortality, and is not inferior to the multisource comorbidity score. M-CDS can be used for population risk stratification, for risk-adjustment in association studies and to predict the individual risk of death.