The Drug Derived Complexity Index (DDCI) Predicts Mortality, Unplanned Hospitalization and Hospital Readmissions at the Population Level

Abstract

Objectiveto develop and validate the Drug Derived Complexity Index (DDCI), a predictive model derived from drug prescriptions able to stratify the general population according to the risk of death, unplanned hospital admission, and readmission, and to compare the new predictive index with the Charlson Comorbidity Index (CCI).DesignPopulation-based cohort study, using a record-linkage analysis of prescription databases, hospital discharge records, and the civil registry. The predictive model was developed based on prescription patterns indicative of chronic diseases, using a random sample of 50% of the population. Multivariate Cox proportional hazards regression was used to assess weights of different prescription patterns and drug classes. The predictive properties of the DDCI were confirmed in the validation cohort, represented by the other half of the population. The performance of DDCI was compared to the CCI in terms of calibration, discrimination and reclassification.Setting6 local health authorities with 2.0 million citizens aged 40 years or above.ResultsOne year and overall mortality rates, unplanned hospitalization rates and hospital readmission rates progressively increased with increasing DDCI score. In the overall population, the model including age, gender and DDCI showed a high performance. DDCI predicted 1-year mortality, overall mortality and unplanned hospitalization with an accuracy of 0.851, 0.835, and 0.584, respectively. If compared to CCI, DDCI showed discrimination and reclassification properties very similar to the CCI, and improved prediction when used in combination with the CCI.Conclusions and RelevanceDDCI is a reliable prognostic index, able to stratify the entire population into homogeneous risk groups. DDCI can represent an useful tool for risk-adjustment, policy planning, and the identification of patients needing a focused approach in everyday practice.

Predicting 3-year mortality and admission to acute-care hospitals, skilled nursing facilities, and long-term care facilities in Medicare beneficiaries

“…Such models can be used on an aggregate basis to forecast and plan for population-level health services’ needs, and as a basis for risk adjustment in the context of evaluating quality of care, costs, and medical effectiveness. Our findings build and expand on previously developed models (Billings et al, 2012; Kansagara et al, 2011; McCormick et al, 1991; Robusto et al, 2016; van Walraven et al, 2010), particularly for those with limitations in performing ADLs and IADLs. The potential utility of these models for such purposes is enhanced by their basis in variables routinely ascertained in the MCBS.…”

“…Robusto and colleagues developed and validated the drug derived complexity index, which stratified the general population aged 40 years and older according to one year and overall mortality and unplanned hospital admission derived from drug prescriptions (Robusto et al, 2016). McCormick and colleagues developed a predictive index for planned patient care in LTC settings among persons with HIV/AIDS (McCormick, Inui, Deyo, & Wood, 1991).…”

Predictors of unplanned hospitalisation in the older population: The role of polypharmacy and other medication and chronic disease‐related factors

“…HF and atherosclerotic disease are known predictors for unplanned hospitalisation, and diuretics and antiplatelets have also been reported by previous studies as predicting unplanned hospitalisation. 12,17,40 We found these diseases and drugs to predict hospitalisation in our study. It is often difficult to disentangle whether adverse outcomes associated with polypharmacy are due to the medications, diseases or both.…”

Treatment with insulin is associated with worse outcome in patients with chronic heart failure and diabetes

“…A logistic regression model including age, sex, presence of micro‐vascular and macro‐vascular complications, overall severity of co‐morbid conditions, summarized by the Drug Derived Complexity Index (DDCI), previous hospitalization for arrhythmia, cancer, chronic obstructive pulmonary disease, and chronic liver disease, year of the event and diabetes duration as covariates was used to predict the probability (propensity score) to receive insulin. The DDCI was previously shown to be a strong predictor of hospitalization, short‐term mortality, and long‐term mortality . An 8‐to‐1 greedy matching algorithm was used to identify a unique matched control for each case according to the propensity score.…”