ArticlePloS one2016
The Drug Derived Complexity Index (DDCI) Predicts Mortality, Unplanned Hospitalization and Hospital Readmissions at the Population Level.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Two years of SARS-CoV-2 pandemic and COVID-19 in Lombardy, Italy.Internal and emergency medicine · 2023Article
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- Association between Preoperative Medication Lists and Postoperative Hospital Length of Stay after Endoscopic Transsphenoidal Pituitary Surgery.Journal of clinical medicine · 2022Article
- Effectiveness and safety of GLP-1 receptor agonists versus SGLT-2 inhibitors in type 2 diabetes: an Italian cohort study.Cardiovascular diabetology · 2022Article
- Cancer Incidence and Mortality According to Pre-Existing Heart Failure in a Community-Based Cohort.JACC. CardioOncology · 2022Article
- Health information technology to improve care for people with multiple chronic conditions.Health services research · 2021Review
- Development of a specialty intensity score to estimate a patient's need for care coordination across physician specialties.Health science reports · 2021Article
- Clinical and pharmacological characteristics of elderly patients admitted for bleeding: impact on in-hospital mortality.Annals of medicine · 2020Article
- Modified-Chronic Disease Score (M-CDS): Predicting the individual risk of death using drug prescriptions.PloS one · 2020Article
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8 authors.
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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 RELEVANCE: DDCI 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.
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