Objective To develop and validate a novel comorbidity score (multisource comorbidity score (MCS)) predictive of mortality, hospital admissions and healthcare costs using multiple source information from the administrative Italian National Health System (NHS) databases. Methods An index of 34 variables (measured from inpatient diagnoses and outpatient drug prescriptions within 2 years before baseline) independently predicting 1-year mortality in a sample of 500 000 individuals aged 50 years or older randomly selected from the NHS beneficiaries of the Italian region of Lombardy (training set) was developed. The corresponding weights were assigned from the regression coefficients of a Weibull survival model. MCS performance was evaluated by using an internal (ie, another sample of 500 000 NHS beneficiaries from Lombardy) and three external (each consisting of 500 000 NHS beneficiaries from Emilia-Romagna, Lazio and Sicily) validation sets. Discriminant power and net reclassification improvement were used to compare MCS performance with that of other comorbidity scores. MCS ability to predict secondary health outcomes (ie, hospital admissions and costs) was also investigated. Results Primary and secondary outcomes progressively increased with increasing MCS value. MCS improved the net 1-year mortality reclassification from 27% (with respect to the Chronic Disease Score) to 69% (with respect to the Elixhauser Index). MCS discrimination performance was similar in the four regions of Italy we tested, the area under the receiver operating characteristic curves (95% CI) being 0.78 (0.77 to 0.79) in Lombardy, 0.78 (0.77 to 0.79) in Emilia-Romagna, 0.77 (0.76 to 0.78) in Lazio and 0.78 (0.77 to 0.79) in Sicily. Conclusion MCS seems better than conventional scores for predicting health outcomes, at least in the general population from Italy. This may offer an improved tool for risk adjustment, policy planning and identifying patients in need of a focused treatment approach in the everyday medical practice.

Developing and validating a novel multisource comorbidity score from administrative data: a large population-based cohort study from Italy

Scondotto S;
2017-01-01

Abstract

Objective To develop and validate a novel comorbidity score (multisource comorbidity score (MCS)) predictive of mortality, hospital admissions and healthcare costs using multiple source information from the administrative Italian National Health System (NHS) databases. Methods An index of 34 variables (measured from inpatient diagnoses and outpatient drug prescriptions within 2 years before baseline) independently predicting 1-year mortality in a sample of 500 000 individuals aged 50 years or older randomly selected from the NHS beneficiaries of the Italian region of Lombardy (training set) was developed. The corresponding weights were assigned from the regression coefficients of a Weibull survival model. MCS performance was evaluated by using an internal (ie, another sample of 500 000 NHS beneficiaries from Lombardy) and three external (each consisting of 500 000 NHS beneficiaries from Emilia-Romagna, Lazio and Sicily) validation sets. Discriminant power and net reclassification improvement were used to compare MCS performance with that of other comorbidity scores. MCS ability to predict secondary health outcomes (ie, hospital admissions and costs) was also investigated. Results Primary and secondary outcomes progressively increased with increasing MCS value. MCS improved the net 1-year mortality reclassification from 27% (with respect to the Chronic Disease Score) to 69% (with respect to the Elixhauser Index). MCS discrimination performance was similar in the four regions of Italy we tested, the area under the receiver operating characteristic curves (95% CI) being 0.78 (0.77 to 0.79) in Lombardy, 0.78 (0.77 to 0.79) in Emilia-Romagna, 0.77 (0.76 to 0.78) in Lazio and 0.78 (0.77 to 0.79) in Sicily. Conclusion MCS seems better than conventional scores for predicting health outcomes, at least in the general population from Italy. This may offer an improved tool for risk adjustment, policy planning and identifying patients in need of a focused treatment approach in the everyday medical practice.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11387/167390
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