Risk Management and Healthcare Policy (Mar 2020)
Performance of Comprehensive Risk Adjustment for the Prediction of In-Hospital Events Using Administrative Healthcare Data: The Queralt Indices
Abstract
David Monterde,1 Miguel Cainzos-Achirica,2,3 Yolima Cossio-Gil,4,5 Luis García-Eroles,6 Pol Pérez-Sust,7 Miquel Arrufat,1 Candela Calle,8 Josep Comin-Colet,3,9 César Velasco4,5 1Catalan Institute of Health, Barcelona, Spain; 2Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins Medical Institutions, Baltimore, MD, USA; 3Bellvitge University Hospital, L’Hospitalet de Llobregat, Barcelona, Spain; 4Vall d’Hebron Hospital, Barcelona, Spain; 5Vall d’Hebron Research Institute (VHIR), Barcelona, Spain; 6Catalan Health Service, Barcelona, Spain; 7Catalan Health Department, Barcelona, Spain; 8Catalan Institute of Oncology (ICO), Barcelona, Spain; 9University of Barcelona, Barcelona, SpainCorrespondence: David MonterdeDepartment of Statistics, Information Systems, Catalan Institute of Health, Gran via De Les Corts Catalanes 587, Barcelona 08007, SpainTel +34 934824642Email [email protected]: Accurate risk adjustment is crucial for healthcare management and benchmarking.Purpose: We aimed to compare the performance of classic comorbidity functions (Charlson’s and Elixhauser’s), of the All Patients Refined Diagnosis Related Groups (APR-DRG), and of the Queralt Indices, a family of novel, comprehensive comorbidity indices for the prediction of key clinical outcomes in hospitalized patients.Material and Methods: We conducted an observational, retrospective cohort study using administrative healthcare data from 156,459 hospital discharges in Catalonia (Spain) during 2018. Study outcomes were in-hospital death, long hospital stay, and intensive care unit (ICU) stay. We evaluated the performance of the following indices: Charlson’s and Elixhauser’s functions, Queralt’s Index for secondary hospital discharge diagnoses (Queralt DxS), the overall Queralt’s Index, which includes pre-existing comorbidities, in-hospital complications, and principal discharge diagnosis (Queralt Dx), and the APR-DRG. Discriminative ability was evaluated using the area under the curve (AUC), and measures of goodness of fit were also computed. Subgroup analyses were conducted by principal discharge diagnosis, by age, and type of admission.Results: Queralt DxS provided relevant risk adjustment information in a larger number of patients compared to Charlson’s and Elixhauser’s functions, and outperformed both for the prediction of the 3 study outcomes. Queralt Dx also outperformed Charlson’s and Elixhauser’s indices, and yielded superior predictive ability and goodness of fit compared to APR-DRG (AUC for in-hospital death 0.95 for Queralt Dx, 0.77– 0.93 for all other indices; for ICU stay 0.84 for Queralt Dx, 0.73– 0.83 for all other indices). The performance of Queralt DxS was at least as good as that of the APR-DRG in most principal discharge diagnosis subgroups.Conclusion: Our findings suggest that risk adjustment should go beyond pre-existing comorbidities and include principal discharge diagnoses and in-hospital complications. Validation of comprehensive risk adjustment tools such as the Queralt indices in other settings is needed.Keywords: benchmarking, case-mix, comorbidity, discrimination, multimorbidity, Queralt’s indices, risk
