<p>Performance of Comprehensive Risk Adjustment for the Prediction of In-Hospital Events Using Administrative Healthcare Data: The Queralt Indices</p>

Abstract

Background: 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.

Comorbidity accounts for severe COVID-19 risk, but how do we measure it? Retrospective assessment of the performance of three measures of comorbidity using 4,607 hospitalizations

“…For descriptive purposes only, patients were also stratified in four risk levels (i.e., low, moderate, high, and very high) based on the 50 th , 80 th , and 95 th percentiles of Queralt DxS, as described previously. 19 Since the primary objective was to compare the performance of each index of comorbidity when added to a given multivariate risk prediction model, we built five logistic regression models to predict the composite outcome of critical illness: a baseline model with age and sex, three models including the baseline and each of the comorbidity measures (i.e., Charlson index, Elixhauser index, and Queralt DxS), and one including the 27 diagnoses included in the Elixhauser index separately. The performance of each model was evaluated using five statistical measures of model performance: the deviance, the Akaike information criterion (AIC), 25 the Bayesian Information Criterion (BIC), 26 the area under the curve of the receiving operating characteristics (AUROCC) curve, and the area under the precision-recall (AUPRC) curve.…”

“…During 2020, the rapid spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the severity of coronavirus disease 2019 (COVID- 19) led to the collapse of many healthcare systems worldwide, particularly hospital and intensive care unit (ICU) resources.…”

“…18 In 2020, we developed a comprehensive risk index tool for hospitalized patients, the Queralt Index, which includes a measure of pre-existing comorbidities (Queralt DxS). 19 The Queralt DxS combines and weighs more than 2,100 relevant acute and chronic diagnostic codes.…”

“…Queralt DxS has shown optimal performance for risk adjustment when measuring comorbidity burden in hospitalized patients. 19 In this study, we assessed the performance of Queralt DxS as a comorbidity measure in models for predicting outcomes in hospitalized COVID-19 patients and compared it with other widely used comorbidity measures: the Charlson and Elixhauser indices.…”

Performance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients

“…Queralt DxS has shown optimal performance for risk adjustment when measuring comorbidity burden in hospitalized patients. 19 Understanding the risk factors, such as comorbidities associated with hospital outcomes in COVID-19 patients, is crucial for healthcare planning during future waves of the infection. However, most predicting models published to date rely on simple counts of comorbidities, and no studies have explored the appropriate measure for adjusting for multimorbidity in the COVID-19 setting.…”