All Patient Refined-Diagnosis Related Groups' (APR-DRGs) Severity of Illness and Risk of Mortality as predictors of in-hospital mortality

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Santos, JV (Author) FMUP; Viana, J (Author) FMUP; Pinto, C (Author) Other; Souza, J (Author) Other; Lopes, F (Author) Other; Freitas A (Author) FMUP; Lopes, S (Author) Other

Journal

Title: Journal of Medical Systems

Vol. 46

ISSN: 0148-5598

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 15 Citations

Scopus - 9 Citations

Other information

Authenticus ID: P-00W-J31

DOI: 10.1007/s10916-022-01805-3

Abstract (EN): The aims of this study were to assess All-Patient Refined Diagnosis-Related Groups' (APR-DRG) Severity of Illness (SOI) and Risk of Mortality (ROM) as predictors of in-hospital mortality, comparing with Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI) scores. We performed a retrospective observational study using mainland Portuguese public hospitalizations of adult patients from 2011 to 2016. Model discrimination (C-statistic/ area under the curve) and goodness-of-fit (R-squared) were calculated. Our results comprised 4,176,142 hospitalizations with 5.9% in-hospital deaths. Compared to the CCI and ECI models, the model considering SOI, age and sex showed a statistically significantly higher discrimination in 49.6% (132 out of 266) of APR-DRGs, while in the model with ROM that happened in 33.5% of APR-DRGs. Between these two models, SOI was the best performer for nearly 20% of APR-DRGs. Some particular APR-DRGs have showed good discrimination (e.g. related to burns, viral meningitis or specific transplants). In conclusion, SOI or ROM, combined with age and sex, perform better than more widely used comorbidity indices. Despite ROM being the only score specifically designed for in-hospital mortality prediction, SOI performed better. These findings can be helpful for hospital or organizational models benchmarking or epidemiological analysis.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12