Literature DB >> 35524075

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

João Vasco Santos1,2,3,4, João Viana5,6, Carla Pinto7, Júlio Souza5,6, Fernando Lopes5,6, Alberto Freitas5,6, Sílvia Lopes8,9.   

Abstract

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.
© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Charlson Comorbidity Index; Diagnosis-Related Groups; Elixhauser Comorbidity Index; In-hospital mortality; Prediction; Risk of Mortality; Severity of Illness

Mesh:

Year:  2022        PMID: 35524075     DOI: 10.1007/s10916-022-01805-3

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  5 in total

1.  Risk-adjusting acute myocardial infarction mortality: are APR-DRGs the right tool?

Authors:  P S Romano; B K Chan
Journal:  Health Serv Res       Date:  2000-03       Impact factor: 3.402

2.  Importance of coding co-morbidities for APR-DRG assignment: Focus on cardiovascular and respiratory diseases.

Authors:  Julio Souza; João Vasco Santos; Veronica Bolon Canedo; Amparo Betanzos; Domingos Alves; Alberto Freitas
Journal:  Health Inf Manag       Date:  2019-05-01       Impact factor: 3.185

3.  Health records as the basis of clinical coding: Is the quality adequate? A qualitative study of medical coders' perceptions.

Authors:  Vera Alonso; João Vasco Santos; Marta Pinto; Joana Ferreira; Isabel Lema; Fernando Lopes; Alberto Freitas
Journal:  Health Inf Manag       Date:  2019-02-11       Impact factor: 3.185

Review 4.  Machine learning and statistical methods for predicting mortality in heart failure.

Authors:  Dineo Mpanya; Turgay Celik; Eric Klug; Hopewell Ntsinjana
Journal:  Heart Fail Rev       Date:  2020-11-09       Impact factor: 4.214

5.  A system for cost and reimbursement control in hospitals.

Authors:  R B Fetter; J D Thompson; R E Mills
Journal:  Yale J Biol Med       Date:  1976-05
  5 in total

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