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Predict models for prolonged ICU stay using APACHE II, APACHE III and SAPS II scores: A Japanese multicenter retrospective cohort study

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  • Daiki Takekawa
  • Hideki Endo
  • Eiji Hashiba
  • Kazuyoshi Hirota

Abstract

Prolonged ICU stays are associated with high costs and increased mortality. Thus, early prediction of such stays would help clinicians to plan initial interventions, which could lead to efficient utilization of ICU resources. The aim of this study was to develop models for predicting prolonged stays in Japanese ICUs using APACHE II, APACHE III and SAPS II scores. In this multicenter retrospective cohort study, we analyzed the cases of 85,558 patients registered in the Japanese Intensive care Patient Database between 2015 and 2019. Prolonged ICU stay was defined as an ICU stay of >14 days. Multivariable logistic regression analyses were performed to develop three predictive models for prolonged ICU stay using APACHE II, APACHE III and SAPS II scores, respectively. After exclusions, 79,620 patients were analyzed, 2,364 of whom (2.97%) experienced prolonged ICU stays. Multivariable logistic regression analyses showed that severity scores, BMI, MET/RRT, postresuscitation, readmission, length of stay before ICU admission, and diagnosis at ICU admission were significantly associated with higher risk of prolonged ICU stay in all models. The present study developed predictive models for prolonged ICU stay using severity scores. These models may be helpful for efficient utilization of ICU resources.

Suggested Citation

  • Daiki Takekawa & Hideki Endo & Eiji Hashiba & Kazuyoshi Hirota, 2022. "Predict models for prolonged ICU stay using APACHE II, APACHE III and SAPS II scores: A Japanese multicenter retrospective cohort study," PLOS ONE, Public Library of Science, vol. 17(6), pages 1-16, June.
    • Handle: RePEc:plo:pone00:0269737
    DOI: 10.1371/journal.pone.0269737
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    References listed on IDEAS

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    1. Ilona W M Verburg & Nicolette F de Keizer & Evert de Jonge & Niels Peek, 2014. "Comparison of Regression Methods for Modeling Intensive Care Length of Stay," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-11, October.
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