The Predictive Factors on Extended Hospital Length of Stay in Patients with AMI: Laboratory and Administrative Data

Teresa Magalhães Barbosa, Sílvia Lopes, João Gomes, Filipe Seixo

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The length of hospital stay (LOS) is an important measure of efficiency in the use of hospital resources. Acute Myocardial Infarction (AMI), as one of the diseases with higher mortality and LOS variability in the OECD countries, has been studied with predominant use of administrative data, particularly on mortality risk adjustment, failing investigation in the resource planning and specifically in LOS. This paper presents results of a predictive model for extended LOS (LOSE - above 75th percentile of LOS) using both administrative and clinical data, namely laboratory data, in order to develop a decision support system. Laboratory and administrative data of a Portuguese hospital were included, using logistic regression to develop this predictive model. A model with three laboratory data and seven administrative data variables (six comorbidities and age ≥ 69 years), with excellent discriminative ability and a good calibration, was obtained. The model validation shows also good results. Comorbidities were relevant predictors, mainly diabetes with complications, showing the highest odds of LOSE (OR = 37,83; p = 0,001). AMI patients with comorbidities (diabetes with complications, cerebrovascular disease, shock, respiratory infections, pulmonary oedema), with pO2 above level, aged 69 years or older, with cardiac dysrhythmia, neutrophils above level, pO2 below level, and prothrombin time above level, showed increased risk of extended LOS. Our findings are consistent with studies that refer these variables as predictors of increased risk.

Original languageEnglish
Article number2
JournalJournal of Medical Systems
Volume40
Issue number1
DOIs
Publication statusPublished - 1 Jan 2016

Keywords

  • Acute myocardial infarction
  • Decision support system
  • Efficiency
  • Laboratory data
  • Length of hospital stay
  • Predictive model

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