Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Intensive Care Decision Making: Using Prognostic Models for Resource Allocation Publisher Pubmed



Atashi A1 ; Sarbaz M2 ; Marashi S1 ; Hajialiasgari F1 ; Eslami S3
Authors
Show Affiliations
Authors Affiliations
  1. 1. E-health Department, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Department of Medical Records and Health Information Technology, School of Paramedical Sciences, Mashhad University of Medical Sciences, Mashhad, Iran
  3. 3. Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

Source: Studies in Health Technology and Informatics Published:2018


Abstract

Accurate outcome prediction by the means of available clinical contributing factors will support researchers and administrators in realistic planning, workload determination, resource optimization, and evidence-based quality control process. This study is aimed to evaluate APACHE II and SAPS II prediction models in an Iranian population. A a prospective cross-sectional study was conducted in four tertiary care referral centers located in the top two most populated cities in Iran, from August 2013 to August 2015. The Brier score, Area under the Receiver Operating Characteristics Curve (AUC), and Hosmer-Lemeshow (H-L) goodness-of-fit test were employed to quantify models' performance. A total of 1799 patients (58.5% males and 41.5% females) were included for further score calculation. The overall observed mortality (24.4%) was more than international rates due to APACHE II categories. The Brier score for APACHE II and SAPS II were 0.17 and 0.196, respectively. Both scoring systems were associated with acceptable AUCs (APACHE II = 0.745 and SAPS II = 0.751). However, none of prediction models were fitted to dataset (H-L ρ value < 0.01). With regards to poor performance measures of APACHE II and SAPS II in this study, finding recalibrated version of current prediction models is considered as an obligatory research question before applying it as a clinical prioritization or quality control instrument. © 2018 The authors and IOS Press. All rights reserved.