Intensive Care Decision Making: Using Prognostic Models for Resource Allocation

Intensive Care Decision Making: Using Prognostic Models for Resource Allocation Publisher Pubmed

Atashi A¹ ; Sarbaz M² ; Marashi S¹ ; Hajialiasgari F¹ ; Eslami S³

Show Affiliations

1. E-health Department, Tehran University of Medical Sciences, Tehran, Iran
2. Department of Medical Records and Health Information Technology, School of Paramedical Sciences, Mashhad University of Medical Sciences, Mashhad, Iran
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.

Style	Citing Format
MLA	Atashi A, et al.. "Intensive Care Decision Making: Using Prognostic Models for Resource Allocation." Studies in Health Technology and Informatics, vol. 251, no. , 2018, pp. 145-148.
APA	Atashi A, Sarbaz M, Marashi S, Hajialiasgari F, Eslami S (2018). Intensive Care Decision Making: Using Prognostic Models for Resource Allocation. Studies in Health Technology and Informatics, 251(), 145-148.
Chicago	Atashi A, Sarbaz M, Marashi S, Hajialiasgari F, Eslami S. "Intensive Care Decision Making: Using Prognostic Models for Resource Allocation." Studies in Health Technology and Informatics 251, no. (2018): 145-148.
Harvard	Atashi A et al. (2018) 'Intensive Care Decision Making: Using Prognostic Models for Resource Allocation', Studies in Health Technology and Informatics, 251(), pp. 145-148.
Vancouver	Atashi A, Sarbaz M, Marashi S, Hajialiasgari F, Eslami S. Intensive Care Decision Making: Using Prognostic Models for Resource Allocation. Studies in Health Technology and Informatics. 2018;251():145-148.
BibTex	@article{ author = {Atashi A and Sarbaz M and Marashi S and Hajialiasgari F and Eslami S}, title = {Intensive Care Decision Making: Using Prognostic Models for Resource Allocation}, journal = {Studies in Health Technology and Informatics}, volume = {251}, number = {}, pages = {145-148}, year = {2018} }
RIS	TY - JOUR AU - Atashi A AU - Sarbaz M AU - Marashi S AU - Hajialiasgari F AU - Eslami S TI - Intensive Care Decision Making: Using Prognostic Models for Resource Allocation JO - Studies in Health Technology and Informatics VL - 251 IS - SP - 145 EP - 148 PY - 2018 ER -

Science Communicator Platform

Authors

Authors Affiliations

Abstract