Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/30744
Title: External validation of a surgical mortality risk prediction model for inpatient noncardiac surgery in an Australian private health insurance dataset.
Austin Authors: Reilly, Jennifer Richelle;Wong, Darren;Brown, Wendy Ann;Gabbe, Belinda Jane;Myles, Paul Stewart
Affiliation: Gastroenterology and Hepatology
Department of Anaesthesiology and Perioperative Medicine, Alfred Health, Melbourne, Victoria, Australia
School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
Department of Surgery, Central Clinical School, Monash University, Melbourne, Victoria, Australia
Department of Surgery, Alfred Health, Melbourne, Victoria, Australia
Department of Medicine, University of Melbourne, Parkville, Victoria, Australia
Department of Anaesthesiology and Perioperative Medicine, Monash University, Melbourne, Victoria, Australia
Issue Date: 18-Aug-2022
Date: 2022
Publication information: ANZ Journal of Surgery 2022; 92(11)
Abstract: We previously conducted a systematic review to identify surgical mortality risk prediction tools suitable for adapting in the Australian context and identified the Surgical Outcome Risk Tool (SORT) as an ideal model. The primary aim was to investigate the external validity of SORT for predicting in-hospital mortality in a large Australian private health insurance dataset. A cohort study using a prospectively collected Australian private health insurance dataset containing over 2 million deidentified records. External validation was conducted by applying the predictive equation for SORT to the complete case analysis dataset. Model re-estimation (recalibration) was performed by logistic regression. The complete case analysis dataset contained 161 277 records. In-hospital mortality was 0.2% (308/161277). The mean estimated risk given by SORT was 0.2% and the median (IQR) was 0.01% (0.003%-0.08%). Discrimination was high (c-statistic 0.96) and calibration was accurate over the range 0%-10%, beyond which mortality was over-predicted but confidence intervals included or closely approached the perfect prediction line. Re-estimation of the equation did not improve over-prediction. Model diagnostics suggested the presence of outliers or highly influential values. The low perioperative mortality rate suggests the dataset was not representative of the overall Australian surgical population, primarily due to selection bias and classification bias. Our results suggest SORT may significantly under-predict 30-day mortality in this dataset. Given potential differences in perioperative mortality, private health insurance status and hospital setting should be considered as covariables when a locally validated national surgical mortality risk prediction model is developed.
URI: https://ahro.austin.org.au/austinjspui/handle/1/30744
DOI: 10.1111/ans.17946
ORCID: 0000-0002-7109-8493
0000-0003-1490-0547
0000-0002-0137-2688
0000-0001-7096-7688
0000-0002-3324-5456
Journal: ANZ Journal of Surgery
PubMed URL: 35979735
PubMed URL: https://pubmed.ncbi.nlm.nih.gov/35979735/
Type: Journal Article
Subjects: anaesthesia
mortality
outcome
perioperative
risk
risk score
surgery
Appears in Collections:Journal articles

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