Preoperative Prediction of Unplanned Reoperation in a Broad Surgical Population.
American college of surgeons national surgical quality improvement program
Chronological validation
Reoperation
Risk prediction
Surgical outcomes
Surgical risk preoperative assessment system
Journal
The Journal of surgical research
ISSN: 1095-8673
Titre abrégé: J Surg Res
Pays: United States
ID NLM: 0376340
Informations de publication
Date de publication:
05 2023
05 2023
Historique:
received:
03
05
2022
revised:
07
11
2022
accepted:
24
12
2022
pmc-release:
01
05
2024
pubmed:
15
1
2023
medline:
3
3
2023
entrez:
14
1
2023
Statut:
ppublish
Résumé
Unplanned reoperation is an undesirable outcome with considerable risks and an increasingly assessed quality of care metric. There are no preoperative prediction models for reoperation after an index surgery in a broad surgical population in the literature. The Surgical Risk Preoperative Assessment System (SURPAS) preoperatively predicts 12 postoperative adverse events using 8 preoperative variables, but its ability to predict unplanned reoperation has not been assessed. This study's objective was to determine whether the SURPAS model could accurately predict unplanned reoperation. This was a retrospective analysis of the American College of Surgeons' National Surgical Quality Improvement Program adult database, 2012-2018. An unplanned reoperation was defined as any unintended operation within 30 d of an initial scheduled operation. The 8-variable SURPAS model and a 29-variable "full" model, incorporating all available American College of Surgeons' National Surgical Quality Improvement Program nonlaboratory preoperative variables, were developed using multiple logistic regression and compared using discrimination and calibration metrics: C-indices (C), Hosmer-Lemeshow observed-to-expected plots, and Brier scores (BSs). The internal chronological validation of the SURPAS model was conducted using "training" (2012-2017) and "test" (2018) datasets. Of 5,777,108 patients, 162,387 (2.81%) underwent an unplanned reoperation. The SURPAS model's C-index of 0.748 was 99.20% of that for the full model (C = 0.754). Hosmer-Lemeshow plots showed good calibration for both models and BSs were similar (BS = 0.0264, full; BS = 0.0265, SURPAS). Internal chronological validation results were similar for the training (C = 0.749, BS = 0.0268) and test (C = 0.748, BS = 0.0250) datasets. The SURPAS model accurately predicted unplanned reoperation and was internally validated. Unplanned reoperation can be integrated into the SURPAS tool to provide preoperative risk assessment of this outcome, which could aid patient risk education.
Identifiants
pubmed: 36640606
pii: S0022-4804(22)00838-1
doi: 10.1016/j.jss.2022.12.016
pmc: PMC9975057
mid: NIHMS1861973
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1-12Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR002535
Pays : United States
Informations de copyright
Copyright © 2022 Elsevier Inc. All rights reserved.
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