A comparison of the National Surgical Quality Improvement Program and the Society of Thoracic Surgery Cardiac Surgery preoperative risk models: a cohort study.
Journal
International journal of surgery (London, England)
ISSN: 1743-9159
Titre abrégé: Int J Surg
Pays: United States
ID NLM: 101228232
Informations de publication
Date de publication:
01 Aug 2023
01 Aug 2023
Historique:
received:
28
02
2023
accepted:
09
05
2023
medline:
22
8
2023
pubmed:
19
5
2023
entrez:
19
5
2023
Statut:
epublish
Résumé
Cardiac surgery prediction models and outcomes from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) have not been reported. The authors sought to develop preoperative prediction models and estimates of postoperative outcomes for cardiac surgery using the ACS-NSQIP and compare these to the Society of Thoracic Surgeons Adult Cardiac Surgery Database (STS-ACSD). In a retrospective analysis of the ACS-NSQIP data (2007-2018), cardiac operations were identified using cardiac surgeon primary specialty and sorted into cohorts of coronary artery bypass grafting (CABG) only, valve surgery only, and valve+CABG operations using CPT codes. Prediction models were created using backward selection of the 28 non-laboratory preoperative variables in ACS-NSQIP. Rates of nine postoperative outcomes and performance statistics of these models were compared to published STS 2018 data. Of 28 912 cardiac surgery patients, 18 139 (62.8%) were CABG only, 7872 (27.2%) were valve only, and 2901 (10.0%) were valve+CABG. Most outcome rates were similar between the ACS-NSQIP and STS-ACSD, except for lower rates of prolonged ventilation and composite morbidity and higher reoperation rates in ACS-NSQIP (all P <0.0001). For all 27 comparisons (9 outcomes × 3 operation groups), the c-indices for the ACS-NSQIP models were lower by an average of ~0.05 than the reported STS models. The ACS-NSQIP preoperative risk models for cardiac surgery were almost as accurate as the STS-ACSD models. Slight differences in c-indexes could be due to more predictor variables in STS-ACSD models or the use of more disease- and operation-specific risk variables in the STS-ACSD models.
Sections du résumé
BACKGROUND
BACKGROUND
Cardiac surgery prediction models and outcomes from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) have not been reported. The authors sought to develop preoperative prediction models and estimates of postoperative outcomes for cardiac surgery using the ACS-NSQIP and compare these to the Society of Thoracic Surgeons Adult Cardiac Surgery Database (STS-ACSD).
METHODS
METHODS
In a retrospective analysis of the ACS-NSQIP data (2007-2018), cardiac operations were identified using cardiac surgeon primary specialty and sorted into cohorts of coronary artery bypass grafting (CABG) only, valve surgery only, and valve+CABG operations using CPT codes. Prediction models were created using backward selection of the 28 non-laboratory preoperative variables in ACS-NSQIP. Rates of nine postoperative outcomes and performance statistics of these models were compared to published STS 2018 data.
RESULTS
RESULTS
Of 28 912 cardiac surgery patients, 18 139 (62.8%) were CABG only, 7872 (27.2%) were valve only, and 2901 (10.0%) were valve+CABG. Most outcome rates were similar between the ACS-NSQIP and STS-ACSD, except for lower rates of prolonged ventilation and composite morbidity and higher reoperation rates in ACS-NSQIP (all P <0.0001). For all 27 comparisons (9 outcomes × 3 operation groups), the c-indices for the ACS-NSQIP models were lower by an average of ~0.05 than the reported STS models.
CONCLUSIONS
CONCLUSIONS
The ACS-NSQIP preoperative risk models for cardiac surgery were almost as accurate as the STS-ACSD models. Slight differences in c-indexes could be due to more predictor variables in STS-ACSD models or the use of more disease- and operation-specific risk variables in the STS-ACSD models.
Identifiants
pubmed: 37204450
doi: 10.1097/JS9.0000000000000490
pii: 01279778-202308000-00020
pmc: PMC10442082
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2334-2343Informations de copyright
Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.
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