Key result
ACS-NSQIP preoperative risk models for cardiac surgery were almost as accurate as STS-ACSD models, with c-indices lower by an average of ~0.05 across 27 comparisons.
Why the study?
Cardiac surgery prediction models and outcomes from the ACS-NSQIP had not been reported.
Does the ACS-NSQIP preoperative risk model perform as well as the STS-ACSD model in predicting postoperative outcomes in cardiac surgery patients?
Cohort (n=28,912)
Yes
Does the ACS-NSQIP preoperative risk model perform as well as the STS-ACSD model in predicting postoperative outcomes in cardiac surgery patients?
Effect estimate: lower by an average of ~0.05
The ACS-NSQIP preoperative risk models for cardiac surgery are slightly less accurate than the STS-ACSD models, likely due to fewer disease- and operation-specific predictor variables.
ACS-NSQIP models approach STS discrimination for cardiac surgery; extends accessible risk tools for non-STS centers but leaves open clinical impact.
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: 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: 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: 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.
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Dyas et al. (2023) conducted a cohort in cardiac surgery (n=28,912). ACS-NSQIP preoperative risk models vs. STS-ACSD models was evaluated on c-indices for postoperative outcomes (lower by an average of ~0.05). ACS-NSQIP preoperative risk models for cardiac surgery were almost as accurate as STS-ACSD models, with c-indices lower by an average of ~0.05 across 27 comparisons.
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