{Reference Type}: Journal Article
{Title}: 2024 Update of The Society of Thoracic Surgeons Short-Term Esophagectomy Risk Model: More Inclusive and Improved Calibration.
{Author}: Velotta JB;Seder CW;Bonnell L;Hayanga JA;Kidane B;Inra M;Shahian DM;Habib RH; ;
{Journal}: Ann Thorac Surg
{Volume}: 0
{Issue}: 0
{Year}: 2024 Jun 29
{Factor}: 5.102
{DOI}: 10.1016/j.athoracsur.2024.05.044
{Abstract}: <B>BACKGROUND: </B>The Society of Thoracic Surgeons General Thoracic Surgery Database (STS-GTSD) previously reported short-term risk models for esophagectomy for esophageal cancer. We sought to update existing models using more inclusive contemporary cohorts, with consideration of additional risk factors based on clinical evidence.<BR><B>METHODS: </B>The study population consisted of adult patients in the STS-GTSD who underwent esophagectomy for esophageal cancer between January 2015 and December 2022. Separate esophagectomy risk models were derived for three primary endpoints: operative mortality, major morbidity, and composite morbidity or mortality. Logistic regression with backward selection was used with predictors retained in models if p<0.10. All derived models were validated using 9-fold cross validation. Model discrimination and calibration were assessed for the overall cohort and specified subgroups.<BR><B>RESULTS: </B>A total of 18,503 patients from 254 centers underwent esophagectomy for esophageal cancer. Operative mortality, morbidity, and composite morbidity or mortality rates were 3.4%, 30.5% and 30.9%, respectively. Novel predictors of short-term outcomes in the updated models included body surface area and insurance payor type. Overall discrimination was similar or superior to previous GTSD models for operative mortality [C-statistic = 0.72] and for composite morbidity or mortality [C-statistic = 0.62], Model discrimination was comparable across procedure- and demographic-specific sub-cohorts. Model calibration was excellent in all patient sub-groups.<BR><B>CONCLUSIONS: </B>The newly derived esophagectomy risk models showed similar or superior performance compared to previous models, with broader applicability and clinical face validity. These models provide robust preoperative risk estimation and can be used for shared decision-making, assessment of provider performance, and quality improvement.