Standardized use of venous thromboembolism (VTE) risk assessment models (RAMs) in surgical patients has been limited, in part due to the cumbersome workflow addition required to use available models. The COBRA score-capturing cancer diagnosis, (old) age, body mass index, race, and American Society of Anesthesiologists Physical Status score-has been reported as a potentially automatable VTE RAM that circumvents the cumbersome workflow addition that most RAMs represent. We aimed to test the ability of the COBRA model to effectively risk-stratify patients across various populations.
Patients were included from the 2014-2019 American College of Surgeons National Surgical Quality Improvement Program (NSQIP) Participant Use Data File for two hospitals, representing colorectal, endocrine, breast, transplant, plastic, and general surgery services. COBRA score was calculated for each patient using preoperative characteristics. We calculated negative predictive value (NPV) for VTE outcomes and compared the COBRA score to NSQIP’s expected VTE rate for all patients, between the two hospitals, and between subspecialty service lines.
Of the 10,711 patients included, those with COBRA <4 (31%) had projected median VTE rate of 0.21% (interquartile range, 0.09-0.68%; mean, 0.54%). Patients with higher scores (69%) had median rate of 0.88% (0.26-2.07%; 1.46%); relative rate 2.7. The median projected VTE rates for patients identified as low risk were 0.21% and 0.16% and as high risk were 0.87% and 0.89% at hospitals one and 2, respectively. The median projected VTE rates for patients identified as low risk were 0.17%, 0.61%, and 0.08% and as high risk were 0.52%, 1.43%, and 0.18% among general, colorectal, and endocrine surgery patients, respectively. COBRA had NPV of 0.995 and sensitivity of 0.871 as compared to NPV 0.997 and sensitivity 0.857 of the NSQIP model.
The COBRA score is concordant with the traditional gold standard NSQIP VTE RAM and demonstrates interhospital and service-specific generalizability, although performance was limited in especially low-risk patients. The model adequately risk-stratifies surgical patients preoperatively, potentially providing clinical decision support for perioperative workflows.

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