Application of machine learning to predict postoperative gastrointestinal bleed in bariatric surgery

Hsu, Justin L.; Chen, Kevin A; Butler, Logan R.; Bahraini, Anoosh; Kapadia, Muneera R.; Gomez, Shawn M.; Farrell, Timothy M.

doi:10.1007/s00464-023-10156-0

Cited by 5 publications

(1 citation statement)

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“…Recent developments in artificial intelligence offer a chance to improve the accuracy of risk stratification through effective analysis of large volumes of patient data. Recently, Hsu et al [ 40 ] developed a machine learning model that outperformed logistic regression in predicting postoperative gastrointestinal bleeding in bariatric surgery patients. However, machine learning has not been used in VTE risk prediction in bariatric patients.…”

Section: Introductionmentioning

confidence: 99%

Predicting short-term thromboembolic risk following Roux-en-Y gastric bypass using supervised machine learning

Ali,

Inayat,

Moond

et al. 2024

World J Gastrointest Surg

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BACKGROUND Roux-en-Y gastric bypass (RYGB) is a widely recognized bariatric procedure that is particularly beneficial for patients with class III obesity. It aids in significant weight loss and improves obesity-related medical conditions. Despite its effectiveness, postoperative care still has challenges. Clinical evidence shows that venous thromboembolism (VTE) is a leading cause of 30-d morbidity and mortality after RYGB. Therefore, a clear unmet need exists for a tailored risk assessment tool for VTE in RYGB candidates. AIM To develop and internally validate a scoring system determining the individualized risk of 30-d VTE in patients undergoing RYGB. METHODS Using the 2016–2021 Metabolic and Bariatric Surgery Accreditation Quality Improvement Program, data from 6526 patients (body mass index ≥ 40 kg/m2) who underwent RYGB were analyzed. A backward elimination multivariate analysis identified predictors of VTE characterized by pulmonary embolism and/or deep venous thrombosis within 30 d of RYGB. The resultant risk scores were derived from the coefficients of statistically significant variables. The performance of the model was evaluated using receiver operating curves through 5-fold cross-validation. RESULTS Of the 26 initial variables, six predictors were identified. These included a history of chronic obstructive pulmonary disease with a regression coefficient (Coef) of 2.54 (P < 0.001), length of stay (Coef 0.08, P < 0.001), prior deep venous thrombosis (Coef 1.61, P < 0.001), hemoglobin A1c > 7% (Coef 1.19, P < 0.001), venous stasis history (Coef 1.43, P < 0.001), and preoperative anticoagulation use (Coef 1.24, P < 0.001). These variables were weighted according to their regression coefficients in an algorithm that was generated for the model predicting 30-d VTE risk post-RYGB. The risk model's area under the curve (AUC) was 0.79 [95% confidence interval (CI): 0.63-0.81], showing good discriminatory power, achieving a sensitivity of 0.60 and a specificity of 0.91. Without training, the same model performed satisfactorily in patients with laparoscopic sleeve gastrectomy with an AUC of 0.63 (95%CI: 0.62-0.64) and endoscopic sleeve gastroplasty with an AUC of 0.76 (95%CI: 0.75-0.78). CONCLUSION This simple risk model uses only six variables to assist clinicians in the preoperative risk stratification of RYGB patients, offering insights into factors that heighten the risk of VTE events.

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Section: Introductionmentioning

confidence: 99%