Premenopausal women, who account for more than half of patients for bariatric surgery, are at higher risk of developing postoperative iron deficiency anemia (IDA) than postmenopausal women and men. We aimed at establishing a machine learning model to evaluate the risk of newly onset IDA in premenopausal women 12 months after sleeve gastrectomy (SG). Premenopausal women with complete clinical records and undergoing SG were enrolled in this retrospective study. Newly onset IDA after surgery, the main outcome, was defined according to the age- and gender-specific World Health Organization criteria. A linear support vector machine model was developed to predict the risk of IDA after SG with the top five important features identified during feature selection. Four hundred and seven subjects aged 31.0 (Interquartile range (IQR): 26.0-36.0) years with a median follow-up period of 12 (IQR 7-13) months were analyzed. They were divided into a training set and a validation set with 285 and 122 individuals, respectively. Preoperative ferritin, age, hemoglobin, creatinine, and fasting C-peptide were included. The model showed moderate discrimination in both sets (area under curve 0.858 and 0.799, respectively, p < 0.001). The calibration curve indicated acceptable consistency between observed and predicted results in both sets. Moreover, decision curve analysis showed substantial clinical benefits of the model in both sets. Our machine learning model could accurately predict newly onset IDA in Chinese premenopausal women with obesity 12 months after SG. External validation was required before the model was used in clinical practice.
Keywords: iron deficiency anemia; machine learning; sleeve gastrectomy; support vector machine.