A novel machine learning model for predicting clinical pregnancy after laparoscopic tubal anastomosis - Nan Ding; Jian Zhang; Peili Wang; Fang Wang (BMC, 2023)
Abstract Background Laparoscopic tubal anastomosis (LTA) is a treatment for women who require reproduction after ligation, and there are no reliable prediction models or clinically useful tools for predicting clinical pregnancy in women who receive this procedure. The prediction model we developed aims to predict the individual probability of clinical pregnancy in women after receiving LTA. Methods Retrospective analysis of clinical data of patients undergoing LAT in the Second Hospital of Lanzhou University from July 2017 to December 2021. Least absolute shrinkage and selection operator (LASSO) regression was used for data dimension reduction and feature selection. We incorporated the patients' basic characteristics, preoperative laboratory tests and laparoscopic tubal anastomosis procedure signature and obtained a nomogram. The model performance was evaluated in terms of its calibration, discrimination, and clinical applicability. The prediction model was further internally validated using 200 bootstrap resamplings. Results A total of 95 patients were selected to build the predictive model for clinical pregnancy after LTA. The LASSO method identified age, intrauterine polyps, pelvic adhesion and thyroid stimulating hormone(TSH) as independent predictors of the clinical pregnancy rate. The prediction nomogram included the abovementioned four predictive parameters. The model showed good discrimination with an area under the curve (AUC) value of 0.752. The Hosmer‒Lemeshow test of calibration showed that χ2 was 4.955 and the p value was 0.838, which indicates a satisfactory goodness-of-fit. Decision curve analysis demonstrated that the nomogram was clinically useful. Internal validation shows that the predictive model performs well. Conclusion This study presents a nomogram incorporating age, intrauterine polyps, pelvic adhesion and TSH based on the LASSO regression model, which can be conveniently used to facilitate the individualized prediction of clinical pregnancy in women after LTA.