Machine learning model integrating radiomics and clinical features for predicting postoperative bleeding after percutaneous nephrolithotomy

Postoperative statistics revealed that the postoperative bleeding rate was 31.1% (n = 47), the blood transfusion rate was 1.99% (n = 3), and the final probability of requiring vascular embolization was 1.32% (n = 2). The accuracy rates for predicting postoperative bleeding in patients with PCNL using logistic regression, RF and SVM were 75.6%, 75.6%, and 71.1%, respectively. The corresponding area under the curve AUC (95% CI) were 0.760 (0.719-0.802), 0.740 (0.691-0.800) and 0.631 (0.558-0.693). The top four prediction importance scores in logistic regression and RF were wavelet-HLH_glrlm_ShortRunLowGrayLevelEmphasis, wavelet-HLH_glrlm_LowGrayLevelRunEmphasis, stone shape, operation time and stone shape, stone diameter, operation time, wavelet-HLH_glrlm_ShortRunLowGrayLevelEmphasis. The DCA decision curve demonstrates that the logistic regression model's prediction of postoperative bleeding offers patients enhanced clinical benefits. The SHAP analysis further clarifies the contribution of each variable in the prediction process CONCLUSION: The machine learning model has demonstrated excellent performance in predicting the bleeding situation after percutaneous nephrolithotomy. This model may provide a basis for surgical risk stratification, thereby facilitating the development of more personalized postoperative monitoring plans.