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Objectives:This study aimed to develop severe pain prediction models for patients after percutaneous nephrolithotomy (PCNL), to identify and intervene the risk factors causing pain in a timely manner so as to improve patient satisfaction after surgery.Methods:The clinical data of 331 PCNL patients were retrospectively analyzed. Data of 265 patients (80%) were randomly selected as the training cohort and those of 66 (20%) patients were selected as the validation cohort. We choosed clinical data through the stepwise Akaike information criterion and best subset selection to build severe pain prediction models by the training data set. The validity of the model was verified and analyzed using the test data set.Results:The first clinical model was developed using the American Society of Anesthesiologists (ASA) classification, number of kidney stones, history of hydronephrosis, duration of surgery, and presence of stone remnants after surgery. This model had an area under the curve (AUC) of 0.8682 (95% confidence interval [CI], 0.8205–0.9160), threshold of 0.4771, sensitivity of 97.25%, specificity of 63.86%, and negative predictive value (NPV) of 91.38%. The second model was developed using the mentioned above six clinical features and size of kidney stones. This model showed a stronger discriminatory power (P=0.208), with an AUC of 0.8710 (95% CI, 0.8242–0.9187), threshold of 0.4813, sensitivity of 97.25%, specificity of 83.86%, and NPV of 91.38%. In the subsequent validation cohort (N = 66), the AUC (95% CI) was 0.9093 (0.8378–0.9808) for the first clinical model and 0.9098 (0.8373–0.9823) for the second clinical models.Conclusion:We developed two prediction models for postoperative pain severity in PCNL patients, which were validated internally.Trial registration:This is a retrospective study that does not include sensitive patient data, but only data provided by professionals from selected centres about their daily clinical practice. Therefore, our ethics Committee did not consider it necessary to register the study. |
