Data Sheet 1_A clinical predictive model for hearing recovery after middle ear cholesteatoma surgery based on machine learning.csv

ObjectiveTo explore various factors influencing postoperative hearing recovery in patients with middle ear cholesteatoma and to construct and validate a clinical prediction model for postoperative hearing recovery.
MethodsClinical data from 548 patients diagnosed with middle ear cholesteatoma, gathered between May 2019 and December 2023, were randomly split into a training cohort and a validation cohort in a ratio of 7:3. To enhance feature selection, we utilized univariate logistic regression analysis, multivariate logistic regression analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model to identify significant variables and develop the prediction model. The model’s ability to predict outcomes was assessed using the Receiver Operating Characteristic (ROC) curve, while its clinical relevance was evaluated through calibration curves and clinical decision curves. Ultimately, the study findings were visually illustrated with a nomogram.
ResultsThe findings from both univariate and multivariate logistic regression analyses suggest that several predictive factors are significant. These factors encompass the completeness of the ossicular chain, granulation tissue presence within the ossicular chain, the use of ossicular prostheses, eustachian tube functionality, instances of mixed hearing loss, ear conditions (either dry or wet), diabetes, and hypertension. For the training cohort, the area under the curve (AUC) was calculated to be 0.992 (95% CI 0.84–0.99), with the Hosmer-Lemeshow test yielding X2 = 10.54 and p = 0.29. In the validation cohort, the AUC was 0.977 (95% CI 0.82–0.98), and the Hosmer-Lemeshow test revealed X2 = 8.54 and p = 0.42. After implementing strict post-split preprocessing to mitigate overfitting and data leakage risks, the model was re-evaluated. The bootstrap-corrected AUC for the training cohort was 0.980 (95% CI, 0.82–0.99), and the cross-validated, optimism-corrected AUC for the validation cohort was 0.965 (95% CI, 0.80–0.98). A nomogram has been developed to visually forecast postoperative hearing recovery in individuals diagnosed with middle ear cholesteatoma. Additionally, the calibration curve, along with the clinical decision curve, indicates that this predictive model is both stable and trustworthy.
ConclusionThis nomogram is an effective tool for predicting hearing recovery in patients with middle ear cholesteatoma, providing evidence-based support for clinical practice.

目的 探讨中耳胆脂瘤患者术后听力恢复的各类影响因素，并构建并验证术后听力恢复的临床预测模型。
方法 收集2019年5月至2023年12月期间收治的548例确诊中耳胆脂瘤患者的临床资料，按照7:3的比例随机划分为训练队列与验证队列。为优化特征筛选流程，本研究采用单因素logistic回归分析、多因素logistic回归分析以及最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）回归模型筛选显著变量，以构建预测模型。采用受试者工作特征（Receiver Operating Characteristic, ROC）曲线评估模型的预测效能，通过校准曲线与临床决策曲线评价其临床实用性。最终，列线图（nomogram）用于可视化展示本研究结果。
结果 单因素与多因素logistic回归分析结果显示，多项预测因素具有统计学意义，包括听骨链完整性、听骨链内肉芽组织存在情况、听骨假体使用情况、咽鼓管功能、混合性听力损失发生情况、耳部干湿状态、糖尿病与高血压病史。训练队列的曲线下面积（area under the curve, AUC）为0.992（95%置信区间CI：0.84~0.99），Hosmer-Lemeshow检验结果为χ²=10.54，P=0.29。验证队列的AUC为0.977（95%CI：0.82~0.98），Hosmer-Lemeshow检验结果为χ²=8.54，P=0.42。在实施严格的拆分后预处理以缓解过拟合与数据泄露风险后，本研究对模型进行了重新评估：训练队列的bootstrap校正AUC为0.980（95%CI：0.82~0.99），验证队列的交叉验证乐观校正AUC为0.965（95%CI：0.80~0.98）。本研究已构建列线图以可视化预测中耳胆脂瘤患者的术后听力恢复情况。此外，校准曲线与临床决策曲线结果表明，该预测模型具有良好的稳定性与可靠性。
结论 该列线图可有效预测中耳胆脂瘤患者的术后听力恢复情况，为临床实践提供循证支持。