Liu Ming, Yang Ping, Gou Yunpeng, Chen Qiang, Xu Dong
Department of Pediatric Surgery, Suining Central Hospital, Suining, Sichuan Province, China.
Front Pediatr. 2024 Dec 13;12:1441263. doi: 10.3389/fped.2024.1441263. eCollection 2024.
The aim of this research was to develop and internally validate a nomogram for forecasting the length of hospital stay following laparoscopic appendectomy in pediatric patients diagnosed with appendicitis.
We developed a prediction model based on a training dataset of 415 pediatric patients with appendicitis, and hospitalization data were collected retrospectively from January 2021 and December 2022. The primary outcome measure in this study was hospital length of stay (LOS), with prolonged LOS defined as admission for a duration equal to or exceeding the 75th percentile of LOS, including the discharge day. Risk factor analysis was conducted through univariate and multivariate logistic regression analyses. Based on the regression coefficients, a nomogram prediction model was developed. The discriminative performance of the predicting model was evaluated using the C-index, and an adjusted C-index was computed through bootstrapping validation. Calibration curves were generated to assess the accuracy of the nomogram. Decision curve analysis was conducted to determine the clinical utility of the predicting model.
Predictors contained in the prediction nomogram included Age, neutrophil-to-lymphocyte ratio, C-reactive protein level, operative time, appendiceal fecalith, and drainage tube. The C-index of the prediction nomogram was determined to be 0.873 (95% CI: 0.838-0.908), with a corrected C-index of 0.8625 obtained through bootstrapping validation (1,000 resamples), indicating the model's favorable discrimination. Calibration curves illustrated a strong agreement between predicted and observed outcomes. According to the decision curve analysis of the nomogram, the predictive model demonstrates a net benefit at threshold probabilities exceeding 2%.
This nomogram, incorporating variables such as Age, neutrophil-to-lymphocyte ratio, C-reactive protein level, operative time, appendiceal fecalith, and drainage tube, offers a convenient method for assessing the duration of hospitalization in pediatric patients with appendicitis.
本研究旨在开发并内部验证一种列线图，用于预测诊断为阑尾炎的儿科患者腹腔镜阑尾切除术后的住院时间。
我们基于415例儿科阑尾炎患者的训练数据集开发了一个预测模型，住院数据从2021年1月至2022年12月进行回顾性收集。本研究的主要结局指标是住院时间（LOS），延长的LOS定义为住院时间等于或超过LOS第75百分位数（包括出院日）。通过单因素和多因素逻辑回归分析进行危险因素分析。基于回归系数，开发了列线图预测模型。使用C指数评估预测模型的判别性能，并通过自举验证计算调整后的C指数。生成校准曲线以评估列线图的准确性。进行决策曲线分析以确定预测模型的临床实用性。
预测列线图中的预测因素包括年龄、中性粒细胞与淋巴细胞比值、C反应蛋白水平、手术时间、阑尾粪石和引流管。预测列线图的C指数确定为0.873（95%CI：0.838 - 0.908），通过自举验证（1000次重采样）获得的校正C指数为0.8625，表明该模型具有良好的判别能力。校准曲线表明预测结果与观察结果之间有很强的一致性。根据列线图的决策曲线分析，预测模型在阈值概率超过2%时显示出净效益。
该列线图纳入了年龄、中性粒细胞与淋巴细胞比值、C反应蛋白水平、手术时间、阑尾粪石和引流管等变量，为评估儿科阑尾炎患者的住院时间提供了一种便捷的方法。
