开发并验证一种简单易用的列线图模型，用于预测儿童难治性肺炎支原体肺炎。
Development and validation of a simple-to-use nomogram for predicting refractory Mycoplasma pneumoniae pneumonia in children.
机构信息
Xiangya Hospital, Central South University, Changsha, Hunan Province, China.
Department of Respiratory Disease, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China.
出版信息
Pediatr Pulmonol. 2020 Apr;55(4):968-974. doi: 10.1002/ppul.24684. Epub 2020 Feb 10.
OBJECTIVE
This study aimed to develop and validate a simple-to-use nomogram for predicting refractory Mycoplasma pneumoniae pneumonia (RMPP) in children.
METHODS
A total of 73 children with RMPP and 146 children with general Mycoplasma pneumoniae pneumonia were included. Clinical, laboratory, and radiological data were obtained. A least absolute shrinkage and selection operator (LASSO) regression model was used to determine optimal predictors. The nomogram was plotted by multivariable logistic regression. The performance of the nomogram was assessed by calibration, discrimination, and clinical utility.
RESULTS
The LASSO regression analysis identified lactate dehydrogenase, albumin, neutrophil ratio, and high fever as significant predictors of RMPP. This nomogram-illustrated model showed good discrimination, calibration, and clinical value. The area under the receiver operating characteristic curve of the nomogram was 0.884 (95% CI, 0.823-0.945) in the training set and 0.881 (95% CI, 0.807-0.955) in the validating set. Calibration curve and Hosmer-Lemeshow test showed good consistency between the predictions of the nomogram and the actual observations, and decision curve analysis showed that the nomogram was clinically useful.
CONCLUSION
A simple-to-use nomogram for predicting RMPP in early stage was developed and validated. This may help physicians recognize RMPP earlier.
目的
本研究旨在开发和验证一种简单易用的列线图，用于预测儿童难治性肺炎支原体肺炎（RMPP）。
方法
共纳入 73 例 RMPP 患儿和 146 例普通肺炎支原体肺炎患儿。收集临床、实验室和影像学数据。采用最小绝对收缩和选择算子（LASSO）回归模型确定最佳预测因素。通过多变量逻辑回归绘制列线图。通过校准、区分和临床实用性评估列线图的性能。
结果
LASSO 回归分析确定乳酸脱氢酶、白蛋白、中性粒细胞比值和高热是 RMPP 的显著预测因素。该列线图模型显示出良好的区分度、校准度和临床价值。列线图在训练集中的受试者工作特征曲线下面积为 0.884（95%CI，0.823-0.945），在验证集中为 0.881（95%CI，0.807-0.955）。校准曲线和 Hosmer-Lemeshow 检验显示列线图预测与实际观察结果之间具有良好的一致性，决策曲线分析表明该列线图具有临床实用性。
结论
开发和验证了一种用于预测 RMPP 的简单易用的列线图。这有助于医生更早地识别 RMPP。
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