Development and validation of a simple-to-use nomogram for predicting refractory Mycoplasma pneumoniae pneumonia in
Siying Cheng1, Jilei Lin2, Xuexiang Zheng2
1Xiangya Hospital, Central South University, Changsha, Hunan Province, China.
Insights
A new nomogram helps predict refractory Mycoplasma pneumoniae pneumonia (RMPP) in children. This tool uses key indicators to aid early RMPP recognition and management.
Area of Science:
- Pediatric Pulmonology
- Infectious Diseases
- Medical Diagnostics
Background:
- Mycoplasma pneumoniae pneumonia (MPP) is common in children.
- Refractory MPP (RMPP) presents diagnostic challenges.
- Early identification of RMPP is crucial for timely intervention.
Purpose of the Study:
- To develop and validate a user-friendly nomogram for predicting RMPP in pediatric patients.
- To identify key clinical, laboratory, and radiological predictors of RMPP.
Main Methods:
- A retrospective study included 73 children with RMPP and 146 with general MPP.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression identified significant predictors.
- Multivariable logistic regression was used to construct the nomogram.
- Nomogram performance was evaluated using calibration, discrimination, and clinical utility metrics.
Main Results:
- Lactate dehydrogenase, albumin, neutrophil ratio, and high fever were identified as significant RMPP predictors.
- The nomogram demonstrated strong predictive performance with an Area Under the Curve (AUC) of 0.884 in the training set and 0.881 in the validation set.
- Calibration and clinical utility analyses confirmed the nomogram's reliability and usefulness in practice.
Conclusions:
- A validated, simple-to-use nomogram for early RMPP prediction in children has been developed.
- This nomogram can assist clinicians in the earlier recognition of RMPP.
- The tool has demonstrated good discrimination, calibration, and clinical utility.
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.
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