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. 2023 Oct 10;23(1):383.
doi: 10.1186/s12890-023-02684-1.

Recognition of refractory Mycoplasma pneumoniae pneumonia among Myocoplasma pneumoniae pneumonia in hospitalized children: development and validation of a predictive nomogram model

Meng Li #  1   2 , Xiang Wei #  1 , Shan-Shan Zhang #  1 , Shan Li  3 , Su-Hong Chen  1 , Su-Jie Shi  1 , Shao-Hong Zhou  1 , Da-Quan Sun  1 , Qian-Ye Zhao  4   5 , Yan Xu  6
Affiliations

Recognition of refractory Mycoplasma pneumoniae pneumonia among Myocoplasma pneumoniae pneumonia in hospitalized children: development and validation of a predictive nomogram model

Meng Li et al. BMC Pulm Med. .

Abstract

Backgroud: The current diagnostic criteria for refractory Mycoplasma pneumoniae pneumonia (RMPP) among Mycoplasma pneumoniae Pneumonia (MPP) are insufficient for early identification, and potentially delayed appropriate treatment. This study aimed to develop an effective individualized diagnostic prediction nomogram for pediatric RMPP.

Methods: A total of 517 hospitalized children with MPP, including 131 with RMPP and 386 without RMPP (non-RMPP), treated at Lianyungang Maternal and Child Health Care Hospital from January 2018 to December 2021 were retrospectively enrolled as a development (modeling) cohort to construct an RMPP prediction nomogram. Additionally, 322 pediatric patients with MPP (64 with RMPP and 258 with non-RMPP, who were treated at the Affiliated Hospital of Xuzhou Medical University from June 2020 to May 2022 were retrospectively enrolled as a validation cohort to assess the prediction accuracy of model. Univariable and multivariable logistic regression analyses were used to identify RMPP risk factors among patients with MPP. Nomogram were generated based on these risk factors using the rms package of R, and the predictive performance was evaluated based on receiver operating characteristic (ROC) curves and using decision curve analysis (DCA).

Results: Multivariate analysis revealed five significant independent predictors of RMPP among patients with MPP: age (hazard ratio [HR] 1.16, 95% confidence interval [CI] 1.08-1.33, P = 0.038), fever duration (HR 1.34, 95%CI 1.20-1.50, P < 0.001), lymphocyte count (HR 0.45, 95%CI 0.23-0.89, P = 0.021), serum D-dimer (D-d) level (HR 1.70, 95%CI 1.16-2.49, P = 0.006), and pulmonary imaging score (HR 5.16, 95%CI 2.38-11.21, P < 0.001). The area under the ROC curve was 90.7% for the development cohort and 96.36% for the validation cohort. The internal and external verification calibration curves were almost linear with slopes of 1, and the DCA curve revealed a net benefit with the final predictive nomogram.

Conclusion: This study proposes a predictive nomogram only based on five variables. The nomogram can be used for early identification of RMPP among pediatric patients with MPP, thereby facilitating more timely and effective intervention.

Keywords: Calibration curve; Decision curve analysis; Nomogram; Receiver operating characteristic curve; Refractory Mycoplasma pneumoniae pneumonia.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Screening flowchart of patients with Mycoplasma. pneumoniae pneumonia in the modeling cohort and validation cohorts
Fig. 2
Fig. 2
Forest chart and nomogram model. A Forest chart of the analysis of independent risk factors in the modeling cohort. B Nomogram model for early prediction of refractory Mycoplasma pneumoniae pneumonia. Instructions for using the nomogram: (a) Draw a line perpendicular from each of the five variables to the top line labeled “Points” to obtain the corresponding number of points; (b) add the points obtained from each of the five variables to obtain the total number of points; and (c) draw a line descending from the axis labeled “Total points” until it intercepts the RMPP type. The risk value corresponding to the RMPP type represents the specific risk at which RMPP will occur
Fig. 3
Fig. 3
Receiver operating characteristic curves to predict refractory Mycoplasma pneumoniae pneumonia. A The modeling cohort and B the validation cohort
Fig. 4
Fig. 4
Calibration and validation curve to predict refractory Myocplasma pneumoniae pneumonia. The calibration curve in the modeling (A) and validation (B) cohorts. The nomogram model to predict RMPP is plotted on the x-axis, and the actual RMPP is plotted on the y-axis. The reference line is 45° and indicates perfect calibration. The validation curve in the modeling (C) and validation (D) cohorts. The y-axis indicates the net benefit. The straight line represents the assumption that all patients will develop RMPP, and the horizontal line represents the assumption that no patient will develop RMPP
See this image and copyright information in PMC

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