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Predictive Model for Discrimination of Tuberculous Pleural Effusion From Severe Mycoplasma pneumoniae Pneumonic Effusion in Children

Xu, Hui MD*; Feng, Guoshuang PhD; Cai, Siyu MD; Liu, Jinrong MD*; Tang, Xiaolei MD*; Liu, Hui MD*; Yang, Haiming MD*; Li, Huiming MD*; Zhao, Shunying MD*

The Pediatric Infectious Disease Journal: November 2019 - Volume 38 - Issue 11 - p 1100–1103
doi: 10.1097/INF.0000000000002438
Original Studies
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Background: Tuberculous pleural effusion (TPE) is often misdiagnosed as severe Mycoplasma pneumoniae pneumonic effusion (SMPPE) in children at early stage. The aim of this study was to develop a predictive model based on clinical and laboratory indices to make accurate differential diagnosis.

Methods: Patients included in this study were 167 children (83 patients with TPE and 84 with SMPPE), containing 117 patients for predictive model development and 50 patients for external validation. Multivariate logistic regression analysis was conducted to select potentially useful characteristics for discrimination of TPEs. External validation was performed for model evaluation.

Results: Multivariate analysis revealed that blood neutrophils and serum lactate dehydrogenase were significant independent factors to discriminate between TPEs and SMPPEs. The results indicated that blood neutrophils ≤69.6% and concentration of serum lactate dehydrogenase ≤297 U/L were the extremely important discrimination factors of TPEs. The area under the receiver operating characteristic curve of the model was 0.9839. The accuracy rate, sensitivity and specificity of the model were 94.02%, 98.28% and 89.83%, respectively. Meanwhile, the accuracy rate of the external validation from the 50 patients was 94.0%.

Conclusions: Applying a predictive model with clinical and laboratory indices can facilitate the differential diagnosis of TPE from SMPPE in children, which seems helpful when a microbiologic or histologic diagnosis of pleural tuberculosis could not be established.

From the *Department of Respiratory Medicine, Beijing Children’s Hospital

Big Data and Engineering Research Center, Beijing Children’s Hospital

Center for Clinical Epidemiology and Evidence-based Medicine, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China.

Accepted for publication July 10, 2019.

The authors have no funding or conflicts of interest to disclose.

H.X. designed the study, conducted the analysis, and drafted and revised the initial manuscript. J.L., X.T., HLiu, H.Y. and HLi advised on the design of the analysis and revised the manuscript. G.F. and S.C. supported the help of statistical analysis. S.Z. conceived and designed the study, analyzed and interpreted the data, and agreed with manuscript results and conclusions.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (www.pidj.com).

Address for Correspondence: Shunying Zhao, Department of Respiratory Medicine, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, NO.56, Nanlishi Road, Xicheng District, Beijing 100045, China. E-mail: zhaoshunyingdoc@sina.com

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