Risk Prediction Models for Refractory Mycoplasma Pneumoniae Pneumonia in Children: a Systematic Review

Background Mycoplasma pneumoniae pneumonia (MPP) is a common respiratory disease in children, which can easily develop into refractory Mycoplasma pneumoniae pneumonia (RMPP). Children with RMPP have complex conditions, are difficult to treat, and are associated with multiple serious complications. A risk prediction model for RMPP in children can help healthcare professionals to identify children at high risk of RMPP and provide timely therapeutic countermeasures. Objective To systematically review the risk prediction models for RMPP in children. Methods CNKI, Wanfang Data, VIP, CBM, PubMed, Embase, Web of Science and Cochrane Library were searched to collect the related studies on risk prediction models for RMPP in Children to February 20th, 2024. Two reviewers independently screened the literature, extracted data and evaluated the quality of the included studies. Meta-analysis of the incidence and predictors of RMPP in children was performed using Stata 18.0 software. Results A total of 14 papers were included, containing 17 risk prediction models. Ten studies were internal validated, and only four studies were external validated, all of which had AUC>0.7. PROBAST results showed that 14 included papers were all at high risk of bias. The results of Meta-analysis showed that the incidence of RMPP in children was 28.2% (95%CI=21.2%-35.1%), hyperthermia, lactate dehydrogenase, C-reactive protein, age, neutrophil ratio, procalcitonin and D-dimer were independent influences on the occurrence of RMPP in children (P<0.05) . Conclusion Risk prediction models for RMPP in children have good predictive performance, but the overall risk of bias was high and external validation was lacking. The future risk prediction model should focus on hyperthermia, lactate dehydrogenase, C-reactive protein, age, neutrophil ratio, procalcitonin and D-dimer.