Mycoplasma pneumoniae (M. pneumoniae) is one of the primary causes of community-acquired pneumonia (CAP) in the pediatric population (1, 2). Most cases of M. pneumoniae pneumonia (MPP) in children present with mild symptoms and without hospitalization. However, a handful of pediatric patients progress to severe MPP (SMPP), and epidemiological surveys have documented a gradual increase in the incidence of SMPP in recent years (3, 4). SMPP reflects the high severity of MPP and meets the diagnostic criteria for severe CAP (5). Notably, SMPP is prone to potentially fatal conditions, including myocarditis, nephritis, encephalitis and hemolytic anemia (6), highlighting the urgency of early and accurate identification of high-risk children to enable timely intervention and reduce mortality.

To date, several predictive tools for pediatric SMPP have been developed, including machine learning models and nomograms (7–11). Nevertheless, these existing models have significant limitations: some are established based on small sample data and fail to clearly distinguish differences in disease occurrence among children of different age groups, resulting in limited data representativeness; others focus solely on inflammatory markers or coagulation function indicators, lacking integration of multi-system parameters; and still others regard lung auscultation as a key predictive factor, but different physicians may yield inconsistent assessment results for the same case, which in turn undermines the model's stability. In contrast, a nomogram integrated with multiple accessible objective indicators can comprehensively weigh the predictive value of each factor, thereby improving the accuracy and practicability of early SMPP identification. Therefore, in this retrospective study, we developed and validated a nomogram-based diagnostic prediction model for pediatric SMPP, aiming to enhance the practicality and accuracy of early SMPP identification and ultimately reduce the occurrence of adverse clinical outcomes.

M. pneumoniae has long been a major pathogenic cause of CAP in children and adolescents (8, 13). Most MPP cases are mild, with only a small minority presenting as the severe form (SMPP); however, the overall proportion of SMPP has been on the rise in recent years (3, 14). Among 494 children with MPP admitted to the specialized ward of Children's Hospital of Fudan University between August and September 2023, as many as 124 cases (25.1%) were diagnosed with SMPP (14). Xu et al. reported that 46.6% of pediatric MPP inpatients (173/371) in Wuhan from 2020 to 2022 presented with severe clinical manifestations (15), and another study indicated that 50.6% of MPP cases were severe and complicated with more extrapulmonary abnormalities (16). In the present study, the proportion of SMPP among enrolled patients was 31.8% (151 cases). The observed discrepancies in SMPP incidence across these studies may be attributed to three main factors: first, variations in study populations and sample sizes; second, regional differences in epidemiological and clinical characteristics of MPP; third, the lack of a unified and standardized diagnostic criteria for severe Mycoplasma pneumoniae pneumonia to date.

Severe MPP is characterized by severe pulmonary involvement and multiple extrapulmonary complications (6). Pediatric patients are particularly prone to SMPP progression, attributable to nonspecific clinical manifestations in the early stage, immature immune systems, and underdeveloped tissue and organ function (2, 17). Children with SMPP face a high risk of developing necrotizing pneumonitis, myocarditis, hemolytic anemia, acute pancreatitis, and a wide array of other extrapulmonary complications (6), which poses a substantial threat to pediatric health. Accordingly, early prediction of SMPP can assist clinicians in the rational allocation of critical care resources and timely identification of high-risk patients for prompt diagnosis and intervention, thereby enabling more targeted and optimized clinical management. We created and validated a nomogram in this study to predict SMPP in children with MPP. The nomogram incorporated 3 variables, including duration of fever, RBC count and ALB level. The ROC, DCA, and calibration curves all revealed that the nomogram possesses good discriminatory power, accurate calibration, and clinical usefulness. Collectively, our study findings may contribute to the advancement of early screening and predictive assessment for SMPP in pediatric clinical practice.

Previous studies have confirmed that fever characteristics are closely associated with the progression of SMPP (18)A study by Ken B et al. clarified that both the intensity (high-grade fever ≥39.0 °C) and duration (≥8 days) of fever can serve as early screening indicators for SMPP, and the fever characteristics of severe cases are closely correlated with elevated inflammatory factors such as CRP and LDH (19). Furthermore, another study verified that preadmission fever duration is an independent risk factor for necrotizing pneumonia (NP) and refractory MPP (RMPP), and prolonged fever directly indicates an increased risk of severe disease (13). Notably, a recent multicenter study in South Korea further validated that patients with lobular/lobar consolidation, a core radiological marker for MPP severity, had significantly longer fever duration (median 8 days vs. 7 days, P = 0.020) and a higher proportion of high-grade fever (71.0% vs. 60.1%, P = 0.034) (20). These findings are highly consistent with the data of the present study: the median fever duration was 6.33 days in the non-SMPP group and 9.49 days in the SMPP group. Importantly, our fever duration was recorded within 24 h of admission (defined as the interval from first fever onset to admission), making it an early accessible indicator—this aligns with Lee et al.'s validation of pre-admission fever duration as an early SMPP predictor (20). Moreover, our multivariate analyses identified fever duration as an independent risk factor for the progression of pediatric MPP to SMPP, a relationship that may be linked to the excessive inflammatory response and severe lung injury induced by M. pneumoniae infection.

During M. pneumoniae infection, the RBC count tends to decrease, and a few patients may develop significant anemia (21–23). MP infection stimulates the body to produce polyclonal IgM-type cold agglutinins, which specifically recognize the I antigen on the surface of red blood cells, activate complement, and ultimately lead to red blood cell destruction through extravascular hemolysis (23). In our study, the RBC count of children with SMPP was significantly lower than that of children with non-SMPP (4.38 ± 0.36 × 10¹²/L vs. 4.52 ± 0.37 × 10¹²/L, P < 0.001). This observation may be attributed to the excessive systemic inflammatory response triggered by SMPP, which exacerbates immune-mediated hemolysis and thereby further reduces the RBC count (24). In addition, in our prediction model, RBC count was included as an independent risk factor for the first time, indicating that RBC count is a useful marker for predicting the severity of MPP in children.

Albumin is the most abundant plasma protein. Under inflammatory conditions, increased capillary permeability leads to massive leakage of albumin into the interstitium, coupled with enhanced albumin degradation, thereby resulting in hypoalbuminemia. Albumin is negatively correlated with the degree of inflammation, lower levels indicate more severe inflammation (25). Our study showed that there was a significant difference in serum albumin levels between children with SMPP and those with non-SMPP: the serum albumin level in the SMPP group was 37.89 ± 3.30 g/L, which was significantly lower than that in the non-SMPP group (41.38 ± 2.75 g/L), with a statistically significant difference (P < 0.001). This finding is consistent with previous studies (24, 26, 27). Additionally, albumin, either alone or as a component of composite indicators, has been proven to be an effective predictor of disease severity and mortality in conditions such as sepsis and Kawasaki disease (28, 29). In the present study, albumin was an independent risk factor for SMPP in children (OR = 0.795, 95% CI: 0.704–0.898, P = 0.0002), with lower albumin levels indicating a higher risk of developing SMPP. Therefore, monitoring changes in peripheral serum albumin can facilitate the early prediction of the occurrence and progression of severe Mycoplasma pneumoniae pneumonia.

This study has several limitations that should be acknowledged. First, it was a single-center retrospective study, which may introduce selection bias and restrict the generalizability of the findings, as regional variations in the progression of MPP) could exist. Second, external validation of our results was not performed to confirm their robustness across different populations. Thus, our conclusions require further verification through large-scale prospective multicenter studies to enhance their reliability and clinical applicability.

In conclusion, this study established a nomogram for predicting the risk of SMPP, which integrates three key indicators: duration of fever, RBC count, and ALB. This tool offers a valuable complement to existing strategies for identifying SMPP, and facilitates the timely prevention and optimized clinical management of children at risk of developing SMPP.