Abstract

Objectives: We aimed to explore the association between human metapneumovirus (hMPV) infection and extrapulmonary involvement risk in children admitted to the pediatric intensive care unit (PICU), and identify risk factors for severe hMPV infections. Methods: A retrospective cohort study was conducted on 752 hospitalized children with hMPV infection from January 2022 to June 2023. Demographic, clinical, laboratory, imaging, extrapulmonary involvement, comorbidity and co-infection data were collected. Logistic regression was used to screen factors linked to PICU admission, and the variance inflation factor (VIF) was applied to identify independent risk factors. Subgroup analyses and interaction tests were performed to verify the stability of the association between extrapulmonary involvement and PICU admission risk. Results: The PICU admission rate was 4.9% (37/752). Univariate analysis showed a significantly higher incidence of extrapulmonary involvement in the PICU group than in the general ward group. Variables with severe multicollinearity (VIF > 5) were excluded from the final model. After adjustment, gastrointestinal involvement was an independent risk factor for PICU admission (OR = 5.52, 95% CI: 1.33 - 22.93). Multiple diagnoses, viral co-infections, respiratory distress, congenital airway anomalies and metabolic disorders also independently increased PICU admission risk. The association between gastrointestinal involvement and PICU admission was strengthened in children with severe pneumonia or without multiple diagnoses, and amplified by respiratory distress. No significant interactions were found between sex and major comorbidity subgroups (all P > 0.05). Conclusion: hMPV can cause severe infections leading to PICU admission. Gastrointestinal involvement may serve as a critical clinical indicator for PICU admission in hospitalized children with hMPV infection.

Keywords

Human Metapneumovirus, Children, Pediatric Intensive Care Unit, Gastrointestinal Involvement

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1. Introduction

HMPV, first identified in 2001, belongs to the Metapneumovirus genus in the family Pneumoviridae [1]. It is considered a common respiratory virus causing mild upper respiratory infections, primarily affecting infants and children under five years of age. In China, an important cause of pediatric respiratory infections is hMPV, accounting for approximately 7.8% of cases and increasing to 12.7% among children with severe community-acquired pneumonia (CAP) [2]. Estimates suggest that hMPV causes approximately 1.42 million cases of acute lower respiratory tract infection and about 16,100 deaths annually among children under five years of age worldwide [3]. In a proportion of pediatric cases, hMPV infection can progress to severe disease characterized by respiratory distress, hypoxemia, and multisystem involvement, and may further deteriorate into critical illness requiring mechanical ventilation or multi-organ support. Although comprehensive supportive management in the PICU has substantially improved outcomes [4] [5], the high resource demands of critical care highlight the importance of establishing effective early risk stratification strategies to optimize clinical management.

Previous studies on severe hMPV infection have largely focused on respiratory involvement, pneumonia severity, or isolated laboratory markers, such as insulin values [6], oxygenation targets [7], C-reactive protein, or procalcitonin [8] [9]. Although informative, these studies often rely on single indicators or focus narrowly on respiratory parameters, which may lack the predictive power for PICU admission [10] [11]. Airin Veronese et al.’s found that the primary reasons for hospitalization in patients with hMPV infection were hypoxemia and dehydration [12]. Notably, extrapulmonary manifestations (gastrointestinal, neurological, circulatory) are increasingly recognized [13]. Neurological symptoms include irritability, somnolence, drowsiness, seizures, or altered mental status. Gastrointestinal symptoms include refusal to eat, severe vomiting, diarrhea, or dehydration. However, few studies have explored identifying risk factors for hMPV in children identifying risk factors of hMPV in children.

To address these gaps, we conducted a retrospective cohort study of hospitalized children with laboratory-confirmed hMPV infection in Pediatric Emergency Department of Guangzhou Women and Children’s Medical Center. We compared clinical characteristics between PICU-admitted and ward-managed patients. Using multivariable logistic regression analysis, we evaluated whether extrapulmonary involvement was associated with PICU admission among children with hMPV infection. Given that extrapulmonary involvement may represent a progressive and evolving process over the course of illness rather than an acute isolated event, we further examined whether this association was consistent across subgroups, aiming to provide evidence for timely and appropriate PICU referral decisions in children with hMPV infection.

2. Methods

2.1. Participants

This study retrospectively collected clinical data from 752 hospitalized pediatric patients through the hospital’s electronic medical record system. The extracted data encompassed multidimensional information including demographic characteristics, clinical details, laboratory test results, imaging features, extrapulmonary involvement, and co-infections. Severe or critical human metapneumovirus (hMPV) infection is defined as meeting diagnostic criteria for hMPV infection and presenting with at least one of the following clinical manifestations: 1) Respiratory failure necessitating mechanical ventilation; 2) Occurrence of shock; 3) Involvement of other organ failure requiring intensive care unit (ICU) admission [14] [15].

Inclusion criteria: 1) Age ≤ 14 years; 2) Confirmed hMPV infection by nucleic acid testing of respiratory specimens.

Exclusion criteria: 1) Hospital-acquired infection; 2) Incomplete case records.

The decision to transfer pediatric patients to the PICU is determined by the attending physician based on the severity of their condition and their need for advanced life support.

Respiratory secretion specimens were collected via throat swabs and used for PCR detection of respiratory pathogens, including hMPV, adenovirus, RSV, parainfluenza virus, bocavirus, influenza virus A/B, rhinovirus, enterovirus, Mycoplasma pneumoniae, and Chlamydia pneumoniae. Additionally, deep sputum or bronchoalveolar lavage fluid was collected for external testing using targeted next-generation sequencing (tNGS) to detect multiple respiratory pathogens, including 49 bacterial species (e.g., Haemophilus influenzae, Streptococcus pneumoniae), 35 viral species (e.g., hMPV, adenovirus), and 16 fungal species (e.g., Mycoplasma, Chlamydia, Rickettsia, Candida albicans).

2.2. Covariates

This study incorporated demographic data as covariates into the analysis to control for potential confounding effects. Demographic data encompass basic characteristics and pre-existing conditions. Basic characteristics include age and gender. Pre-existing conditions include prior comorbidities and multiple diagnosis status. Prior comorbidities encompass congenital cardiovascular disease, congenital airway anomalies, neurological disorders, metabolic disorders, and immunodeficiency. Multiple diagnosis status is defined as the simultaneous presence of two or more confirmed diagnoses.

2.3. Exposure

We collected various clinical, laboratory, and imaging indicators associated with PICU admission risk. Extrapulmonary involvement is a binary variable encompassing gastrointestinal, neurological, and circulatory system involvement, all diagnosed based on clinical symptoms and relevant examinations. Infection-related characteristics included viral co-infection status, pneumonia severity, fever duration, and respiratory distress status.

2.4. Statistical Analysis

All data in this study were analyzed using R-4.5.1 statistical software. Prior to formal statistical analysis, systematic quality control was conducted on the raw data. Through logical verification and missing value imputation, the integrity and accuracy of the dataset included in the analysis were ensured. For different types of research data, corresponding descriptive statistical methods were employed: Quantitative data (e.g., age, hemoglobin levels, inflammatory markers, and other laboratory indicators) were expressed as mean ± standard deviation ($\overline{x}$ ± s) after confirming normal distribution via the Shapiro-Wilk test. Independent samples t-tests were used for intergroup comparisons.

To avoid the interference of multicollinearity on regression analysis results, we first employed the Variance Inflation Factor (VIF) for collinearity diagnosis. VIF reflecting the strength of multicollinearity by quantifying the degree to which one independent variable is linearly explained by other independent variables [16] [17]. A VIF value exceeding 5 was used as the criterion for identifying significant multicollinearity, and variables meeting this criterion were subsequently excluded. Variables screened for multicollinearity were incorporated into a logistic regression model. The final results of the regression analysis are presented as odds ratios (OR) and 95% confidence intervals (95% CI).

This study conducted pre-specified subgroup analyses based on clinical characteristics. The proposed subgroups include sex subgroup (females, males), disease severity subgroups (mild-to-moderate pneumonia, severe pneumonia), and underlying disease subgroups (presence or absence of congenital disorders). By comparing the differences in OR values and 95% CIs across these subgroups, we will assess the consistency of risk factor effects across different populations, thereby providing a basis for individualized clinical risk assessment. The statistical significance level for this study is defined as a P-value < 0.05.

3. Results

3.1. Baseline Characteristics

Among 752 hospitalized children (Table 1), 37 (4.9%) required PICU admission while 715 (95.1%) were managed in the general ward. The mean age was similar between groups (2.40 ± 1.65 vs. 2.66 ± 2.57 years, P = 0.547), and sex distribution did not differ (males: 60.28% vs. 62.16%, P = 0.819). Compared with ward patients, PICU patients showed markedly lower hemoglobin levels (101.78 ± 14.38 vs. 116.08 ± 11.77 g/L, P < 0.001), higher neutrophil percentages (63.00 ± 15.04 vs. 48.90% ± 18.34%, P < 0.001), lower lymphocyte percentages (29.24 ± 12.88 vs. 40.33 ± 19.36%, P < 0.001), higher CRP (63.15 ± 89.61 vs. 29.73 ± 42.62 mg/L, P = 0.03), and higher PCT (5.78 ± 9.26 vs. 1.72 ± 3.28, P = 0.012). Co-infection was more common in the PICU (48.65% vs. 18.18%, P < 0.001), as was bronchoscopy use (24.32% vs. 2.80%, P < 0.001). Severe pneumonia predominated among PICU patients (89.19% vs. 13.01%), whereas mild-to-moderate pneumonia was rare (5.41% vs. 79.58%; both P < 0.001). Multiple diagnoses were substantially more frequent in the PICU (72.97% vs. 8.53%, P < 0.001). Five pre-existing comorbidities including congenital cardiovascular disease (8.11% vs. 1.96%, P = 0.046), congenital airway anomalies (13.51% vs. 3.08%, P = 0.004), neurological disorders (16.22% vs. 2.24%, P < 0.001), metabolic disorders (8.11% vs. 0.14%, P < 0.001), and immunodeficiency (5.41% vs. 0.28%, P = 0.013) were significantly enriched in the PICU group. PICU children had longer fever duration (8.59 ± 5.39 vs. 4.87 ± 1.91 days, P < 0.001) and markedly prolonged hospital stay (19.43 ± 16.14 vs. 6.05 ± 3.05 days, P < 0.001), as well as a higher rate of respiratory distress (83.78% vs. 12.31%, P < 0.001). Extrapulmonary involvement was also more prevalent in the PICU, including gastrointestinal (37.84% vs. 6.57%), neurological (48.65% vs. 7.27%), and circulatory involvement (8.11% vs. 0.70%; all P < 0.001). Radiologically, PICU patients more frequently exhibited patchy opacities/infiltrates (64.86% vs. 52.45%), pleural effusion (5.41% vs. 0.14%), and pneumonic/inflammatory changes (21.62% vs. 10.07%; P < 0.001). On CT, pneumonia/inflammation (40.54% vs. 5.87%) and airway disease (5.41% vs. 1.96%) were notably higher in the PICU group, whereas “normal/none” findings were much less frequent (43.24% vs. 87.27%; P < 0.001).

Table 1. Baseline characteristics of participants (N = 752).

3.2. Variables Selection

In the initial VIF assessment (Table 2), Seventeen predictors demonstrated substantial multicollinearity, with HGB showing the highest VIF value (97.05), followed by cough (56.26), neutrophil percentage (49.80), SII (25.75), CT category (23.57), lymphocyte percentage (23.48), NLR (22.38), PLT (19.97), fever (18.98), LDH (10.42), PLR (8.11), duration of fever (8.06), WBC (7.33), X-ray category (6.42), AST (6.29), albumin (5.22), and hospital stay (5.11); all of these variables were removed based on the VIF > 5 threshold. After filtering, the remaining variables displayed substantially lower VIF values. For example, age decreased from 4.57 to 2.03 (−55.6%), ALT from 3.70 to 1.53 (−58.5%), PNR from 3.10 to 1.48 (−52.3%), sex from 2.69 to 2.11 (−21.5%), PICU status from 2.37 to 1.90 (−20.0%), and CRP from 2.88 to 2.50 (−13.2%). Smaller reductions were observed in respiratory distress (3.28 to 3.04), multiple diagnoses (1.67 to 1.60), pathogen detection (1.37 to 1.30), congenital airway anomalies (1.30 to 1.20), and gastrointestinal involvement (1.30 to 1.27). Three predictors had VIF values close to 1 both before and after filtering, including hematologic involvement (1.04 to 1.02), renal involvement (1.03 to 1.02), and renal disease (1.01 to 1.00).

Table 2. VIF assessment before and after variable filtering.

Figure 1 presents the correlation heatmaps of all study variables before and after VIF-based filtering. Panel (a) shows the full correlation matrix, in which many variables exhibit moderate to strong positive correlations, reflected by concentrated red blocks along the diagonal and multiple off-diagonal clusters, indicating substantial multicollinearity across hematologic markers, inflammatory indicators, clinical features, and radiological categories. Panel (b) shows the correlation structure after removing variables with VIF > 5. The reduced matrix displays markedly fewer high-correlation clusters, and the remaining variables demonstrate weaker and more sparse correlation patterns, indicating a substantial reduction in multicollinearity.

(a)

(b)

Figure 1. Comparison of heatmaps before and after VIF filtering. (a) Heatmap of all variables prior to VIF assessment; (b) Heatmap after exclusion of variables with VIF > 5.

3.3. Multivariable Logistic Regression Analysis

Table 3 shows the associations between extrapulmonary involvement and PICU admission based on univariate and multivariate logistic regression analyses. In univariate models, higher CRP (OR = 1.01; 95% CI: 1.01 - 1.01; P < 0.001), higher PCT (OR = 1.11; 95% CI: 1.06 - 1.17; P < 0.001), higher ALT (OR = 1.01; 95% CI: 1.01 - 1.01; P = 0.028), and lower PNR (OR = 0.84; 95% CI: 0.74 - 0.96; P = 0.010) were associated with PICU admission, although none of these remained significant after adjustment. Compared with mild-to-moderate pneumonia, severe pneumonia showed a strong univariate association with PICU admission (OR = 100.95; 95% CI: 23.82 - 427.79; P < 0.001), which decreased substantially in the multivariate model (OR = 11.12; 95% CI: 0.99 - 125.49; P = 0.051). The presence of multiple diagnoses was consistently associated with higher odds of PICU admission both before (OR = 28.95; 95% CI: 13.38 - 62.62; P < 0.001) and after adjustment (OR = 36.44; 95% CI: 8.76 - 151.53; P < 0.001). Co-infection also showed significant associations in both analyses (univariate OR = 4.26; adjusted OR = 6.18; both P ≤ 0.004). Among comorbidities, congenital airway anomalies remained significant after adjustment (OR = 7.86; 95% CI: 1.23 - 50.04; P = 0.029), while metabolic disorders also retained significance (OR = 51.35; 95% CI: 1.53 - 1724.87; P = 0.028). Respiratory distress demonstrated a strong association with PICU admission (univariate OR = 36.81; 95% CI: 14.93 - 90.74; P < 0.001), and remained significant in the multivariate model (OR = 9.96; 95% CI: 1.66 - 59.95; P = 0.012). For extrapulmonary involvement, gastrointestinal involvement (adjusted OR = 5.52; 95% CI: 1.33 - 22.93; P = 0.019) maintained a significant association with PICU admission, whereas neurological and circulatory involvement lost significance after adjustment.

Table 3. Multivariable logistic regression analysis of the association between extrapulmonary involvement and PICU admission.

3.4. Subgroup Analysis

In the overall cohort of 752 children (Figure 2), gastrointestinal involvement was associated with higher odds of PICU admission (OR = 5.52; 95% CI: 1.33 - 22.93; P = 0.019). Across sex groups, the association was not statistically significant for either females (OR = 17.58; 95% CI: 0.60 - 516.15; P = 0.096) or males (OR = 4.18; 95% CI: 0.55 - 31.95; P = 0.168), with no evidence of interaction (P_interaction = 0.476). Among diagnostic categories, the association remained significant only in severe pneumonia (OR = 11.59; 95% CI: 1.63 - 82.55; P = 0.014), while P_interaction was 0.409. Similar associations were observed in children with bronchoscopy not performed (OR = 9.56; 95% CI: 1.48 - 61.76; P = 0.018). In the subgroup without multiple diagnoses, gastrointestinal involvement remained associated with PICU admission (OR = 47.08; 95% CI: 1.09 - 2040.30; P = 0.045), whereas the interaction test was not significant (P_interaction = 0.600). Significant associations were also observed in children without congenital cardiovascular disease (OR = 5.84; P = 0.016), without congenital airway anomalies (OR = 4.54; P = 0.050), without neurological disorders (OR = 4.69; P = 0.116, nonsignificant), without metabolic disorders (OR = 8.56; P = 0.007), without hematologic disorders (OR = 5.55; P = 0.018), without immunodeficiency (OR = 5.43; P = 0.020), and without respiratory distress (OR = 0.00; P = 1.000), while the interaction P-values remained above 0.05 across all comorbidity subgroups. For clinical symptoms, wheezing (P_interaction = 0.519) and hoarseness (P_interaction = 0.216) did not modify the association. Respiratory distress showed a statistically significant interaction (P_interaction = 0.038), with strong associations in children with respiratory distress (OR = 38.63; 95% CI: 3.72 - 400.92; P = 0.002). Across extrapulmonary involvement subgroups—including renal, neurological, circulatory, and hematologic involvement—no significant effect modification was detected, with interaction P-values all above 0.20.

Figure 2. Subgroup analysis of the association between extrapulmonary involvement and PICU admission.

4. Discussion

This study demonstrates that gastrointestinal involvement is an independent risk factor for PICU admission in children hospitalized with hMPV infection (OR = 5.52; 95% CI: 1.33 - 22.93; P = 0.019), even after adjusting for key confounders. Subgroup analysis indicated that the association between gastrointestinal involvement and PICU admission was more pronounced in the severe pneumonia subgroup (OR = 11.59, 95% CI: 1.63 - 82.55, P = 0.014) and the subgroup without multiple diagnoses (OR = 47.08, 95% CI: 1.09 - 2040.30, P = 0.045). Leveraging a large cohort, our analysis provides robust insights into the role of gastrointestinal involvement in severe hMPV outcomes.

Gastrointestinal involvement serves as an independent risk factor for admission to the PICU with hMPV, reinforcing the notion that hMPV may exert systemic effects. This is further supported by reports of its neurological complication [18]. Furthermore, this aligns with a broader pattern in pediatric respiratory virology, in which extrapulmonary involvement is a critical determinant of disease severity [19]. This pattern is well illustrated by the association of gastrointestinal symptoms with more severe illness in children with RSV infection [20]. Additionally, the key predictors of PICU admission identified in this study are corroborated by prior research: Heneghan et al. confirmed that children with multiple underlying conditions exhibit significantly increased intensive care requirements [21]. In contrast, our finding that co-infection was a significant risk factor diverges from the conclusions of Li et al.’s meta-analysis [22]. This discrepancy may stem from differences in outcome definitions or adjustments for confounding factors across studies. Collectively, while these comparisons provide external validation for several of our predictors, our core contribution lies in establishing GI involvement as a novel, independent risk factor within the systemic spectrum of severe hMPV disease.

Previous studies on extrapulmonary manifestations of hMPV infection have primarily focused on neurological and cardiovascular involvement, with limited attention given to gastrointestinal involvement. Furthermore, there is a lack of validation from large-scale cohort studies. This study is the first to confirm, through univariate and multivariate logistic regression analyses, that gastrointestinal involvement is an independent risk factor for PICU admission in children with hMPV infection. It also found that respiratory distress significantly amplifies this association effect. This finding lends clinical support to the “gut-lung axis” hypothesis in respiratory viral infections [23], providing new avenues for investigating the molecular mechanisms by which extrapulmonary involvement may drive progression to severe disease. Our study identified an independent risk factor for predicting the risk of pediatric patients with hMPV infection requiring admission to the PICU. Gastrointestinal involvement provides key indicators for establishing a clinical early warning system. Future efforts may focus on implementing early gastrointestinal interventions for pediatric patients at moderate to high risk, while systematically documenting details of gastrointestinal symptoms in children with hMPV infection (onset time, duration, severity), intervention measures, and prognostic data. Further optimize the risk prediction model for severe disease based on gastrointestinal involvement (incorporating factors such as age, underlying conditions, and co-infections), ultimately developing a practical clinical alert score to enhance the accuracy of risk assessment.

There are some limitations. At first, this study is a single-center retrospective cohort study. Data were derived from the hospital’s electronic medical record system, inevitably introducing selection bias and information bias. Moreover, the study scope was limited to hospitalized children with acute respiratory infections in southern China, excluding children with acute respiratory infections in northern China and outpatient patients. Therefore, these findings may not be generalizable to the entire Chinese population. Although this study excluded variables with severe multicollinearity through VIF analysis, the variable selection process relied solely on a threshold of VIF > 5 for exclusion. The absence of more precise variable screening methods, such as stepwise regression or LASSO regression, may have resulted in the omission of certain indicators with potential influence on PICU admission. This study focused solely on the risk of PICU admission among children infected with hMPV and did not track long-term outcomes such as length of hospitalization, complication rates, or readmission rates after discharge. Finally, it cannot assess the impact of factors like gastrointestinal involvement on the children’s long-term health.

5. Conclusion

hMPV can cause severe infections that lead to PICU admission. Gastrointestinal involvement may serve as an important clinical indicator of PICU admission among hospitalized children with hMPV infection.

Acknowledgements

The study was supported by the Guangzhou Municipal Science and Technology Bureau Project (Grant No. 2025A03J4385)-Establishment of a Regional Critical Care Pediatric Transport System and Its Application in Pertussis Case Transfers.

Authors’ Contributions

Huarong Deng contributed to drafting of the manuscript. Tiefu Fang and Wanqi Li contributed to data collection.

Daojiu Jiang and Xiaodan Li carried out statistical analysis. Guangming Liu and Qiang Wang contributed to editing of the manuscript, study design and review of the manuscript.

Data Availability Statement

We declare that data can be shared and used.

NOTES

*These authors are co-first authors.

^#Corresponding authors.

Conflicts of Interest

All authors declare that they have no competing interests.

References