Predictive value of the prognostic nutritional index and aggregate index of systemic inflammation for pulmonary complications in children with influenza.

BACKGROUND

Pulmonary complications drive morbidity in pediatric influenza, but early risk stratification remains challenging. This study evaluated the predictive utility of the prognostic nutritional index (PNI) and the aggregate index of systemic inflammation (AISI)-accessible biomarkers of immune-nutritional and inflammatory status-for pulmonary complications in pediatric influenza.

METHODS

This retrospective study analyzed 248 pediatric patients with influenza admitted between January 2022 and December 2024. PNI and AISI were calculated from admission laboratory data. Multivariable logistic regression and receiver operating characteristic (ROC) curves were utilized to evaluate the independent and combined predictive performance of PNI and AISI for the development of pulmonary complications.

RESULTS

Pulmonary complications developed in 86 patients (34.7%). These patients exhibited significantly lower PNI and higher AISI than those without complications (both P<0.001). Furthermore, PNI correlated inversely, and AISI positively, with disease severity markers including C-reactive protein (CRP), procalcitonin, fever duration, and hospital length of stay (all P<0.001). Multivariable analysis identified prolonged fever, decreased PNI, and elevated AISI as independent predictors of pulmonary complications. The combined PNI-AISI model showed higher apparent discriminatory performance than PNI or AISI alone [the area under the receiver operating characteristic curve (AUC): 0.913, sensitivity: 87.2%, specificity: 85.2%]; however, further validation is required to confirm its generalizability.

CONCLUSIONS

Decreased PNI and elevated AISI independently predict pulmonary complications and disease severity in pediatric influenza. Integrating these indices provides a highly accurate, accessible, and physiologically grounded tool for early risk stratification, optimizing clinical decision-making.