Development and Validation of a Nomogram to Predict Respiratory Failure With Influenza

Area of Science:

• Pulmonology
• Infectious Diseases
• Medical Informatics

Background:

• Influenza can cause severe respiratory failure, leading to multi-organ dysfunction or death.
• Early prediction and intervention are crucial for reducing mortality in patients with influenza-induced respiratory failure.

Purpose of the Study:

• To develop and validate a predictive model for respiratory failure in influenza patients.
• To identify key risk factors associated with the development of respiratory failure following influenza infection.

Main Methods:

• A retrospective analysis of 182 influenza-positive patients was conducted, with external validation on 78 patients.
• Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression were used to select variables.
• A nomogram was constructed and validated using ROC curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).

Main Results:

• The incidence of respiratory failure in the training group was 29.1% (53/182).
• Key predictors identified were age, tumor presence, influenza type, and red cell distribution width coefficient of variation (RDW-CV).
• The nomogram demonstrated good predictive accuracy with an Area Under the Curve (AUC) of 0.79 in the training set and 0.73 in the validation set, showing excellent clinical utility.

Conclusions:

• A nomogram incorporating RDW-CV, influenza type, tumor status, and age is an effective tool for early identification of respiratory failure in influenza patients.
• This predictive model can guide clinical decision-making for prevention and treatment strategies.
• Further research may refine this model for broader clinical application.