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

Influenza-induced respiratory failure is a severe complication of influenza that can rapidly progress to multi-organ failure or even death due to severe impairment of pulmonary ventilation or gas exchange function. Early prediction and intervention can decrease the mortality in patients with respiratory failure.

A comprehensive analysis was conducted on 182 influenza-positive patients who were admitted in Affiliated Hospital of Chengde Medical University between December 2018 and May 2019. 78 patients with influenza were used for external validation at Hebei Chest Hospital. We assessed the relationship between respiratory failure and demographic characteristics, preexisting diseases, and laboratory test results in the training group. First, the variables of the nomogram of respiratory failure with influenza were selected using Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. A nomogram was developed to predict respiratory failure due to influenza. The accuracy of the proposed model was validated by utilizing the area under the receiver operating characteristic (ROC) and calibration curve. The clinical utility of the proposed model was evaluated using decision curve analysis (DCA) and clinical impact curve (CIC).

A total of 182 influenza-positive patients were included, with the incidence of respiratory failure reaching 29.1% (n = 53). Risk factors contributing to respiratory failure encompassed age, tumor, influenza type, and red cell distribution width coefficient of variation (RDW.CV). Utilizing ROC and calibration curve assessment, the constructed nomogram exhibited accurate prediction of respiratory failure risk, and the AUC value in the training group was 0.79. Of the 78 patients diagnosed with influenza, 35 (accounting for 44.9%) developed respiratory failure. Using the ROC curve, the established nomogram accurately predicted the risk of respiratory failure, and the area under the curve was 0.73 in the validation cohort. Decision curve analysis and clinical impact curve verified that this model demonstrated excellent clinical utility in predicting respiratory failure among influenza patients.

A nomogram based on the expression levels of RDW.CV, influenza type, tumor, and age was an efficient model for the early identification of respiratory failure in patients with influenza. These results will be useful for guiding the prevention and treatment of respiratory failure caused by influenza.