OBJECTIVE  To explore the predictive value of a risk factor model based on blood routine parameters, creatine kinase isoenzyme （CK-MB） and neuron-specific enolase （NSE） for secondary pulmonary infection following upper respiratory tract infection, thereby achieving early risk identification and providing evidences for clinical prevention and intervention.

METHODS  A retrospective analysis was conducted on 141 patients with upper respiratory tract infection treated at the 905 Hospital of the People's Liberation Army Navy from Jan. 2024 to Dec. 2024, where etiological findings were considered as the gold standard. Patients were divided into a pneumonia group （n=52） and a non-pneumonia group （n=89） based on pulmonary infection status. Multivariate logistic regression analysis was employed to screen for risk factors, establish a risk factor model, evaluate its predictive value, and construct a nomogram model for validation.

RESULTS  Elevated white blood cell count （WBC）, CK-MB and NSE levels, as well as decreased platelet （PLT） levels, were identified as risk factors for secondary pulmonary infection following upper respiratory tract infection （P＜0.05）. The area under the curve （AUC） for the combined prediction of this infection by WBC, CK-MB, PLT and NSE was higher than that of individual predictions （P＜0.05）. A nomogram model was constructed based on the risk factors for this infection. The 141 patients were divided into a training set （n=99） and a validation set （n=42） at a ratio of 7∶3. The AUCs of the receiver operating characteristic curves for the nomogram model in the training and validation sets were 0.882 （0.813–0.950） and 0.814 （0.662–0.951）, respectively. Bootstrap sampling with 1,000 resamples was performed, and a calibration curve was plotted to evaluate the consistency of the model. The results showed that the predicted values of the nomogram were close to the actual observations. A clinical decision curve was further plotted to assess the clinical utility of the model, with threshold probability on the x-axis and net benefit on the y-axis. The nomogram was found to provide higher net benefit in the threshold probability range of 40%-100%.

CONCLUSION  The risk factor model for secondary pulmonary infection following upper respiratory tract infection, constructed based on routine blood parameters, CK-MB and NSE, demonstrates high predictive value.