Objective To explore the value of platelet ocrit,lymphocyte percentage and monocyte percentage in the differential diagnosis of COVID-19 and influenza A virus infection and the derivation of the prediction model. Methods Collecting data of 101 patients successively infected with SARS-CoV-2 and A influenza virus in the Affiliated Hospital of Shaoxing University from December 2022 to June 2023,statistically analysed the blood routine indexes and their ratio of the two groups;multivariate logistic regression analysis was applied to derive 10 prediction models for diagnose COVID-19 and influenza A virus infection. The blood routine indexes of 100 confirmed patients were randomly selected into 10 prediction models for validation. Receiver operating characteristic (ROC) curves were drawn to evaluate the efficacy of each single index and 10 indicators to predict the differential diagnosis of COVID-19. Results Univariate statistical analysis showed that basophil count,neutrophil percentage,lymphocyte percentage,percentage of monocytesred,blood cell distribution width-CV,platelet count and platelet pressure were statistical differences present in patients successively infected with SARS-CoV-2and A influenza virus(P<0.05). Applying the ROC to evaluate the results of the univariate and 10 logistic regression models and the validation set found that the combined model of platelet ocrit,lymphocyte percentage and monocyte percentage standard 9∶Y=10.023×PCT+0.051×LYMPH_p-0.140×MONO_p-1.953 had the best efficacy for differential diagnosis COVID-19,The AUC for the test and validation sets were 0.737 and 0.756. According to the best cutoff value determined by the optimal model of ROC curve in the test set,the test could still achieve good specificity in the verification set. Conclusion The platelet ocrit,lymphocyte percentage and monocyte percentage model have clinical significance in the differential diagnosis of COVID-19 and A virus infection.
COVID-19Influenza A virus infectionPlatelet ocritLymphocyte percentageMonocyte percentage
万方数据
维普
