A predictive model for differentiating hemorrhagic fever with renal syndrome and scrub typhus in southwestern China

BackgroundBoth hemorrhagic fever with renal syndrome (HFRS) and scrub typhus (ST) are acute zoonotic infectious diseases. There is an overlap in their epidemiological characteristics and clinical manifestations, posing challenges for early differential diagnosis. This study aims to identify predictive factors for these two diseases to provide a basis for early diagnosis. Method/FindingsA retrospective analysis was conducted on the clinical data of patients diagnosed with HFRS and ST at the First Affiliated Hospital of Dali University. Logistic regression analysis was employed to explore independent risk factors for the early differential diagnosis of these two diseases, and a nomogram model was constructed based on these risk factors. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC). The nomogram was utilized to visually present the predictive variables. Decision curve analysis (DCA) was performed to assess the clinical utility of the model. ResultsA total of 235 patients each with HFRS and ST were included in this study. After adjusting for confounding factors, the results of multivariate logistic regression analysis revealed that sex (male) (adjusted odds ratio [ajOR]: 2.093, 95% confidence interval [CI]: 1.107 - 3.957, P = 0.018), positive proteinuria (ajOR: 4.937, 95% CI: 2.427 - 10.042, P < 0.001), creatinine (CREA) (ajOR: 1.009, 95% CI: 1.003 - 1.015, P = 0.005), heart rate (ajOR: 0.981, 95% CI: 0.966 - 0.997, P = 0.018), and conjunctival congestion (ajOR: 16.167, 95% CI: 5.326 - 49.072, P < 0.001) were independent risk factors for differentiating HFRS from ST. The AUC of the model constructed based on these five independent risk factors was 0.856. ConclusionSex (male), positive proteinuria, elevated CREA, decreased heart rate, and conjunctival congestion are effective predictive factors.