Derivation and Internal Validation of a Clinical Prediction Model for Diagnosis of Spotted Fever Group Rickettsioses in Northern Tanzania

Abstract

Spotted fever group rickettsioses (SFGR) pose a global threat as emerging zoonotic infectious diseases; however, timely and cost-effective diagnostic tools are currently limited. We used data from 449 patients presenting to 2 hospitals in northern Tanzania between 2007 and 2008, of which 71 (15.8%) met criteria for acute SFGR based on ≥4-fold rise in antibody titers between acute and convalescent serum samples. We fit random forest classifiers incorporating clinical and demographic data from hospitalized febrile participants as well as Earth observation hydrometeorological predictors from the Kilimanjaro Region. In cross-validation, a prediction model with 10 clinical predictors achieved an area under the receiver operating characteristic curve of 0.65 (95% confidence interval, .48–.82). A combined prediction model with clinical, hydrometeorological, and environmental predictors (20 predictors total) did not significantly improve model performance. Novel strategies are needed to improve the diagnosis of acute SFGR, including the identification of diagnostic biomarkers that could enhance clinical prediction models.