Using body temperature and variables commonly available in the EHR to predict acute infection: A proof-of-concept study showing improved pretest probability estimates for acute COVID-19 infection among discharged emergency department patients

Berdahl, C. T.; Nguyen, A. T.; Diniz, M. A.; Henreid, A. J.; Nuckols, T. K.; Libby, C. P.; Pevnick, J. M.

Objectives: Obtaining body temperature is a quick and easy method to screen for acute infection such as COVID-19. Currently, the predictive value of body temperature for acute infection is inhibited by failure to account for other readily available variables that affect temperature values. In this proof-of-concept study, we sought to improve COVID-19 pretest probability estimation by incorporating covariates known to be associated with body temperature, including patient age, sex, comorbidities, month, time of day. Methods: For patients discharged from an academic hospital emergency department after testing for COVID-19 in March and April of 2020, we abstracted clinical data. We reviewed physician documentation to retrospectively generate estimates of pretest probability for COVID-19. Using patient COVID-19 PCR test results as a gold standard, we compared AUCs of logistic regression models predicting COVID-19 positivity that used: 1) body temperature alone; 2) body temperature and pretest probability; 3) body temperature, pretest probability, and body temperature-relevant covariates. Calibration plots and bootstrap validation were used to assess predictive performance for model #3. Results: Data from 117 patients were included. The models AUCs were: 1) 0.69 2) 0.72, and 3) 0.76, respectively. The absolute difference in AUC was 0.029 (95%CI -0.057 to 0.114, p=0.25) between model 2 and 1 and 0.038 (95%CI -0.021 to 0.097, p=0.10) between model 3 and 2. Conclusions: By incorporating covariates known to affect body temperature, we demonstrated improved pretest probability estimates of acute COVID-19 infection. Future work should be undertaken to further develop and validate our model in a larger, multi-institutional sample.