Qual Manag Health Care. 2023 Jan-Mar 01;32(Suppl 1):S29-S34. doi: 10.1097/QMH.0000000000000397.
ABSTRACT
BACKGROUND AND OBJECTIVES: COVID-19 symptoms change after onset-some show early, others later. This article examines whether the order of occurrence of symptoms can improve diagnosis of COVID-19 before test results are available.
METHODS: In total, 483 individuals who completed a COVID-19 test were recruited through Listservs. Participants then completed an online survey regarding their symptoms and test results. The order of symptoms was set according to (a) whether the participant had a “history of the symptom” due to a prior condition; and (b) whether the symptom “occurred first,” or prior to, other symptoms of COVID-19. Two LASSO (Least Absolute Shrinkage and Selection Operator) regression models were developed. The first model, referred to as “time-invariant,” used demographics and symptoms but not the order of symptom occurrence. The second model, referred to as “time-sensitive,” used the same data set but included the order of symptom occurrence.
RESULTS: The average cross-validated area under the receiver operating characteristic (AROC) curve for the time-invariant model was 0.784. The time-sensitive model had an AROC curve of 0.799. The difference between the 2 accuracy levels was statistically significant (α < .05).
CONCLUSION: The order of symptom occurrence made a statistically significant, but small, improvement in the accuracy of the diagnosis of COVID-19.
PMID:36579706 | DOI:10.1097/QMH.0000000000000397
