Abstract
INTRODUCTION: Acute encephalitis syndrome (AES) is one of the important causes of mortality among children in India. Active management of the cases, followed by addressing the cause of AES, is the key strategy for preventing mortality. Lack of laboratory facility and difficulty of sampling blood and cerebrospinal fluid (CSF) for assessing causes is one of the important barriers to early initiation of treatment. The main objective of the study is to validate the Scrub Typhus Encephalitis Assessment Tool (SEAT) for the management of AES. METHODS: The study is a continuation of a study conducted in a tertiary care hospital in Eastern Uttar Pradesh. A machine learning (LightGBM) model was built to predict the probability of scrub typhus diagnosis among patients with acute encephalitis. Three models were built: one with sociodemographic characters, the second with Model 1 variables and blood parameters, and the third with Model 2 variables and CSF parameters. RESULTS: The sensitivity of diagnosing the scrub typhus case was 71%, 77.5%, and 83% in Model 1, Model 2, and Model 3, respectively, and specificity was 61.5%, 75.5%, and 76.3%, respectively, in the models. In Model 1 fever duration, in Models 2 and 3, neutrophil/lymphocyte ratio was the most important predictor for differentiating the scrub and nonscrub cases. CONCLUSION: With the available sensitivity and specificity of the tool, the SEAT can be a valuable tool for the prediction of scrub typhus as a cause of AES cases in remote areas.