Validation of a clinical risk-scoring algorithm for scrub typhus severity in South India

Shivali Gulati, Kiran Chunduru, Mridula Madiyal, Maninder S. Setia, Kavitha Saravu

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Background: A clinical risk-scoring algorithm (CRSA) to forecast the scrub typhus severity was developed from two general hospitals in Thailand where patients were classified into three groups-nonsevere, severe, and fatal. In this study, an attempt was made to validate the risk-scoring algorithm for prognostication of scrub typhus severity in India. Patients and methods: This prospective study was conducted at a hospital in South India between November 2017 and March 2019. Patients of scrub typhus were categorized into nonsevere, severe, and fatal according to the CRSA. The patients were also grouped into severe and nonsevere according to the definition of severe scrub typhus which was used as a gold standard. The obtained CRSA score was validated against the classification based on the definition of severe scrub typhus. Receiver operating characteristics (ROC) curve for the scores was plotted and the Youden’s index for optimal cutoff was used. Results: A total of 198 confirmed cases of scrub typhus were included in the study. According to the ROC curve, at a severity score ≥7, an optimal combination of sensitivity of 75.9% and specificity of 77.5% was achieved. It correctly predicted 76.77% (152 of 198) of patients as severe, with an underestimation of 10.61% (21 patients) and an overestimation of 12.63% (25 patients). Conclusion: In the present study setting, a cutoff of ≥7 for severity prediction provides an optimum combination of sensitivity and specificity. These findings need to be validated in further studies.

Original languageEnglish
Pages (from-to)551-556
Number of pages6
JournalIndian Journal of Critical Care Medicine
Issue number5
Publication statusPublished - 2021

All Science Journal Classification (ASJC) codes

  • Critical Care and Intensive Care Medicine


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