Indian Journal of Critical Care Medicine

Register      Login

SEARCH WITHIN CONTENT

FIND ARTICLE

Volume / Issue

Online First

Archive
Related articles

VOLUME 25 , ISSUE 5 ( May, 2021 ) > List of Articles

ORIGINAL RESEARCH

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

Keywords : Clinical Risk-scoring Algorithm, Prognosis, Scrub typhus, Severe scrub typhus

Citation Information : Gulati S, Chunduru K, Madiyal M, Setia MS, Saravu K. Validation of a Clinical Risk-scoring Algorithm for Scrub Typhus Severity in South India. Indian J Crit Care Med 2021; 25 (5):551-556.

DOI: 10.5005/jp-journals-10071-23828

License: CC BY-NC 4.0

Published Online: 01-05-2021

Copyright Statement:  Copyright © 2021; Jaypee Brothers Medical Publishers (P) Ltd.


Abstract

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. Materials 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.


PDF Share
  1. Rahi M, Gupte MD, Bhargava A, Varghese GM, Arora R. DHR-ICMR guidelines for diagnosis & management of Rickettsial diseases in India. Indian J Med Res 2015;141(4):417–422. DOI: 10.4103/0971-5916.159279.
  2. Luce-Fedrow A, Lehman ML, Kelly DJ, Mullins K, Maina AN, Stewart RL, et al. A review of scrub typhus (Orientia tsutsugamushi and related organisms): then, now, and tomorrow. Trop Med Infect Dis 2018;3(1):8. DOI: 10.3390/tropicalmed3010008.
  3. Bonell A, Lubell Y, Newton PN, Crump JA, Paris DH. Estimating the burden of scrub typhus: a systematic review. PLoS Negl Trop Dis 2017;11(9):e0005838–e0005838. DOI: 10.1371/journal.pntd.0005838.
  4. Varghese GM, Janardhanan J, Trowbridge P, Peter JV, Prakash JAJ, Sathyendra S, et al. Scrub typhus in South India: clinical and laboratory manifestations, genetic variability, and outcome. Int J Infect Dis 2013;17(11):e981–e987. DOI: 10.1016/j.ijid.2013.05.017.
  5. Varghese GM, Trowbridge P, Janardhanan J, Thomas K, Peter JV, Mathews P, et al. Clinical profile and improving mortality trend of scrub typhus in South India. Int J Infect Dis 2014;23:39–43. DOI: 10.1016/j.ijid.2014.02.009.
  6. Kundavaram A, Jonathan A, Nathaniel S, Varghese G. Eschar in scrub typhus: a valuable clue to the diagnosis. J Postgrad Med 2013;59(3):177–178. DOI: 10.4103/0022-3859.118033.
  7. Koralur M, Bairy I, Singh R, Varma D, Stenos J. Molecular confirmation of scrub typhus infection and characterization of Orientia tsutsugamushi genotype from Karnataka, India, vol. 53. 2016. 185 p.
  8. Prakash JAJ. Scrub typhus: risks, diagnostic issues, and management challenges. Res Rep Trop Med 2017;8:73–83. DOI: 10.2147/RRTM.S105602.
  9. Chakraborty S, Sarma N. Scrub typhus: an emerging threat. Indian J Dermatol 2017;62(5):478–485. DOI: 10.4103/ijd.IJD_388_17.
  10. Chauhan V, Thakur A, Thakur S. Eschar is associated with poor prognosis in scrub typhus. Indian J Med Res 2017;145(5):693–696. DOI: 10.4103/ijmr_1888_15.
  11. Rajapakse S, Rodrigo C, Fernando D. Scrub typhus: pathophysiology, clinical manifestations and prognosis. Asian Pac J Trop Med 2012;5(4):261–264. DOI: 10.1016/S1995-7645(12)60036-4.
  12. Takhar R, Bunkar M, Arya S, Mirdha N, Mohd A. Scrub typhus: a prospective, observational study during an outbreak in Rajasthan, India. Natl Med J India 2017;30(2):69–72.
  13. Attur RP, Kuppasamy S, Bairy M, Nagaraju SP, Pammidi NR, Kamath V, et al. Acute kidney injury in scrub typhus. Clin Exp Nephrol 2013;17(5):725–729. DOI: 10.1007/s10157-012-0753-9.
  14. Jayaprakash V, Vamsikrishna M, Indhumathi E, Jayakumar M. Scrub typhus-associated acute kidney injury: a study from a South Indian Tertiary Care Hospital. Saudi J Kidney Dis Transpl 2019;30(4):883. DOI: 10.4103/1319-2442.265464.
  15. Viswanathan S, Muthu V, Iqbal N, Remalayam B, George T. Scrub typhus meningitis in South India — a retrospective study. PLoS One 2013;8(6):e66595. DOI: 10.1371/journal.pone.0066595.
  16. Taylor AJ, Paris DH, Newton PN. A systematic review of mortality from untreated scrub typhus (Orientia tsutsugamushi). PLoS Negl Trop Dis 2015;9(8):e0003971. DOI: 10.1371/journal.pntd.0003971.
  17. Jain D, Nand N, Giri K, Bhutani J. Scrub typhus infection, not a benign disease: an experience from a tertiary care center in Northern India. Med Pharm Rep 2019;92(1):36–42. DOI: 10.15386/cjmed-1088.
  18. Shivalli S. Diagnostic evaluation of rapid tests for scrub typhus in the Indian population is needed. Infect Dis Poverty 2016;5(1):40. DOI: 10.1186/s40249-016-0137-6.
  19. España PP, Capelastegui A, Gorordo I, Esteban C, Oribe M, Ortega M, et al. Development and validation of a clinical prediction rule for severe community-acquired pneumonia. Am J Respir Crit Care Med 2006;174(11):1249–1256. DOI: 10.1164/rccm.200602-177OC.
  20. Tanner L, Schreiber M, Low JGH, Ong A, Tolfvenstam T, Lai YL, et al. Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness. PLOS Negl Trop Dis 2008;2(3):e196. DOI: 10.1371/journal.pntd.0000196.
  21. Lee VJ, Lye DC, Sun Y, Leo YS. Decision tree algorithm in deciding hospitalization for adult patients with dengue haemorrhagic fever in Singapore. Trop Med Int Health 2009;14(9):1154–1159. DOI: 10.1111/j.1365-3156.2009.02337.x.
  22. Butt E, Foster JA, Keedwell E, Bell JE, Titball RW, Bhangu A, et al. Derivation and validation of a simple, accurate and robust prediction rule for risk of mortality in patients with Clostridium difficile infection. BMC Infect Dis 2013;13(1):316. DOI: 10.1186/1471-2334-13-316.
  23. Mohapatra M, Das P. The malaria severity score: a method for severity assessment and risk prediction of hospital mortality for falciparum malaria in adults. J Assoc Physicians India 2009;57:119–126.
  24. Sriwongpan P, Krittigamas P, Tantipong H, Patumanond J, Tawichasri C, Namwongprom S. Clinical risk-scoring algorithm to forecast scrub typhus severity. Risk Manag Healthc Policy 2013;7:11–17. DOI: 10.2147/RMHP.S55305.
  25. Sriwongpan P, Patumanond J, Krittigamas P, Tantipong H, Tawichasri C, Namwongprom S. Validation of a clinical risk-scoring algorithm for severe scrub typhus. Risk Manag Healthc Policy 2014;7:29–34. DOI: 10.2147/RMHP.S56974.
PDF Share
PDF Share

© Jaypee Brothers Medical Publishers (P) LTD.