Validation of a clinical risk-scoring algorithm for severe scrub typhus
Authors Sriwongpan P, Patumanond J, Krittigamas P, Tantipong H, Tawichasri C, Namwongprom S
Received 1 November 2013
Accepted for publication 23 December 2013
Published 18 February 2014 Volume 2014:7 Pages 29—34
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Pamornsri Sriwongpan,1,2 Jayanton Patumanond,3 Pornsuda Krittigamas,4 Hutsaya Tantipong,5 Chamaiporn Tawichasri,6 Sirianong Namwongprom1,7
1Clinical Epidemiology Program, Faculty of Medicine, Chiang Mai University, Chiang Mai, 2Department of Social Medicine, Chiangrai Prachanukroh Hospital, Chiang Rai, 3Clinical Epidemiology Program, Faculty of Medicine, Thammasat University, Bangkok, 4Department of General Pediatrics, Nakornping Hospital, Chiang Mai, 5Department of Medicine, Chonburi Hospital, Chonburi, 6Clinical Epidemiology Society at Chiang Mai, Chiang Mai, 7Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
Objective: The aim of the study reported here was to validate the risk-scoring algorithm for prognostication of scrub typhus severity.
Methods: The risk-scoring algorithm for prognostication of scrub typhus severity developed earlier from two general hospitals in Thailand was validated using an independent dataset of scrub typhus patients in one of the hospitals from a few years later. The predictive performances of the two datasets were compared by analysis of the area under the receiver-operating characteristic curve (AuROC). Classification of patients into non-severe, severe, and fatal cases was also compared.
Results: The proportions of non-severe, severe, and fatal patients by operational definition were similar between the development and validation datasets. Patient, clinical, and laboratory profiles were also similar. Scores were similar in both datasets, both in terms of discriminating non-severe from severe and fatal patients (AuROC =88.74% versus 91.48%, P=0.324), and in discriminating fatal from severe and non-severe patients (AuROC =88.66% versus 91.22%, P=0.407). Over- and under-estimations were similar and were clinically acceptable.
Conclusion: The previously developed risk-scoring algorithm for prognostication of scrub typhus severity performed similarly with the validation data and the first dataset. The scoring algorithm may help in the prognostication of patients according to their severity in routine clinical practice. Clinicians may use this scoring system to help make decisions about more intensive investigations and appropriate treatments.
Keywords: severity, clinical prediction rule, algorithm, prognosis, Thailand
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