Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/52768
Title: Clinical risk-scoring algorithm to forecast scrub typhus severity
Authors: Pamornsri Sriwongpan
Pornsuda Krittigamas
Hutsaya Tantipong
Jayanton Patumanond
Chamaiporn Tawichasri
Sirianong Namwongprom
Keywords: Medicine
Issue Date: 12-Dec-2013
Abstract: Purpose: To develop a simple risk-scoring system to forecast scrub typhus severity. Patients and methods: Seven years' retrospective data of patients diagnosed with scrub typhus from two university-affiliated hospitals in the north of Thailand were analyzed. Patients were categorized into three severity groups: nonsevere, severe, and dead. Predictors for severity were analyzed under multivariable ordinal continuation ratio logistic regression. Significant coefficients were transformed into item score and summed to total scores. Results: Predictors of scrub typhus severity were age >15 years, (odds ratio [OR] =4.09), pulse rate >100/minute (OR 3.19), crepitation (OR 2.97), serum aspartate aminotransferase >160 IU/L (OR 2.89), serum albumin ≤3.0 g/dL (OR 4.69), and serum creatinine >1.4 mg/dL (OR 8.19). The scores which ranged from 0 to 16, classified patients into three risk levels: non-severe (score ≤5, n=278, 52.8%), severe (score 6-9, n=143, 27.2%), and fatal (score ≥10, n=105, 20.0%). Exact severity classification was obtained in 68.3% of cases. Underestimations of 5.9% and overestimations of 25.8% were clinically acceptable. Conclusion: The derived scrub typhus severity score classified patients into their severity levels with high levels of prediction, with clinically acceptable under- and overestimations. This classification may assist clinicians in patient prognostication, investigation, and management. The scoring algorithm should be validated by independent data before adoption into routine clinical practice. © 2014 Sriwongpan et al.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84890540839&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/52768
ISSN: 11791594
Appears in Collections:CMUL: Journal Articles

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