Validation of a clinical risk-scoring algorithm for severe scrub typhus

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 inde...

Full description

Saved in:
Bibliographic Details
Main Authors: Pamornsri Sriwongpan, Jayanton Patumanond, Pornsuda Krittigamas, Hutsaya Tantipong, Chamaiporn Tawichasri, Sirianong Namwongprom
Format: Journal
Published: 2018
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84896736683&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/45162
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Chiang Mai University
id th-cmuir.6653943832-45162
record_format dspace
spelling th-cmuir.6653943832-451622018-01-24T06:06:09Z Validation of a clinical risk-scoring algorithm for severe scrub typhus Pamornsri Sriwongpan Jayanton Patumanond Pornsuda Krittigamas Hutsaya Tantipong Chamaiporn Tawichasri Sirianong Namwongprom 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. © 2014 Sriwongpan et al. This work is published by Dove Medical Press Limited. 2018-01-24T06:06:09Z 2018-01-24T06:06:09Z 2014-02-18 Journal 11791594 2-s2.0-84896736683 10.2147/RMHP.S56974 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84896736683&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/45162
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
description 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. © 2014 Sriwongpan et al. This work is published by Dove Medical Press Limited.
format Journal
author Pamornsri Sriwongpan
Jayanton Patumanond
Pornsuda Krittigamas
Hutsaya Tantipong
Chamaiporn Tawichasri
Sirianong Namwongprom
spellingShingle Pamornsri Sriwongpan
Jayanton Patumanond
Pornsuda Krittigamas
Hutsaya Tantipong
Chamaiporn Tawichasri
Sirianong Namwongprom
Validation of a clinical risk-scoring algorithm for severe scrub typhus
author_facet Pamornsri Sriwongpan
Jayanton Patumanond
Pornsuda Krittigamas
Hutsaya Tantipong
Chamaiporn Tawichasri
Sirianong Namwongprom
author_sort Pamornsri Sriwongpan
title Validation of a clinical risk-scoring algorithm for severe scrub typhus
title_short Validation of a clinical risk-scoring algorithm for severe scrub typhus
title_full Validation of a clinical risk-scoring algorithm for severe scrub typhus
title_fullStr Validation of a clinical risk-scoring algorithm for severe scrub typhus
title_full_unstemmed Validation of a clinical risk-scoring algorithm for severe scrub typhus
title_sort validation of a clinical risk-scoring algorithm for severe scrub typhus
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84896736683&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/45162
_version_ 1681422694019497984