Preventing nerve function impairment in leprosy: validation and updating of a prediction rule.

BACKGROUND: To validate and update a prediction rule for estimating the risk of leprosy-related nerve function impairment (NFI). METHODOLOGY/PRINCIPAL FINDINGS: Prospective cohort using routinely collected data, in which we determined the discriminative ability of a previously published rule and an...

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Bibliographic Details
Published in:PLoS Neglected Tropical Diseases
Main Authors: Ron P Schuring, Jan H Richardus, Ewout W Steyerberg, David Pahan, William R Faber, Linda Oskam
Format: Article in Journal/Newspaper
Language:English
Published: Public Library of Science (PLoS) 2008
Subjects:
Arctic medicine. Tropical medicine
RC955-962
Public aspects of medicine
RA1-1270
Arctic
Online Access:https://doi.org/10.1371/journal.pntd.0000283
https://doaj.org/article/91901f9ed44642d9beecee11b04999e8
Description
Summary:BACKGROUND: To validate and update a prediction rule for estimating the risk of leprosy-related nerve function impairment (NFI). METHODOLOGY/PRINCIPAL FINDINGS: Prospective cohort using routinely collected data, in which we determined the discriminative ability of a previously published rule and an updated rule with a concordance statistic (c). Additional risk factors were analyzed with a Cox proportional hazards regression model. The population consisted of 1,037 leprosy patients newly diagnosed between 2002 and 2003 in the health care facilities of the Rural Health Program in Nilphamari and Rangpur districts in northwest Bangladesh. The primary outcome was the time until the start of treatment. An NFI event was defined as the decision to treat NFI with corticosteroids after diagnosis. NFI occurred in 115 patients (13%; 95% confidence interval 11%-16%). The original prediction rule had adequate discriminative ability (c = 0.79), but could be improved by substituting one predicting variable: 'long-standing nerve function impairment at diagnosis' by 'anti-PGL-I antibodies'. The adjusted prediction rule was slightly better (c = 0.81) and identified more patients with NFI (80%) than the original prediction rule (72%). CONCLUSIONS/SIGNIFICANCE: NFI can well be predicted by using the risk variables 'leprosy classification' and 'anti-PGL-I antibodies'. The use of these two variables that do not include NFI offer the possibility of predicting NFI, even before it occurs for the first time. Surveillance beyond the treatment period can be targeted to those most likely to benefit from preventing permanent disabilities.

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