TY - JOUR KW - Adolescent KW - Adrenal Cortex Hormones KW - Adult KW - Aged KW - Aged, 80 and over KW - Child KW - Child, Preschool KW - Female KW - Humans KW - Leprostatic Agents KW - leprosy KW - Male KW - Middle Aged KW - Peripheral Nervous System Diseases KW - Proportional Hazards Models KW - Reproducibility of Results KW - Risk Factors KW - Treatment Outcome KW - Young Adult AU - Schuring RP AU - Richardus JH AU - Steyerberg EW AU - Pahan D AU - Faber WR AU - Oskam L AB -
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.
BT - PLoS neglected tropical diseases C1 - http://www.ncbi.nlm.nih.gov/pubmed/18846229?dopt=Abstract CN - SCHURING 2008 DA - 2008 DO - 10.1371/journal.pntd.0000283 IS - 8 J2 - PLoS Negl Trop Dis LA - eng N2 -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.
PY - 2008 EP - e283 T2 - PLoS neglected tropical diseases TI - Preventing nerve function impairment in leprosy: validation and updating of a prediction rule. UR - http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2565693/pdf/pntd.0000283.pdf VL - 2 SN - 1935-2735 ER -