Fig 1.
Flow chart of the follow-up study of household contacts (HHC).
Biological samples from leprosy index cases and endemic controls clinically evaluated in 2011 were submitted to q-PCR and ELISA assays (anti-LID-1 and anti-ND-O-LID). Using data from these assays, the random forest algorithm was established for the prediction of a dichotomous model of Sick/Healthy. Then the same tests were applied to the HHC in 2011, 2012, and 2016. The random forest algorithm was appliedto predict Sick/Healthy individuals during the period of follow-up of HHC. q-PCR: quantitative polymerase chain reaction; ND-O-LID: natural disaccharide-octyl-leprosy IDRI diagnostic-1; LID-1: leprosy IDRI diagnostic-1.
Fig 2.
ROC curve for evaluation of qPCR performance in leprosy diagnosis.
The ROC curve established the optimal cutoff point for qPCR. Sens: sensitivity; Spec: specificity; AUC: area under the curve; LR+: likelihood ratio positive; LR-: likelihood ratio negative.
Table 1.
Frequency of positive household contacts identified by qPCR SSS in 2011, 2012, and 2016.
Fig 3.
ROC curve analysis to evaluate ELISA assays.
The ROC curve established the optimal cutoff point for ND-O-LID (A) and LID-1 (B). Sens: sensitivity; Spec: specificity; AUC: area under the curve; LR+: likelihood ratio positive; LR-: likelihood ratio negative.
Fig 4.
Comparison of the specific antibody responses in the EC, PB, MB, and HHC in 2011, 2012, and 2016 by ELISA assay.
A) ND-O-LID; B) LID-1. EC: endemic control; PB: paucibacillary; MB: multibacillary; HHCPB: household contact of paucibacillary patient; HHCMB: household contact of multibacillary patient.
Table 2.
Comparison of specific antibody responses in the EC, PB, MB and HHC groups in 2011, 2012, and 2016.
Table 3.
Confusion matrix for the dichotomous model (Sick/Healthy).
Fig 5.
Levels of importance of the variables in the prediction model by random forest.
TT: treatment time; qPCR SSS: quantitative PCR using samples of slit skin smears; BI: bacilloscopic index; GEN: gender.
Fig 6.
Error convergence curve according to the number of trees used in the random forest model.
Fig 7.
Example of a decision tree in the random forest model.
Error 12.8%. BI: bacilloscopy index; TT: treatment time.
Table 4.
Frequency of positive results using various methods for diagnosis of PB and MB leprosy.
Fig 8.
Prediction Force (PF) defined by the dichotomous model (Sick/Healthy).
The bars represent each individual with the respective identification number. The height of the bar indicates the PF: low (PF ≤ 0.25), moderate (0.25 < PF ≤ 0.50), high (0.50 < PF≤0.75), and very high (PF > 0.75). The color scale emphasizes the PF—Healthy represented by green and Sick by red. The arrowhead indicates individuals (38, 134, 40, and 22 highlighted) who were clinically diagnosed as leprosy cases in 2012, and the white arrow shows an individual clinically diagnosed as a leprosy case in 2017. The white bars represent individuals who were lost during follow-up.