Table 1.
Clinical variables according to a stable or worsening PKDL state.
Fig 1.
Patient metadata and feature scheme. Created in BioRender. Torres, A. (2025) https://BioRender.com/j75e448.
Table 2.
Variable analysis using conventional statistics.
Fig 2.
Heatmap indicating r values for all-against-all properties > 80% non-zero numbers.
The chart was built with the heatmap function of the seaborn Python library.
Fig 3.
Dimensionality reduction by PCA.
(A) Dimensionality reduction using robust normalized data. Cumulative variance by PCA. (B) Bidimensional scatter plot showing patient data for “stable” (blue) and “worsening” (red) PKDL.
Fig 4.
(A) K-means clustering inertia for k values from 2 to 20. (B) Significance of clinical phenotype enrichment in patients with stable (blue) or worsening (red) PKDL disease for clusters with k values from 2 to 20. Sphere diameter is proportional to cluster size (number of patients). Dashed grey lines indicate significant (p < 0.05) and highly significant (p < 0.01) values for enrichment in either stable or worsening patients. (C) Boxplot panel showing value distributions of features, both PCA-selected and non-selected, significantly differing in cluster cl5-k8 compared to the remaining subjects. Boxes represent the interquartile range; the line is the mean. Whiskers indicate up to 1.5 fold the interquartile range. Outliers are shown in circles. Significance: *p < 0.05, **p < 0.01, ***p < 0.001.
Table 3.
Cluster cl5-k8 values. Mean ± SD for original (non-normalized) PCA variables are shown with respect to the same ciphers for the remaining cluster subjects (control).
Table 4.
Variables defining worsening PKDL.
Fig 5.
Spatial representation of three of fourteen features found to define the worsening PKDL phenotype.
Red dots: worsening patients in cluster cl5-k8; Orange dots: stable patients in cluster cl5-k8; Black dots: worsening patients outside cluster cl5-k8; Grey dots: stable patients outside cluster cl5-k8.