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. 2017 Jul;92(1):179–191. doi: 10.1016/j.kint.2017.01.017

Figure 6.

Figure 6

Local immune fingerprints associated with poor clinical outcomes. (a) Performance of Random Forest (RF), Support Vector Machine (SVM), and artificial neural network (ANN)–based feature elimination models for the prediction of technique failure over the next 90 days (catheter removal, transfer to hemodialysis, or peritonitis-related death; N = 23) against all other episodes of peritonitis (N = 60), shown as area under the curve (AUC) depending on the number of biomarkers. Red symbols depict the maximum AUC for each model. (b) Kurtosis and skewness of the top 5 biomarkers selected by RF-based feature elimination. (c) Receiver operating characteristic analysis showing specificity and sensitivity of the top 5 biomarkers. (d) Tukey plots of the top 5 biomarkers in patients with subsequent technique failure and all other patients, as assessed by Mann-Whitney tests. (e) Heat map showing the top 5 biomarkers across all patients presenting with acute peritonitis. MMP, matrix metalloproteinase; sIL-6R, soluble IL-6 receptor; TGF-β, transforming growth factor-β.