An Improved Clinical and Genetics-Based Prediction Model for Diabetic Foot Ulcer Healing

Abstract

Objective: The goal of this investigation was to use comprehensive prediction modeling tools and available genetic information to try to improve upon the performance of simple clinical models in predicting whether a diabetic foot ulcer (DFU) will heal. Approach: We utilized a cohort study (n = 206) that included clinical factors, measurements of circulating endothelial precursor cells (CEPCs), and fine sequencing of the NOS1AP gene. We derived and selected relevant predictive features from this patient-level information using statistical and machine learning techniques. We then developed prognostic models using machine learning approaches and assessed predictive performance. The presentation is consistent with TRIPOD requirements. Results: Models using baseline clinical and CEPC data had an area under the receiver operating characteristic curve (AUC) of 0.73 (0.66-0.80). Models using only single nucleotide polymorphisms (SNPs) of the NOS1AP gene had an AUC of 0.67 (95% confidence interval, CI: [0.59-0.75]). However, models incorporating baseline and SNP information resulted in improved AUC (0.80, 95% CI [0.73-0.87]). Innovation: We provide a rigorous analysis demonstrating the predictive potential of genetic information in DFU healing. In this process, we present a framework for using advanced statistical and bioinformatics techniques for creating superior prognostic models and identify potentially predictive SNPs for future research. Conclusion: We have developed a new benchmark for which future predictive models can be compared against. Such models will enable wound care experts to more accurately predict whether a patient will heal and aid clinical trialists in designing studies to evaluate therapies for subjects likely or unlikely to heal.

Keywords: diabetic foot ulcers; epidemiology; machine learning; prognostic models.

David J. Margolis, MD, PhD

Figure 1.

A visualization of feature selection stability. We consider sets of SNP features selected using data from 179 patients with complete data and then create 10 cross-validation training-testing splits. Within each training fold, we assess whether each feature was also selected by a LASSO model trained on these preselected SNP and baseline features (LASSO) or identified when replicating our feature selection approach (Within-Fold Selection). LASSO, least absolute shrinkage and selection operator; SNP, single nucleotide polymorphism.