The impact of clinical history on the predictive performance of machine learning and deep learning models for renal complications of diabetes

Background and Objective

Diabetes is a chronic disease characterised by a high risk of developing diabetic nephropathy. The early identification of individuals at heightened risk of such complications or their exacerbation can be crucial to set a correct course of treatment. However, there are currently no widely accepted predictive tools for this task and, additionally, most of these models rely only on information at a single baseline visit. Considering this, we investigate the potential predictive role of patients’ clinical history over multiple levels of renal disease severity while, at the same time, developing an effective predictive model.

Methods:

From the data collected in the DARWIN–Renal (DApagliflozin Real-World evIdeNce-Renal) study, a nationwide multicentre retrospective real-world study, we develop four different types of machine learning models, namely, logistic regression, random forest, Cox proportional hazards regression, and a deep learning model based on recurrent neural network to predict the crossing of 5 clinically relevant glomerular filtration rate thresholds for patients with type 2 diabetes.

Results:

The predictive performance of all models is satisfactory for all outcomes, even without the introduction of information referring to past visits, with AUROC and C-index between 0.69 and 0.98 and average precision well above the random model. The introduction of past information results into a clear improvement in performance for all the models, with percentage increases of up to 12% for both AUROC and C-index and 300% for average precision. The usefulness of past information is further corroborated by a feature importance analysis.

Conclusions:

Incorporating data from the patients’ clinical history into the predictive models greatly improves their performance, particularly for recurrent neural network where the full sequence of values for dynamic variables is provided compared to synthetic indicators of past history.