A SuperLearner approach for predicting diabetic kidney disease upon the initial diagnosis of T2DM in hospital.

ABSTRACT:

Background

Diabetic kidney disease (DKD) is a serious complication of diabetes mellitus (DM), with patients typically remaining asymptomatic until reaching an advanced stage. We aimed to develop and validate a predictive model for DKD in patients with an initial diagnosis of type 2 diabetes mellitus (T2DM) using real-world data.

Methods

We retrospectively examined data from 3,291 patients (1740 men, 1551 women) newly diagnosed with T2DM at Ningbo Municipal Hospital of Traditional Chinese Medicine (2011-2023). The dataset was randomly divided into training and validation cohorts. Forty-six readily available medical characteristics at initial diagnosis of T2DM from the electronic medical records were used to develop prediction models based on linear, non-linear, and SuperLearn

SUBMITTER: Lin X 

PROVIDER: S-EPMC11948915 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature