Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms

Abstract

Objectives Type 2 diabetes mellitus (T2DM) imposes a great burden on healthcare systems, and these patients experience higher long-term risks for developing end-stage renal disease (ESRD). Managing diabetic nephropathy becomes more challenging when kidney function starts declining. Therefore, developing predictive models for the risk of developing ESRD in newly diagnosed T2DM patients may be helpful in clinical settings. Methods We established machine learning models constructed from a subset of clinical features collected from 53,477 newly diagnosed T2DM patients from January 2008 to December 2018 and then selected the best model. The cohort was divided, with 70% and 30% of patients randomly assigned to the training and testing sets, respectively. Results The discriminative ability of our machine learning models, including logistic regression, extra tree classifier, random forest, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine were evaluated across the cohort. XGBoost yielded the highest area under the receiver operating characteristic curve (AUC) of 0.953, followed by extra tree and GBDT, with AUC values of 0.952 and 0.938 on the testing dataset. The SHapley Additive explanation summary plot in the XGBoost model illustrated that the top five important features included baseline serum creatinine, mean serum creatine within 1 year before the diagnosis of T2DM, high-sensitivity C-reactive protein, spot urine protein-to-creatinine ratio and female gender. Conclusions Because our machine learning prediction models were based on routinely collected clinical features, they can be used as risk assessment tools for developing ESRD. By identifying high-risk patients, intervention strategies may be provided at an early stage.

Machine Learning Models for Prediction of Diabetic Microvascular Complications

“…We identified 74 studies that met our eligibility criteria. Of these, 66 studies presented the original version of the ML model, 10-75 and an additional eight studies externally validated these models. 76-…”

Using Artificial Intelligence Algorithms to Predict the Overall Survival of Hemodialysis Patients during the COVID-19 Pandemic: A Prospective Cohort Study

“…11 However, the complexity and interrelation of clinical characteristics and extensive electronic health record data pose challenges for traditional predictive models based on classification point systems. 12,13 In recent years, the rapid advancement of artificial intelligence has brought about groundbreaking achievements in machine learning and big data analytics, leading to transformative innovations across various domains, including the development of predictive models. [12][13][14][15] These state-of-the-art techniques empower researchers to delve into vast and diverse data sources, enabling comprehensive exploration and integration that result in remarkably accurate predictions and enhanced risk assessment.…”

“…12,13 In recent years, the rapid advancement of artificial intelligence has brought about groundbreaking achievements in machine learning and big data analytics, leading to transformative innovations across various domains, including the development of predictive models. [12][13][14][15] These state-of-the-art techniques empower researchers to delve into vast and diverse data sources, enabling comprehensive exploration and integration that result in remarkably accurate predictions and enhanced risk assessment. By harnessing the extraordinary capabilities of artificial intelligence algorithms, vast volumes of data can be efficiently analyzed, unveiling intricate patterns and valuable insights that were once inaccessible.…”

“…11 However, the complexity and interrelation of clinical characteristics and extensive electronic health record data pose challenges for traditional predictive models based on classification point systems. 12,13…”

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

“…First, from the study design perspective, the selection of the study population in most previous studies was either unmatched with real-world hospital-visited patients with T2DM or introduced bias due to inappropriate requirements for data completeness. For instance, in certain studies, patients without baseline estimated glomerular ltration rate (eGFR) were excluded [14][15][16][17][18] . This exclusion criterion could potentially introduce selection bias, as individuals who underwent creatinine testing due to physician suspicion of kidney disease were more likely to have pre-existing renal conditions [19,20] .…”