Predicting the risks for stroke, cardiovascular disease, and peripheral vascular disease among people with type 2 diabetes with artificial intelligence models: a systematic review and meta-analysis
Authors: Nur Aqsha , Tjandra Sydney , Yumnanisha Defin , Keane Arnold , Bachtiar Adang 
Published: August 2024

AbstractObjectivesThis systematic review and meta-analysis aim to explore the performance of machine learning algorithms in predicting the risk of macrovascular complications among individuals with T2DM, specifically, the predictive capabilities of AI models in forecasting stroke, CVD, and PVD in LMICs.DesignSystematic review and meta-analysis of studies reporting on AI prediction models for macrovascular complications in T2DM patients.SettingThe review included studies conducted in various healthcare settings, primarily from LMICs, upper-middle-income countries (UMICs), and high-income countries (HICs).Participants46 studies were included, with a total of 184 AI models. Participants were diverse in age, sex, and geographical locations, reflecting a broad range of healthcare settings.InterventionsThe intervention analyzed was the application of AI models, including machine learning algorithms, to predict macrovascular complications such as stroke, CVD, and PVD.Primary and Secondary Outcome MeasuresThe primary outcome was the predictive performance of AI models, measured by the area under the receiver operating characteristic curve (AUROC). Secondary outcomes included subgroup analyses based on predictor types and an assessment of AI model applicability in low-resource settings.ResultsTwelve included studies yielded 184 AI models with an overall AUROC of 0.753 (95%CI: 0.74-0.766; I2=99.99%; p<0.001). For 80 models of cardiovascular outcomes, an AUROC of 0.741 (95%CI: 0.721-0.76; I2=99.78%; p<0.001) was obtained. Meanwhile, 25 models of peripheral vascular disease and 38 models of cerebrovascular diseases obtained AUROCs of 0.794 (95%CI: 0.758-0.831; I2=97.23%; p<0.001) and 0.77 (95%CI: 0.743-0.797; I2=99.73%; p<0.001) respectively. Subgroup analysis revealed that models with lab-only predictors were superior to those with mixed or no-lab predictors. This signalled the lack of AI capability for history-taking and physical examination data alone, primarily available in low-resource settings.ConclusionsArtificial intelligence is promising in predicting diabetes complications. Nevertheless, future studies should explore accessible features in low-resource settings and employ external validation to ensure the robustness of the prediction models.Article SummaryStrengths and limitations of this studyInclusion of studies from both health-related and computer science databases (such as IEEE Xplore) ensured a comprehensive assessment of AI models for predicting diabetes complications.The study analyzed a wide range of models from various countries with different income levels, enhancing the generalizability of the findings.Detailed subgroup analyses provided insights into the impact of predictor types (lab vs. non-lab) and machine learning algorithms on model performance.High heterogeneity across studies, stemming from variations in populations, data sources, and algorithms, was observed, reflecting a common issue in AI model performance meta-analyses.A significant limitation was the lack of external validation in most included studies, which raises concerns about the generalizability and applicability of the AI models in diverse clinical settings.Key MessagesWhat is already known on this topicArtificial intelligence (AI) holds great potential for diabetes care.Previous meta-analyses have shown its promise in diabetes predictions, but none has been done for diabetes complication predictions.What this study addsAI model performance aggregates provided promising results.Subgroup analyses exposed characteristics facilitating prediction performances, namely gradient-boosting algorithms, lab predictors, cross- validation, and detailed missing data.How this study might affect research, practice, or policyAlbeit promising, ethical open-source models enabling multiple external validations and interdisciplinary collaboration are vital before broader implementation.