SynthesisCardiovascular diabetology2023
Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review.
Synthesis in Cardiovascular diabetology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 5 syntheses or guidelines pooled it, 67 citations in OpenAlex.
- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods: a Korean translation.Ewha medical journal · 2025Guideline
- Pooled it
- Overcoming Missing Data: Accurately Predicting Cardiovascular Risk in Type 2 Diabetes, A Systematic Review.Journal of diabetes · 2025Pooled it
- Machine learning in the prediction and detection of new-onset atrial fibrillation in ICU: a systematic review.Journal of anesthesia · 2024Pooled it
- Recommendations for prediction models in clinical practice guidelines for cardiovascular diseases are over-optimistic: a global survey utilizing a systematic literature search.Frontiers in cardiovascular medicine · 2024Pooled it
- Machine Learning Integration of Clinical and Molecular Biomarkers to Predict Vascular Complications in Type 2 Diabetes.Diagnostics (Basel, Switzerland) · 2026Article
- Continuous Glucose Monitoring-Derived Metrics and Cardiovascular Risk Among People With Diabetes: Systematic Scoping Review.JMIR diabetes · 2026Review
- A bimodal dataset for diabetes research.Scientific data · 2026Article
- A Sophisticated Onscreen Smart Framework for Predicting Diabetes in Remote Healthcare.Diagnostics (Basel, Switzerland) · 2026Article
- AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application.Frontiers in medicine · 2026Article
- Narrative review of the development of an ischaemic heart disease prognostic scoring tool (i-IHD score) among patients with type 2 diabetes mellitus in Malaysia.Malaysian family physician : the official journal of the Academy of Family Physicians of Malaysia · 2026Review
- Secondary Prevention of AFAIS: Deploying Traditional Regression, Machine Learning, and Deep Learning Models to Validate and Update CHA2DS2-VASc for 90-Day Recurrence.Journal of clinical medicine · 2025Article
- Machine learning-based models to predict type 2 diabetes combined with coronary heart disease and feature analysis-based on interpretable SHAP.Acta diabetologica · 2025Article
- Artificial intelligence algorithm for predicting cardio-cerebrovascular risk in type 2 diabetes: concordance with clinical and instrumental assessments.Diabetology & metabolic syndrome · 2025Article
- Left-atrioventricular interaction and left-atrial deformation in patients with type 2 diabetes mellitus with or without chronic aortic regurgitation: a 3.0-T cardiac magnetic resonance feature-tracking study.Quantitative imaging in medicine and surgery · 2025Article
- PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods.BMJ (Clinical research ed.) · 2025Article
- Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction.PloS one · 2025Article
- AI hybrid survival assessment for advanced heart failure patients with renal dysfunction.Nature communications · 2024Article
- Article
- Prediction model for cardiovascular disease in patients with diabetes using machine learning derived and validated in two independent Korean cohorts.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction model has been the focus of studies since the last century in the diagnosis and prognosis of various diseases. With the advancement in computational technology, machine learning (ML) has become the widely used tool to develop a prediction model. This review is to investigate the current development of prediction model for the risk of cardiovascular disease (CVD) among type 2 diabetes (T2DM) patients using machine learning. A systematic search on Scopus and Web of Science (WoS) was conducted to look for relevant articles based on the research question. The risk of bias (ROB) for all articles were assessed based on the Prediction model Risk of Bias Assessment Tool (PROBAST) statement. Neural network with 76.6% precision, 88.06% sensitivity, and area under the curve (AUC) of 0.91 was found to be the most reliable algorithm in developing prediction model for cardiovascular disease among type 2 diabetes patients. The overall concern of applicability of all included studies is low. While two out of 10 studies were shown to have high ROB, another studies ROB are unknown due to the lack of information. The adherence to reporting standards was conducted based on the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) standard where the overall score is 53.75%. It is highly recommended that future model development should adhere to the PROBAST and TRIPOD assessment to reduce the risk of bias and ensure its applicability in clinical settings. Potential lipid peroxidation marker is also recommended in future cardiovascular disease prediction model to improve overall model applicability.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.