ArticleDiabetes care2020
Predicting 10-Year Risk of End-Organ Complications of Type 2 Diabetes With and Without Metabolic Surgery: A Machine Learning Approach.
Article in Diabetes care, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 34 papers, 6 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
34 citing papers in PubMed, 6 syntheses or guidelines pooled it, 94 citations in OpenAlex.
- The Evolution of Bariatric and Metabolic Surgery in the Artificial Intelligence Era: A Comprehensive Systematic Review of Current Applications and Clinical Implications.Obesity surgery · 2026Pooled it
- Artificial Intelligence-Driven Transformation of Pediatric Diabetes Care: A Systematic Review and Epistemic Meta-Analysis of Diagnostic, Therapeutic, and Self-Management Applications.International journal of molecular sciences · 2026Pooled it
- Pooled it
- Machine Learning Models for Prediction of Diabetic Microvascular Complications.Journal of diabetes science and technology · 2024Pooled it
- Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature.Journal of nursing management · 2022Pooled it
- Artificial Intelligence in Bariatric Surgery: Current Status and Future Perspectives.Obesity surgery · 2022Pooled it
- Intermittent Fasting and Risk of Diabetic Retinopathy: Retrospective Data from the National Health and Nutrition Examination Survey.Nutrients · 2026Article
- Metabolic polygenic risk scores for prediction of obesity, type 2 diabetes, and related morbidities.Cell metabolism · 2026Article
- Prognostic Models for Predicting Coronary Heart Disease Risk in Patients with Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence for Diabetes Complication Prediction: A Systematic Review of Current Applications and Future Directions.Journal of diabetes science and technology · 2025Review
- Article
- Artificial Intelligence Algorithm to Screen for Diabetic Neuropathy: A Pilot Study.Biomedicines · 2025Article
- Predicting pregnancy at the first year following metabolic-bariatric surgery: development and validation of machine learning models.Surgical endoscopy · 2025Article
- Clinical decision support to improve primary care obesity management in adults with diabetes: Clinic-randomized study design.Contemporary clinical trials · 2025Article
- Prognostic models for cardiovascular and kidney outcomes in people with type 2 diabetes: living systematic review and meta-analysis of observational studies.BMJ medicine · 2025Article
- Metabolic surgery versus usual care effects on mortality among patients with obesity and type 2 diabetes: A systematic review and meta-analysis.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2025Review
- Machine learning techniques to predict the risk of developing diabetic nephropathy: a literature review.Journal of diabetes and metabolic disorders · 2024Article
- The Pulse of AI: Implementation of Artificial Intelligence in Healthcare and its Potential Hazards.The open respiratory medicine journal · 2024Article
- Machine Learning Approach to ClassifyInfection and drug resistance · 2024Article
- Mortality prediction in patients with hyperglycaemic crisis using explainable machine learning: a prospective, multicentre study based on tertiary hospitals.Diabetology & metabolic syndrome · 2023Article
Corrections and comments
- Commented on by
- Erratum issued
Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveTo construct and internally validate prediction models to estimate the risk of long-term end-organ complications and mortality in patients with type 2 diabetes and obesity that can be used to inform treatment decisions for patients and practitioners who are considering metabolic surgery. RESEARCH DESIGN AND
methodsA total of 2,287 patients with type 2 diabetes who underwent metabolic surgery between 1998 and 2017 in the Cleveland Clinic Health System were propensity-matched 1:5 to 11,435 nonsurgical patients with BMI ≥30 kg/m
resultsThe prediction tools demonstrated the following discrimination ability based on the area under the receiver operating characteristic curve (1 = perfect discrimination and 0.5 = chance) at 10 years in the surgical and nonsurgical groups, respectively: all-cause mortality (0.79 and 0.81), coronary artery events (0.66 and 0.67), heart failure (0.73 and 0.75), and nephropathy (0.73 and 0.76). When a patient's data are entered into the IDC application, it estimates the individualized 10-year morbidity and mortality risks with and without undergoing metabolic surgery.
conclusionsThe IDC Risk Scores can provide personalized evidence-based risk information for patients with type 2 diabetes and obesity about future cardiovascular outcomes and mortality with and without metabolic surgery based on their current status of obesity, diabetes, and related cardiometabolic conditions.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.