Prognostic models for cardiovascular and kidney outcomes in people with type 2 diabetes: living systematic review and meta-analysis of observational studies
Notice bibliographique
Résumé
Objective: To summarise available evidence regarding the performance metrics of validated prognostic models on cardiovascular and kidney outcomes in adults with type 2 diabetes mellitus. Design: Living systematic review and meta-analysis of observational studies. Data sources: Medline, Embase, Central, and the Cochrane Database of Systematic Reviews from 1 January 2020 to 17 January 2024. Eligibility criteria for selecting studies: Studies validating prognostic models that predicted all cause and cardiovascular mortality, admission to hospital for heart failure, kidney failure, myocardial infarction, or ischaemic stroke in adults with type 2 diabetes mellitus, including people with established cardiovascular disease or chronic kidney disease, or both. Risk models evaluating composite outcomes were not eligible. Data synthesis: For each model and outcome, using a random effects model, the reported discrimination measures were pooled, reported as c statistics. Furthermore, when available, calibration plots were reconstructed and interpreted narratively. The Prediction Model Risk of Bias Assessment (PROBAST) tool was used to assess the risk of bias of each analysed study cohort and the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach to evaluate our certainty in the evidence. Results: 6529 publications were identified, of which 35 studies reporting on 13 models were included, all of which were developed for general populations with type 2 diabetes but no established cardiovascular disease or chronic kidney disease. Among the identified models, the Risk Equations for Complications of Type 2 Diabetes (RECODe) and the UK Prospective Diabetes Study Outcomes Model 2 (UKPDS-OM2) evaluated all outcomes except for admission to hospital for heart failure. Relative to a threshold c statistic of 0.7, RECODe had an acceptable discrimination for cardiovascular mortality (0.79, high certainty), probably has an acceptable discrimination for myocardial infarction (0.72, moderate certainty) and stroke (0.71, moderate certainty), and may have an acceptable discrimination for kidney failure (0.76, low certainty). High certainty evidence suggests that UKPDS-OM2 has unacceptable discrimination for myocardial infarction (0.64) and stroke (0.65). RECODe showed acceptable calibration for cardiovascular mortality (high certainty), myocardial infarction (high certainty), and kidney failure (moderate certainty) but had unacceptable calibration for stroke (moderate certainty). UKPDS-OM2 showed acceptable calibration for cardiovascular mortality (moderate certainty), stroke (moderate certainty), and kidney failure (low certainty), but may have unacceptable calibration for myocardial infarction (moderate certainty). Conclusion: 13 unique models were identified that evaluated cardiovascular and kidney outcomes in patients with type 2 diabetes. Two models, RECODe and UKPDS-OM2, evaluated all outcomes except for admission to hospital for heart failure. Of all the appraised prognostic models, RECODe had acceptable discrimination and calibration in validation studies for most outcomes; although, additional studies directly comparing models are needed. Study registration number: PROSPERO, CRD42023423075. Readers’ note: This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is the original article.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,037 | 0,098 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,019 | 0,044 |
| Bibliométrie | 0,011 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».