ReviewDiagnostic and prognostic research2019
Untapped potential of multicenter studies: a review of cardiovascular risk prediction models revealed inappropriate analyses and wide variation in reporting.
Review in Diagnostic and prognostic research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Early warning scores for detecting deterioration in adult hospital patients: systematic review and critical appraisal of methodology.BMJ (Clinical research ed.) · 2020Pooled it
- Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisalBMJ (Clinical research ed.) · 2020Pooled it
- Comparison of the Cox Proportional Hazards Model and Random Survival Forest Algorithm for Predicting Patient-Specific Survival Probabilities in Clinical Trial Data.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Association between GLIM-defined malnutrition and in-hospital costs in adults with sepsis: A sub analysis of a prospective cohort study.JPEN. Journal of parenteral and enteral nutrition · 2026Article
- Machine Learning Methods to Predict the Length of Stay for Acute Stroke: A Scoping Review and Meta-Analysis.Health care science · 2026Review
- Development and validation of a machine learning-based prediction model for malnutrition risk in peritoneal dialysis patients: a multi‑center retrospective study.International urology and nephrology · 2026Article
- Clustered flexible calibration plots for binary outcomes using random effects modeling.Research synthesis methods · 2026Article
- Serotype-specific pneumococcal conjugate vaccine effectiveness in Canadian children: an indirect cohort study using Bayesian hierarchical modeling of multisite data.BMC infectious diseases · 2026Article
- Machine learning approaches to early detection of delayed wound healing following gastric cancer surgery.World journal of gastrointestinal oncology · 2026Article
- Large Language Models in Critical Care Medicine: Scoping Review.JMIR medical informatics · 2025Article
- Early Prediction of ICU Mortality in Patients with Acute Hypoxemic Respiratory Failure Using Machine Learning: The MEMORIAL Study.Journal of clinical medicine · 2025Article
- The Impact of Multi-Institution Datasets on the Generalizability of Machine Learning Prediction Models in the ICU.Critical care medicine · 2024Observational
- Targeted Development and Validation of Clinical Prediction Models in Secondary Care Settings: Opportunities and Challenges for Electronic Health Record Data.JMIR medical informatics · 2024Article
- Tutorial: dos and don'ts in clinical prediction research for venous thromboembolism.Research and practice in thrombosis and haemostasis · 2024Article
- There is no such thing as a validated prediction model.BMC medicine · 2023Article
- Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration.BMJ (Clinical research ed.) · 2023Article
- Reporting of Model Performance and Statistical Methods in Studies That Use Machine Learning to Develop Clinical Prediction Models: Protocol for a Systematic Review.JMIR research protocols · 2022Article
- Clinical decision support systems in orthodontics: A narrative review of data science approaches.Orthodontics & craniofacial research · 2021Review
- Reducing employee turnover in hospitals: estimating the effects of hypothetical improvements in the psychosocial work environment.Scandinavian journal of work, environment & health · 2021Article
- Risk prediction in multicentre studies when there is confounding by cluster or informative cluster size.BMC medical research methodology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical prediction models are often constructed using multicenter databases. Such a data structure poses additional challenges for statistical analysis (clustered data) but offers opportunities for model generalizability to a broad range of centers. The purpose of this study was to describe properties, analysis, and reporting of multicenter studies in the Tufts PACE Clinical Prediction Model Registry and to illustrate consequences of common design and analyses choices.
methodsFifty randomly selected studies that are included in the Tufts registry as multicenter and published after 2000 underwent full-text screening. Simulated examples illustrate some key concepts relevant to multicenter prediction research.
resultsMulticenter studies differed widely in the number of participating centers (range 2 to 5473). Thirty-nine of 50 studies ignored the multicenter nature of data in the statistical analysis. In the others, clustering was resolved by developing the model on only one center, using mixed effects or stratified regression, or by using center-level characteristics as predictors. Twenty-three of 50 studies did not describe the clinical settings or type of centers from which data was obtained. Four of 50 studies discussed neither generalizability nor external validity of the developed model.
conclusionsRegression methods and validation strategies tailored to multicenter studies are underutilized. Reporting on generalizability and potential external validity of the model lacks transparency. Hence, multicenter prediction research has untapped potential. REGISTRATION: This review was not registered.
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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.