ArticleDiagnostic and prognostic research2021
Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems?
Article in Diagnostic and prognostic research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 3 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.
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Who cites it
56 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Predictive performance of risk prediction models for lung cancer incidence in Western and Asian countries: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review.BMC medical research methodology · 2022Pooled it
- Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis.BMJ (Clinical research ed.) · 2022Pooled it
- Reimagining antimicrobial resistance: AI-driven predictive epidemiology and the C-AMRE framework for next-generation antibiotic discovery.The Journal of antibiotics · 2026Review
- Detecting Performance Drift in AI Models for Medical Image Analysis Using CUSUM Chart.Journal of imaging informatics in medicine · 2026Article
- Validation is not enough: Longitudinal evidence of post-deployment fragility in clinical AI systems.PLOS digital health · 2026Article
- Validation of a prediction model for postpartum hospital use in geographic contexts with greater rural representation.American journal of obstetrics & gynecology MFM · 2026Article
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- The challenges of implementing hybrid baselines for the interpretation of longitudinal behavioral data from individuals.NPJ digital medicine · 2026Article
- Enhancing cause of death prediction: development and validation of machine learning models using multimodal data across multiple health-care sites.JAMIA open · 2026Article
- Performance of the Leicester risk assessment and Leicester practice risk scores for assessing the risk of undiagnosed type 2 diabetes or prediabetes in diverse populations: protocol for a systematic review of published validations and updates.Diagnostic and prognostic research · 2026Review
- Redefining cardiometabolic biomarkers in the big data era: toward a personalized medicine-centered reconstruction of risk prediction models.Frontiers in cardiovascular medicine · 2026Article
- Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine.Nature reviews. Clinical oncology · 2025Review
- Development and evaluation of a lymph node invasion risk prediction model in intermediate- and high-risk prostate cancer patients.Acta oncologica (Stockholm, Sweden) · 2025Article
- CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation.Interdisciplinary sciences, computational life sciences · 2025Article
- Mortality Prediction Performance Under Geographical, Temporal, and COVID-19 Pandemic Dataset Shift: External Validation of the Global Open-Source Severity of Illness Score Model.Critical care explorations · 2025Article
- Adverse drug reactions and events in an Ageing PopulaTion risk Prediction (ADAPTiP) tool: the development and validation of a model for predicting adverse drug reactions and events in older patients.European geriatric medicine · 2025Article
- Predictive modeling of methadone poisoning outcomes in children ≤ 5 years: utilizing machine learning and the National Poison Data System for improved clinical decision-making.European journal of pediatrics · 2025Article
- Article
- Enhancing bronchopulmonary dysplasia prediction in preterm infants using artificial intelligence and multimodal data integration.Frontiers in pediatrics · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Clinical prediction models (CPMs) have become fundamental for risk stratification across healthcare. The CPM pipeline (development, validation, deployment, and impact assessment) is commonly viewed as a one-time activity, with model updating rarely considered and done in a somewhat ad hoc manner. This fails to address the fact that the performance of a CPM worsens over time as natural changes in populations and care pathways occur. CPMs need constant surveillance to maintain adequate predictive performance. Rather than reactively updating a developed CPM once evidence of deteriorated performance accumulates, it is possible to proactively adapt CPMs whenever new data becomes available. Approaches for validation then need to be changed accordingly, making validation a continuous rather than a discrete effort. As such, "living" (dynamic) CPMs represent a paradigm shift, where the analytical methods dynamically generate updated versions of a model through time; one then needs to validate the system rather than each subsequent model revision.
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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.