Observational studyCritical care explorations2025
Multicenter Development and Prospective Validation of eCARTv5: A Gradient-Boosted Machine-Learning Early Warning Score.
Observational study in Critical care explorations, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis 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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Factors underpinning the performance of implemented artificial intelligence-based patient deterioration prediction systems: reasons for selection and implications for hospitals and researchers.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- DETERIO-LLM: enhancing traditional deterioration risk scores with clinical context and advanced reasoning.JAMIA open · 2026Article
- How Best to Explain Machine Learning Models to Clinicians: A User Study of Explanation Types.medRxiv : the preprint server for health sciences · 2026Article
- Real-time prediction of cardiorespiratory deterioration during paediatric critical care transport using interpretable machine learning.PLOS digital health · 2026Article
- Early Warning Scores in Emergency Department Patients Aged 80 Years or Older.JAMA network open · 2026Article
- Beyond Human Error: Building Intelligent Resilience for Medication Safety in the ICU.Healthcare (Basel, Switzerland) · 2026Review
- A High-Precision Time-Varying Survival Model for Early Prediction of Patient Deterioration: A Retrospective Cohort Study.Journal of clinical medicine · 2026Article
- Treatment Recommendations for Clinical Deterioration on the Wards: Development and Validation of Machine Learning Models.JMIR AI · 2026Article
- Comparison of Multimodal Deep Learning Approaches for Predicting Clinical Deterioration in Ward Patients: Observational Cohort Study.Journal of medical Internet research · 2025Observational
- Predictions With a Purpose: Elevating Standards for Clinical Modeling Research.Critical care explorations · 2025Article
- Prospective external validation of a deep-learning-based early-warning system for major adverse events in general wards in South Korea.Acute and critical care · 2025Article
- When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2024Article
- Early Warning Scores With and Without Artificial Intelligence.JAMA network open · 2024Article
- Clinician perspectives on how situational context and augmented intelligence design features impact perceived usefulness of sepsis prediction scores embedded within a simulated electronic health record.Journal of the American Medical Informatics Association : JAMIA · 2024Article
Corrections and comments
- Update of
Authors and funding
12 authors.
Funding
Abstract
backgroundEarly detection of clinical deterioration using machine-learning early warning scores may improve outcomes. However, most implemented scores were developed using logistic regression, only underwent retrospective validation, and were not tested in important subgroups.
objectiveThe objective of our multicenter retrospective and prospective observational study was to develop and prospectively validate a gradient-boosted machine model (eCARTv5) for identifying clinical deterioration on the wards. DERIVATION COHORT: All adult patients admitted to the inpatient medical-surgical wards at seven hospitals in three health systems for model development (2006-2022). VALIDATION COHORT: All adult patients admitted to the inpatient medical-surgical wards and at 21 hospitals from three health systems for retrospective (2009-2023) and prospective (2023-2024) external validation. PREDICTION MODEL: Predictor variables (demographics, vital signs, documentation, and laboratory values) were used in a gradient-boosted trees algorithm to predict ICU transfer or death in the next 24 hours. The developed model (eCARTv5) was compared with the Modified Early Warning Score (MEWS), the National Early Warning Score (NEWS), and eCARTv2 using the area under the receiver operating characteristic curve (AUROC).
resultsThe development cohort included 901,491 admissions, the retrospective validation cohort included 1,769,461 admissions, and the prospective validation cohort included 205,946 admissions. In retrospective validation, eCARTv5 had the highest AUROC (0.834; 95% CI, 0.834-0.835), followed by eCARTv2 (0.775 [95% CI, 0.775-0.776]), NEWS (0.766 [95% CI, 0.766-0.767]), and MEWS (0.704 [95% CI, 0.703-0.704]). eCARTv5's performance remained high (AUROC ≥0.80) across a range of patient demographics, clinical conditions, and during prospective validation.
conclusionWe developed eCARTv5, which performed better than eCARTv2, NEWS, and MEWS retrospectively, prospectively, and across a range of subgroups. These results served as the foundation for Food and Drug Administration clearance for its use in identifying deterioration in hospitalized ward patients.
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.