SynthesisJournal of hospital medicine2019
Statistical Modeling and Aggregate-Weighted Scoring Systems in Prediction of Mortality and ICU Transfer: A Systematic Review.
Synthesis in Journal of hospital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of universal early warning scores in different patient subgroups and clinical settings: a systematic review.BMJ open · 2021Pooled it
- Early warning scores for detecting deterioration in adult hospital patients: systematic review and critical appraisal of methodology.BMJ (Clinical research ed.) · 2020Pooled it
- MultiCardioNet: A Multimodal Deep Learning Model for Early Cardiovascular Deterioration Prediction in ICU Patients.Bioengineering (Basel, Switzerland) · 2026Article
- Association of Vital Sign and Laboratory Abnormalities Detected via Remote Monitoring with ICU Readmission and Mortality: An Observational Cohort Study.Joint Commission journal on quality and patient safety · 2026Observational
- Unplanned transfers from wards to intensive care units: how well does NEWS identify patients in need of urgent escalation of care?Scandinavian journal of trauma, resuscitation and emergency medicine · 2025Article
- Machine learning-based early warning system for hemodynamic deterioration in cardiovascular ICU patients: a bidirectional cross-validation study.Frontiers in cardiovascular medicine · 2025Article
- Clinical evaluation of a machine learning-based early warning system for patient deterioration.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2024Article
- Beyond Unplanned ICU Transfers: Linking a Revised Definition of Deterioration to Patient Outcomes.Critical care medicine · 2024Article
- Validation of a Proprietary Deterioration Index Model and Performance in Hospitalized Adults.JAMA network open · 2023Article
- Developing and Validating a Prediction Model For Death or Critical Illness in Hospitalized Adults, an Opportunity for Human-Computer Collaboration.Critical care explorations · 2023Article
- Chinese experts' consensus on the application of intensive care big data.Frontiers in medicine · 2023Article
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- Article
- Implementing machine learning in medicine.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2021Article
- Use of Multiprognostic Index Domain Scores, Clinical Data, and Machine Learning to Improve 12-Month Mortality Risk Prediction in Older Hospitalized Patients: Prospective Cohort Study.Journal of medical Internet research · 2021Article
- Technical considerations for evaluating clinical prediction indices: a case study for predicting code blue events with MEWS.Physiological measurement · 2021Article
- Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Predicting Intensive Care Transfers and Other Unforeseen Events: Analytic Model Validation Study and Comparison to Existing Methods.JMIR medical informatics · 2021Article
- Machine Learning-Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review.Journal of medical Internet research · 2021Article
- Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration.The New England journal of medicine · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundThe clinical deterioration of patientsin general hospital wards is an important safety issue. Aggregate-weighted early warning systems (EWSs) may not detect risk until patients present with acute decline. PURPOSE: We aimed to compare the prognostic test accuracy and clinical workloads generated by EWSs using statistical modeling (multivariable regression or machine learning) versus aggregate-weighted tools. DATA SOURCES: We searched PubMed and CINAHL using terms that described clinical deterioration and use of an advanced EWS. STUDY SELECTION: The outcome was clinical deterioration (intensive care unit transfer or death) of adult patients on general hospital wards. We included studies published from January 1, 2012 to September 15, 2018. DATA EXTRACTION: Following 2015 PRIMSA systematic review protocol guidelines; 2015 TRIPOD criteria for predictive model evaluation; and the Cochrane Collaboration guidelines, we reported model performance, adjusted positive predictive value (PPV), and conducted simulations of workup-to-detection ratios. DATA SYNTHESIS: Of 285 articles, six studies reported the model performance of advanced EWSs, and five were of high quality. All EWSs using statistical modeling identified at-risk patients with greater precision than aggregate-weighted EWSs (mean AUC 0.80 vs 0.73). EWSs using statistical modeling generated 4.9 alerts to find one true positive case versus 7.1 alerts in aggregate-weighted EWSs; a nearly 50% relative workload increase for aggregate-weighted EWSs.
conclusionsCompared with aggregate-weighted tools, EWSs using statistical modeling consistently demonstrated superior prognostic performance and generated less workload to identify and treat one true positive case. A standardized approach to reporting EWS model performance is needed, including outcome definitions, pretest probability, observed and adjusted PPV, and workup-to-detection ratio.
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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.