SynthesisJournal of the American Heart Association2020
Clinical Predictive Models of Sudden Cardiac Arrest: A Survey of the Current Science and Analysis of Model Performances.
Synthesis in Journal of the American Heart Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 3 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 3 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- Application of Machine Learning for Patients With Cardiac Arrest: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Predicting neurological outcome in adult patients with cardiac arrest: systematic review and meta-analysis of prediction model performance.Critical care (London, England) · 2022Pooled it
- Clinical Predictive Models of Sudden Cardiac Arrest: A Survey of the Current Science and Analysis of Model Performances.Journal of the American Heart Association · 2020Pooled it
- Association Between Serum Potassium Levels and Cardiac Arrest Risk in Emergency Resuscitation Room Patients: A Retrospective Cohort Study.Journal of clinical medicine · 2026Article
- Practical adaptability of a pre-hospital prognostic prediction model for patients following out-of-hospital cardiac arrest during the COVID-19 pandemic.BMJ health & care informatics · 2026Article
- Distinguishing Subtypes of Unwitnessed Out-of-Hospital Cardiac Arrest by Estimated Last Seen Alive Time.Prehospital emergency care · 2026Article
- A pipeline for developing deep learning prognostic prediction models in cardiac magnetic resonance image analysis.European heart journal. Digital health · 2026Article
- Observational
- The association between the severity of out-of-hospital cardiac arrest and the effectiveness of target temperature management: a retrospective study based on prediction models.International journal of emergency medicine · 2025Article
- Comparison of return of spontaneous circulation prediction scores in patients with cardiac arrest during ambulance transport.BMC emergency medicine · 2025Article
- The Prognosis After Cardiac Arrest: Evidence on the Short- and Long-Term Course.Deutsches Arzteblatt international · 2025Review
- Bland-Altman plot to assess the consistency of arterial and venous blood lactate in the emergency room: a retrospective cohort study.Scientific reports · 2025Article
- Seeking a Treatable Cause of Out-of-Hospital Cardiac Arrest during and after Resuscitation.Journal of clinical medicine · 2024Review
- External Validation of Updated Prediction Models for Neurological Outcomes at 90 Days in Patients With Out-of-Hospital Cardiac Arrest.Journal of the American Heart Association · 2024Article
- Article
- Clinical Applications, Methodology, and Scientific Reporting of Electrocardiogram Deep-Learning Models: A Systematic Review.JACC. Advances · 2023Article
- Six-Lead Electrocardiography Enables Identification of Rhythm and Conduction Anomalies of Patients in the Telemedicine-Based, Hospital-at-Home Setting: A Prospective Validation Study.Sensors (Basel, Switzerland) · 2023Article
- Predicting survival post-cardiac arrest: An observational cohort study.Resuscitation plus · 2023Article
- External validation of the PROLOGUE score to predict neurological outcome in adult patients after cardiac arrest: a prospective cohort study.Scandinavian journal of trauma, resuscitation and emergency medicine · 2023Article
- Combination of Cerebral Computed Tomography and Simplified Cardiac Arrest Hospital Prognosis (sCAHP) Score for Predicting Neurological Recovery in Cardiac Arrest Survivors.Reviews in cardiovascular medicine · 2023Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Background More than 500 000 sudden cardiac arrests (SCAs) occur annually in the United States. Clinical predictive models (CPMs) may be helpful tools to differentiate between patients who are likely to survive or have good neurologic recovery and those who are not. However, which CPMs are most reliable for discriminating between outcomes in SCA is not known. Methods and Results We performed a systematic review of the literature using the Tufts PACE (Predictive Analytics and Comparative Effectiveness) CPM Registry through February 1, 2020, and identified 81 unique CPMs of SCA and 62 subsequent external validation studies. Initial cardiac rhythm, age, and duration of cardiopulmonary resuscitation were the 3 most commonly used predictive variables. Only 33 of the 81 novel SCA CPMs (41%) were validated at least once. Of 81 novel SCA CPMs, 56 (69%) and 61 of 62 validation studies (98%) reported discrimination, with median c-statistics of 0.84 and 0.81, respectively. Calibration was reported in only 29 of 62 validation studies (41.9%). For those novel models that both reported discrimination and were validated (26 models), the median percentage change in discrimination was -1.6%. We identified 3 CPMs that had undergone at least 3 external validation studies: the out-of-hospital cardiac arrest score (9 validations; median c-statistic, 0.79), the cardiac arrest hospital prognosis score (6 validations; median c-statistic, 0.83), and the good outcome following attempted resuscitation score (6 validations; median c-statistic, 0.76). Conclusions Although only a small number of SCA CPMs have been rigorously validated, the ones that have been demonstrate good discrimination.
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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.