ArticleJournal of clinical monitoring and computing2019
Machine learning based framework to predict cardiac arrests in a paediatric intensive care unit : Prediction of cardiac arrests.
Article in Journal of clinical monitoring and computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications.Applied clinical informatics · 2022Pooled it
- Automated Classification of Mitral and Tricuspid Regurgitation With Explainability and Real-World Practice Experience.Journal of the American Heart Association · 2026Article
- Data pipeline quality: development and validation of a quality assessment tool for data-driven algorithms and artificial intelligence in healthcare.BMJ health & care informatics · 2026Article
- Prediction cardiovascular deterioration in a paediatric intensive care unit (PicEWS): a machine learning modelling study of routinely collected health-care data.EClinicalMedicine · 2025Article
- Review
- Incidence of cardiac arrest following implementation of a predictive analytics display in a pediatric intensive care unit.Resuscitation plus · 2025Article
- Assessing the Predictive Capabilities of Autoregressive Integrated Moving Average and Linear Regression Models for Acute Changes in Clinical and Selected Laboratory Parameters in Children After Cardiac Surgery in the ICU.Children (Basel, Switzerland) · 2024Article
- Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.Circulation · 2024Review
- Development of a deep learning model for predicting critical events in a pediatric intensive care unit.Acute and critical care · 2024Article
- Exploring the clinical relevance of vital signs statistical calculations from a new-generation clinical information system.Scientific reports · 2023Observational
- Predicting Cardiac Arrest in Children with Heart Disease: A Novel Machine Learning Algorithm.Journal of clinical medicine · 2023Article
- Artificial Intelligence in Resuscitation: A Scoping Review.Journal of clinical medicine · 2023Article
- Novel approaches to capturing and using continuous cardiorespiratory physiological data in hospitalized children.Pediatric research · 2023Review
- Artificial Intelligence in NICU and PICU: A Need for Ecological Validity, Accountability, and Human Factors.Healthcare (Basel, Switzerland) · 2022Article
- Machine Learning-Based Systems for the Anticipation of Adverse Events After Pediatric Cardiac Surgery.Frontiers in pediatrics · 2022Article
- Prediction of Acute Respiratory Failure Requiring Advanced Respiratory Support in Advance of Interventions and Treatment: A Multivariable Prediction Model From Electronic Medical Record Data.Critical care explorations · 2021Article
- Wireless monitoring and real-time adaptive predictive indicator of deterioration.Scientific reports · 2020Observational
- Journal of Clinical Monitoring and Computing end of year summary 2019: hemodynamic monitoring and management.Journal of clinical monitoring and computing · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A cardiac arrest is a life-threatening event, often fatal. Whilst clinicians classify some of the cardiac arrests as potentially predictable, the majority are difficult to identify even in a post-incident analysis. Changes in some patients' physiology when analysed in detail can however be predictive of acute deterioration leading to cardiac or respiratory arrests. This paper seeks to exploit the causally-related changing patterns in signals such as heart rate, respiration rate, systolic blood pressure and peripheral cutaneous oxygen saturation to evaluate the predictability of cardiac arrests in critically ill paediatric patients in intensive care. In this paper we report the results of a framework constituting feature space embedding and time series forecasting methods to build an automated prediction system. The results were compared with clinical assessment of predictability. A sensitivity of 71% and specificity of 69% was obtained when the maximum value of Anomaly Index (12) in the 50 min (starting one hour and ending 10 min) before the arrest was considered for the case patients and a random 50 min of data was considered for the control set patients. A positive predictive value of 11% and negative predictive value of 98% was obtained with a prevalence of 5% by our method of prediction. While clinicians predicted 4 out of the 69 cardiac arrests (6%), the prediction system predicted 63 (91%) cardiac arrests. Prospective validation of the automated system remains.
Indexed as
Identifiers
30264218What 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.