ArticlePediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies2015
Using Time Series Analysis to Predict Cardiac Arrest in a PICU.
Article in Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Incorporating repeated measurements into prediction models in the critical care setting: a framework, systematic review and meta-analysis.BMC medical research methodology · 2019Pooled it
- Time series electrocardiography (ECG) data for early prediction of cardiac arrest.Scientific reports · 2026Article
- Incidence of cardiac arrest following implementation of a predictive analytics display in a pediatric intensive care unit.Resuscitation plus · 2025Article
- Maximizing Survival in Pediatric Congenital Cardiac Surgery Using Machine Learning, Explainability, and Simulation Techniques.Journal of clinical medicine · 2024Article
- Prediction of In-Hospital Cardiac Arrest in the Intensive Care Unit: Machine Learning-Based Multimodal Approach.JMIR medical informatics · 2024Article
- Development of a deep learning model for predicting critical events in a pediatric intensive care unit.Acute and critical care · 2024Article
- Article
- 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
- Multivariate longitudinal data for survival analysis of cardiovascular event prediction in young adults: insights from a comparative explainable study.BMC medical research methodology · 2023Observational
- Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care.Frontiers in pediatrics · 2022Article
- Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review.JMIR medical informatics · 2021Article
- Machine learning for early prediction of in-hospital cardiac arrest in patients with acute coronary syndromes.Clinical cardiology · 2021Article
- Inadequate oxygen delivery index dose is associated with cardiac arrest risk in neonates following cardiopulmonary bypass surgery.Resuscitation · 2019Article
- Machine learning based framework to predict cardiac arrests in a paediatric intensive care unit : Prediction of cardiac arrests.Journal of clinical monitoring and computing · 2019Article
- Multiple imputation for time series data with Amelia package.Annals of translational medicine · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectivesTo build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data.
designRetrospective cohort study.
settingThirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. SUBJECTS: Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes.
interventionsNone. MEASUREMENTS AND MAIN
resultsOne hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve.
conclusionsCardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.
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.