ReviewTranslational pediatrics2018
Current monitoring and innovative predictive modeling to improve care in the pediatric cardiac intensive care unit.
Review in Translational pediatrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Congenital heart disease diagnosis using machine learning: a systematic literature review.Frontiers in medicine · 2026Pooled it
- Leveraging ChatGPT to strengthen pediatric healthcare systems: a systematic review.European journal of pediatrics · 2025Pooled it
- Big Data in cardiac surgery: real world and perspectives.Journal of cardiothoracic surgery · 2022Pooled it
- A clinical decision support system for predicting drug-related adverse effects in pediatric patients with congenital heart disease: Development and retrospective evaluation.The Journal of international medical research · 2026Article
- Artificial Intelligence in Cardiopulmonary Resuscitation.Medicina (Kaunas, Lithuania) · 2025Review
- Investigation of a Novel Noninvasive Risk Analytics Algorithm With Laboratory Central Venous Oxygen Saturation Measurements in Critically Ill Pediatric Patients.Critical care explorations · 2025Article
- Role of artificial intelligence in pediatric intensive care: a survey of healthcare staff perspectives in Saudi Arabia.Frontiers in pediatrics · 2025Article
- Statistical process monitoring creates a hemodynamic trajectory map after pediatric cardiac surgery: A case study of the arterial switch operation.Bioengineering & translational medicine · 2024Article
- Development and Temporal Validation of a Machine Learning Model to Predict Clinical Deterioration.Hospital pediatrics · 2024Article
- Accurately assessing congenital heart disease using artificial intelligence.PeerJ. Computer science · 2024Article
- Risk of in-hospital Deterioration for Children with Single Ventricle Physiology.Pediatric cardiology · 2023Article
- Time series analysis of non-invasive hemodynamic monitoring data in neonates with hypoxic-ischemic encephalopathy.Frontiers in pediatrics · 2023Article
- Role of artificial intelligence in early detection of congenital heart diseases in neonates.Frontiers in digital health · 2023Review
- The Compensatory Reserve Index Responds to Acute Hemodynamic Changes in Patients with Congenital Heart Disease: A Proof of Concept Study.Pediatric cardiology · 2020Article
- Implementing Artificial Intelligence and Digital Health in Resource-Limited Settings? Top 10 Lessons We Learned in Congenital Heart Defects and Cardiology.Omics : a journal of integrative biology · 2020Review
- A deep learning model for real-time mortality prediction in critically ill children.Critical care (London, England) · 2019Observational
- A framework to characterize the performance of early warning index alarm systems for patient monitoring.MethodsX · 2019Article
- Artificial intelligence in pediatric cardiology and cardiac surgery: Irrational hype or paradigm shift?Annals of pediatric cardiologyArticle
- Artificial intelligence in pediatric cardiology: Where do we stand in 2024?Annals of pediatric cardiologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objectives of this review are (I) to describe the challenges associated with monitoring patients in the pediatric cardiac intensive care unit (PCICU) and (II) to discuss the use of innovative statistical and artificial intelligence (AI) software programs to attempt to predict significant clinical events. Patients cared for in the PCICU are clinically fragile and at risk for fatal decompensation. Current monitoring modalities are often ineffective, sometimes inaccurate, and fail to detect a deteriorating clinical status in a timely manner. Predictive models created by AI and machine learning may lead to earlier detection of patients at risk for clinical decompensation and thereby improve care for critically ill pediatric cardiac patients.
Indexed as
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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.