---
title: "Artificial intelligence in paediatric cardiac critical care: predictive analytics to continuous ph"
id: "pubmed-42845159"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42845159"
content_type: "clinical_feed_article"
specialty: "Critical Care"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42845159/"
doi: "10.1017/S1047951126124342"
published_at: "2026-10-08T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Artificial intelligence in paediatric cardiac critical care: predictive analytics to continuous ph
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42845159
- **Specialty:** [Critical Care](https://medichelpline.com/clinical-feed/critical-care.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42845159/)
- **DOI:** [10.1017/S1047951126124342](https://doi.org/10.1017%2FS1047951126124342)
- **Published At:** 2026-10-08T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- This narrative review summarizes applications of **artificial intelligence** in **paediatric cardiology** and the **cardiac intensive care unit (ICU)**, focusing on imaging, electrocardiography, predictive analytics, waveform intelligence, and outcomes after surgery. - The authors searched PubMed, MEDLINE, and Scopus for studies from 2018 through 2026 addressing AI, machine learning, or deep learning in paediatric cardiology, congenital heart disease (CHD), congenital heart surgery, and cardiac critical care. - Included studies covered echocardiography, cardiac MRI, electrocardiography, ICU monitoring, waveform analytics, and postoperative complication prediction, with data extracted on populations, AI methods, inputs, outcomes, validation, and limitations. - AI development is most advanced in cardiac imaging and electrocardiography, whereas paediatric- and complex CHD-specific evidence is less mature. - In the cardiac ICU, models using electronic health record variables, high-frequency physiologic data, and risk indices have been evaluated for events including cardiac arrest, low cardiac output physiology, hyperlactatemia, extubation failure, and vasoactive de-escalation. - Major gaps identified are lack of external validation, prospective deployment, calibration, and implementation that is safe for clinical workflows; measurable clinical benefit and prospective validation are required before autonomous use. - The review concludes AI has potential to improve diagnostic consistency, risk stratification, and early recognition of deterioration but emphasizes that prospective validation and demonstrable clinical benefit are needed prior to routine autonomous deployment.
## Clinical Analysis & Structured Key Points
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Online ahead of print. # Artificial intelligence in paediatric cardiac critical care: from predictive analytics to continuous physiologic intelligence [Fabio Savorgnan](https://pubmed.ncbi.nlm.nih.gov/?term=Savorgnan+F&cauthor_id=42845159)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42845159/#full-view-affiliation-1 "Pediatrics, Baylor College of Medicine, USA."), [Pranathi Pilla](https://pubmed.ncbi.nlm.nih.gov/?term=Pilla+P&cauthor_id=42845159)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42845159/#full-view-affiliation-2 "Baylor College of Medicine, USA."), [Rohit Loomba](https://pubmed.ncbi.nlm.nih.gov/?term=Loomba+R&cauthor_id=42845159)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42845159/#full-view-affiliation-3 "Northwestern University, USA."), [Saul Flores](https://pubmed.ncbi.nlm.nih.gov/?term=Flores+S&cauthor_id=42845159)[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42845159/#full-view-affiliation-4 "Pediatrics-Critical Care, Baylor College of Medicine, USA.") Affiliations Expand ### Affiliations * 1 Pediatrics, Baylor College of Medicine, USA. * 2 Baylor College of Medicine, USA. * 3 Northwestern University, USA. * 4 Pediatrics-Critical Care, Baylor College of Medicine, USA. * PMID: **42845159** * DOI: [ 10.1017/S1047951126124342 ](https://doi.org/10.1017/s1047951126124342) Item in Clipboard Review # Artificial intelligence in paediatric cardiac critical care: from predictive analytics to continuous physiologic intelligence Fabio Savorgnan et al. Cardiol Young. 2026. Show details Display options Display options Format Abstract PubMed PMID Cardiol Young Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Cardiol+Young%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Cardiol+Young%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42845159/) . 2026 Oct 8:1-7. doi: 10.1017/S1047951126124342. Online ahead of print. ### Authors [Fabio Savorgnan](https://pubmed.ncbi.nlm.nih.gov/?term=Savorgnan+F&cauthor_id=42845159)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42845159/#short-view-affiliation-1 "Pediatrics, Baylor College of Medicine, USA."), [Pranathi Pilla](https://pubmed.ncbi.nlm.nih.gov/?term=Pilla+P&cauthor_id=42845159)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42845159/#short-view-affiliation-2 "Baylor College of Medicine, USA."), [Rohit Loomba](https://pubmed.ncbi.nlm.nih.gov/?term=Loomba+R&cauthor_id=42845159)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42845159/#short-view-affiliation-3 "Northwestern University, USA."), [Saul Flores](https://pubmed.ncbi.nlm.nih.gov/?term=Flores+S&cauthor_id=42845159)[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42845159/#short-view-affiliation-4 "Pediatrics-Critical Care, Baylor College of Medicine, USA.") ### Affiliations * 1 Pediatrics, Baylor College of Medicine, USA. * 2 Baylor College of Medicine, USA. * 3 Northwestern University, USA. * 4 Pediatrics-Critical Care, Baylor College of Medicine, USA. * PMID: **42845159** * DOI: [ 10.1017/S1047951126124342 ](https://doi.org/10.1017/s1047951126124342) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID Linked Discoveries ## Abstract **Objective:** Artificial intelligence is increasingly applied in paediatric cardiology and cardiac intensive care, where CHD, postoperative physiologic instability, and continuous bedside monitoring generate complex datasets. This review summarises current applications of artificial intelligence in paediatric cardiology and the cardiac ICU, focusing on cardiac imaging, electrocardiography, predictive analytics, waveform intelligence, and postoperative outcome prediction. **Data sources:** A narrative review of PubMed, MEDLINE, and Scopus was performed for studies published from 2018 through 2026 evaluating artificial intelligence, machine learning, or deep learning in paediatric cardiology, CHD, congenital heart surgery, and cardiac critical care. **Study selection:** Studies addressing paediatric or CHD-focused applications in echocardiography, cardiac MRI, electrocardiography, cardiac ICU monitoring, waveform analytics, or postoperative complication prediction were included, together with relevant reviews. **Data extraction:** Information was extracted on study population, artificial intelligence method, clinical task, model inputs, outcomes, validation strategy, and major limitations. **Data synthesis:** Artificial intelligence is most developed in imaging and electrocardiography, while paediatric and complex CHD evidence remains less mature. In cardiac ICU, models using electronic health record variables, high-frequency physiologic data, and risk indices have been evaluated for cardiac arrest, low cardiac output physiology, hyperlactatemia, extubation failure, and vasoactive de-escalation. External validation, prospective deployment, calibration, and workflow-safe implementation remain major gaps. **Conclusions:** Artificial intelligence may improve diagnostic consistency, risk stratification, and early recognition of deterioration, but prospective validation and measurable clinical benefit are needed before autonomous use. **Keywords:** Artificial intelligence; CHD; cardiac intensive care; paediatric cardiology; predictive analytics; waveform analytics. 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