Training for Pediatric Sepsis—A Medical Education Perspective and Potential Role of Artificial Intelligence
Abstract
1. Introduction
2. Methods
- Lectures;
- Seminars;
- E-learning;
- Hybrid learning;
- Technology-enhanced learning (gamification, virtual reality (VR) and augmented reality (AR) simulation;
- Artificial intelligence;
- Barriers to implementation of artificial intelligence in sepsis education.Pediatric sepsis definitions vary across different studies:
3. Results
3.1. Lectures and Seminars
3.2. Lectures
3.3. Seminars
3.4. E-Learning and Hybrid Learning
3.5. E-Learning
3.6. Hybrid Learning
3.7. Technology-Enhanced Learning and AI
3.8. Gamification
3.9. Simulation
3.10. Virtual and Augmented Reality Simulation
3.11. Barriers to the Implementation of AI in Sepsis Education
3.12. Limitations
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
| ED | Emergency Department |
| AI | Artificial Intelligence |
| IV | Intravenous |
| CI | Confidence Interval |
| PALS | Pediatric Advanced Life Support |
| QI | Quality Improvement |
| OR | Odds Ratio |
| LMIC | Low- and Middle-Income Countries |
| PEAK | Pediatric Education and Advocacy Kit |
| EMSC | Emergency Medical Services for Children |
| SIRS | Systemic Inflammatory Response Syndrome |
| SOAR | Strengths, Opportunities, Aspirations, Results |
| VR/AR | Virtual Reality/Augmented Reality |
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| 1. Recognize possible septic shock signs: tachycardia, abnormal capillary refill, abnormal mental status, systolic hypotension, widened pulse pressure, elevated lactate |
| 2. Initiate early IV access, with large diameter and short length preferred |
| 3. Initiate IV fluid resuscitation with the appropriate agent and volume |
| 4. Deliver IV fluids rapidly using rapid delivery system and specifically not with an IV pump |
| 5. Deliver appropriate antibiotics within 180 min of sepsis recognition |
| 6. Recognize appropriate responses to fluid therapy by normalization of or improvement in heart rate, blood pressure, capillary refill time, lactate, and/or urine output |
| 7. Continue fluid resuscitation and prepare for potential vasoactive agent requirement when septic shock persists despite appropriate initial rapid fluid resuscitation |
| 8. Recognize fluid overload during resuscitation and appropriately choose adjunctive therapy |
| 9. Initiate vasoactive infusion through any access available when hypotension is present despite appropriate rapid fluid resuscitation |
| 10. Obtain blood culture prior to giving antibiotics |
| 11. Recognize and correct hypoglycemia |
| 12. Appropriate pressor choice |
| 13. Consider corticosteroids in catecholamine-resistant shock. Treat and test appropriately |
| 14. Recognize and correct hypocalcemia |
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Karageorgos, S.; Hibberd, O.; Ren, D.; Hornsby, Y.; Roland, D.; Koutroulis, I. Training for Pediatric Sepsis—A Medical Education Perspective and Potential Role of Artificial Intelligence. Children 2025, 12, 1542. https://doi.org/10.3390/children12111542
Karageorgos S, Hibberd O, Ren D, Hornsby Y, Roland D, Koutroulis I. Training for Pediatric Sepsis—A Medical Education Perspective and Potential Role of Artificial Intelligence. Children. 2025; 12(11):1542. https://doi.org/10.3390/children12111542
Chicago/Turabian StyleKarageorgos, Spyridon, Owen Hibberd, Dennis Ren, Yasmin Hornsby, Damian Roland, and Ioannis Koutroulis. 2025. "Training for Pediatric Sepsis—A Medical Education Perspective and Potential Role of Artificial Intelligence" Children 12, no. 11: 1542. https://doi.org/10.3390/children12111542
APA StyleKarageorgos, S., Hibberd, O., Ren, D., Hornsby, Y., Roland, D., & Koutroulis, I. (2025). Training for Pediatric Sepsis—A Medical Education Perspective and Potential Role of Artificial Intelligence. Children, 12(11), 1542. https://doi.org/10.3390/children12111542

