Yapay Zekâ Eşliğinde Kardiak Arreste Yaklaşım Sağ Kalım Oranını Artırır mı? Nörolojik Sonuçlar İyileşir mi?

Yıl 2024, Cilt: 2 Sayı: 2, 88 - 91, 30.06.2024

Tayfun Karatas Fatma Tortum

https://doi.org/10.61845/agrimedical.1499441

Öz

Hastane içi ve hastane dışı acil uygulama gerektiren durumlarda Yapay zekâ (AI) kullanımına olan ilgi son yıllarda artış göstermiştir. Bu derlemede, kardiyak arrest yönetimi için hastane içi ve hastane dışı yapay zekâ ile yapılmış güncel çalışmaların bir özeti sunulmaktadır. Kardiyak arrest kalpteki aktivitenin hayatı tehdit eden bir şekilde durması olarak bilinir ve erken teşhis ve müdahale oldukça önemlidir. Bu nedenle, AI teknolojileri risk altındaki hastaların daha öncesinde belirlenmesine imkân sağlamasından dolayı günümüzde daha fazla kullanılmaktadır.

Anahtar Kelimeler

Yapay zekâ, kardiyak arrest, tahmin

Does Artificial Intelligence Guided Approach to Cardiac Arrest Increase Survival Rate? Will Neurological Outcomes Improve?

Öz

Interest in the use of Artificial intelligence (AI) in in-hospital and out-of-hospital emergency situations has increased in recent years. In this review, we present a summary of recent studies using in-hospital and out-of-hospital AI for cardiac arrest management. Cardiac arrest is known as a life-threatening cessation of cardiac activity, and early diagnosis and intervention are crucial. For this reason, AI technologies are being used more and more nowadays as they allow for earlier identification of patients at risk.

Anahtar Kelimeler

artificial intelligence, cardiac arrest, prediction

Kaynakça

• Alamgir A, Mousa O, Shah Z. Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review. JMIR Med Inform. 2021; 17;9(12):e30798. doi: 10.2196/30798.
• Yan S, Gan Y, Jiang N, Wang R, Chen Y, Luo Z, Zong Q, Chen S, Lv C. The global survival rate among adult out-of-hospital cardiac arrest patients who received cardiopulmonary resuscitation: a systematic review and meta-analysis. Crit Care. 2020; 22;24(1):61. doi: 10.1186/s13054-020- 2773-2.
• Berdowski J, Berg RA, Tijssen JG, Koster RW. Global incidences of out-of-hospital cardiac arrest and survival rates: systematic review of 67 prospective studies. Resuscitation. 2010; 81(11):1479– 87. doi: 10.1016/j.resuscitation.2010.08.006.S0300-9572(10)00432-6
• Chan PS, Krein SL, Tang F, Iwashyna TJ, Harrod M, Kennedy M, Lehrich J, Kronick S, Nallamothu BK, American Heart Association's Get With the Guidelines–Resuscitation Investigators Resuscitation practices associated with survival after in-hospital cardiac arrest: a nationwide survey. JAMA Cardiol. 2016; 1(2):189–97. doi: 10.1001/jamacardio.2016.0073
• Blomberg SN, Folke F, Ersbøll AK, Christensen HC, Torp-Pedersen C, Sayre MR, Counts CR, Lippert FK. Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Resuscitation. 2019;138:322-329. doi: 10.1016/j.resuscitation.2019.01.015.
• Byrsell F, Claesson A, Ringh M, Svensson L, Jonsson M, Nordberg P, Forsberg S, Hollenberg J, Nord A. Machine learning can support dispatchers to better and faster recognize out-of-hospital cardiac arrest during emergency calls: A retrospective study. Resuscitation. 2021; 162:218-226. doi: 10.1016/j.resuscitation
• Lee YJ, Cho KJ, Kwon O, Park H, Lee Y, Kwon JM, Park J, Kim JS, Lee MJ, Kim AJ, Ko RE, Jeon K, Jo YH. A multicentre validation study of the deep learning-based early warning score for predicting in-hospital cardiac arrest in patients admitted to general wards. Resuscitation. 2021; 22;163:78- 85. doi: 10.1016/j.resuscitation.2021.04.013.
• Kwon JM, Lee Y, Lee Y, Lee S, Park J. An Algorithm Based on Deep Learning for Predicting In- Hospital Cardiac Arrest. J Am Heart Assoc. 2018; 7(13):e008678. doi: 10.1161/JAHA.118.008678.
• Park SJ, Cho KJ, Kwon O, Park H, Lee Y, Shim WH, Park CR, Jhang WK. Development and validation of a deep-learning-based pediatric early warning system: A single-center study. Biomed J. 2022; 45(1):155-168. doi: 10.1016/j.bj.2021.01.003.
• Jang DH, Kim J, Jo YH, Lee JH, Hwang JE, Park SM, Lee DK, Park I, Kim D, Chang H. Developing neural network models for early detection of cardiac arrest in emergency department. Am J Emerg Med. 2020; 38(1):43-49. doi: 10.1016/j.ajem.2019.04.006.
• Haro Alonso D, Wernick MN, Yang Y, Germano G, Berman DS, Slomka P. Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning. J Nucl Cardiol. 2019; 26(5):1746-1754. doi: 10.1007/s12350-018-1250-7.
• Lee SY, Song KJ, Shin SD, Hong KJ, Kim TH. Comparison of the effects of audio-instructed and video-instructed dispatcher-assisted cardiopulmonary resuscitation on resuscitation outcomes after out-of-hospital cardiac arrest. Resuscitation. 2020; 1(147):12-20. doi: 10.1016/j. resuscitation.2019.12.004.
• Otero-Agra M, Jorge-Soto C, Cosido-Cobos ÓJ, Blanco-Prieto J, Alfaya-Fernández C, García- Ordóñez E, Barcala-Furelos R. Can a voice assistant help bystanders save lives? A feasibility pilot study chatbot in beta version to assist OHCA bystanders. Am J Emerg Med. 2022; 61:169-174. doi: 10.1016/j.ajem.2022.09.013.
• Chin KC, Hsieh TC, Chiang WC, Chien YC, Sun JT, Lin HY, Hsieh MJ, Yang CW, Chen AY, Ma MH. Early recognition of a caller's emotion in out-of-hospital cardiac arrest dispatching: An artificial intelligence approach. Resuscitation. 2021; 167:144-150. doi: 10.1016/j.resuscitation.2021.08.032. 15. Isasi I, Irusta U, Aramendi E, Eftestøl T, Kramer-Johansen J, Wik L. Rhythm Analysis during Cardiopulmonary Resuscitation Using Convolutional Neural Networks. Entropy (Basel). 2020; 22(6):595. doi: 10.3390/e22060595.
• Jekova I, Krasteva V. Optimization of End-to-End Convolutional Neural Networks for Analysis of Out-of-Hospital Cardiac Arrest Rhythms during Cardiopulmonary Resuscitation. Sensors (Basel). 2021; 21(12): 4105. doi: 10.3390/s21124105.
• Liu X, Liu T, Zhang Z, Kuo PC, Xu H, Yang Z, Lan K, Li P, Ouyang Z, Ng YL, Yan W, Li D. TOP-Net Prediction Model Using Bidirectional Long Short-term Memory and Medical-Grade Wearable Multisensor System for Tachycardia Onset: Algorithm Development Study. JMIR Med Inform. 2021; 9(4):e18803. doi: 10.2196/18803.
• Martinez-Alanis M, Bojorges-Valdez E, Wessel N, Lerma C. Prediction of Sudden Cardiac Death Risk with a Support Vector Machine Based on Heart Rate Variability and Heartprint Indices. Sensors (Basel). 2020; 20(19):5483. doi: 10.3390/s20195483.
• Thannhauser J, Nas J, Rebergen DJ, Westra SW, Smeets JLRM, Van Royen N, Bonnes JL, Brouwer MA. Computerized Analysis of the Ventricular Fibrillation Waveform Allows Identification of Myocardial Infarction: A Proof-of-Concept Study for Smart Defibrillator Applications in Cardiac Arrest. J Am Heart Assoc. 2020; 9(19):e016727. doi: 10.1161/JAHA.120.016727.
• Scquizzato T, Burkart R, Greif R, Monsieurs KG, Ristagno G, Scapigliati A, Semeraro F. Mobile phone systems to alert citizens as first responders and to locate automated external defibrillators: A European survey. Resuscitation. 2020; 151:39-42. doi: 10.1016/j.resuscitation.2020.03.009.
• Claesson A, Bäckman A, Ringh M, Svensson L, Nordberg P, Djärv T, Hollenberg J. Time to Delivery of an Automated External Defibrillator Using a Drone for Simulated Out-of-Hospital Cardiac Arrests vs Emergency Medical Services. JAMA. 2017; 317(22):2332-2334. doi: 10.1001/ jama.2017.3957.
• Chu J, Leung KHB, Snobelen P, Nevils G, Drennan IR, Cheskes S, Chan TCY. Machine learningbased dispatch of drone-delivered defibrillators for out-of-hospital cardiac arrest. Resuscitation. 2021; 162:120-127. doi: 10.1016/j.resuscitation.2021.02.028.
• Kajino K, Daya MR, Onoe A, Nakamura F, Nakajima M, Sakuramoto K, Ong MEH, Kuwagata Y. Development and validation of a prehospital termination of resuscitation (TOR) rule for out of hospital cardiac arrest (OHCA) cases using general purpose artificial intelligence (AI). Resuscitation. 2024;197:110165. doi: 10.1016/j.resuscitation.2024.110165.
• Amacher SA, Arpagaus A, Sahmer C, Becker C, Gross S, Urben T, Tisljar K, Sutter R, Marsch S, Hunziker S. Prediction of outcomes after cardiac arrest by a generative artificial intelligence model. Resusc Plus. 2024; 22;18:100587. doi: 10.1016/j.resplu.2024.100587.
• Heo JH, Kim T, Shin J, Suh GJ, Kim J, Jung YS, Park SM, Kim S; For SNU CARE investigators. Prediction of Neurological Outcomes in Out-of-hospital Cardiac Arrest Survivors Immediately after Return of Spontaneous Circulation: Ensemble Technique with Four Machine Learning Models. J Korean Med Sci. 2021; 36(28):e187. doi: 10.3346/jkms.2021.36.e187.