Solunum Yetmezliği ve Ventilatör Yönetiminde Yapay Zeka

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Referanslar

Parcha V, Kalra R, Bhatt S et al (2020) Trends and geographic variation in acute respiratory failure and ARDS mortality in the United States. Chest 159(4):1460–1472

Kempker JA, Abril MK, Chen Y et al (2020) The epidemiology of respiratory failure in the United States 2002–2017: a serial cross-sectional study. Crit Care Explor 2(6):e0128

Bellani G, Laffey JG, Pham T, Fan E, Brochard L, Esteban A, et al. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA. 2016;315(8):788–800.

Kangelaris KN, Ware LB, Wang CY, Janz DR, Zhuo H, Matthay MA, et al. Timing of intubation and clinical outcomes in adults with acute respiratory distress syndrome. Crit Care Med. 2016;44(1):120–9.

Mietto C, Pinciroli R, Patel N, Berra L. Ventilator associated pneumonia: evolving definitions and preventive strategies. Respir Care. 2013;58(6):990–1007.

Girard TD, Shintani AK, Jackson JC, Gordon SM, Pun BT, Henderson MS, et al. Risk factors for post-traumatic stress disorder symptoms following critical illness requiring mechanical ventilation: a prospective cohort study. Crit Care. 2007;11(1):R28.

Trillo-Alvarez C, Cartin-Ceba R, Kor DJ, Kojicic M, Kashyap R, Thakur S, et al. Acute lung injury prediction score: derivation and validation in a population-based sample. Eur Respir J. 2011;37(3):604–9.

Roca O, Messika J, Caralt B, Garcia-de-Acilu M, Sztrymf B, Ricard JD, et al. Predicting success of high-flow nasal cannula in pneumonia patients with hypoxemic respiratory failure: the utility of the ROX index. J Crit Care. 2016;35:200–5.

Chen D, Heunks L, Pan C, Xie J, Qiu H, Yang Y, et al. A novel index to predict the failure of High-Flow nasal cannula in patients with acute hypoxemic respiratory failure: A pilot study. Am J Respir Crit Care Med. 2022;206(7):910–3.

Roussos C, Koutsoukou A. Respiratory failure. Eur Respir J Suppl. 2003;47:3s–14. doi: 10.1183/09031936.03.00038503.

Serin SO, Karaoren G, Esquinas AM. Delayed admission to ICU in acute respiratory failure: critical time for critical conditions. Am J Emerg Med. 2017;35:1571–1572. doi: 10.1016/j.ajem.2017.04.026.

Gajic O, Dabbagh O, Park PK, Adesanya A, Chang SY, Hou P, et al. Early identification of patients at risk of acute lung injury: evaluation of lung injury prediction score in a multicenter cohort study. Am J Respir Crit Care Med. 2011;183:462–470. doi: 10.1164/rccm.201004-0549OC.

Ferguson ND, Frutos-Vivar F, Esteban A, Gordo F, Honrubia T, Peñuelas O, et al. Clinical risk conditions for acute lung injury in the intensive care unit and hospital ward: a prospective observational study. Crit Care. 2007;11:R96. doi: 10.1186/cc6113.

Liu V, Kipnis P, Rizk NW, Escobar GJ. Adverse outcomes associated with delayed intensive care unit transfers in an integrated healthcare system. J Hosp Med. 2012;7:224–230. doi: 10.1002/jhm.964.

Subbe CP, Slater A, Menon D, Gemmell L. Validation of physiological scoring systems in the accident and emergency department. Emerg Med J. 2006;23:841–845. doi: 10.1136/emj.2006.035816.

Yu S, Leung S, Heo M, Soto GJ, Shah RT, Gunda S, et al. Comparison of risk prediction scoring systems for ward patients: a retrospective nested case-control study. Crit Care. 2014;18:R132. doi: 10.1186/cc13947.

Dziadzko MA, Novotny PJ, Sloan J, Gajic O, Herasevich V, Mirhaji P, et al. Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital. Crit Care. 2018;22:286. doi: 10.1186/s13054-018-2194-7.

Shortliffe EH, Sepúlveda MJ. Clinical decision support in the era of artificial intelligence. JAMA. 2018;320:2199–2200. doi: 10.1001/jama.2018.17163.

Martín-González F, González-Robledo J, Sánchez-Hernández F, Moreno-García MN. Success/failure prediction of noninvasive mechanical ventilation in intensive care units. Using multiclassifiers and feature selection methods. Methods Inf Med. 2016;55:234–241. doi: 10.3414/ME14-01-0015.

Zeiberg D, Prahlad T, Nallamothu BK, Iwashyna TJ, Wiens J, Sjoding MW. Machine learning for patient risk stratification for acute respiratory distress syndrome. PLoS One. 2019;14:e0214465. doi: 10.1371/journal.pone.0214465.

Bolourani S, Brenner M, Wang P, McGinn T, Hirsch JS, Barnaby D, et al. A machine learning prediction model of respiratory failure within 48 hours of patient admission for COVID-19: model development and validation. J Med Internet Res. 2021;23:e24246. doi: 10.2196/24246.

Ferrari D, Milic J, Tonelli R, Ghinelli F, Meschiari M, Volpi S, et al. Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia-challenges, strengths, and opportunities in a global health emergency. PLoS One. 2020;15:e0239172. doi: 10.1371/journal.pone.0239172.

Bendavid I, Statlender L, Shvartser L, Teppler S, Azullay R, Sapir R, et al. A novel machine learning model to predict respiratory failure and invasive mechanical ventilation in critically ill patients suffering from COVID-19. Sci Rep. 2022;12:10573. doi: 10.1038/s41598-022-14758-x.

Kennedy HL. Heart rate variability--a potential, noninvasive prognostic index in the critically ill patient. Crit Care Med. 1998;26:213–214. doi: 10.1097/00003246-199802000-00010.

Yoon D, Jang JH, Choi BJ, Kim TY, Han CH. Discovering hidden information in biosignals from patients using artificial intelligence. Korean J Anesthesiol. 2020;73:275–284. doi: 10.4097/kja.19475.

Q. Dai, S. Wang, R. Liu, H. Wang, J. Zheng, K. Yu. Risk factors for outcomes of acute respiratory distress syndrome patients: a retrospective study. J Thorac Dis, 11 (3) (2019), pp. 673-685, doi: 10.21037/jtd.2019.02.84

K. Kambas, M.M. Markiewski, I.A. Pneumatikos, S.S. Rafail, V. Theodorou, D. Konstantonis, et al. C5a and TNF-alpha up-regulate the expression of tissue factor in intra-alveolar neutrophils of patients with the acute respiratory distress syndrome. J Immunol, 180 (11) (2008), pp. 7368-7375, doi: 10.4049/jimmunol.180.11.7368

B.T. Thompson, M. Moss. A new definition for the acute respiratory distress syndrome. Semin Respir Crit Care Med, 34 (4) (2013), pp. 441-447, doi: 10.1055/s-0033-1351162

B.A. McNicholas, G.M. Rooney, J.G. Laffey. Lessons to learn from epidemiologic studies in ARDS. Curr Opin Crit Care, 24 (1) (2018), pp. 41-48, doi: 10.1097/mcc.0000000000000473

M.I. García-Laorden, J.A. Lorente, C. Flores, A.S. Slutsky, J. Villar.Biomarkers for the acute respiratory distress syndrome: how to make the diagnosis more precise. Ann Transl Med, 5 (14) (2017), p. 283, doi: 10.21037/atm.2017.06.49

J. Villar, C. Martín-Rodríguez, A.M. Domínguez Berrot, L. Fernández, C. Ferrando, J.A. Soler, et al. A quantile analysis of plateau and driving pressures: effects on mortality in patients with acute respiratory distress syndrome receiving lung-protective ventilation. Crit Care Med, 45 (5) (2017), pp. 843-850, doi:10.1097/ccm.0000000000002330

E.D. Riviello, E. Buregeya, T. Twagirumugabe.Diagnosing acute respiratory distress syndrome in resource limited settings: the Kigali modification of the Berlin definition. Curr Opin Crit Care, 23 (1) (2017), pp. 18-23, doi: 10.1097/mcc.0000000000000372

P. Wohlrab, F. Kraft, V. Tretter, R. Ullrich, K. Markstaller, K.U. Klein. Recent advances in understanding acute respiratory distress syndrome. F1000Res, 7 (2018), doi: 10.12688/f1000research.11148.1

Z. Zhang. Prediction model for patients with acute respiratory distress syndrome: use of a genetic algorithm to develop a neural network model. PeerJ, 7 (2019), Article e7719, doi: 10.7717/peerj.7719

X.F. Ding, J.B. Li, H.Y. Liang, Z.Y. Wang, T.T. Jiao, Z. Liu, et al. Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study. J Transl Med, 17 (1) (2019), p. 326, doi: 10.1186/s12967-019-2075-0

Wunsch, H.; Wagner, J.; Herlim, M.; Chong, D.H.; Kramer, A.A.; Halpern, S.D. ICU Occupancy and Mechanical Ventilator Use in the United States. Crit. Care Med. 2013, 41, 2712–2719.

Cooper, L.M.; Linde-Zwirble, W.T. Medicare Intensive Care Unit Use: Analysis of Incidence, Cost, and Payment. Crit. Care Med. 2004, 32, 2247–2253.]

Marti, J.; Hall, P.; Hamilton, P.; Lamb, S.; McCabe, C.; Lall, R.; Darbyshire, J.; Young, D.; Hulme, C. One-Year Resource Utilisation, Costs and Quality of Life in Patients with Acute Respiratory Distress Syndrome (ARDS): Secondary Analysis of a Randomised Controlled Trial. J. Intensive Care 2016, 4, 56.

Boles, J.-M.; Bion, J.; Connors, A.; Herridge, M.; Marsh, B.; Melot, C.; Pearl, R.; Silverman, H.; Stanchina, M.; Vieillard-Baron, A.; et al. Weaning from Mechanical Ventilation. Eur. Respir. J. 2007, 29, 1033–1056.

Bigatello, L.M.; Stelfox, H.T.; Berra, L.; Schmidt, U.; Gettings, E.M. Outcome of Patients Undergoing Prolonged Mechanical Ventilation after Critical Illness. Crit. Care Med. 2007, 35, 2491–2497.

Esteban, A. Characteristics and Outcomes in Adult Patients Receiving Mechanical VentilationA 28-Day International Study. JAMA 2002, 287, 345.

Hughes, C.G.; McGrane, S.; Pandharipande, P.P. Sedation in the Intensive Care Setting. CPAA 2012, 2012, 53–63.

Wagner, D.P. Economics of Prolonged Mechanical Ventilation. Am. Rev. Respir. Dis. 1989, 140, S14–S18.

Tobin, M.J. Advances in Mechanical Ventilation. N. Engl. J. Med. 2001, 344, 1986–1996.

Gowardman, J.R.; Huntington, D.; Whiting, J. The Effect of Extubation Failure on Outcome in a Multidisciplinary Australian Intensive Care Unit. Crit. Care Resusc. 2006, 8, 328–333.

Krinsley, J.S.; Reddy, P.K.; Iqbal, A. What Is the Optimal Rate of Failed Extubation? Crit. Care 2012, 16, 111.

Liao, K.-M.; Ko, S.-C.; Liu, C.-F.; Cheng, K.-C.; Chen, C.-M.; Sung, M.-I.; Hsing, S.-C.; Chen, C.-J. Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers. Diagnostics 2022, 12, 975.

Hanson, C.W.; Marshall, B.E. Artificial Intelligence Applications in the Intensive Care Unit. Crit. Care Med. 2001, 29, 427–435.

Komorowski, M.; Celi, L.A.; Badawi, O.; Gordon, A.C.; Faisal, A.A. The Artificial Intelligence Clinician Learns Optimal Treatment Strategies for Sepsis in Intensive Care. Nat. Med. 2018, 24, 1716–1720

Liu, C.-F.; Hung, C.-M.; Ko, S.-C.; Cheng, K.-C.; Chao, C.-M.; Sung, M.-I.; Hsing, S.-C.; Wang, J.-J.; Chen, C.-J.; Lai, C.-C.; et al. An Artificial Intelligence System to Predict the Optimal Timing for Mechanical Ventilation Weaning for Intensive Care Unit Patients: A Two-Stage Prediction Approach. Front. Med. 2022, 9, 935366.

Gallifant, J.; Zhang, J.; Del Pilar Arias Lopez, M.; Zhu, T.; Camporota, L.; Celi, L.A.; Formenti, F. Artificial Intelligence for Mechanical Ventilation: Systematic Review of Design, Reporting Standards, and Bias. Br. J. Anaesth. 2022, 128, 343–351.

Stivi, T.; Padawer, D.; Dirini, N.; Nachshon, A.; Batzofin, B.M.; Ledot, S. Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome. J. Clin. Med. 2024, 13, 1505.

Misseri, G.; Piattoli, M.; Cuttone, G.; Gregoretti, C.; Bignami, E.G. Artificial Intelligence for Mechanical Ventilation: A Transformative Shift in Critical Care. Ther. Adv. Pulm. Crit. Care Med. 2024, 19, 29768675241298918.

Handel, C.; Frerichs, I.; Weiler, N.; Bergh, B. Prediction and Simulation of PEEP Setting Effects with Machine Learning Models. Med. Intensiv. Engl. Ed. 2024, 48, 191–199.

Baptistella, A.; Carvalho, D.; Dallacosta, F.; Nunes Filho, J.R. NexoVent: Artificial Intelligence Applied to the Management of Mechanical Ventilation. In Proceedings of the Respiratory Failure and Mechanical Ventilation, Berlin, Germany, 15–17 February 2024; European Respiratory Society: Lausanne, Switzerland, 2024; p. 57.

Gao, S.; Zhang, Z.; Brunelli, A.; Chen, C.; Chen, C.; Chen, G.; Chen, H.; Chen, J.-S.; Cassivi, S.; Chai, Y.; et al. The Society for Translational Medicine: Clinical Practice Guidelines for Mechanical Ventilation Management for Patients Undergoing Lobectomy. J. Thorac. Dis. 2017, 9, 3246–3254.

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