Machine learning in the prediction and detection of new-onset atrial fibrillation in ICU: a systematic review

Bosch NA, Cimini J, Walkey AJ. Atrial fibrillation in the ICU. Chest. 2018;154(6):1424–34.

Article  PubMed  PubMed Central  Google Scholar 

Wetterslev M, Hylander Møller M, Granholm A, Hassager C, Haase N, Lange T, Myatra SN, Hästbacka J, Arabi YM, Shen J, Cronhjort M, Lindqvist E, Aneman A, Young PJ, Szczeklik W, Siegemund M, Koster T, Aslam TN, Bestle MH, Girkov MS, Kalvit K, Mohanty R, Mascarenhas J, Pattnaik M, Vergis S, Haranath SP, Shah M, Joshi Z, Wilkman E, Reinikainen M, Lehto P, Jalkanen V, Pulkkinen A, An Y, Wang G, Huang L, Huang B, Liu W, Gao H, Dou L, Li S, Yang W, Tegnell E, Knight A, Czuczwar M, Czarnik T, Perner A, AFIB-ICU Collaborators. Atrial fibrillation (AFIB) in the ICU: incidence, risk factors, and outcomes: the international AFIB-ICU cohort study. Crit Care Med. 2023;51(9):1124–37.

Article  CAS  PubMed  Google Scholar 

Santhanakrishnan R, Wang N, Larson MG, Magnani JW, McManus DD, Lubitz SA, Ellinor PT, Cheng S, Vasan RS, Lee DS, Wang TJ, Levy D, Benjamin EJ, Ho JE. Atrial fibrillation begets heart failure and vice versa: temporal associations and differences in preserved versus reduced ejection fraction. Circulation. 2016;133(5):484–92.

Article  PubMed  PubMed Central  Google Scholar 

Benjamin EJ, Wolf PA, D’Agostino RB, Silbershatz H, Kannel WB, Levy D. Impact of atrial fibrillation on the risk of death: the Framingham Heart Study. Circulation. 1998;98(10):946–52.

Article  CAS  PubMed  Google Scholar 

Klein Klouwenberg PM, Frencken JF, Kuipers S, Ong DS, Peelen LM, van Vught LA, Schultz MJ, van der Poll T, Bonten MJ, Cremer OL, MARS Consortium. Incidence, predictors, and outcomes of new-onset atrial fibrillation in critically ill patients with sepsis. A cohort study. Am J Respir Crit Care Med. 2017;195(2):205–11.

Article  PubMed  Google Scholar 

Moss TJ, Calland JF, Enfield KB, Gomez-Manjarres DC, Ruminski C, DiMarco JP, Lake DE, Moorman JR. New-onset atrial fibrillation in the critically ill. Crit Care Med. 2017;45(5):790–7.

Article  PubMed  PubMed Central  Google Scholar 

Jolley SE, Bunnell AE, Hough CL. ICU-acquired weakness. Chest. 2016;150(5):1129–40.

Article  PubMed  PubMed Central  Google Scholar 

Mariscalco G, Engström KG. Atrial fibrillation after cardiac surgery: risk factors and their temporal relationship in prophylactic drug strategy decision. Int J Cardiol. 2008;129(3):354–62.

Article  PubMed  Google Scholar 

Amar D, Shi W, Hogue CW Jr, Zhang H, Passman RS, Thomas B, Bach PB, Damiano R, Thaler HT. Clinical prediction rule for atrial fibrillation after coronary artery bypass grafting. J Am Coll Cardiol. 2004;44(6):1248–53.

Article  PubMed  Google Scholar 

Mathew JP, Fontes ML, Tudor IC, Ramsay J, Duke P, Mazer CD, Barash PG, Hsu PH, Mangano DT, Investigators of the Ischemia Research and Education Foundation, Multicenter Study of Perioperative Ischemia Research Group. Mangano DT A multicenter risk index for atrial fibrillation after cardiac surgery. JAMA. 2004;291(14):1720–9.

Article  CAS  PubMed  Google Scholar 

Thorén E, Hellgren L, Jidéus L, Ståhle E. Prediction of postoperative atrial fibrillation in a large coronary artery bypass grafting cohort. Interact Cardiovasc Thorac Surg. 2012;14(5):588–93.

Article  PubMed  PubMed Central  Google Scholar 

Mariscalco G, Biancari F, Zanobini M, Cottini M, Piffaretti G, Saccocci M, Banach M, Beghi C, Angelini GD. Bedside tool for predicting the risk of postoperative atrial fibrillation after cardiac surgery: the POAF score. J Am Heart Assoc. 2014;3(2): e000752.

Article  PubMed  PubMed Central  Google Scholar 

Viderman D, Abdildin YG, Batkuldinova K, Badenes R, Bilotta F. Artificial intelligence in resuscitation: a scoping review. J Clin Med. 2023;12(6):2254. https://doi.org/10.3390/jcm12062254. (PMID:36983255;PMCID:PMC10054374).

Article  PubMed  PubMed Central  Google Scholar 

Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med. 2023;388(13):1201–8. https://doi.org/10.1056/NEJMra2302038. (PMID: 36988595).

Article  CAS  PubMed  Google Scholar 

Bellini V, Valente M, Bertorelli G, Pifferi B, Craca M, Mordonini M, Lombardo G, Bottani E, Del Rio P, Bignami E. Machine learning in perioperative medicine: a systematic review. J Anesth Analg Crit Care. 2022;2(1):2. https://doi.org/10.1186/s44158-022-00033-y. (PMID:37386544;PMCID:PMC8761048).

Article  PubMed  PubMed Central  Google Scholar 

Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–58.

Article  PubMed  Google Scholar 

Harmon DM, Sehrawat O, Maanja M, Wight J, Noseworthy PA. Artificial intelligence for the detection and treatment of atrial fibrillation. Arrhythm Electrophysiol Rev. 2023;12: e12.

Article  PubMed  PubMed Central  Google Scholar 

Jentzer JC, Kashou AH, Murphree DH. Clinical applications of artificial intelligence and machine learning in the modern cardiac intensive care unit. Intell Based Med. 2023;7:100089.

Article  Google Scholar 

Karri R, Kawai A, Thong YJ, Ramson DM, Perry LA, Segal R, Smith JA, Penny-Dimri JC. Machine learning outperforms existing clinical scoring tools in the prediction of postoperative atrial fibrillation during intensive care unit admission after cardiac surgery. Heart Lung Circ. 2021;30(12):1929–37.

Article  PubMed  Google Scholar 

Bashar SK, Han D, Zieneddin F, Ding E, Fitzgibbons TP, Walkey AJ, McManus DD, Javidi B, Chon KH. Novel density poincaré plot based machine learning method to detect atrial fibrillation from premature atrial/ventricular contractions. IEEE Trans Biomed Eng. 2021;68(2):448–60.

Article  PubMed  PubMed Central  Google Scholar 

Bashar SK, Hossain MB, Ding E, Walkey AJ, McManus DD, Chon KH. Atrial fibrillation detection during sepsis: study on MIMIC III ICU data. IEEE J Biomed Health Inform. 2020;24(11):3124–35.

Article  PubMed  PubMed Central  Google Scholar 

Verhaeghe J, De Corte T, Sauer CM, Hendriks T, Thijssens OWM, Ongenae F, Elbers P, De Waele J, Van Hoecke S. Generalizable calibrated machine learning models for real-time atrial fibrillation risk prediction in ICU patients. Int J Med Inform. 2023;175:105086.

Article  PubMed  Google Scholar 

Chen B, Javadi G, Hamilton A, Sibley S, Laird P, Abolmaesumi P, Maslove D, Mousavi P. Quantifying deep neural network uncertainty for atrial fibrillation detection with limited labels. Sci Rep. 2022;12(1):20140.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Gue Y, Correa E, Thompson JLP, Homma S, Qian M, Lip GYH. Machine learning predicting atrial fibrillation as an adverse event in the Warfarin and aspirin in reduced cardiac ejection fraction (WARCEF) Trial. Am J Med. 2023;21:S0002-9343.

Google Scholar 

Gong KD, Lu R, Bergamaschi TS, Sanyal A, Guo J, Kim HB, Nguyen HT, Greenstein JL, Winslow RL, Stevens RD. Predicting intensive care delirium with machine learning: model development and external validation. Anesthesiology. 2023;138(3):299–311.

Article  CAS  PubMed  Google Scholar 

N, Abdul Murad NA, Chin SF, Jaafar R, Abdullah N. Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review. Cardiovasc Diabetol. 2023, 22(1):13.

Fischer MA, Mahajan A, Cabaj M, Kimball TH, Morselli M, Soehalim E, Chapski DJ, Montoya D, Farrell CP, Scovotti J, Bueno CT, Mimila NA, Shemin RJ, Elashoff D, Pellegrini M, Monte E, Vondriska TM. DNA methylation-based prediction of post-operative atrial fibrillation. Front Cardiovasc Med. 2022;9:837725.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chequel M, Ollitrault P, Saloux E, Parienti JJ, Fischer MO, Desgué J, Allouche S, Milliez P, Alexandre J. Preoperative plasma aldosterone levels and postoperative atrial fibrillation occurrence following cardiac surgery: a review of literature and design of the ALDO-POAF study (ALDOsterone for prediction of post-operative atrial fibrillation). Curr Clin Pharmacol. 2016;11(3):150–8.

Article  CAS  PubMed  Google Scholar 

Zhou Y, Wu Q, Ni G, Hong Y, Xiao S, Liu C, Yu Z. Immune-associated pivotal biomarkers identification and competing endogenous RNA network construction in post-operative atrial fibrillation by comprehensive bioinformatics and machine learning strategies. Front Immunol. 2022;13:974935.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, Clarke M, Devereaux PJ, Kleijnen J, Moher D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339:b2700. https://doi.org/10.1136/bmj.b2700. (PMID:19622552;PMCID:PMC2714672).

Article  PubMed  PubMed Central  Google Scholar 

留言 (0)

沒有登入
gif