Development and validation of an interpretable prehospital return of spontaneous circulation (P-ROSC) score for patients with out-of-hospital cardiac arrest using machine learning: A retrospective study
Tài liệu tham khảo
Baldi, 2020, An Utstein-based model score to predict survival to hospital admission: the UB-ROSC score, Int J Cardiol, 308, 84, 10.1016/j.ijcard.2020.01.032
Berdowski, 2010, Global incidences of out-of-hospital cardiac arrest and survival rates: systematic review of 67 prospective studies, Resuscitation, 81, 1479, 10.1016/j.resuscitation.2010.08.006
Adrie, 2006, Predicting survival with good neurological recovery at hospital admission after successful resuscitation of out-of-hospital cardiac arrest: the OHCA score, Eur Heart J, 27, 2840, 10.1093/eurheartj/ehl335
Bisbal, 2014, Effectiveness of SAPS III to predict hospital mortality for post-cardiac arrest patients, Resuscitation, 85, 939, 10.1016/j.resuscitation.2014.03.302
Grasner, 2011, ROSC after cardiac arrest–the RACA score to predict outcome after out-of-hospital cardiac arrest, Eur Heart J, 32, 1649, 10.1093/eurheartj/ehr107
Aschauer, 2014, A prediction tool for initial out-of-hospital cardiac arrest survivors, Resuscitation, 85, 1225, 10.1016/j.resuscitation.2014.06.007
Myat, 2018, Out-of-hospital cardiac arrest: current concepts, Lancet, 391, 970, 10.1016/S0140-6736(18)30472-0
Carrick, 2020, Clinical predictive models of sudden cardiac arrest: a survey of the current science and analysis of model performances, J Am Heart Assoc, 9, 10.1161/JAHA.119.017625
Wnent, 2015, Difficult intubation and outcome after out-of-hospital cardiac arrest: a registry-based analysis, Scand J Trauma Resusc Emerg Med, 23, 43, 10.1186/s13049-015-0124-0
Kupari, 2017, External validation of the ROSC after cardiac arrest (RACA) score in a physician staffed emergency medical service system, Scand J Trauma Resusc Emerg Med, 25, 34, 10.1186/s13049-017-0380-2
Caputo, 2019, Validation of the return of spontaneous circulation after cardiac arrest (RACA) score in two different national territories, Resuscitation, 134, 62, 10.1016/j.resuscitation.2018.11.012
Liu, 2020, Validation of the ROSC after cardiac arrest (RACA) score in Pan-Asian out-of-hospital cardiac arrest patients, Resuscitation, 149, 53, 10.1016/j.resuscitation.2020.01.029
Neukamm, 2011, The impact of response time reliability on CPR incidence and resuscitation success: a benchmark study from the German Resuscitation Registry, Crit Care, 15, R282, 10.1186/cc10566
Schewe, 2015, Outcome of out-of-hospital cardiac arrest over a period of 15 years in comparison to the RACA score in a physician staffed urban emergency medical service in Germany, Resuscitation, 96, 232, 10.1016/j.resuscitation.2015.07.025
Xie, 2020, AutoScore: a machine learning-based automatic clinical score generator and its application to mortality prediction using electronic health records, JMIR Med Inform, 21798, 10.2196/21798
Xie, 2021, Development and assessment of an interpretable machine learning triage tool for estimating mortality after emergency admissions, JAMA Netw Open, 4, 10.1001/jamanetworkopen.2021.18467
Ong, 2015, Outcomes for out-of-hospital cardiac arrests across 7 countries in Asia: the Pan Asian resuscitation outcomes study (PAROS), Resuscitation, 96, 100, 10.1016/j.resuscitation.2015.07.026
Ong, 2011, Pan-Asian resuscitation outcomes study (PAROS): rationale, methodology, and implementation, Acad Emerg Med, 18, 890, 10.1111/j.1553-2712.2011.01132.x
Ong, 2013, Comparison of emergency medical services systems in the pan-Asian resuscitation outcomes study countries: report from a literature review and survey, Emerg Med Australas EMA, 25, 55, 10.1111/1742-6723.12032
Moons, 2015, Transparent Reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration, Ann Intern Med, 162, W1, 10.7326/M14-0698
Xie F, Ning Y, Yuan H, Saffari E, Chakraborty, Liu N, Package ‘AutoScore’: an interpretable machine learning-based automatic clinical score generator, R package version 0.2.0, 2021. Available from https://cran.r-project.org/package=AutoScore.
Verikas, 2011, Mining data with random forests: a survey and results of new tests, Pattern Recognit, 44, 330, 10.1016/j.patcog.2010.08.011
Tan, 2021, Prediction of breakthrough pain during labour neuraxial analgesia: comparison of machine learning and multivariable regression approaches, Int J Obstet Anesth, 45, 99, 10.1016/j.ijoa.2020.08.010
Fawcett, 2006, An introduction to ROC analysis, Pattern Recognit Lett, 27, 861, 10.1016/j.patrec.2005.10.010
Yoshida K and Bartel A. tableone: Create 'Table 1' to Describe Baseline Characteristics with or without Propensity Score Weights. R package version 0.13.0. 2022. https://CRAN.R-project.org/package=tableone.
Robin, 2011, pROC: an open-source package for R and S+ to analyze and compare ROC curves, BMC Bioinform, 12, 77, 10.1186/1471-2105-12-77
Tanaka, 2018, Modifiable factors associated with survival after out-of-hospital cardiac arrest in the Pan-Asian resuscitation outcomes study, Ann Emerg Med, 71, 608, 10.1016/j.annemergmed.2017.07.484
Jacobs, 2011, Effect of adrenaline on survival in out-of-hospital cardiac arrest: a randomised double-blind placebo-controlled trial, Resuscitation, 82, 1138, 10.1016/j.resuscitation.2011.06.029
Hajian-Tilaki, 2014, Sample size estimation in diagnostic test studies of biomedical informatics, J Biomed Inform, 48, 193, 10.1016/j.jbi.2014.02.013
Pfitzner, 2021, Federated learning in a medical context: a systematic literature review, ACM Trans Internet Technol, 21, 50, 10.1145/3412357
Warnat-Herresthal, 2021, Swarm learning for decentralized and confidential clinical machine learning, Nature, 594, 265, 10.1038/s41586-021-03583-3
Lam, 2017, Simulation-based decision support framework for dynamic ambulance redeployment in Singapore, Int J Med Inform, 106, 37, 10.1016/j.ijmedinf.2017.06.005
Rea, 2021, Out of hospital cardiac arrest: past, present, and future, Resuscitation, 165, 101, 10.1016/j.resuscitation.2021.06.010