|
|
|
|
|
Received: 10 December 2023
|
|
|
|
|
|
| [1] |
袁 维, 张润峰. 心脏骤停动物模型研究进展[J]. 四川医学, 2020, 41(11): 1200-1203.
|
| [1] |
袁 维, 张润峰. 心脏骤停动物模型研究进展[J]. 四川医学, 2020, 41(11): 1200-1203.
|
| [2] |
Dilibero J, Misto K. Outcomes of in-hospital cardiac arrest: a review of the evidence[J]. Crit Care Nurs Clin North Am, 2021, 33(3):343-356.
|
| [3] |
Holmberg M J, Ross C E, Fitzmaurice G M, et al. Annual incidence of adult and pediatric in-hospital cardiac arrest in The United States[J]. Circ Cardiovasc Qual Outcomes, 2019, 12(7):e005580.
|
| [2] |
Dilibero J, Misto K. Outcomes of in-hospital cardiac arrest: a review of the evidence[J]. Crit Care Nurs Clin North Am, 2021, 33(3):343-356.
|
| [4] |
Xie X, Zheng J, Zheng W, et al. Efforts to improve survival outcomes of out-of-hospital cardiac arrest in china: basic-ohca[J]. Circ Cardiovasc Qual Outcomes, 2023, 16(2): e008856.
|
| [3] |
Holmberg M J, Ross C E, Fitzmaurice G M, et al. Annual incidence of adult and pediatric in-hospital cardiac arrest in The United States[J]. Circ Cardiovasc Qual Outcomes, 2019, 12(7):e005580.
|
| [5] |
Deo R C. Machine learning in medicine[J]. Circulation, 2015, 132(20):1920-1930.
|
| [4] |
Xie X, Zheng J, Zheng W, et al. Efforts to improve survival outcomes of out-of-hospital cardiac arrest in china: basic-ohca[J]. Circ Cardiovasc Qual Outcomes, 2023, 16(2): e008856.
|
| [6] |
Handelman G S, Kok H K, Chandra R V, et al. eDoctor: machine learning and the future of medicine[J]. J Intern Med, 2018, 284(6): 603-619.
|
| [5] |
Deo R C. Machine learning in medicine[J]. Circulation, 2015, 132(20):1920-1930.
|
| [7] |
Lee Y J, Cho K J, Kwon O, et al. A multicentre validation study of the deep learning-based early warning score for predicting in-hospital cardiac arrest in patients admitted to general wards[J]. Resuscitation, 2021, 163: 78-85
|
| [6] |
Handelman G S, Kok H K, Chandra R V, et al. eDoctor: machine learning and the future of medicine[J]. J Intern Med, 2018, 284(6): 603-619.
|
| [8] |
Mackenzie M J, Hagel C, Lin Y, et al. The reliability of the resuscitation assessment tool (rat) in assessing emergency medicine resident competence in pediatric resuscitation scenarios: a prospective observational pilot study[J]. Cureus, 2023, 15(3): e35869.
|
| [7] |
Lee Y J, Cho K J, Kwon O, et al. A multicentre validation study of the deep learning-based early warning score for predicting in-hospital cardiac arrest in patients admitted to general wards[J]. Resuscitation, 2021, 163: 78-85
|
| [9] |
Sun J T, Chang C C, Lu T C, et al. External validation of a triage tool for predicting cardiac arrest in the emergency department[J]. Sci Rep, 2022, 12(1):8779.
|
| [8] |
Mackenzie M J, Hagel C, Lin Y, et al. The reliability of the resuscitation assessment tool (rat) in assessing emergency medicine resident competence in pediatric resuscitation scenarios: a prospective observational pilot study[J]. Cureus, 2023, 15(3): e35869.
|
| [10] |
Høybye M, Stankovic N, Holmberg M, et al. In-hospital vs. out-of-hospital cardiac arrest: patient characteristics and survival[J]. Resuscitation. 2021, 158: 157-165.
|
| [9] |
Sun J T, Chang C C, Lu T C, et al. External validation of a triage tool for predicting cardiac arrest in the emergency department[J]. Sci Rep, 2022, 12(1):8779.
|
| [11] |
Moosajee U S, Saleem S G, Iftikhar S, et al. Outcomes following cardiopulmonary resuscitation in an emergency department of a low-and middle-income country[J]. Int J Emerg Med, 2018, 11(1): 40.
|
| [10] |
Høybye M, Stankovic N, Holmberg M, et al. In-hospital vs. out-of-hospital cardiac arrest: patient characteristics and survival[J]. Resuscitation. 2021, 158: 157-165.
|
| [12] |
Ruangsomboon O, Surabenjawongse U, Jantataeme P, et al. Association between cardiopulmonary resuscitation audit results with in-situ simulation and in-hospital cardiac arrest outcomes and key performance indicators[J]. BMC Cardiovasc Disord, 2023, 23(1): 299.
|
| [11] |
Moosajee U S, Saleem S G, Iftikhar S, et al. Outcomes following cardiopulmonary resuscitation in an emergency department of a low-and middle-income country[J]. Int J Emerg Med, 2018, 11(1): 40.
|
| [13] |
Chae M, Han S, Gil H, et al. Prediction of In-hospital cardiac arrest using shallow and deep learning[J]. Diagnostics (Basel), 2021, 11(7): 1255.
|
| [12] |
Ruangsomboon O, Surabenjawongse U, Jantataeme P, et al. Association between cardiopulmonary resuscitation audit results with in-situ simulation and in-hospital cardiac arrest outcomes and key performance indicators[J]. BMC Cardiovasc Disord, 2023, 23(1): 299.
|
| [14] |
Choi A, Choi S Y, Chung K, et al. Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department[J]. Sci Rep, 2023, 13(1): 8561.
|
| [13] |
Chae M, Han S, Gil H, et al. Prediction of In-hospital cardiac arrest using shallow and deep learning[J]. Diagnostics (Basel), 2021, 11(7): 1255.
|
| [15] |
Jones N W, Song S L, Thomasian N, et al. Behavioral health decision Support systems and user interface design in the emergency department[J]. Appl Clin Inform, 2023, 14(4):705-713.
|
| [14] |
Choi A, Choi S Y, Chung K, et al. Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department[J]. Sci Rep, 2023, 13(1): 8561.
|
| [16] |
Ong M E, Lee Ng C H, Goh K, et al. Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score[J]. Crit Care, 2012, 16(3): R108.
|
| [15] |
Jones N W, Song S L, Thomasian N, et al. Behavioral health decision Support systems and user interface design in the emergency department[J]. Appl Clin Inform, 2023, 14(4):705-713.
|
| [17] |
Lu T C, Wang C H, Chou F Y, et al. Machine learning to predict in-hospital cardiac arrest from patients presenting to the emergency department[J]. Intern Emerg Med, 2023, 18(2): 595-605.
|
| [16] |
Ong M E, Lee Ng C H, Goh K, et al. Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score[J]. Crit Care, 2012, 16(3): R108.
|
| [18] |
Kim J H, Choi A, Kim M J, et al. Development of a machine-learning algorithm to predict in-hospital cardiac arrest for emergency department patients using a nationwide database[J]. Sci Rep, 2022, 12(1):21797.
|
| [17] |
Lu T C, Wang C H, Chou F Y, et al. Machine learning to predict in-hospital cardiac arrest from patients presenting to the emergency department[J]. Intern Emerg Med, 2023, 18(2): 595-605.
|
| [19] |
Churpek M M, Yuen T C, Park S Y, et al. Derivation of a cardiac arrest prediction model using ward vital signs [J]. Crit Care Med, 2012, 40(7): 2102-2108.
|
| [18] |
Kim J H, Choi A, Kim M J, et al. Development of a machine-learning algorithm to predict in-hospital cardiac arrest for emergency department patients using a nationwide database[J]. Sci Rep, 2022, 12(1):21797.
|
| [20] |
Kwon J M, Lee Y, Lee Y, et al. An algorithm based on deep learning for predicting in-hospital cardiac arrest[J]. J Am Heart Assoc, 2018, 7(13): e008678.
|
| [19] |
Churpek M M, Yuen T C, Park S Y, et al. Derivation of a cardiac arrest prediction model using ward vital signs [J]. Crit Care Med, 2012, 40(7): 2102-2108.
|
| [21] |
Cho K J, Kwon O, Kwon J M, et al. Detecting patient deterioration using artificial intelligence in a rapid response system[J]. Crit Care Med, 2020, 48(4): e285-e289.
|
| [20] |
Kwon J M, Lee Y, Lee Y, et al. An algorithm based on deep learning for predicting in-hospital cardiac arrest[J]. J Am Heart Assoc, 2018, 7(13): e008678.
|
| [22] |
Bartkowiak B, Snyder A M, Benjamin A, et al. Validating the electronic cardiac arrest risk triage (ecart) score for risk stratification of surgical inpatients in the postoperative setting: retrospective cohort study[J]. Ann Surg, 2019, 269(6): 1059-1063.
|
| [21] |
Cho K J, Kwon O, Kwon J M, et al. Detecting patient deterioration using artificial intelligence in a rapid response system[J]. Crit Care Med, 2020, 48(4): e285-e289.
|
| [23] |
Wu T T, Lin X Q, Mu Y, et al. Machine learning for early prediction of in-hospital cardiac arrest in patients with acute coronary syndromes [J]. Clin Cardiol, 2021, 44(3): 349-356.
|
| [22] |
Bartkowiak B, Snyder A M, Benjamin A, et al. Validating the electronic cardiac arrest risk triage (ecart) score for risk stratification of surgical inpatients in the postoperative setting: retrospective cohort study[J]. Ann Surg, 2019, 269(6): 1059-1063.
|
| [24] |
Rasmussen T P, Riley D J, Sarazin M V, et al. Variation across hospitals in in-hospital cardiac arrest incidence among medicare beneficiaries[J]. JAMA Netw Open, 2022, 5(2): e2148485.
|
| [23] |
Wu T T, Lin X Q, Mu Y, et al. Machine learning for early prediction of in-hospital cardiac arrest in patients with acute coronary syndromes [J]. Clin Cardiol, 2021, 44(3): 349-356.
|
| [25] |
吴秋硕, 陆宗庆, 刘 瑜, 等. 机器学习应用于心脏骤停早期预测模型的系统评价[J]. 中国循证医学杂志, 2021, 21(8): 942-952.
|
| [24] |
Rasmussen T P, Riley D J, Sarazin M V, et al. Variation across hospitals in in-hospital cardiac arrest incidence among medicare beneficiaries[J]. JAMA Netw Open, 2022, 5(2): e2148485.
|
| [26] |
Benjamin E J, Muntner P, Alonso A, et al. Heart disease and stroke statistics-2019 update: a report from the american heart association[J]. Circulation, 2019, 139(10): e56-e528.
|
| [25] |
吴秋硕, 陆宗庆, 刘 瑜, 等. 机器学习应用于心脏骤停早期预测模型的系统评价[J]. 中国循证医学杂志, 2021, 21(8): 942-952.
|
| [27] |
Jerkeman M, Sultanian P, Lundgren P, et al. Trends in survival after cardiac arrest: a swedish nationwide study over 30 years[J]. Eur Heart J, 2022, 43(46): 4817-4829.
|
| [26] |
Benjamin E J, Muntner P, Alonso A, et al. Heart disease and stroke statistics-2019 update: a report from the american heart association[J]. Circulation, 2019, 139(10): e56-e528.
|
| [28] |
Mayampurath A, Bashiri F, Hagopian R, et al. Predicting neurological outcomes after in-hospital cardiac arrests for patients with coronavirus disease 2019[J]. Resuscitation, 2022, 178:55-62.
|
| [27] |
Jerkeman M, Sultanian P, Lundgren P, et al. Trends in survival after cardiac arrest: a swedish nationwide study over 30 years[J]. Eur Heart J, 2022, 43(46): 4817-4829.
|
| [29] |
Chou S Y, Bamodu O A, Chiu W T, et al. Artificial neural network-boosted cardiac arrest survival post-resuscitation in-hospital (caspri) score accurately predicts outcome in cardiac arrest patients treated with targeted temperature management[J]. Sci Rep, 2022, 12(1): 7254.
|
| [28] |
Mayampurath A, Bashiri F, Hagopian R, et al. Predicting neurological outcomes after in-hospital cardiac arrests for patients with coronavirus disease 2019[J]. Resuscitation, 2022, 178:55-62.
|
| [30] |
Kim B, Hong S I, Kim Y J, et al. predicting the probability of good neurological outcome after in-hospital cardiac arrest based on prearrest factors: validation of the good outcome following attempted resuscitation 2 (go-far 2) score[J]. Intern Emerg Med, 2023, 18(6): 1807-1813.
|
| [29] |
Chou S Y, Bamodu O A, Chiu W T, et al. Artificial neural network-boosted cardiac arrest survival post-resuscitation in-hospital (caspri) score accurately predicts outcome in cardiac arrest patients treated with targeted temperature management[J]. Sci Rep, 2022, 12(1): 7254.
|
| [31] |
Aellen F M, Alnes S L, Loosli F, et al. Auditory stimulation and deep learning predict awakening from coma after cardiac arrest[J]. Brain, 2023, 146(2):778-788.
|
| [30] |
Kim B, Hong S I, Kim Y J, et al. predicting the probability of good neurological outcome after in-hospital cardiac arrest based on prearrest factors: validation of the good outcome following attempted resuscitation 2 (go-far 2) score[J]. Intern Emerg Med, 2023, 18(6): 1807-1813.
|
| [32] |
Chen C C, Massey S L, Kirschen M P, et al. Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: a systematic review[J]. Resuscitation, 2024, 194:110049.
|
| [31] |
Aellen F M, Alnes S L, Loosli F, et al. Auditory stimulation and deep learning predict awakening from coma after cardiac arrest[J]. Brain, 2023, 146(2):778-788.
|
| [33] |
Chung C C, Chiu W T, Huang Y H, et al. Identifying prognostic factors and developing accurate outcome predictions for in-hospital cardiac arrest by using artificial neural networks[J]. J Neurol Sci, 2021, 425:117445.
|
| [32] |
Chen C C, Massey S L, Kirschen M P, et al. Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: a systematic review[J]. Resuscitation, 2024, 194:110049.
|
| [34] |
Mayampurath A, Hagopian R, Venable L, et al. Comparison of machine learning methods for predicting outcomes after in-hospital cardiac arrest[J]. Crit Care Med, 2022, 50(2):e162-e172.
|
| [33] |
Chung C C, Chiu W T, Huang Y H, et al. Identifying prognostic factors and developing accurate outcome predictions for in-hospital cardiac arrest by using artificial neural networks[J]. J Neurol Sci, 2021, 425:117445.
|
| [35] |
Li Z, Qin Y, Liu X, et al. Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning[J]. Funct Integr Genomics, 2023, 23(2): 83.
|
| [34] |
Mayampurath A, Hagopian R, Venable L, et al. Comparison of machine learning methods for predicting outcomes after in-hospital cardiac arrest[J]. Crit Care Med, 2022, 50(2):e162-e172.
|
| [36] |
Grandbois van Ravenhorst C, Schluep M, Endeman H, et al. Prognostic models for outcome prediction following in-hospital cardiac arrest using pre-arrest factors: a systematic review, meta-analysis and critical appraisal[J]. Crit Care, 2023, 27(1):32.
|
| [35] |
Li Z, Qin Y, Liu X, et al. Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning[J]. Funct Integr Genomics, 2023, 23(2): 83.
|
| [36] |
Grandbois van Ravenhorst C, Schluep M, Endeman H, et al. Prognostic models for outcome prediction following in-hospital cardiac arrest using pre-arrest factors: a systematic review, meta-analysis and critical appraisal[J]. Crit Care, 2023, 27(1):32.
|
|
|
2024, Vol. 35 
