{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T11:32:39Z","timestamp":1788780759251,"version":"build-2803163510"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 111-2314-B-038-132-MY3"],"award-info":[{"award-number":["NSTC 111-2314-B-038-132-MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["113FRP-14"],"award-info":[{"award-number":["113FRP-14"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018493","name":"Taipei Medical University-Shuang Ho Hospital","doi-asserted-by":"publisher","award":["NSTC 111-2314-B-038-132-MY3"],"award-info":[{"award-number":["NSTC 111-2314-B-038-132-MY3"]}],"id":[{"id":"10.13039\/100018493","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018493","name":"Taipei Medical University-Shuang Ho Hospital","doi-asserted-by":"publisher","award":["113FRP-14"],"award-info":[{"award-number":["113FRP-14"]}],"id":[{"id":"10.13039\/100018493","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Therapeutic hypothermia (TH) represents a critical therapeutic intervention for patients with cardiac arrest, although treatment efficacy and prognostic factors may vary between individuals. Precise, personalized outcome predictions can empower better clinical decisions. Methods: In this multi-center retrospective cohort study involving nine medical centers in Taiwan, we developed machine learning algorithms to predict neurological outcomes in patients who experienced cardiac arrest with shockable rhythms and underwent TH. The study cohort comprised 209 patients treated between January 2014 and September 2019. The models were trained on patients\u2019 pre-treatment characteristics collected during this study period. The optimal artificial neural network (ANN) model was interpretable using the SHapley Additive exPlanations (SHAP) method. Results: Among the 209 enrolled patients, 79 (37.80%) demonstrated favorable neurological outcomes at discharge. The ANN model achieved an area under the curve value of 0.9089 (accuracy = 0.8330, precision = 0.7984, recall = 0.7492, specificity = 0.8846) for outcome prediction. SHAP analysis identified vital predictive features, including the dose of epinephrine during resuscitation, diabetes status, body temperature at return of spontaneous circulation (ROSC), whether the cardiac arrest was witnessed, and diastolic blood pressure at ROSC. Using real-life case examples, we demonstrated how the ANN model provides personalized prognostic predictions tailored to individuals\u2019 distinct profiles. Conclusion: Our machine learning approach delivers personalized forecasts of TH outcomes in cardiac arrest patients with shockable rhythms. By accounting for each patient\u2019s unique health history and cardiac arrest event details, the ANN model empowers more precise risk stratification, tailoring clinical decision-making regarding TH prognostication and optimizing personalized treatment planning.<\/jats:p>","DOI":"10.3390\/diagnostics15030267","type":"journal-article","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T12:24:09Z","timestamp":1737635049000},"page":"267","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Personalized Predictions of Therapeutic Hypothermia Outcomes in Cardiac Arrest Patients with Shockable Rhythms Using Explainable Machine Learning"],"prefix":"10.3390","volume":"15","author":[{"given":"Chien-Tai","family":"Hong","sequence":"first","affiliation":[{"name":"Department of Neurology, Taipei Medical University, Shuang Ho Hospital, New Taipei City 235, Taiwan"},{"name":"Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 110, Taiwan"},{"name":"Taipei Neuroscience Institute, Taipei Medical University, Shuang Ho Hospital, New Taipei City 235, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8229-0408","authenticated-orcid":false,"given":"Oluwaseun Adebayo","family":"Bamodu","sequence":"additional","affiliation":[{"name":"Department of Prevention and Community Health, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA"},{"name":"Directorate of Postgraduate Studies, School of Clinical Medicine, Muhimbili University of Health and Allied Sciences, Ilala District, Dar es Salaam P.O. Box 65001, Tanzania"},{"name":"Ocean Road Cancer Institute, Ilala District, Dar es Salaam P.O. Box 3592, Tanzania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6919-8199","authenticated-orcid":false,"given":"Hung-Wen","family":"Chiu","sequence":"additional","affiliation":[{"name":"Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei City 110, Taiwan"},{"name":"Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei City 110, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Ting","family":"Chiu","sequence":"additional","affiliation":[{"name":"Department of Neurology, Taipei Medical University, Shuang Ho Hospital, New Taipei City 235, Taiwan"},{"name":"Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 110, Taiwan"},{"name":"Taipei Neuroscience Institute, Taipei Medical University, Shuang Ho Hospital, New Taipei City 235, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5795-4460","authenticated-orcid":false,"given":"Lung","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Neurology, Taipei Medical University, Shuang Ho Hospital, New Taipei City 235, Taiwan"},{"name":"Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 110, Taiwan"},{"name":"Taipei Neuroscience Institute, Taipei Medical University, Shuang Ho Hospital, New Taipei City 235, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6743-6667","authenticated-orcid":false,"given":"Chen-Chih","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Neurology, Taipei Medical University, Shuang Ho Hospital, New Taipei City 235, Taiwan"},{"name":"Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 110, Taiwan"},{"name":"Taipei Neuroscience Institute, Taipei Medical University, Shuang Ho Hospital, New Taipei City 235, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s13054-019-2721-1","article-title":"High Quality Targeted Temperature Management (TTM) After Cardiac Arrest","volume":"24","author":"Taccone","year":"2020","journal-title":"Crit. Care"},{"key":"ref_2","first-page":"Cd004128","article-title":"Hypothermia for neuroprotection in adults after cardiopulmonary resuscitation","volume":"2","author":"Arrich","year":"2016","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.1056\/NEJMoa2100591","article-title":"Hypothermia versus Normothermia after Out-of-Hospital Cardiac Arrest","volume":"384","author":"Dankiewicz","year":"2021","journal-title":"N. Engl. J. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2327","DOI":"10.1056\/NEJMoa1906661","article-title":"Targeted Temperature Management for Cardiac Arrest with Nonshockable Rhythm","volume":"381","author":"Lascarrou","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref_5","first-page":"1360","article-title":"Targeted temperature management after cardiac arrest","volume":"370","author":"Nielsen","year":"2014","journal-title":"N. Engl. J. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.resuscitation.2021.08.040","article-title":"Targeted temperature management in adult cardiac arrest: Systematic review and meta-analysis","volume":"167","author":"Granfeldt","year":"2021","journal-title":"Resuscitation"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.amjcard.2020.07.038","article-title":"Meta-Analysis of the Usefulness of Therapeutic Hypothermia After Cardiac Arrest","volume":"133","author":"Rout","year":"2020","journal-title":"Am. J. Cardiol."},{"key":"ref_8","first-page":"47","article-title":"Therapeutic Hypothermia Is Associated With a Decrease in All-cause Mortality in Cardiac Arrest Due to Shockable Rhythm","volume":"21","author":"Nasir","year":"2022","journal-title":"Crit. Pathw. Cardiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.ajem.2023.06.040","article-title":"Therapeutic hypothermia in patients after cardiac arrest: A systematic review and meta-analysis of randomized controlled trials","volume":"71","author":"Chiu","year":"2023","journal-title":"Am. J. Emerg. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e005689","DOI":"10.1161\/CIRCEP.117.005689","article-title":"Health Care Costs After Cardiac Arrest in the United States","volume":"11","author":"Damluji","year":"2018","journal-title":"Circ. Arrhythmia Electrophysiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"117445","DOI":"10.1016\/j.jns.2021.117445","article-title":"Identifying prognostic factors and developing accurate outcome predictions for in-hospital cardiac arrest by using artificial neural networks","volume":"425","author":"Chung","year":"2021","journal-title":"J. Neurol. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1080\/14017431.2018.1450991","article-title":"Importance of comorbidities in comatose survivors of shockable and non-shockable out-of-hospital cardiac arrest treated with target temperature management","volume":"52","author":"Salam","year":"2018","journal-title":"Scand. Cardiovasc. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.jfma.2021.04.006","article-title":"Factors affecting outcomes in patients with cardiac arrest who receive target temperature management: The multi-center TIMECARD registry","volume":"121","author":"Chang","year":"2022","journal-title":"J. Formos. Med. Assoc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"55","DOI":"10.31083\/j.rcm2402055","article-title":"Identifying Risk Factors for Prolonged Length of Stay in Hospital and Developing Prediction Models for Patients with Cardiac Arrest Receiving Targeted Temperature Management","volume":"24","author":"Chiu","year":"2023","journal-title":"Rev. Cardiovasc. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.resuscitation.2018.11.007","article-title":"Multimodal approach for neurologic prognostication of out-of-hospital cardiac arrest patients undergoing targeted temperature management","volume":"134","author":"Kim","year":"2019","journal-title":"Resuscitation"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"95","DOI":"10.5603\/CJ.a2019.0035","article-title":"Predicting survival in out-of-hospital cardiac arrest patients undergoing targeted temperature management: The Polish Hypothermia Registry Risk Score","volume":"28","author":"Czajkowska","year":"2021","journal-title":"Cardiol. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.jfma.2022.11.012","article-title":"TIMECARD score: An easily operated prediction model of unfavorable neurological outcomes in out-of-hospital cardiac arrest patients with targeted temperature management","volume":"122","author":"Lin","year":"2023","journal-title":"J. Formos. Med. Assoc."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chou, S.Y., Bamodu, O.A., Chiu, W.T., Hong, C.T., Chan, L., and Chung, C.C. (2022). 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. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-11201-z"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.jfma.2021.07.004","article-title":"Predicting the survivals and favorable neurologic outcomes after targeted temperature management by artificial neural networks","volume":"121","author":"Chiu","year":"2022","journal-title":"J. Formos. Med. Assoc. Taiwan Yi Zhi"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chung, C.-C., Su, E.C.-Y., Chen, J.-H., Chen, Y.-T., and Kuo, C.-Y. (2023). XGBoost-Based Simple Three-Item Model Accurately Predicts Outcomes of Acute Ischemic Stroke. Diagnostics, 13.","DOI":"10.3390\/diagnostics13050842"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.jfma.2020.07.036","article-title":"Post-cardiac arrest care and targeted temperature management: A consensus of scientific statement from the Taiwan Society of Emergency & Critical Care Medicine, Taiwan Society of Critical Care Medicine and Taiwan Society of Emergency Medicine","volume":"120","author":"Chiu","year":"2021","journal-title":"J. Formos. Med. Assoc."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"(2002). Mild therapeutic hypothermia to improve the neurologic outcome after cardiac arrest. N. Engl. J. Med., 346, 549\u2013556.","DOI":"10.1056\/NEJMoa012689"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1111\/j.1553-2712.2011.01185.x","article-title":"Logistic regression: A brief primer","volume":"18","author":"Stoltzfus","year":"2011","journal-title":"Acad. Emerg. Med."},{"key":"ref_24","first-page":"11409","article-title":"Logistic regression and artificial neural network-based simple predicting models for obstructive sleep apnea by age, sex, and body mass index","volume":"19","author":"Kuan","year":"2022","journal-title":"Math. Biosci. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e41084","DOI":"10.1016\/j.heliyon.2024.e41084","article-title":"Predictive modeling and interpretative analysis of risks of instability in patients with Myasthenia Gravis requiring intensive care unit admission","volume":"10","author":"Kuo","year":"2024","journal-title":"Heliyon"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cheng, C.Y., Chiu, I.M., Zeng, W.H., Tsai, C.M., and Lin, C.R. (2021). Machine Learning Models for Survival and Neurological Outcome Prediction of Out-of-Hospital Cardiac Arrest Patients. BioMed Res. Int., 2021.","DOI":"10.1155\/2021\/9590131"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10462-020-09896-5","article-title":"A comparative analysis of gradient boosting algorithms","volume":"54","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1186\/s13054-021-03505-9","article-title":"Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network algorithm","volume":"25","author":"Andersson","year":"2021","journal-title":"Crit. Care"},{"key":"ref_30","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_31","first-page":"1089","article-title":"No Unbiased Estimator of the Variance of K-Fold Cross-Validation","volume":"5","author":"Bengio","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref_32","unstructured":"Lundberg, S.M., and Lee, S.I. (2017, January 4\u20139). A Unified Approach to Interpreting Model Predictions. Proceedings of the 31st Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shapley, L. (1953). A Value for n-Person Games. Contributions to the Theory of Games II, Princeton University Press.","DOI":"10.1515\/9781400881970-018"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1007\/s10822-020-00314-0","article-title":"Interpretation of machine learning models using shapley values: Application to compound potency and multi-target activity predictions","volume":"34","author":"Bajorath","year":"2020","journal-title":"J. Comput. Aided Mol. Des."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1089\/ther.2015.0030","article-title":"Effect of Therapeutic Hypothermia on Survival and Neurologic Outcome in the Elderly","volume":"6","author":"Bosson","year":"2016","journal-title":"Ther. Hypothermia Temp. Manag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.ajem.2017.07.087","article-title":"Age is related to neurological outcome in patients with out-of-hospital cardiac arrest (OHCA) receiving therapeutic hypothermia (TH)","volume":"36","author":"Oh","year":"2018","journal-title":"Am. J. Emerg. Med."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1097\/CCM.0000000000002367","article-title":"Dysglycemia, Glycemic Variability, and Outcome After Cardiac Arrest and Temperature Management at 33 \u00b0C and 36 \u00b0C","volume":"45","author":"Borgquist","year":"2017","journal-title":"Crit. Care Med."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1089\/ther.2019.0004","article-title":"Increased Glucose Variability During Long-Term Therapeutic Hypothermia as a Predictor of Poor Neurological Outcomes and Mortality: A Retrospective Study","volume":"10","author":"Oh","year":"2020","journal-title":"Ther. Hypothermia Temp. Manag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e26199","DOI":"10.1016\/j.heliyon.2024.e26199","article-title":"Beyond diagnosis: Leveraging routine blood and urine biomarkers to predict severity and functional outcome in acute ischemic stroke","volume":"10","author":"Bamodu","year":"2024","journal-title":"Heliyon"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.ajem.2024.07.038","article-title":"The compliance with TTM protocol may benefit outcomes in cardiac arrest survivors: A retrospective cohort study","volume":"84","author":"Lee","year":"2024","journal-title":"Am. J. Emerg. Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1186\/s13054-020-03103-1","article-title":"Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care","volume":"24","author":"Johnsson","year":"2020","journal-title":"Crit. 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