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Rawshani A, Hessulf F, Deminger J, Sultanian P, Gupta V, Lundgren P, Mohammed M, Abu Alchay M, Siöland T, Gryska E, Piasecki A. Prediction of neurologic outcome after out-of-hospital cardiac arrest: An interpretable approach with machine learning. Resuscitation 2024; 202:110359. [PMID: 39142467 DOI: 10.1016/j.resuscitation.2024.110359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2024] [Revised: 08/02/2024] [Accepted: 08/08/2024] [Indexed: 08/16/2024]
Abstract
Out-of-hospital cardiac arrest (OHCA) is a critical condition with low survival rates. In patients with a return of spontaneous circulation, brain injury is a leading cause of death. In this study, we propose an interpretable machine learning approach for predicting neurologic outcome after OHCA, using information available at the time of hospital admission. METHODS The study population were 55 615 OHCA cases registered in the Swedish Cardiopulmonary Resuscitation Registry between 2010 and 2020. The dataset was split to training and validation sets (for model development) and test set (for evaluation of the final model). We used an XGBoost algorithm with stratified, repeated 10-fold cross-validation along with Optuna framework for hyperparameters tuning. The final model was trained on 10 features selected based on the importance scores and evaluated on the test set in terms of discrimination, calibration and bias-variance tradeoff. We used SHapley Additive exPlanations to address the 'black-box' model and align with eXplainable artificial intelligence. RESULTS The final model achieved: area under the receiver operating characteristic value 0.964 (95% confidence interval (CI) [0.960-0.968]), sensitivity 0.606 (95% CI [0.573-0.634]), specificity 0.975 (95% CI [0.972-0.978]), positive predictive value (PPV) 0.664 (95% CI [0.625-0.696]), negative predictive value (NPV) 0.969 (95% CI [0.966-0.972]), macro F1 0.803 (95% CI [0.788-0.816]), and showed a very good calibration. SHAP features with the highest impact on the model's output were:'ROSC on arrival to hospital', 'Initial rhythm asystole' and 'Conscious on arrival to hospital'. CONCLUSIONS The XGBoost machine learning model with 10 features available at the time of hospital admission showed good performance for predicting neurologic outcome after OHCA, with no apparent signs of overfitting.
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Affiliation(s)
- Araz Rawshani
- Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Wallenberg Laboratory, Blå stråket 5, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Blå stråket 5, 413 45 Gothenburg, Sweden; The Swedish Registry for Cardiopulmonary Resuscitation, Medicinaregatan 18G, 413 90 Gothenburg, Sweden
| | - Fredrik Hessulf
- Department of Anesthesiology and Intensive Care, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Blå stråket 5, 413 45 Gothenburg, Sweden; Department of Anesthesiology and Intensive Care, Sahlgrenska University Hospital, Göteborgsvägen 31, 431 30 Mölndal, Sweden
| | - John Deminger
- Department of Medicine and Emergency Care, Sahlgrenska University Hospital, Göteborgsvägen 33, 431 30 Mölndal, Sweden
| | - Pedram Sultanian
- Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Wallenberg Laboratory, Blå stråket 5, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - Vibha Gupta
- Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Wallenberg Laboratory, Blå stråket 5, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - Peter Lundgren
- Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Wallenberg Laboratory, Blå stråket 5, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Blå stråket 5, 413 45 Gothenburg, Sweden
| | - Mohammed Mohammed
- Department of Cardiology, Sahlgrenska University Hospital, Blå stråket 5, 413 45 Gothenburg, Sweden
| | - Monér Abu Alchay
- Department of Cardiology, Sahlgrenska University Hospital, Blå stråket 5, 413 45 Gothenburg, Sweden
| | - Tobias Siöland
- Department of Anesthesiology and Intensive Care, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Blå stråket 5, 413 45 Gothenburg, Sweden; Department of Anesthesiology and Intensive Care, Sahlgrenska University Hospital, Göteborgsvägen 31, 431 30 Mölndal, Sweden
| | - Emilia Gryska
- Department of Hand Surgery, Sahlgrenska University Hospital, Göteborgsvägen 31, 431 30 Mölndal, Sweden; Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Adam Piasecki
- Department of Anesthesiology and Intensive Care, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Blå stråket 5, 413 45 Gothenburg, Sweden; Department of Anesthesiology and Intensive Care, Sahlgrenska University Hospital, Göteborgsvägen 31, 431 30 Mölndal, Sweden.
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Heidet M, Benjamin Leung KH, Bougouin W, Alam R, Frattini B, Liang D, Jost D, Canon V, Deakin J, Hubert H, Christenson J, Vivien B, Chan T, Cariou A, Dumas F, Jouven X, Marijon E, Bennington S, Travers S, Souihi S, Mermet E, Freyssenge J, Arrouy L, Lecarpentier E, Derkenne C, Grunau B. Improving EMS response times for out-of-hospital cardiac arrest in urban areas using drone-like vertical take-off and landing air ambulances: An international, simulation-based cohort study. Resuscitation 2023; 193:109995. [PMID: 37813148 DOI: 10.1016/j.resuscitation.2023.109995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/12/2023] [Accepted: 10/02/2023] [Indexed: 10/11/2023]
Abstract
BACKGROUND Advances in vertical take-off and landing (VTOL) technologies may enable drone-like crewed air ambulances to rapidly respond to out-of-hospital cardiac arrest (OHCA) in urban areas. We estimated the impact of incorporating VTOL air ambulances on OHCA response intervals in two large urban centres in France and Canada. METHODS We included adult OHCAs occurring between Jan. 2017-Dec. 2018 within Greater Paris in France and Metro Vancouver in Canada. Both regions utilize tiered OHCA response with basic (BLS)- and advanced life support (ALS)-capable units. We simulated incorporating 1-2 ALS-capable VTOL air ambulances dedicated to OHCA response in each study region, and computed time intervals from call reception by emergency medical services (EMS) to arrival of the: (1) first ALS unit ("call-to-ALS arrival interval"); and (2) first EMS unit ("call-to-first EMS arrival interval"). RESULTS There were 6,217 OHCAs included during the study period (3,760 in Greater Paris and 2,457 in Metro Vancouver). Historical median call-to-ALS arrival intervals were 21 min [IQR 16-29] in Greater Paris and 12 min [IQR 9-17] in Metro Vancouver, while median call-to-first EMS arrival intervals were 11 min [IQR 8-14] and 7 min [IQR 5-8] respectively. Incorporating 1-2 VTOL air ambulances improved median call-to-ALS arrival intervals to 7-9 min and call-to-first EMS arrival intervals to 6-8 min in both study regions (all P < 0.001). CONCLUSION VTOL air ambulances dedicated to OHCA response may improve EMS response intervals, with substantial improvements in ALS response metrics.
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Affiliation(s)
- Matthieu Heidet
- Assistance Publique-Hôpitaux de Paris (AP-HP), SAMU 94, Henri Mondor University Hospital, Créteil, France; Université Paris-Est Créteil (UPEC), CIR/TincNet (EA-3956), Créteil, France.
| | - K H Benjamin Leung
- Department of Mechanical and Industrial Engineering University of Toronto, Toronto, Canada
| | - Wulfran Bougouin
- Université de Paris, INSERM U970, Paris Cardiovascular Research Center (PARCC), European Georges Pompidou Hospital, Paris, France; Paris Sudden Death Expertise Center, Paris, France; Medical Intensive Care Unit, Ramsay Générale de Santé, Hôpital Privé Jacques Cartier, Massy, France
| | - Rejuana Alam
- Department of Mechanical and Industrial Engineering University of Toronto, Toronto, Canada
| | | | - Danny Liang
- Department of Emergency Medicine, University of Calgary, Calgary, Canada
| | - Daniel Jost
- Paris Fire Brigade (BSPP), Paris, France; Paris Sudden Death Expertise Center, Paris, France
| | | | | | | | - Jim Christenson
- Centre for Health Evaluation and Outcome Sciences (CHEOS), Vancouver, Canada; Department of Emergency Medicine, St Paul's Hospital and University of British Columbia, Vancouver, Canada
| | - Benoît Vivien
- AP-HP, SAMU 75, Necker University Hospital, Paris, France
| | - Timothy Chan
- Department of Mechanical and Industrial Engineering University of Toronto, Toronto, Canada
| | - Alain Cariou
- Paris Sudden Death Expertise Center, Paris, France; AP-HP, Medical Intensive Care Unit, Cochin University Hospital, Paris, France
| | - Florence Dumas
- Université de Paris, INSERM U970, Paris Cardiovascular Research Center (PARCC), European Georges Pompidou Hospital, Paris, France; Paris Sudden Death Expertise Center, Paris, France; AP-HP, Emergency Department, Cochin-Hotel-Dieu University Hospital, Paris, France
| | - Xavier Jouven
- Université de Paris, INSERM U970, Paris Cardiovascular Research Center (PARCC), European Georges Pompidou Hospital, Paris, France; Paris Sudden Death Expertise Center, Paris, France; AP-HP, Cardiology Department, European Georges Pompidou University Hospital, Paris, France
| | - Eloi Marijon
- Université de Paris, INSERM U970, Paris Cardiovascular Research Center (PARCC), European Georges Pompidou Hospital, Paris, France; Paris Sudden Death Expertise Center, Paris, France; AP-HP, Cardiology Department, European Georges Pompidou University Hospital, Paris, France
| | - Steven Bennington
- Assistance Publique-Hôpitaux de Paris (AP-HP), SAMU 94, Henri Mondor University Hospital, Créteil, France
| | | | - Sami Souihi
- Université Paris-Est Créteil (UPEC), CIR/TincNet (EA-3956), Créteil, France
| | - Eric Mermet
- Centre National pour la Recherche scientifique (CNRS), TSE-R, UMR 5314, Toulouse, France; Toulouse School of Economics (TSE), Toulouse, France
| | - Julie Freyssenge
- Université Claude Bernard Lyon 1, INSERME U1290, Research on Healthcare Performance (RESHAPE), Lyon, France; Urgences-ARA Network, ARS Auvergne Rhône-Alpes, Lyon, France
| | - Laurence Arrouy
- AP-HP, Emergency Department, Paris Ile-de-France Ouest University Hospitals, Ambroise Paré University Hospital, Boulogne-Billancourt, France
| | - Eric Lecarpentier
- Assistance Publique-Hôpitaux de Paris (AP-HP), SAMU 94, Henri Mondor University Hospital, Créteil, France
| | - Clément Derkenne
- Medical Intensive Care Unit, Ramsay Générale de Santé, Hôpital Privé Jacques Cartier, Massy, France
| | - Brian Grunau
- Centre for Health Evaluation and Outcome Sciences (CHEOS), Vancouver, Canada; Department of Emergency Medicine, St Paul's Hospital and University of British Columbia, Vancouver, Canada
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Kawai Y, Yamamoto K, Miyazaki K, Asai H, Fukushima H. Machine learning-based analysis of regional differences in out-of-hospital cardiopulmonary arrest outcomes and resuscitation interventions in Japan. Sci Rep 2023; 13:15884. [PMID: 37741881 PMCID: PMC10518013 DOI: 10.1038/s41598-023-43210-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 09/21/2023] [Indexed: 09/25/2023] Open
Abstract
Refining out-of-hospital cardiopulmonary arrest (OHCA) resuscitation protocols for local emergency practices is vital. The lack of comprehensive evaluation methods for individualized protocols impedes targeted improvements. Thus, we employed machine learning to assess emergency medical service (EMS) records for examining regional disparities in time reduction strategies. In this retrospective study, we examined Japanese EMS records and neurological outcomes from 2015 to 2020 using nationwide data. We included patients aged ≥ 18 years with cardiogenic OHCA and visualized EMS activity time variations across prefectures. A five-layer neural network generated a neurological outcome predictive model that was trained on 80% of the data and tested on the remaining 20%. We evaluated interventions associated with changes in prognosis by simulating these changes after adjusting for time factors, including EMS contact to hospital arrival and initial defibrillation or drug administration. The study encompassed 460,540 patients, with the model's area under the curve and accuracy being 0.96 and 0.95, respectively. Reducing transport time and defibrillation improved outcomes universally, while combining transport time and drug administration showed varied efficacy. In conclusion, the association of emergency activity time with neurological outcomes varied across Japanese prefectures, suggesting the need to set targets for reducing activity time in localized emergency protocols.
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Affiliation(s)
- Yasuyuki Kawai
- Department of Emergency and Critical Care Medicine, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8522, Japan.
| | - Koji Yamamoto
- Department of Emergency and Critical Care Medicine, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8522, Japan
| | - Keita Miyazaki
- Department of Emergency and Critical Care Medicine, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8522, Japan
| | - Hideki Asai
- Department of Emergency and Critical Care Medicine, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8522, Japan
| | - Hidetada Fukushima
- Department of Emergency and Critical Care Medicine, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8522, Japan
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Sun Y, He Z, Ren J, Wu Y. Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning. BMC Anesthesiol 2023; 23:178. [PMID: 37231340 DOI: 10.1186/s12871-023-02138-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 05/13/2023] [Indexed: 05/27/2023] Open
Abstract
BACKGROUND Both in-hospital cardiac arrest (IHCA) and out-of-hospital cardiac arrest (OHCA) have higher incidence and lower survival rates. Predictors of in-hospital mortality for intensive care unit (ICU) admitted cardiac arrest (CA) patients remain unclear. METHODS The Medical Information Mart for Intensive Care IV (MIMIC-IV) database was used to perform a retrospective study. Patients meeting the inclusion criteria were identified from the MIMIC-IV database and randomly divided into training set (n = 1206, 70%) and validation set (n = 516, 30%). Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Independent risk factors for in-hospital mortality were screened using the least absolute shrinkage and selection operator (LASSO) regression model and the extreme gradient boosting (XGBoost) in the training set. Multivariate logistic regression analysis was used to build prediction models in training set, and then validated in validation set. Discrimination, calibration and clinical utility of these models were compared using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). After pairwise comparison, the best performing model was chosen to build a nomogram. RESULTS Among the 1722 patients, in-hospital mortality was 53.95%. In both sets, the LASSO, XGBoost,the logistic regression(LR) model and the National Early Warning Score 2 (NEWS 2) models showed acceptable discrimination. In pairwise comparison, the prediction effectiveness was higher with the LASSO,XGBoost and LR models than the NEWS 2 model (p < 0.001). The LASSO,XGBoost and LR models also showed good calibration. The LASSO model was chosen as our final model for its higher net benefit and wider threshold range. And the LASSO model was presented as the nomogram. CONCLUSIONS The LASSO model enabled good prediction of in-hospital mortality in ICU admission CA patients, which may be widely used in clinical decision-making.
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Affiliation(s)
- Yiwu Sun
- Department of Anesthesiology, Dazhou Central Hospital, No.56 Nanyuemiao Street, Tongchuan District, Dazhou, Sichuan, 635000, China.
| | - Zhaoyi He
- Department of Anesthesiology, The Third Affiliated Hospital of Harbin Medical University, No.150 Haping Road, Nangang District, Harbin, Heilongjiang, 150000, China
| | - Jie Ren
- Department of Anesthesiology, Guizhou Provincial People's Hospital, No.83 Zhongshan East Road, Nanming District, Guiyang, Guizhou, 550002, China
| | - Yifan Wu
- Department of Anesthesiology, Shanghai Sixth People's Hospital, No.600 Yishan Road, Xuhui District, Shanghai, 200030, China
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Sung CW, Chang WT, Chen WY, Jaw FS, Shieh JS. Simulation of a real-time dual-loop control system for high-quality personalized cardiopulmonary resuscitation. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Fordyce CB, Kramer AH, Ainsworth C, Christenson J, Hunter G, Kromm J, Lopez Soto C, Scales DC, Sekhon M, van Diepen S, Dragoi L, Josephson C, Kutsogiannis J, Le May MR, Overgaard CB, Savard M, Schnell G, Wong GC, Belley-Côté E, Fantaneanu TA, Granger CB, Luk A, Mathew R, McCredie V, Murphy L, Teitelbaum J. Neuroprognostication in the Post Cardiac Arrest Patient: A Canadian Cardiovascular Society Position Statement. Can J Cardiol 2023; 39:366-380. [PMID: 37028905 DOI: 10.1016/j.cjca.2022.12.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 12/13/2022] [Accepted: 12/14/2022] [Indexed: 04/08/2023] Open
Abstract
Cardiac arrest (CA) is associated with a low rate of survival with favourable neurologic recovery. The most common mechanism of death after successful resuscitation from CA is withdrawal of life-sustaining measures on the basis of perceived poor neurologic prognosis due to underlying hypoxic-ischemic brain injury. Neuroprognostication is an important component of the care pathway for CA patients admitted to hospital but is complex, challenging, and often guided by limited evidence. Using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system to evaluate the evidence underlying factors or diagnostic modalities available to determine prognosis, recommendations were generated in the following domains: (1) circumstances immediately after CA; (2) focused neurologic exam; (3) myoclonus and seizures; (4) serum biomarkers; (5) neuroimaging; (6) neurophysiologic testing; and (7) multimodal neuroprognostication. This position statement aims to serve as a practical guide to enhance in-hospital care of CA patients and emphasizes the adoption of a systematic, multimodal approach to neuroprognostication. It also highlights evidence gaps.
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Affiliation(s)
- Christopher B Fordyce
- Division of Cardiology, Department of Medicine, Vancouver General Hospital, and the Centre for Cardiovascular Innovation, University of British Columbia, Vancouver, British Columbia.
| | - Andreas H Kramer
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta; Department of Critical Care, University of Calgary, Alberta
| | - Craig Ainsworth
- Division of Cardiology, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Jim Christenson
- Department of Emergency Medicine, University of British Columbia, Vancouver, British Columbia
| | - Gary Hunter
- Division of Neurology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Julie Kromm
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta; Department of Critical Care, University of Calgary, Alberta
| | - Carmen Lopez Soto
- Department of Critical Care, King's College Hospital NHS Foundation Trust, London, United Kingdom
| | - Damon C Scales
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Mypinder Sekhon
- Division of Critical Care, Department of Medicine, Vancouver General Hospital, Djavad Mowafaghian Centre for Brain Health, International Centre for Repair Discoveries, University of British Columbia, Vancouver, British Columbia
| | - Sean van Diepen
- Department of Critical Care Medicine, University of Alberta, Edmonton, Alberta; Division of Cardiology, Department of Medicine, University of Alberta, Edmonton, Alberta
| | - Laura Dragoi
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Colin Josephson
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta; Department of Critical Care, University of Calgary, Alberta
| | - Jim Kutsogiannis
- Department of Critical Care Medicine, University of Alberta, Edmonton, Alberta
| | - Michel R Le May
- Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Christopher B Overgaard
- Division of Cardiology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Martin Savard
- Department of Neurological Sciences CHU de Québec - Hôpital de l'Enfant-Jésus Quebec City, Quebec, Canada
| | - Gregory Schnell
- Division of Cardiology, Department of Medicine, University of Calgary, Calgary, Alberta
| | - Graham C Wong
- Division of Cardiology, Department of Medicine, Vancouver General Hospital, and the Centre for Cardiovascular Innovation, University of British Columbia, Vancouver, British Columbia
| | - Emilie Belley-Côté
- Division of Cardiology, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Tadeu A Fantaneanu
- Division of Neurology, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
| | | | - Adriana Luk
- Division of Cardiology, Department of Medicine, University of Toronto and the Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
| | - Rebecca Mathew
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, and the Faculty of Medicine, Division of Critical Care, University of Ottawa, Ottawa, Ontario, Canada
| | - Victoria McCredie
- Interdepartmental Division of Critical Care Medicine, University of Toronto, the Krembil Research Institute, Toronto Western Hospital, University Health Network, and Department of Critical Care Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Laurel Murphy
- Departments of Emergency Medicine and Critical Care, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Jeanne Teitelbaum
- Neurological Intensive Care Unit, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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Viderman D, Abdildin YG, Batkuldinova K, Badenes R, Bilotta F. Artificial Intelligence in Resuscitation: A Scoping Review. J Clin Med 2023; 12:jcm12062254. [PMID: 36983255 PMCID: PMC10054374 DOI: 10.3390/jcm12062254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/17/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023] Open
Abstract
Introduction: Cardiac arrest is a significant cause of premature mortality and severe disability. Despite the death rate steadily decreasing over the previous decade, only 22% of survivors achieve good clinical status and only 25% of patients survive until their discharge from the hospital. The objective of this scoping review was to review relevant AI modalities and the main potential applications of AI in resuscitation. Methods: We conducted the literature search for related studies in PubMed, EMBASE, and Google Scholar. We included peer-reviewed publications and articles in the press, pooling and characterizing the data by their model types, goals, and benefits. Results: After identifying 268 original studies, we chose 59 original studies (reporting 1,817,419 patients) to include in the qualitative synthesis. AI-based methods appear to be superior to traditional methods in achieving high-level performance. Conclusion: AI might be useful in predicting cardiac arrest, heart rhythm disorders, and post-cardiac arrest outcomes, as well as in the delivery of drone-delivered defibrillators and notification of dispatchers. AI-powered technologies could be valuable assistants to continuously track patient conditions. Healthcare professionals should assist in the research and development of AI-powered technologies as well as their implementation into clinical practice.
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Affiliation(s)
- Dmitriy Viderman
- Department of Surgery, Nazarbayev University School of Medicine (NUSOM), Kerei, Zhanibek khandar Str. 5/1, Astana 010000, Kazakhstan;
| | - Yerkin G. Abdildin
- Department of Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, 53 Kabanbay Batyr Ave., Astana 010000, Kazakhstan
| | - Kamila Batkuldinova
- Department of Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, 53 Kabanbay Batyr Ave., Astana 010000, Kazakhstan
| | - Rafael Badenes
- Department of Anaesthesiology and Intensive Care, Hospital Clìnico Universitario de Valencia, University of Valencia, 46001 Valencia, Spain
- Correspondence:
| | - Federico Bilotta
- Department of Anesthesia and Intensive Care, University La Sapienza, 00185 Rome, Italy
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Lin WC, Huang CH, Chien LT, Tseng HJ, Ng CJ, Hsu KH, Lin CC, Chien CY. Tree-Based Algorithms and Association Rule Mining for Predicting Patients’ Neurological Outcomes After First-Aid Treatment for an Out-of-Hospital Cardiac Arrest During COVID-19 Pandemic: Application of Data Mining. Int J Gen Med 2022; 15:7395-7405. [PMID: 36157293 PMCID: PMC9507444 DOI: 10.2147/ijgm.s384959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 09/13/2022] [Indexed: 11/23/2022] Open
Abstract
Objective The authors performed several tree-based algorithms and an association rules mining as data mining tools to find useful determinants for neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients as well as to assess the effect of the first-aid and basic characteristics in the EMS system. Patients and Methods This was a retrospective cohort study. The outcome was Cerebral Performance Categories grading on OHCA patients at hospital discharge. Decision tree-based models inclusive of C4.5 algorithm, classification and regression tree and random forest were built to determine an OHCA patient’s prognosis. Association rules mining was another data mining method which we used to find the combination of prognostic factors linked to the outcome. Results The total of 3520 patients were included in the final analysis. The mean age was 67.53 (±18.4) year-old and 63.4% were men. To overcome the imbalance outcome issue in machine learning, the random forest has a better predictive ability for OHCA patients in overall accuracy (91.19%), weighted precision (88.76%), weighted recall (91.20%) and F1 score (0.9) by oversampling adjustment. Under association rules mining, patients who had any witness on the spot when encountering OHCA or who had ever ROSC during first-aid would be highly correlated with good CPC prognosis. Conclusion The random forest has a better predictive ability for OHCA patients. This paper provides a role model applying several machine learning algorithms to the first-aid clinical assessment that will be promising combining with Artificial Intelligence for applying to emergency medical services.
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Affiliation(s)
- Wei-Chun Lin
- Department of Emergency Medicine, New Taipei Municipal TuCheng Hospital and Chang Gung University, New Taipei City, Taiwan
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Linkou and College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Chien-Hsiung Huang
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Linkou and College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Graduate Institute of Management, Chang Gung University, Taoyuan, Taiwan
| | - Liang-Tien Chien
- Graduate Institute of Management, Chang Gung University, Taoyuan, Taiwan
- Fire Department, Taoyuan City Government, Taoyuan, Taiwan
| | - Hsiao-Jung Tseng
- Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan
- Biostatistics Unit, Clinical Trial Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Chip-Jin Ng
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Linkou and College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Taipei, Taiwan
| | - Kuang-Hung Hsu
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Linkou and College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Laboratory for Epidemiology, Chang Gung University, Taoyuan, Taiwan
- Laboratory for Epidemiology, Department of Health Care Management, Healthy Aging Research Center, Chang Gung University, Taoyuan, Taiwan
- Research Center for Food and Cosmetic Safety, College of Human Ecology, Chang Gung University of Science and Technology, Taoyuan, Taiwan
- Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, New Taipei City, Taiwan
| | - Chi-Chun Lin
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Linkou and College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Department of Emergency Medicine, Ton-Yen General Hospital, Zhubei, Taiwan
| | - Cheng-Yu Chien
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Linkou and College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Graduate Institute of Management, Chang Gung University, Taoyuan, Taiwan
- Department of Emergency Medicine, Ton-Yen General Hospital, Zhubei, Taiwan
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- Correspondence: Cheng-Yu Chien, Department of Emergency Medicine, Chang Gung Memorial Hospital, No. 5 Fushing St., Gueishan Dist, Taoyuan City, Taiwan, Tel +886-3-3281200 # 2505, Fax +886-3-3287715, Email
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9
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Lee B, Chung HJ, Kang HM, Kim DK, Kwak YH. Development and validation of machine learning-driven prediction model for serious bacterial infection among febrile children in emergency departments. PLoS One 2022; 17:e0265500. [PMID: 35333881 PMCID: PMC8956167 DOI: 10.1371/journal.pone.0265500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 03/02/2022] [Indexed: 12/03/2022] Open
Abstract
Serious bacterial infection (SBI) in children, such as bacterial meningitis or sepsis, is an important condition that can lead to fatal outcomes. Therefore, since it is very important to accurately diagnose SBI, SBI prediction tools such as 'Refined Lab-score' or 'clinical prediction rule' have been developed and used. However, these tools can predict SBI only when there are values of all factors used in the tool, and if even one of them is missing, the tools become useless. Therefore, the purpose of this study was to develop and validate a machine learning-driven model to predict SBIs among febrile children, even with missing values. This was a multicenter retrospective observational study including febrile children <6 years of age who visited Emergency departments (EDs) of 3 different tertiary hospitals from 2016 to 2018. The SBI prediction model was trained with a derivation cohort (data from two hospitals) and externally tested with a validation cohort (data from a third hospital). A total of 11,973 and 2,858 patient records were included in the derivation and validation cohorts, respectively. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) of the RF model was 0.964 (95% confidence interval [CI], 0.943-0.986), and the area under the precision-recall curve (AUPRC) was 0.753 (95% CI, 0.681-0.824). The conventional LR (CLR) model showed corresponding values of 0.902 (95% CI, 0.894-0.910) and 0.573 (95% CI, 0.560-0.586), respectively. In the validation cohort, the AUROC (95% CI) of the RF model was 0.950 (95% CI, 0.945-0.956), the AUPRC was 0.605 (95% CI, 0.593-0.616), and the CLR presented corresponding values of 0.815 (95% CI, 0.789-0.841) and 0.586 (95% CI, 0.553-0.619), respectively. We developed a machine learning-driven prediction model for SBI among febrile children, which works robustly despite missing values. And it showed superior performance compared to CLR in both internal validation and external validation.
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Affiliation(s)
- Bongjin Lee
- Department of Pediatrics, Seoul National University Hospital, Seoul, Korea
| | - Hyun Jung Chung
- Department of Emergency Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Korea
| | - Hyun Mi Kang
- Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Do Kyun Kim
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea
| | - Young Ho Kwak
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea
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10
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Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest. Crit Care Med 2022; 50:e162-e172. [PMID: 34406171 PMCID: PMC8810601 DOI: 10.1097/ccm.0000000000005286] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVES Prognostication of neurologic status among survivors of in-hospital cardiac arrests remains a challenging task for physicians. Although models such as the Cardiac Arrest Survival Post-Resuscitation In-hospital score are useful for predicting neurologic outcomes, they were developed using traditional statistical techniques. In this study, we derive and compare the performance of several machine learning models with each other and with the Cardiac Arrest Survival Post-Resuscitation In-hospital score for predicting the likelihood of favorable neurologic outcomes among survivors of resuscitation. DESIGN Analysis of the Get With The Guidelines-Resuscitation registry. SETTING Seven-hundred fifty-five hospitals participating in Get With The Guidelines-Resuscitation from January 1, 2001, to January 28, 2017. PATIENTS Adult in-hospital cardiac arrest survivors. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Of 117,674 patients in our cohort, 28,409 (24%) had a favorable neurologic outcome, as defined as survival with a Cerebral Performance Category score of less than or equal to 2 at discharge. Using patient characteristics, pre-existing conditions, prearrest interventions, and periarrest variables, we constructed logistic regression, support vector machines, random forests, gradient boosted machines, and neural network machine learning models to predict favorable neurologic outcome. Events prior to October 20, 2009, were used for model derivation, and all subsequent events were used for validation. The gradient boosted machine predicted favorable neurologic status at discharge significantly better than the Cardiac Arrest Survival Post-Resuscitation In-hospital score (C-statistic: 0.81 vs 0.73; p < 0.001) and outperformed all other machine learning models in terms of discrimination, calibration, and accuracy measures. Variables that were consistently most important for prediction across all models were duration of arrest, initial cardiac arrest rhythm, admission Cerebral Performance Category score, and age. CONCLUSIONS The gradient boosted machine algorithm was the most accurate for predicting favorable neurologic outcomes in in-hospital cardiac arrest survivors. Our results highlight the utility of machine learning for predicting neurologic outcomes in resuscitated patients.
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Choi Y, Park JH, Hong KJ, Ro YS, Song KJ, Shin SD. Development and validation of a prehospital-stage prediction tool for traumatic brain injury: a multicentre retrospective cohort study in Korea. BMJ Open 2022; 12:e055918. [PMID: 35022177 PMCID: PMC8756263 DOI: 10.1136/bmjopen-2021-055918] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVES Predicting diagnosis and prognosis of traumatic brain injury (TBI) at the prehospital stage is challenging; however, using comprehensive prehospital information and machine learning may improve the performance of the predictive model. We developed and tested predictive models for TBI that use machine learning algorithms using information that can be obtained in the prehospital stage. DESIGN This was a multicentre retrospective study. SETTING AND PARTICIPANTS This study was conducted at three tertiary academic emergency departments (EDs) located in an urban area of South Korea. The data from adult patients with severe trauma who were assessed by emergency medical service providers and transported to three participating hospitals between 2014 to 2018 were analysed. RESULTS We developed and tested five machine learning algorithms-logistic regression analyses, extreme gradient boosting, support vector machine, random forest and elastic net (EN)-to predict TBI, TBI with intracranial haemorrhage or injury (TBI-I), TBI with ED or admission result of admission or transferred (TBI with non-discharge (TBI-ND)) and TBI with ED or admission result of death (TBI-D). A total of 1169 patients were included in the final analysis, and the proportions of TBI, TBI-I, TBI-ND and TBI-D were 24.0%, 21.5%, 21.3% and 3.7%, respectively. The EN model yielded an area under receiver-operator curve of 0.799 for TBI, 0.844 for TBI-I, 0.811 for TBI-ND and 0.871 for TBI-D. The EN model also yielded the highest specificity and significant reclassification improvement. Variables related to loss of consciousness, Glasgow Coma Scale and light reflex were the three most important variables to predict all outcomes. CONCLUSION Our results inform the diagnosis and prognosis of TBI. Machine learning models resulted in significant performance improvement over that with logistic regression analyses, and the best performing model was EN.
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Affiliation(s)
- Yeongho Choi
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, South Korea
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Jeong Ho Park
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, South Korea
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Ki Jeong Hong
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, South Korea
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Young Sun Ro
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, South Korea
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Kyoung Jun Song
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, South Korea
- Department of Emergency Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, South Korea
| | - Sang Do Shin
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, South Korea
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, South Korea
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12
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Sánchez-Salmerón R, Gómez-Urquiza JL, Albendín-García L, Correa-Rodríguez M, Martos-Cabrera MB, Velando-Soriano A, Suleiman-Martos N. Machine learning methods applied to triage in emergency services: A systematic review. Int Emerg Nurs 2021; 60:101109. [PMID: 34952482 DOI: 10.1016/j.ienj.2021.101109] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 08/23/2021] [Accepted: 10/22/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND In emergency services is important to accurately assess and classify symptoms, which may be improved with the help of technology. One mechanism that could help and improve predictions from health records or patient flow is machine learning (ML). AIM To analyse the effectiveness of ML systems in triage for making predictions at the emergency department in comparison with other triage scales/scores. METHODS Following the PRISMA recommendations, a systematic review was conducted using CINAHL, Cochrane, Cuiden, Medline and Scopus databases with the search equation "Machine learning AND triage AND emergency". RESULTS Eleven studies were identified. The studies show that the use of ML methods consistently predict important outcomes like mortality, critical care outcomes and admission, and the need for hospitalization in comparison with scales like Emergency Severity Index or others. Among the ML models considered, XGBoost and Deep Neural Networks obtained the highest levels of prediction accuracy, while Logistic Regression performed obtained the worst values. CONCLUSIONS Machine learning methods can be a good instrument for helping triage process with the prediction of important emergency variables like mortality or the need for critical care or hospitalization.
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Affiliation(s)
| | - José L Gómez-Urquiza
- Faculty of Health Sciences, University of Granada, Avenida de la Ilustración N. 60, 18016 Granada, Spain.
| | - Luis Albendín-García
- Faculty of Health Sciences, University of Granada, Avenida de la Ilustración N. 60, 18016 Granada, Spain.
| | - María Correa-Rodríguez
- Faculty of Health Sciences, University of Granada, Avenida de la Ilustración N. 60, 18016 Granada, Spain.
| | - María Begoña Martos-Cabrera
- San Cecilio Clinical University Hospital, Andalusian Health Service, Avenida del Conocimiento s/n, 18016 Granada, Spain.
| | - Almudena Velando-Soriano
- San Cecilio Clinical University Hospital, Andalusian Health Service, Avenida del Conocimiento s/n, 18016 Granada, Spain.
| | - Nora Suleiman-Martos
- Faculty of Health Sciences, Ceuta University Campus, University of Granada, C/Cortadura del Valle SN, 51001 Ceuta, Spain.
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Ibrahim O, Sutherland HG, Lea RA, Nasrallah F, Maksemous N, Smith RA, Haupt LM, Griffiths LR. Discriminating head trauma outcomes using machine learning and genomics. J Mol Med (Berl) 2021; 100:303-312. [PMID: 34797388 DOI: 10.1007/s00109-021-02158-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 10/15/2021] [Accepted: 10/20/2021] [Indexed: 10/19/2022]
Abstract
A percentage of the population suffers prolonged and persistent post-concussion symptoms (PCS) following average head injuries or develops severe neurological dysfunction following minor head trauma. Genetic variants that may contribute to individual response to head trauma have been investigated in some studies, but to date none have explored the use of machine learning (ML) methods with genomic data to specifically explore outcomes of head trauma. Whole exome sequencing (WES) was completed for three groups of individuals (N = 60): (a) 16 individuals with severe neurological responses to minor head trauma, (b) 26 individuals with persistent PCS and (c) 18 individuals with normal recovery from concussion or mTBI. Gradient boosted tree algorithms were applied to the data using XGBoost. By using variants with CADD scores above 15 in the training set (randomly sampled 70%), we identified signatures that accurately distinguish to accurately distinguish the test groups with an average area under the curve (AUC) of 0.8 (SE = 0.019). Metrics including positive and negative prediction values, as well as kappa were all within acceptable range to support the prediction accuracy. This study illustrates how ML methods in combination with WES data have the potential to predict severe or prolonged responses to head trauma from healthy recovery. KEY MESSAGES: Linear association analysis has been inconclusive in concussion genetics. Non-linear methods as boosted trees can offer better insights in small samples. Strong discrimination trends can be achieved from exome data of cases and controls.
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Affiliation(s)
- Omar Ibrahim
- Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia
| | - Heidi G Sutherland
- Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia
| | - Rodney A Lea
- Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia
| | - Fatima Nasrallah
- The Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Neven Maksemous
- Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia
| | - Robert A Smith
- Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia
| | - Larisa M Haupt
- Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia
| | - Lyn R Griffiths
- Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia.
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14
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Nishioka N, Kobayashi D, Kiguchi T, Irisawa T, Yamada T, Yoshiya K, Park C, Nishimura T, Ishibe T, Yagi Y, Kishimoto M, Kim SH, Hayashi Y, Sogabe T, Morooka T, Sakamoto H, Suzuki K, Nakamura F, Matsuyama T, Okada Y, Matsui S, Yoshimura S, Kimata S, Kawai S, Makino Y, Kitamura T, Iwami T. Development and validation of early prediction for neurological outcome at 90 days after return of spontaneous circulation in out-of-hospital cardiac arrest. Resuscitation 2021; 168:142-150. [PMID: 34619295 DOI: 10.1016/j.resuscitation.2021.09.027] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 09/08/2021] [Accepted: 09/26/2021] [Indexed: 11/24/2022]
Abstract
AIM To develop and validate a model for the early prediction of long-term neurological outcome in patients with non-traumatic out-of-hospital cardiac arrest (OHCA). METHODS We analysed multicentre OHCA registry data of adult patients with non-traumatic OHCA who experienced return of spontaneous circulation (ROSC) and had been admitted to the intensive care unit between 2013 and 2017. We allocated 1329 (2013-2015) and 1025 patients (2016-2017) to the derivation and validation sets, respectively. The primary outcome was the dichotomized cerebral performance category (CPC) at 90 days, defined as good (CPC 1-2) or poor (CPC 3-5). We developed 2 models: model 1 included variables without laboratory data, and model 2 included variables with laboratory data available immediately after ROSC. Logistic regression with least absolute shrinkage and selection operator regularization was employed for model development. Measures of discrimination, accuracy, and calibration (C-statistics, Brier score, calibration plot, and net benefit) were assessed in the validation set. RESULTS The C-statistic (95% confidence intervals) of models 1 and 2 in the validation set was 0.947 (0.930-0.964) and 0.950 (0.934-0.966), respectively. The Brier score of models 1 and 2 in the validation set was 0.0622 and 0.0606, respectively. The calibration plot showed that both models were well-calibrated to the observed outcome. Decision curve analysis indicated that model 2 was similar to model 1. CONCLUSION The prediction tool containing detailed in-hospital information showed good performance for predicting neurological outcome at 90 days immediately after ROSC in patients with OHCA.
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Affiliation(s)
- Norihiro Nishioka
- Department of Preventive Services, Kyoto University School of Public Health, Kyoto, Japan
| | | | - Takeyuki Kiguchi
- Critical Care and Trauma Center, Osaka General Medical Center, Osaka, Japan
| | - Taro Irisawa
- Department of Traumatology and Acute Critical Medicine, Osaka University Graduate School of Medicine, Suita, Japan
| | - Tomoki Yamada
- Emergency and Critical Care Medical Center, Osaka Police Hospital, Osaka, Japan
| | - Kazuhisa Yoshiya
- Department of Emergency and Critical Care Medicine, Kansai Medical University, Takii Hospital, Moriguchi, Japan
| | - Changhwi Park
- Department of Emergency Medicine, Tane General Hospital, Osaka, Japan
| | - Tetsuro Nishimura
- Department of Critical Care Medicine, Osaka City University, Osaka, Japan
| | - Takuya Ishibe
- Department of Emergency and Critical Care Medicine, Kindai University School of Medicine, Osaka-Sayama, Japan
| | - Yoshiki Yagi
- Osaka Mishima Emergency Critical Care Center, Takatsuki, Japan
| | - Masafumi Kishimoto
- Osaka Prefectural Nakakawachi Medical Center of Acute Medicine, Higashi-Osaka, Japan
| | - Sung-Ho Kim
- Senshu Trauma and Critical Care Center, Osaka, Japan
| | - Yasuyuki Hayashi
- Senri Critical Care Medical Center, Saiseikai Senri Hospital, Suita, Japan
| | - Taku Sogabe
- Traumatology and Critical Care Medical Center, National Hospital Organization Osaka National Hospital, Osaka, Japan
| | - Takaya Morooka
- Emergency and Critical Care Medical Center, Osaka City General Hospital, Osaka, Japan
| | - Haruko Sakamoto
- Department of Pediatrics, Osaka Red Cross Hospital, Osaka, Japan
| | - Keitaro Suzuki
- Emergency and Critical Care Medical Center, Kishiwada Tokushukai Hospital, Osaka, Japan
| | - Fumiko Nakamura
- Department of Emergency and Critical Care Medicine, Kansai Medical University, Hirakata, Osaka, Japan
| | - Tasuku Matsuyama
- Department of Emergency Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yohei Okada
- Department of Preventive Services, Kyoto University School of Public Health, Kyoto, Japan
| | - Satoshi Matsui
- Division of Environmental Medicine and Population Sciences, Department of Social and Environmental Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Satoshi Yoshimura
- Department of Preventive Services, Kyoto University School of Public Health, Kyoto, Japan
| | - Shunsuke Kimata
- Department of Preventive Services, Kyoto University School of Public Health, Kyoto, Japan
| | - Shunsuke Kawai
- Department of Preventive Services, Kyoto University School of Public Health, Kyoto, Japan
| | - Yuto Makino
- Department of Preventive Services, Kyoto University School of Public Health, Kyoto, Japan
| | - Tetsuhisa Kitamura
- Division of Environmental Medicine and Population Sciences, Department of Social and Environmental Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Taku Iwami
- Kyoto University Health Services, Kyoto, Japan
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15
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Park JH, Choi J, Lee S, Shin SD, Song KJ. Use of Time-to-Event Analysis to Develop On-Scene Return of Spontaneous Circulation Prediction for Out-of-Hospital Cardiac Arrest Patients. Ann Emerg Med 2021; 79:132-144. [PMID: 34417073 DOI: 10.1016/j.annemergmed.2021.07.121] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 07/05/2021] [Accepted: 07/14/2021] [Indexed: 12/23/2022]
Abstract
STUDY OBJECTIVE We aimed to train and validate the time to on-scene return of spontaneous circulation prediction models using time-to-event analysis among out-of-hospital cardiac arrest patients. METHODS Using a Korean population-based out-of-hospital cardiac arrest registry, we selected a total of 105,215 adults with presumed cardiac etiologies between 2013 and 2018. Patients from 2013 to 2017 and from 2018 were analyzed for training and test, respectively. We developed 4 time-to-event analyzing models (Cox proportional hazard [Cox], random survival forest, extreme gradient boosting survival, and DeepHit) and 4 classification models (logistic regression, random forest, extreme gradient boosting, and feedforward neural network). Patient characteristics and Utstein elements collected at the scene were used as predictors. Discrimination and calibration were evaluated by Harrell's C-index and integrated Brier score. RESULTS Among the 105,215 patients (mean age 70 years and 64% men), 86,314 and 18,901 patients belonged to the training and test sets, respectively. On-scene return of spontaneous circulation was achieved in 5,240 (6.1%) patients in the former set and 1,709 (9.0%) patients in the latter. The proportion of emergency medical services (EMS) management was higher and scene time interval longer in the latter. Median time from EMS scene arrival to on-scene return of spontaneous circulation was 8 minutes for both datasets. Classification models showed similar discrimination and poor calibration power compared to survival models; Cox showed high discrimination with the best calibration (C-index [95% confidence interval]: 0.873 [0.865 to 0.882]; integrated Brier score at 30 minutes: 0.060). CONCLUSION Incorporating time-to-event analysis could lead to improved performance in prediction models and contribute to personalized field EMS resuscitation decisions.
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Affiliation(s)
- Jeong Ho Park
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea; Department of Emergency Medicine, Seoul National University College of Medicine and Hospital, Seoul, Korea; Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, Korea
| | - Jinwook Choi
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea; Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, Korea.
| | - SangMyeong Lee
- School of Electrical Engineering, Undergraduate School of Korea University, Seoul, Korea
| | - Sang Do Shin
- Department of Emergency Medicine, Seoul National University College of Medicine and Hospital, Seoul, Korea; Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, Korea
| | - Kyoung Jun Song
- Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, Korea; Department of Emergency Medicine, Seoul National University College of Medicine and Seoul National University Boramae Medical Center, Seoul, Korea
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Heo JH, Kim T, Shin J, Suh GJ, Kim J, Jung YS, Park SM, Kim S. Prediction of Neurological Outcomes in Out-of-hospital Cardiac Arrest Survivors Immediately after Return of Spontaneous Circulation: Ensemble Technique with Four Machine Learning Models. J Korean Med Sci 2021; 36:e187. [PMID: 34282605 PMCID: PMC8289719 DOI: 10.3346/jkms.2021.36.e187] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 06/14/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND We performed this study to establish a prediction model for 1-year neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) immediately after ROSC using machine learning methods. METHODS We performed a retrospective analysis of an OHCA survivor registry. Patients aged ≥ 18 years were included. Study participants who had registered between March 31, 2013 and December 31, 2018 were divided into a develop dataset (80% of total) and an internal validation dataset (20% of total), and those who had registered between January 1, 2019 and December 31, 2019 were assigned to an external validation dataset. Four machine learning methods, including random forest, support vector machine, ElasticNet and extreme gradient boost, were implemented to establish prediction models with the develop dataset, and the ensemble technique was used to build the final prediction model. The prediction performance of the model in the internal validation and the external validation dataset was described with accuracy, area under the receiver-operating characteristic curve, area under the precision-recall curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Futhermore, we established multivariable logistic regression models with the develop set and compared prediction performance with the ensemble models. The primary outcome was an unfavorable 1-year neurological outcome. RESULTS A total of 1,207 patients were included in the study. Among them, 631, 139, and 153 were assigned to the develop, the internal validation and the external validation datasets, respectively. Prediction performance metrics for the ensemble prediction model in the internal validation dataset were as follows: accuracy, 0.9620 (95% confidence interval [CI], 0.9352-0.9889); area under receiver-operator characteristics curve, 0.9800 (95% CI, 0.9612-0.9988); area under precision-recall curve, 0.9950 (95% CI, 0.9860-1.0000); sensitivity, 0.9594 (95% CI, 0.9245-0.9943); specificity, 0.9714 (95% CI, 0.9162-1.0000); PPV, 0.9916 (95% CI, 0.9752-1.0000); NPV, 0.8718 (95% CI, 0.7669-0.9767). Prediction performance metrics for the model in the external validation dataset were as follows: accuracy, 0.8509 (95% CI, 0.7825-0.9192); area under receiver-operator characteristics curve, 0.9301 (95% CI, 0.8845-0.9756); area under precision-recall curve, 0.9476 (95% CI, 0.9087-0.9867); sensitivity, 0.9595 (95% CI, 0.9145-1.0000); specificity, 0.6500 (95% CI, 0.5022-0.7978); PPV, 0.8353 (95% CI, 0.7564-0.9142); NPV, 0.8966 (95% CI, 0.7857-1.0000). All the prediction metrics were higher in the ensemble models, except NPVs in both the internal and the external validation datasets. CONCLUSION We established an ensemble prediction model for prediction of unfavorable 1-year neurological outcomes in OHCA survivors using four machine learning methods. The prediction performance of the ensemble model was higher than the multivariable logistic regression model, while its performance was slightly decreased in the external validation dataset.
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Affiliation(s)
- Ji Han Heo
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea
| | - Taegyun Kim
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea
- Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Korea.
| | - Jonghwan Shin
- Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Emergency Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Korea.
| | - Gil Joon Suh
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea
- Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Joonghee Kim
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Yoon Sun Jung
- Division of Critical Care Medicine, Seoul National University Hospital, Seoul, Korea
| | - Seung Min Park
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Sungwan Kim
- Department of Biomedical Engineering, College of Medicine and Institute of Medical & Biological Engineering, Medical Research Center, Seoul National University, Seoul, Korea
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Choi SW, Ko T, Hong KJ, Kim KH. Machine Learning-Based Prediction of Korean Triage and Acuity Scale Level in Emergency Department Patients. Healthc Inform Res 2019; 25:305-312. [PMID: 31777674 PMCID: PMC6859273 DOI: 10.4258/hir.2019.25.4.305] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 10/21/2019] [Accepted: 10/21/2019] [Indexed: 12/23/2022] Open
Abstract
Objectives Triage is a process to accurately assess and classify symptoms to identify and provide rapid treatment to patients. The Korean Triage and Acuity Scale (KTAS) is used as a triage instrument in all emergency centers. The aim of this study was to train and compare machine learning models to predict KTAS levels. Methods This was a cross-sectional study using data from a single emergency department of a tertiary university hospital. Information collected during triage was used in the analysis. Logistic regression, random forest, and XGBoost were used to predict the KTAS level. Results The models with the highest area under the receiver operating characteristic curve (AUROC) were the random forest and XGBoost models trained on the entire dataset (AUROC = 0.922, 95% confidence interval 0.917-0.925 and AUROC = 0.922, 95% confidence interval 0.918-0.925, respectively). The AUROC of the models trained on the clinical data was higher than that of models trained on text data only, but the models trained on all variables had the highest AUROC among similar machine learning models. Conclusions Machine learning can robustly predict the KTAS level at triage, which may have many possibilities for use, and the addition of text data improves the predictive performance compared to that achieved by using structured data alone.
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Affiliation(s)
- Sae Won Choi
- Office of Hospital Information, Seoul National University Hospital, Seoul, Korea.,Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea
| | - Taehoon Ko
- Office of Hospital Information, Seoul National University Hospital, Seoul, Korea
| | - Ki Jeong Hong
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea.,Laboratory of Emergency Medical Services, Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea
| | - Kyung Hwan Kim
- Office of Hospital Information, Seoul National University Hospital, Seoul, Korea.,Department of Thoracic and Cardiovascular Surgery, Seoul National University College of Medicine, Seoul, Korea
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