Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMS
Background: Shock-refractory ventricular fibrillation (VF) or ventricular tachycardia (VT) is a treatment challenge in out-of-hospital cardiac arrest (OHCA). This study aimed to develop and validate machine learning models that could be implemented by emergency medical services (EMS) to predict refr...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-06-01
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| Series: | Resuscitation Plus |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666520424000572 |
| _version_ | 1797257717645049856 |
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| author | Rayhan Erlangga Rahadian Yohei Okada Nur Shahidah Dehan Hong Yih Yng Ng Michael Y.C. Chia Han Nee Gan Benjamin S.H. Leong Desmond R. Mao Wei Ming Ng Nausheen Edwin Doctor Marcus Eng Hock Ong |
| author_facet | Rayhan Erlangga Rahadian Yohei Okada Nur Shahidah Dehan Hong Yih Yng Ng Michael Y.C. Chia Han Nee Gan Benjamin S.H. Leong Desmond R. Mao Wei Ming Ng Nausheen Edwin Doctor Marcus Eng Hock Ong |
| author_sort | Rayhan Erlangga Rahadian |
| collection | DOAJ |
| description | Background: Shock-refractory ventricular fibrillation (VF) or ventricular tachycardia (VT) is a treatment challenge in out-of-hospital cardiac arrest (OHCA). This study aimed to develop and validate machine learning models that could be implemented by emergency medical services (EMS) to predict refractory VF/VT in OHCA patients. Methods: This was a retrospective study examining adult non-traumatic OHCA patients brought into the emergency department by Singapore EMS from the Pan-Asian Resuscitation Outcomes Study (PAROS) registry. Data from April 2010 to March 2020 were extracted for this study. Refractory VF/VT was defined as VF/VT persisting or recurring after at least one shock. Features were selected based on expert clinical opinion and availability to dispatch prior to arrival at scene. Multivariable logistic regression (MVR), LASSO and random forest (RF) models were investigated. Model performance was evaluated using receiver operator characteristic (ROC) area under curve (AUC) analysis and calibration plots. Results: 20,713 patients were included in this study, of which 860 (4.1%) fulfilled the criteria for refractory VF/VT. All models performed comparably and were moderately well-calibrated. ROC-AUC were 0.732 (95% CI, 0.695 – 0.769) for MVR, 0.738 (95% CI, 0.701 – 0.774) for LASSO, and 0.731 (95% CI, 0.690 – 0.773) for RF. The shared important predictors across all models included male gender and public location. Conclusion: The machine learning models developed have potential clinical utility to improve outcomes in cases of refractory VF/VT OHCA. Prediction of refractory VF/VT prior to arrival at patient’s side may allow for increased options for intervention both by EMS and tertiary care centres. |
| first_indexed | 2024-04-24T22:42:05Z |
| format | Article |
| id | doaj.art-eebd8bd8cb454128b01394b1d2caecc1 |
| institution | Directory Open Access Journal |
| issn | 2666-5204 |
| language | English |
| last_indexed | 2024-04-24T22:42:05Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Resuscitation Plus |
| spelling | doaj.art-eebd8bd8cb454128b01394b1d2caecc12024-03-19T04:19:16ZengElsevierResuscitation Plus2666-52042024-06-0118100606Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMSRayhan Erlangga Rahadian0Yohei Okada1Nur Shahidah2Dehan Hong3Yih Yng Ng4Michael Y.C. Chia5Han Nee Gan6Benjamin S.H. Leong7Desmond R. Mao8Wei Ming Ng9Nausheen Edwin Doctor10Marcus Eng Hock Ong11Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, SingaporeHealth Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore; Department of Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Corresponding author at: Health Services and Systems Research, Duke-NUS Medical School, National University of Singapore, Singapore and Department of Preventive Services, School of Public Health, Kyoto University, Kyoto, Japan.Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore; Pre-hospital and Emergency Research Centre, Duke-NUS Medical School, SingaporeEmergency Medical Services Department, Singapore Civil Defence Force, SingaporeLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore; Department of Preventive and Population Medicine, Tan Tock Seng Hospital, Singapore, SingaporeEmergency Department, Tan Tock Seng Hospital, Singapore, SingaporeAccident & Emergency, Changi General Hospital, Singapore, SingaporeEmergency Medicine Department, National University Hospital, Singapore, SingaporeDepartment of Acute and Emergency Care, Khoo Teck Puat Hospital, Singapore, SingaporeEmergency Medicine Department, Ng Teng Fong General Hospital, Singapore, SingaporeDepartment of Emergency Medicine, Sengkang General Hospital, Singapore, SingaporeHealth Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore, SingaporeBackground: Shock-refractory ventricular fibrillation (VF) or ventricular tachycardia (VT) is a treatment challenge in out-of-hospital cardiac arrest (OHCA). This study aimed to develop and validate machine learning models that could be implemented by emergency medical services (EMS) to predict refractory VF/VT in OHCA patients. Methods: This was a retrospective study examining adult non-traumatic OHCA patients brought into the emergency department by Singapore EMS from the Pan-Asian Resuscitation Outcomes Study (PAROS) registry. Data from April 2010 to March 2020 were extracted for this study. Refractory VF/VT was defined as VF/VT persisting or recurring after at least one shock. Features were selected based on expert clinical opinion and availability to dispatch prior to arrival at scene. Multivariable logistic regression (MVR), LASSO and random forest (RF) models were investigated. Model performance was evaluated using receiver operator characteristic (ROC) area under curve (AUC) analysis and calibration plots. Results: 20,713 patients were included in this study, of which 860 (4.1%) fulfilled the criteria for refractory VF/VT. All models performed comparably and were moderately well-calibrated. ROC-AUC were 0.732 (95% CI, 0.695 – 0.769) for MVR, 0.738 (95% CI, 0.701 – 0.774) for LASSO, and 0.731 (95% CI, 0.690 – 0.773) for RF. The shared important predictors across all models included male gender and public location. Conclusion: The machine learning models developed have potential clinical utility to improve outcomes in cases of refractory VF/VT OHCA. Prediction of refractory VF/VT prior to arrival at patient’s side may allow for increased options for intervention both by EMS and tertiary care centres.http://www.sciencedirect.com/science/article/pii/S2666520424000572Machine learningPrediction modelOHCAECPRRefractory VF |
| spellingShingle | Rayhan Erlangga Rahadian Yohei Okada Nur Shahidah Dehan Hong Yih Yng Ng Michael Y.C. Chia Han Nee Gan Benjamin S.H. Leong Desmond R. Mao Wei Ming Ng Nausheen Edwin Doctor Marcus Eng Hock Ong Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMS Resuscitation Plus Machine learning Prediction model OHCA ECPR Refractory VF |
| title | Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMS |
| title_full | Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMS |
| title_fullStr | Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMS |
| title_full_unstemmed | Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMS |
| title_short | Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMS |
| title_sort | machine learning prediction of refractory ventricular fibrillation in out of hospital cardiac arrest using features available to ems |
| topic | Machine learning Prediction model OHCA ECPR Refractory VF |
| url | http://www.sciencedirect.com/science/article/pii/S2666520424000572 |
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