Machine learning-based analysis of regional differences in out-of-hospital cardiopulmonary arrest outcomes and resuscitation interventions in Japan
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 serv...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-43210-x |
| _version_ | 1797452815038152704 |
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| author | Yasuyuki Kawai Koji Yamamoto Keita Miyazaki Hideki Asai Hidetada Fukushima |
| author_facet | Yasuyuki Kawai Koji Yamamoto Keita Miyazaki Hideki Asai Hidetada Fukushima |
| author_sort | Yasuyuki Kawai |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T15:13:09Z |
| format | Article |
| id | doaj.art-d3877f951e404e7ebe2477b370285715 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:13:09Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-d3877f951e404e7ebe2477b3702857152023-11-26T13:15:10ZengNature PortfolioScientific Reports2045-23222023-09-0113111010.1038/s41598-023-43210-xMachine learning-based analysis of regional differences in out-of-hospital cardiopulmonary arrest outcomes and resuscitation interventions in JapanYasuyuki Kawai0Koji Yamamoto1Keita Miyazaki2Hideki Asai3Hidetada Fukushima4Department of Emergency and Critical Care Medicine, Nara Medical UniversityDepartment of Emergency and Critical Care Medicine, Nara Medical UniversityDepartment of Emergency and Critical Care Medicine, Nara Medical UniversityDepartment of Emergency and Critical Care Medicine, Nara Medical UniversityDepartment of Emergency and Critical Care Medicine, Nara Medical UniversityAbstract 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.https://doi.org/10.1038/s41598-023-43210-x |
| spellingShingle | Yasuyuki Kawai Koji Yamamoto Keita Miyazaki Hideki Asai Hidetada Fukushima Machine learning-based analysis of regional differences in out-of-hospital cardiopulmonary arrest outcomes and resuscitation interventions in Japan Scientific Reports |
| title | Machine learning-based analysis of regional differences in out-of-hospital cardiopulmonary arrest outcomes and resuscitation interventions in Japan |
| title_full | Machine learning-based analysis of regional differences in out-of-hospital cardiopulmonary arrest outcomes and resuscitation interventions in Japan |
| title_fullStr | Machine learning-based analysis of regional differences in out-of-hospital cardiopulmonary arrest outcomes and resuscitation interventions in Japan |
| title_full_unstemmed | Machine learning-based analysis of regional differences in out-of-hospital cardiopulmonary arrest outcomes and resuscitation interventions in Japan |
| title_short | Machine learning-based analysis of regional differences in out-of-hospital cardiopulmonary arrest outcomes and resuscitation interventions in Japan |
| title_sort | machine learning based analysis of regional differences in out of hospital cardiopulmonary arrest outcomes and resuscitation interventions in japan |
| url | https://doi.org/10.1038/s41598-023-43210-x |
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