Developing a Time-Adaptive Prediction Model for Out-of-Hospital Cardiac Arrest: Nationwide Cohort Study in Korea
BackgroundOut-of-hospital cardiac arrest (OHCA) is a serious public health issue, and predicting the prognosis of OHCA patients can assist clinicians in making decisions about the treatment of patients, use of hospital resources, or termination of resuscitation. O...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2021-07-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2021/7/e28361/ |
| _version_ | 1797735898990772224 |
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| author | Ji Woong Kim Juhyung Ha Taerim Kim Hee Yoon Sung Yeon Hwang Ik Joon Jo Tae Gun Shin Min Seob Sim Kyunga Kim Won Chul Cha |
| author_facet | Ji Woong Kim Juhyung Ha Taerim Kim Hee Yoon Sung Yeon Hwang Ik Joon Jo Tae Gun Shin Min Seob Sim Kyunga Kim Won Chul Cha |
| author_sort | Ji Woong Kim |
| collection | DOAJ |
| description |
BackgroundOut-of-hospital cardiac arrest (OHCA) is a serious public health issue, and predicting the prognosis of OHCA patients can assist clinicians in making decisions about the treatment of patients, use of hospital resources, or termination of resuscitation.
ObjectiveThis study aimed to develop a time-adaptive conditional prediction model (TACOM) to predict clinical outcomes every minute.
MethodsWe performed a retrospective observational study using data from the Korea OHCA Registry in South Korea. In this study, we excluded patients with trauma, those who experienced return of spontaneous circulation before arriving in the emergency department (ED), and those who did not receive cardiopulmonary resuscitation (CPR) in the ED. We selected patients who received CPR in the ED. To develop the time-adaptive prediction model, we organized the training data set as ongoing CPR patients by the minute. A total of 49,669 patients were divided into 39,602 subjects for training and 10,067 subjects for validation. We compared random forest, LightGBM, and artificial neural networks as the prediction model methods. Model performance was quantified using the prediction probability of the model, area under the receiver operating characteristic curve (AUROC), and area under the precision recall curve.
ResultsAmong the three algorithms, LightGBM showed the best performance. From 0 to 30 min, the AUROC of the TACOM for predicting good neurological outcomes ranged from 0.910 (95% CI 0.910-0.911) to 0.869 (95% CI 0.865-0.871), whereas that for survival to hospital discharge ranged from 0.800 (95% CI 0.797-0.800) to 0.734 (95% CI 0.736-0.740). The prediction probability of the TACOM showed similar flow with cohort data based on a comparison with the conventional model’s prediction probability.
ConclusionsThe TACOM predicted the clinical outcome of OHCA patients per minute. This model for predicting patient outcomes by the minute can assist clinicians in making rational decisions for OHCA patients. |
| first_indexed | 2024-03-12T13:05:44Z |
| format | Article |
| id | doaj.art-c3c673b01cc24eb78030876bff929b82 |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-12T13:05:44Z |
| publishDate | 2021-07-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-c3c673b01cc24eb78030876bff929b822023-08-28T17:00:01ZengJMIR PublicationsJournal of Medical Internet Research1438-88712021-07-01237e2836110.2196/28361Developing a Time-Adaptive Prediction Model for Out-of-Hospital Cardiac Arrest: Nationwide Cohort Study in KoreaJi Woong Kimhttps://orcid.org/0000-0003-0593-0461Juhyung Hahttps://orcid.org/0000-0002-6596-5116Taerim Kimhttps://orcid.org/0000-0002-8149-1205Hee Yoonhttps://orcid.org/0000-0002-1297-9813Sung Yeon Hwanghttps://orcid.org/0000-0002-1352-3009Ik Joon Johttps://orcid.org/0000-0001-8098-1862Tae Gun Shinhttps://orcid.org/0000-0001-9657-1040Min Seob Simhttps://orcid.org/0000-0001-6645-3684Kyunga Kimhttps://orcid.org/0000-0002-0865-2236Won Chul Chahttps://orcid.org/0000-0002-2778-2992 BackgroundOut-of-hospital cardiac arrest (OHCA) is a serious public health issue, and predicting the prognosis of OHCA patients can assist clinicians in making decisions about the treatment of patients, use of hospital resources, or termination of resuscitation. ObjectiveThis study aimed to develop a time-adaptive conditional prediction model (TACOM) to predict clinical outcomes every minute. MethodsWe performed a retrospective observational study using data from the Korea OHCA Registry in South Korea. In this study, we excluded patients with trauma, those who experienced return of spontaneous circulation before arriving in the emergency department (ED), and those who did not receive cardiopulmonary resuscitation (CPR) in the ED. We selected patients who received CPR in the ED. To develop the time-adaptive prediction model, we organized the training data set as ongoing CPR patients by the minute. A total of 49,669 patients were divided into 39,602 subjects for training and 10,067 subjects for validation. We compared random forest, LightGBM, and artificial neural networks as the prediction model methods. Model performance was quantified using the prediction probability of the model, area under the receiver operating characteristic curve (AUROC), and area under the precision recall curve. ResultsAmong the three algorithms, LightGBM showed the best performance. From 0 to 30 min, the AUROC of the TACOM for predicting good neurological outcomes ranged from 0.910 (95% CI 0.910-0.911) to 0.869 (95% CI 0.865-0.871), whereas that for survival to hospital discharge ranged from 0.800 (95% CI 0.797-0.800) to 0.734 (95% CI 0.736-0.740). The prediction probability of the TACOM showed similar flow with cohort data based on a comparison with the conventional model’s prediction probability. ConclusionsThe TACOM predicted the clinical outcome of OHCA patients per minute. This model for predicting patient outcomes by the minute can assist clinicians in making rational decisions for OHCA patients.https://www.jmir.org/2021/7/e28361/ |
| spellingShingle | Ji Woong Kim Juhyung Ha Taerim Kim Hee Yoon Sung Yeon Hwang Ik Joon Jo Tae Gun Shin Min Seob Sim Kyunga Kim Won Chul Cha Developing a Time-Adaptive Prediction Model for Out-of-Hospital Cardiac Arrest: Nationwide Cohort Study in Korea Journal of Medical Internet Research |
| title | Developing a Time-Adaptive Prediction Model for Out-of-Hospital Cardiac Arrest: Nationwide Cohort Study in Korea |
| title_full | Developing a Time-Adaptive Prediction Model for Out-of-Hospital Cardiac Arrest: Nationwide Cohort Study in Korea |
| title_fullStr | Developing a Time-Adaptive Prediction Model for Out-of-Hospital Cardiac Arrest: Nationwide Cohort Study in Korea |
| title_full_unstemmed | Developing a Time-Adaptive Prediction Model for Out-of-Hospital Cardiac Arrest: Nationwide Cohort Study in Korea |
| title_short | Developing a Time-Adaptive Prediction Model for Out-of-Hospital Cardiac Arrest: Nationwide Cohort Study in Korea |
| title_sort | developing a time adaptive prediction model for out of hospital cardiac arrest nationwide cohort study in korea |
| url | https://www.jmir.org/2021/7/e28361/ |
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