Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes

Kwon, Joon‐myoung; Jeon, Ki‐Hyun; Kim, Hyue Mee; Kim, Min Jeong; Lim, Soo Min; Kim, Kyung Hee; Song, Pil Sang; Park, Jinsik; Choi, Rak Kyeong; Oh, Byung Hee

doi:10.1016/j.resuscitation.2019.04.007

Cited by 71 publications

(70 citation statements)

References 23 publications

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“…‡The alternative hypothesis for this p-value was that there is a difference between the ensemble model, combining artificial intelligence and the ESI, and the other predictive methods Meanwhile, deep learning includes feature learning, which allows the model to automatically learn the relationships and characteristics between input variables required to perform a task [30]. As shown in our previous studies, deep learning could be used to understand the connection between features and outperformed conventional and other machine learning methods [9,11,31]. It is important to note that feature learning is not designed by humans in deep learning.…”

Section: Discussionmentioning

confidence: 99%

“…The goal of this study was to develop and validate an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care of patients in EMSs accurately. Deep learning could overcome the limitations of conventional statistical methods and has recently achieved state-of-the-art performance in several domains, including medical imaging and outcome prediction [8][9][10]. To the best of our knowledge, this study is the first to predict severity in EMS using an AI algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services

Kang

Cho²,

Kwon³

et al. 2020

Scand J Trauma Resusc Emerg Med

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Background: In emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS. Methods: We conducted a retrospective observation cohort study. The algorithm was established using development data from the Korean national emergency department information system, which were collected during visits in real time from 151 emergency departments (EDs). We validated the algorithm using EMS run sheets from two EDs. The study subjects comprised adult patients who visited EDs. The endpoint was critical care, and we used age, sex, chief complaint, symptom onset to arrival time, trauma, and initial vital signs as the predicted variables. Results: The number of patients in the development data was 8,981,181, and the validation data comprised 2604 EMS run sheets from two hospitals. The area under the receiver operating characteristic curve of the algorithm to predict the critical care was 0.867 (95% confidence interval, [0.864-0.871]). This result outperformed the Emergency Severity Index (0.839 [0.831-0.846]), Korean Triage and Acuity System (0.824 [0.815-0.832]), National Early Warning Score (0.741 [0.734-0.748]), and Modified Early Warning Score (0.696 [0.691-0.699]). Conclusions: The AI algorithm accurately predicted the need for the critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services

Kang

Cho²,

Kwon³

et al. 2020

Scand J Trauma Resusc Emerg Med

Self Cite

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“…the ability to detect correlations between independent variables in large complex data sets and to find trends or patterns in subsets of data. Recently published studies have shown the potential of machine learning regarding OHCA prediction with very good performance [7,8]. In a study from Kwon et al, over 36,000 OHCA patients were included, and a deep learning-based OHCA prognostic system showed an impressive performance to predict neurologic recovery and survival to discharge of OHCA patients, with an AUC of 0.953 ± 0.001.…”

Section: Discussionmentioning

confidence: 99%

“…However, no information regarding the long-term outcome in these patients was presented, and the overall mortality was very high, inherently increasing the possibility to reach high AUCs. The cohort used in the study was heterogeneous including more than 8000 patients (22%) with cardiac arrest of a traumatic cause, known to have a poor outcome and therefore probably contributing significantly to the predictive performance of the models [8]. In a population with about 50% survival, as for OHCA Increased prediction performance when adding variables.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, machine learning models have been used to predict the outcome in out-of-hospital cardiac arrest (OHCA) cohorts with high accuracy early in the chain of resuscitation, where overall mortality is above 80% [7,8], but these models are not applicable to patients admitted to ICUs after OHCA. Several factors are known to influence the overall outcome in the OHCA population, including patients' age and comorbidities, cardiac arrest characteristics and status on admission [9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care

et al. 2020

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Background: Pre-hospital circumstances, cardiac arrest characteristics, comorbidities and clinical status on admission are strongly associated with outcome after out-of-hospital cardiac arrest (OHCA). Early prediction of outcome may inform prognosis, tailor therapy and help in interpreting the intervention effect in heterogenous clinical trials. This study aimed to create a model for early prediction of outcome by artificial neural networks (ANN) and use this model to investigate intervention effects on classes of illness severity in cardiac arrest patients treated with targeted temperature management (TTM). Methods: Using the cohort of the TTM trial, we performed a post hoc analysis of 932 unconscious patients from 36 centres with OHCA of a presumed cardiac cause. The patient outcome was the functional outcome, including survival at 180 days follow-up using a dichotomised Cerebral Performance Category (CPC) scale with good functional outcome defined as CPC 1-2 and poor functional outcome defined as CPC 3-5. Outcome prediction and severity class assignment were performed using a supervised machine learning model based on ANN. Results: The outcome was predicted with an area under the receiver operating characteristic curve (AUC) of 0.891 using 54 clinical variables available on admission to hospital, categorised as background, pre-hospital and admission data. Corresponding models using background, pre-hospital or admission variables separately had inferior prediction performance. When comparing the ANN model with a logistic regression-based model on the same cohort, the ANN model performed significantly better (p = 0.029). A simplified ANN model showed promising performance with an AUC above 0.852 when using three variables only: age, time to ROSC and first monitored rhythm. The ANN-stratified analyses showed similar intervention effect of TTM to 33°C or 36°C in predefined classes with different risk of a poor outcome.

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Artificial intelligence in emergency medicine: A scoping review

et al. 2020

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Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes

Cited by 71 publications

References 23 publications

Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services

Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services

Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care

Artificial intelligence in emergency medicine: A scoping review

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