Outcome prediction of out-of-hospital cardiac arrest with presumed cardiac aetiology using an advanced machine learning technique

Seki, Tomohisa; Tamura, Tomoyoshi; Suzuki, Mitsuaki

doi:10.1016/j.resuscitation.2019.06.006

Cited by 38 publications

(36 citation statements)

References 28 publications

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“…23% of included sample) whose outcome is predictable with high probability of death or poor neurological recovery was not excluded. A separate study by Tomohisa et al reported favourable prediction capability of a random forest model for 1-year survival of patients with OHCA of presumed cardiac aetiology (AUROC; 0.943), using 35 prehospital variables 16 . However, the aetiology of OHCA is not always evident in a prehospital setting.…”

Section: Discussionmentioning

confidence: 99%

Early outcome prediction for out-of-hospital cardiac arrest with initial shockable rhythm using machine learning models

et al. 2021

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Aim: Early outcome prediction for out-of-hospital cardiac arrest with initial shockable rhythm is useful in selecting the choice of resuscitative treatment by clinicians. This study aimed to develop and validate a machine learning-based outcome prediction model for out-of-hospital cardiac arrest with initial shockable rhythm, which can be used on patient's arrival at the hospital. Methods: Data were obtained from a nationwide out-of-hospital cardiac arrest registry in Japan. Of 43,350 out-of-hospital cardiac arrest patients with initial shockable rhythm registered between 2013 and 2017, patients aged <18 years and those with cardiac arrest caused by external factors were excluded. Subjects were classified into training (n = 23,668, 2013-2016 data) and test (n = 6381, data from 2017) sets for validation. Only 19 prehospital variables were used for the outcome prediction. The primary outcome was death at 1 month or survival with poor neurological function (cerebral performance category 3-5; "poor" outcome). Several machine learning models, including those based on logistic regression, support vector machine, random forest, and multilayer perceptron classifiers were compared. Results: In validation analyses, all machine learning models performed satisfactorily with area under the receiver operating characteristic curve values of 0.882 [95% confidence interval [CI]: 0.869-0.894] for logistic regression, 0.866 [95% CI: 0.853-0.879] for support vector machine, 0.877 [95% CI: 0.865-0.890] for random forest, and 0.888 [95% CI: 0.876-0.900] for multilayer perceptron classifiers. Conclusions: A favourable machine learning-based prognostic model available to use on patient arrival at the hospital was developed for out-ofhospital cardiac arrest with initial shockable rhythm.

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Section: Discussionmentioning

confidence: 99%

Early outcome prediction for out-of-hospital cardiac arrest with initial shockable rhythm using machine learning models

et al. 2021

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“…[36][37][38] Several ML algorithms used EMS data to predict outcomes for out-of-hospital cardiac arrest. [39][40][41][42] Another intervention used supervised ML to automatically link EMS electronic patient care reports to ED records. 43 The out-of-hospital setting presents a unique setting where limited clinical variables are used to make prompt decisions (for example, whether or not to transport to hospital).…”

Section: Discussionmentioning

confidence: 99%

“…Examples of studies in the out‐of‐hospital environment included demand forecast for allocation of ambulances, classification of out‐of‐hospital ECGs, and screening of EMS calls to recognize cardiac arrest 36–38 . Several ML algorithms used EMS data to predict outcomes for out‐of‐hospital cardiac arrest 39–42 . Another intervention used supervised ML to automatically link EMS electronic patient care reports to ED records 43 .…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence in emergency medicine: A scoping review

et al. 2020

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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%

“…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%

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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Outcome prediction of out-of-hospital cardiac arrest with presumed cardiac aetiology using an advanced machine learning technique

Cited by 38 publications

References 28 publications

Early outcome prediction for out-of-hospital cardiac arrest with initial shockable rhythm using machine learning models

Early outcome prediction for out-of-hospital cardiac arrest with initial shockable rhythm using machine learning models

Artificial intelligence in emergency medicine: A scoping review

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

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