A machine learning based model for Out of Hospital cardiac arrest outcome classification and sensitivity analysis

Harford, Samuel; Darabi, Houshang; Rios, Marina Del; Majumdar, Somshubra; Karim, Fazle; Hoek, Terry L. Vanden; Erwin, Kim; Watson, Dennis P.

doi:10.1016/j.resuscitation.2019.03.012

Cited by 26 publications

(30 citation statements)

References 25 publications

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“…Aschauer et al discovered that using 21 variables, an LR model obtained an average AUC of 0.827 for survival probability, with key predictors being prehospital variables, such as the number of minutes to sustained restoration of spontaneous circulation and the first rhythm [ 29 ]. Another study cohort with 2639 patients, comparing several ML models (including decision tree, random forest (RF), k -nearest neighbors, XGB, light gradient boosting machine (GBM), and neural networks), stated that an embedded fully convolutional network model has the best average class sensitivity of 0.825 for neurological outcome prediction [ 18 ]. However, the above models required knowledge of the periods of time with circulatory no-flow and low-flow, limiting its use when prehospital data are unknown or recalled incorrectly.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have suggested that ML methods could predict neurologic and survival outcomes of OHCA patients [18][19][20][21]. Harford et al found that an ML model can be used to support intervention decisions such as CPR or coronary angiography in OHCA patients [18].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning Models for Survival and Neurological Outcome Prediction of Out‐of‐Hospital Cardiac Arrest Patients

Cheng

Chiu

Zeng

et al. 2021

BioMed Research International

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Background. Out-of-hospital cardiac arrest (OHCA) is a major health problem worldwide, and neurologic injury remains the leading cause of morbidity and mortality among survivors of OHCA. The purpose of this study was to investigate whether a machine learning algorithm could detect complex dependencies between clinical variables in emergency departments in OHCA survivors and perform reliable predictions of favorable neurologic outcomes. Methods. This study included adults (≥18 years of age) with a sustained return of spontaneous circulation after successful resuscitation from OHCA between 1 January 2004 and 31 December 2014. We applied three machine learning algorithms, including logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGB). The primary outcome was a favorable neurological outcome at hospital discharge, defined as a Glasgow-Pittsburgh cerebral performance category of 1 to 2. The secondary outcome was a 30-day survival rate and survival-to-discharge rate. Results. The final analysis included 1071 participants from the study period. For neurologic outcome prediction, the area under the receiver operating curve (AUC) was 0.819, 0.771, and 0.956 in LR, SVM, and XGB, respectively. The sensitivity and specificity were 0.875 and 0.751 in LR, 0.687 and 0.793 in SVM, and 0.875 and 0.904 in XGB. The AUC was 0.766 and 0.732 in LR, 0.749 and 0.725 in SVM, and 0.866 and 0.831 in XGB, for survival-to-discharge and 30-day survival, respectively. Conclusions. Prognostic models trained with ML technique showed appropriate calibration and high discrimination for survival and neurologic outcome of OHCA without using prehospital data, with XGB exhibiting the best performance.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Models for Survival and Neurological Outcome Prediction of Out‐of‐Hospital Cardiac Arrest Patients

Cheng

Chiu

Zeng

et al. 2021

BioMed Research International

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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%