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Multicenter Study
. 2018 Jun 26;7(13):e008678.
doi: 10.1161/JAHA.118.008678.

An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest

Joon-Myoung Kwon  1 , Youngnam Lee  2 , Yeha Lee  2 , Seungwoo Lee  2 , Jinsik Park  3
Affiliations
Multicenter Study

An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest

Joon-Myoung Kwon et al. J Am Heart Assoc. .

Abstract

Background: In-hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track-and-trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high false-alarm rates. We propose a deep learning-based early warning system that shows higher performance than the existing track-and-trigger systems.

Methods and results: This retrospective cohort study reviewed patients who were admitted to 2 hospitals from June 2010 to July 2017. A total of 52 131 patients were included. Specifically, a recurrent neural network was trained using data from June 2010 to January 2017. The result was tested using the data from February to July 2017. The primary outcome was cardiac arrest, and the secondary outcome was death without attempted resuscitation. As comparative measures, we used the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), and the net reclassification index. Furthermore, we evaluated sensitivity while varying the number of alarms. The deep learning-based early warning system (AUROC: 0.850; AUPRC: 0.044) significantly outperformed a modified early warning score (AUROC: 0.603; AUPRC: 0.003), a random forest algorithm (AUROC: 0.780; AUPRC: 0.014), and logistic regression (AUROC: 0.613; AUPRC: 0.007). Furthermore, the deep learning-based early warning system reduced the number of alarms by 82.2%, 13.5%, and 42.1% compared with the modified early warning system, random forest, and logistic regression, respectively, at the same sensitivity.

Conclusions: An algorithm based on deep learning had high sensitivity and a low false-alarm rate for detection of patients with cardiac arrest in the multicenter study.

Keywords: artificial intelligence; cardiac arrest; deep learning; machine learning; rapid response system; resuscitation.

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Figures

Figure 1
Figure 1
The process of the DEWS. DEWS indicates deep learning–based early warning system; HR, heart rate; RNN, recurrent neural network; RR, respiratory rate; SBP, systolic blood pressure; BT, body temperature.
Figure 2
Figure 2
The architecture of the recurrent neural network. X t and h t indicate input and output at time t; W, weights.
Figure 3
Figure 3
Study flow chart. DEWS indicates deep learning–based early warning system.
Figure 4
Figure 4
Accuracy for predicting in‐hospital cardiac arrest and death without attempted resuscitation. AUPRC indicates area under the precision–recall curve; AUROC, area under the receiver operating characteristic curve; CI, confidence interval; DEWS, deep learning–based early warning system; MEWS, modified early warning score; SPTTS, single‐parameter track‐and‐trigger system.
Figure 5
Figure 5
Sensitivity according to MACHP for predicting in‐hospital cardiac arrest. DEWS indicates deep learning–based early warning system; MACHP, mean alarm count per hour per patient; MEWS, modified early warning score; SPTTS, single‐parameter track‐and‐trigger system; TTS, track‐and‐trigger system.
Figure 6
Figure 6
Trend of the DEWS score. A, The change in the mean of the DEWS scores over time as a group of patients. B, Cumulative percentage of cardiac arrest patients on detection time before event. We used the DEWS with sensitivity 70% for this plot. DEWS indicates deep learning–based early warning system.
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