Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography

Kwon, Joon‐myoung; Kim, Kyung Hee; Jeon, Ki‐Hyun; Lee, Soo-Youn; Park, Jinsik; Oh, Byung Hee

doi:10.1186/s13049-020-00791-0

Cited by 51 publications

(42 citation statements)

References 29 publications

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“…As the DLM was developed to rapidly screen patients with potential hypokalemia, we evaluated the specificity, positive predictive value, and negative predictive value at a cutoff point selected for high sensitivity in the development data. [ 11 ] Except for the AUC, all the diagnostic performance indicators were based on an accurate 95% confidence interval (CI). The reliability interval of the AUC was determined by using the pROC software package in R (R Foundation) to perform the Sun and Su optimization of the Delong method.…”

Section: Methodsmentioning

confidence: 99%

Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients

Wang

Zhang

Kong

et al. 2021

Chinese Medical Journal

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Background: A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of this life-threatening condition. This study aimed to develop and evaluate the performance of a DLM for the detection of hypokalemia from the ECGs of emergency patients. Methods: We used a total of 9908 ECG data from emergency patients who were admitted at the Second Affiliated Hospital of Nanchang University, Jiangxi, China, from September 2017 to October 2020. The DLM was trained using 12 ECG leads (lead I, II, III, aVR, aVL, aVF, and V 1 – 6 ) to detect patients with serum potassium concentrations <3.5 mmol/L and was validated using retrospective data from the Jiangling branch of the Second Affiliated Hospital of Nanchang University. The blood draw was completed within 10 min before and after the ECG examination, and there was no new or ongoing infusion during this period. Results: We used 6904 ECGs and 1726 ECGs as development and internal validation data sets, respectively. In addition, 1278 ECGs from the Jiangling branch of the Second Affiliated Hospital of Nanchang University were used as external validation data sets. Using 12 ECG leads (leads I, II, III, aVR, aVL, aVF, and V 1 – 6 ), the area under the receiver operating characteristic curve (AUC) of the DLM was 0.80 (95% confidence interval [CI]: 0.77–0.82) for the internal validation data set. Using an optimal operating point yielded a sensitivity of 71.4% and a specificity of 77.1%. Using the same 12 ECG leads, the external validation data set resulted in an AUC for the DLM of 0.77 (95% CI: 0.75–0.79). Using an optimal operating point yielded a sensitivity of 70.0% and a specificity of 69.1%. Conclusions: In this study, using 12 ECG leads, a DLM detected hypokalemia in emergency patients with an AUC of 0.77 to 0.80. Artificial intelligence could be used to analyze an ECG to quickly screen for hypokalemia.

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Section: Methodsmentioning

confidence: 99%

Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients

Wang

Zhang

Kong

et al. 2021

Chinese Medical Journal

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“…Based on 3 years of EMR data, a cardiac arrest algorithm was developed by noting blood pressure, pulse rate, respiration rate, and body temperature to cope with emergency situations. 23 The AI program for predicting the incidence of diabetes mellitus (DM) based on routine health checkup records showed 95% accuracy. 24 The AI algorithm was also developed to predict the risk of developing essential hypertension using EMR data.…”

Section: Ai Using Emrmentioning

confidence: 99%

“…One study developed deep-learning-based artificial intelligence algorithm (DLA) predicting cardiac arrest that validated using ECG. 23 They used 47,505 ECGs of 25,672 adult patients, from October 2016 to September 2019. The areas under the receiver operating characteristic curves of the DLA in predicting cardiac arrest within 24 hours were 0.913 and 0.948, respectively.…”

Section: Ai Using Emrmentioning

confidence: 99%

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Prospect of Artificial Intelligence Based on Electronic Medical Records

Lee

Kim

2021

J Lipid Atheroscler

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With the advent of the big data era, the interest of the international community is focusing on increasing the utilization of medical big data. Many hospitals are attempting to increase the efficiency of their operations and patient management by adopting artificial intelligence (AI) technology that enables the use of electronic medical record (EMR) data. EMR includes information about a patient's health history, such as diagnoses, medicines, tests, allergies, immunizations, treatment plans, personalized medical care, and improvement of medical quality and safety. EMR data can also be used for AI-based new drug development. In particular, it is effective to develop AI that can predict the occurrence of specific diseases or provide individualized customized treatments by classifying the individualized characteristics of patients. In order to improve performance of artificial intelligence research using EMR data, standardization and refinement of data are essential. In addition, since EMR data deal with sensitive personal information of patients, it is also vital to protect the patient's privacy. There are already various supports for the use of EMR data in the Korean government, and researchers are encouraged to be proactive.

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“… 19 ML networks have also demonstrated promise identifying clinically meaningful markers of prognosis, predicting both 1-year mortality 20 and incident cardiac arrest within 24 hours. 21 …”

Section: Introductionmentioning

confidence: 99%

Identifying risk of adverse outcomes in COVID-19 patients via artificial intelligence–powered analysis of 12-lead intake electrocardiogram

Sridhar

Chen

Mayfield

et al. 2022

Cardiovascular Digital Health Journal

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Background Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that AI can help identify subtle signs of myocardial involvement in the 12-lead electrocardiogram (ECG), which could help predict complications. Objective Use intake ECGs from COVID-19 patients to train AI models to predict risk of mortality or major adverse cardiovascular events (MACE). Methods We studied intake ECGs from 1448 COVID-19 patients (60.5% male, 63.4±16.9 years). Records were labeled by mortality (death vs. discharge) or MACE (no events vs. arrhythmic, heart failure [HF], or thromboembolic [TE] events), then used to train AI models; these were compared to conventional regression models developed using demographic and comorbidity data. Results 245 (17.7%) patients died (67.3% male, 74.5±14.4 years); 352 (24.4%) experienced at least one MACE (119 arrhythmic; 107 HF; 130 TE). AI models predicted mortality and MACE with area under the curve (AUC) values of 0.60±0.05 and 0.55±0.07, respectively; these were comparable to AUC values for conventional models (0.73±0.07 and 0.65±0.10). There were no prominent temporal trends in mortality rate or MACE incidence in our cohort; holdout testing with data from after a cutoff date (June 9, 2020) did not degrade model performance. Conclusion Using intake ECGs alone, our AI models had limited ability to predict hospitalized COVID-19 patients’ risk of mortality or MACE. Our models’ accuracy was comparable to that of conventional models built using more in-depth information, but translation to clinical use would require higher sensitivity and positive predictive value. In the future, we hope that mixed-input AI models utilizing both ECG and clinical data may be developed to enhance predictive accuracy.

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Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography

Cited by 51 publications

References 29 publications

Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients

Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients

Prospect of Artificial Intelligence Based on Electronic Medical Records

Identifying risk of adverse outcomes in COVID-19 patients via artificial intelligence–powered analysis of 12-lead intake electrocardiogram

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