Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients

Shinichi Goto, Mai Kimura, Yoshinori Katsumata, Shinya Goto, Takashi Kamatani, Genki Ichihara, Seien Ko, Junichi Sasaki, Keiichi Fukuda, Motoaki Sano, Shinichi Goto, Mai Kimura, Yoshinori Katsumata, Shinya Goto, Takashi Kamatani, Genki Ichihara, Seien Ko, Junichi Sasaki, Keiichi Fukuda, Motoaki Sano

Abstract

Background: Patient with acute coronary syndrome benefits from early revascularization. However, methods for the selection of patients who require urgent revascularization from a variety of patients visiting the emergency room with chest symptoms is not fully established. Electrocardiogram is an easy and rapid procedure, but may contain crucial information not recognized even by well-trained physicians.

Objective: To make a prediction model for the needs for urgent revascularization from 12-lead electrocardiogram recorded in the emergency room.

Method: We developed an artificial intelligence model enabling the detection of hidden information from a 12-lead electrocardiogram recorded in the emergency room. Electrocardiograms obtained from consecutive patients visiting the emergency room at Keio University Hospital from January 2012 to April 2018 with chest discomfort was collected. These data were splitted into validation and derivation dataset with no duplication in each dataset. The artificial intelligence model was constructed to select patients who require urgent revascularization within 48 hours. The model was trained with the derivation dataset and tested using the validation dataset.

Results: Of the consecutive 39,619 patients visiting the emergency room with chest discomfort, 362 underwent urgent revascularization. Of them, 249 were included in the derivation dataset and the remaining 113 were included in validation dataset. For the control, 300 were randomly selected as derivation dataset and another 130 patients were randomly selected for validation dataset from the 39,317 who did not undergo urgent revascularization. On validation, our artificial intelligence model had predictive value of the c-statistics 0.88 (95% CI 0.84-0.93) for detecting patients who required urgent revascularization.

Conclusions: Our artificial intelligence model provides information to select patients who need urgent revascularization from only 12-leads electrocardiogram in those visiting the emergency room with chest discomfort.

Conflict of interest statement

I have read the journal's policy and the authors of this manuscript have the following competing interests: SG (first author) received a grant from Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research Grant Number 18K15859. Shinya Goto acknowledge the financial support from MEXT/JSPS KAKENHI 17K19669 and partly by 18H01726. Shinya Goto acknowledge the financial support from Bristol-Myers Squibb from their independent research support project (33999603). Shinya Goto received research funding from Sanofi, Pfizer, and Ono. Shinya Goto received a modest personal fee from Bayer. Shinya Goto is an associated Editor for Circulation, an associate Editor for Journal of Biorheology, an associate Editor for Archives of Medical Science, section Editor for Thrombosis and Haemostasis.

Figures

Fig 1. Selection of study population.
Fig 1. Selection of study population.
ECG: electrocardiogram.
Fig 2. Structure of the neural network…
Fig 2. Structure of the neural network in our AI model.
Schematic illustration of the neural network model (A). Schematic illustration of bidirectional LSTM(B). Note that two layers of LSTM which have opposite directions of information transfer with the neurons next to each other are stacked up. LSTM: long short-term memory. N: neuron.
Fig 3. Conversion of ECG data to…
Fig 3. Conversion of ECG data to 2D matrix.
A representative plot of a single beat at induction I picked up from a 12-lead ECG recording (A). The recorded data consists of voltage plotted against time. A representative 2-dimention matrix converted from the 12-lead ECG recording (B). The matrix has 2 axis of induction axis and time axis. The value at the point indicated with dotted grey line in A converted to an element in the matrix is highlighted with dotted blue line in B. Voltage for each induction was recorded in each 2 ms. ECG: electrocardiogram.
Fig 4. Diagnostic value of the AI…
Fig 4. Diagnostic value of the AI model.
ROC curve (A) and probability of receiving urgent revascularization for patients stratified to each quartile range of the model output using the derivation cohort(B). The results from same analysis using validation cohort are shown in panel C and D. The p values were calculated using Fisher’s exact test. ROC: receiver operating characteristic. AUC: area under curve. CI: confidence interval.

References

    1. Amsterdam EA, Wenger NK, Brindis RG, Casey DE Jr., Ganiats TG, Holmes DR Jr., et al. 2014 AHA/ACC Guideline for the Management of Patients with Non-ST-Elevation Acute Coronary Syndromes: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;64(24):e139–e228. 10.1016/j.jacc.2014.09.017
    1. Roffi M, Patrono C, Collet J-P, Mueller C, Valgimigli M, Andreotti F, et al. 2015 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevationTask Force for the Management of Acute Coronary Syndromes in Patients Presenting without Persistent ST-Segment Elevation of the European Society of Cardiology (ESC). European Heart Journal. 2016;37(3):267–315. 10.1093/eurheartj/ehv320
    1. Rouan GW, Lee TH, Cook EF, Brand DA, Weisberg MC, Goldman L. Clinical characteristics and outcome of acute myocardial infarction in patients with initially normal or nonspecific electrocardiograms (a report from the Multicenter Chest Pain Study). Am J Cardiol. 1989;64(18):1087–1092.
    1. McCarthy BD, Wong JB, Selker HP. Detecting acute cardiac ischemia in the emergency department: a review of the literature. J Gen Intern Med. 1990;5(4):365–373.
    1. Slater DK, Hlatky MA, Mark DB, Harrell FE Jr., Pryor DB, Califf RM. Outcome in suspected acute myocardial infarction with normal or minimally abnormal admission electrocardiographic findings. Am J Cardiol. 1987;60(10):766–770.
    1. Taghavi Namin S, Esmaeilzadeh M, Najafi M, Brown TB, Borevitz JO. Deep phenotyping: deep learning for temporal phenotype/genotype classification. Plant Methods. 2018;14(1):66 10.1186/s13007-018-0333-4
    1. Chollet F. Keras. . 2015.
    1. Martín Abadi AA, Paul Barham, Eugene Brevdo, Zhifeng Chen CC, Greg S. Corrado, Andy Davis, Jeffrey Dean MD, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp GI, Michael Isard, Rafal Jozefowicz, Yangqing Jia, Lukasz Kaiser MK, Josh Levenberg, Dan Mané, Mike Schuster, Rajat Monga SM, Derek Murray, Chris Olah, Jonathon Shlens, et al. TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorfloworg. 2015.
    1. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12(1):77 10.1186/1471-2105-12-77
    1. Bigi R, Gregori D, Cortigiani L, Desideri A, Chiarotto FA, Toffolo GM. Artificial neural networks and robust Bayesian classifiers for risk stratification following uncomplicated myocardial infarction. International Journal of Cardiology. 2005;101(3):481–487. 10.1016/j.ijcard.2004.07.008
    1. Akay M. Noninvasive diagnosis of coronary artery disease using a neural network algorithm. Biol Cybern. 1992;67(4):361–367.
    1. Furlong JW, Dupuy ME, Heinsimer JA. Neural network analysis of serial cardiac enzyme data. A clinical application of artificial machine intelligence. Am J Clin Pathol. 1991;96(1):134–141.
    1. Tu JV, Weinstein MC, McNeil BJ, Naylor CD. Predicting mortality after coronary artery bypass surgery: what do artificial neural networks learn? The Steering Committee of the Cardiac Care Network of Ontario. Med Decis Making. 1998;18(2):229–235.
    1. Tu JV, Guerriere MR. Use of a neural network as a predictive instrument for length of stay in the intensive care unit following cardiac surgery. Comput Biomed Res. 1993;26(3):220–229.
    1. Haraldsson H, Edenbrandt L, Ohlsson M. Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks. Artificial Intelligence in Medicine. 2004;32(2):127–136. 10.1016/j.artmed.2004.01.003
    1. Myers PD, Scirica BM, Stultz CM. Machine Learning Improves Risk Stratification After Acute Coronary Syndrome. Scientific Reports. 2017;7(1):12692 10.1038/s41598-017-12951-x
    1. Heden B, Ohlin H, Rittner R, Edenbrandt L. Acute myocardial infarction detected in the 12-lead ECG by artificial neural networks. Circulation. 1997;96(6):1798–1802.
    1. de Araujo Goncalves P, Ferreira J, Aguiar C, Seabra-Gomes R. TIMI, PURSUIT, and GRACE risk scores: sustained prognostic value and interaction with revascularization in NSTE-ACS. Eur Heart J. 2005;26(9):865–872. 10.1093/eurheartj/ehi187
    1. Hess EP, Agarwal D, Chandra S, Murad MH, Erwin PJ, Hollander JE, et al. Diagnostic accuracy of the TIMI risk score in patients with chest pain in the emergency department: a meta-analysis. CMAJ: Canadian Medical Association Journal. 2010;182(10):1039–1044. 10.1503/cmaj.092119
    1. Mahmoodzadeh S, Moazenzadeh M, Rashidinejad H, Sheikhvatan M. Diagnostic performance of electrocardiography in the assessment of significant coronary artery disease and its anatomical size in comparison with coronary angiography. Journal of Research in Medical Sciences: The Official Journal of Isfahan University of Medical Sciences. 2011;16(6):750–755.

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe