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Artificial intelligence could select heart failure patients for expensive treatment

May 13, 2019

Lisbon, Portugal - 13 May 2019: Artificial intelligence (AI) has shown promise to select heart failure patients for expensive treatments to prevent lethal arrhythmias, reports a study presented today at ICNC 2019.1 The study is the first to use a machine learning algorithm to predict sudden death in heart failure patients.

The International Conference on Nuclear Cardiology and Cardiac CT (ICNC) is co-organised by the American Society of Nuclear Cardiology (ASNC), the European Association of Cardiovascular Imaging (EACVI) of the European Society of Cardiology (ESC), and the European Association of Nuclear Medicine (EANM).

Around 1-2% of adults in developed countries have heart failure, a clinical syndrome characterised by breathlessness, ankle swelling, and fatigue.2 A high proportion of deaths in these patients, especially those with milder symptoms, occur suddenly due to ventricular arrhythmias. Implantable cardioverter defibrillators (ICDs) or cardiac resynchronisation therapy with a pacemaker and defibrillator (CRT-D) are recommended for some patients to correct potentially lethal arrhythmias and reduce the risk of sudden death. However, these treatments are expensive and do not work in all patients.

Study author Professor Kenichi Nakajima, of Kanazawa University Hospital, Japan, said: "Our model calculated the probability of a sudden arrhythmic event with an area under the curve (AUC) of 0.74, where 1.0 is perfect prediction and 0.5 is a random result. This could be used to identify very low risk patients for whom an ICD or CRT-D is not required, and very high risk patients who should receive a device. Optimising risk evaluation in this way will improve the cost effectiveness of treatment."

The study included 529 heart failure patients with known two-year outcomes for sudden arrhythmic events (including arrhythmic death, sudden cardiac death, and appropriate shock from an ICD) and death due to heart failure.

Machine learning - a type of AI used by the Google search engine and face recognition on smartphones - was used to discover how eight variables used to predict prognosis of heart failure patients were connected and create a formula correlating them to two-year outcomes.

The eight factors were age, sex, heart failure severity (New York Heart Association functional class), heart pumping function (left ventricular ejection fraction), whether heart failure was caused by restricted blood supply (ischaemia), B-type natriuretic peptide level in the blood, kidney function (estimated glomerular filtration rate), and a nuclear imaging parameter.

During the two-year follow-up there were 141 events (27%) consisting of 37 sudden arrhythmic events (7%) and 104 deaths due to heart failure (20%). The AUC for predicting all events was 0.87, while for arrhythmic events and heart failure death it was 0.74 and 0.91, respectively.

Professor Nakajima said: "This is a preliminary study and we can improve the prediction of arrhythmic events by adding variables and continuing to train the machine learning algorithm."

The imaging parameter was heart-to-mediastinum ratio (HMR) of 123Iodine-metaiodobenzylguanidine (MIBG) uptake. MIBG is a radioisotope analogue of norepinephrine and is used to assess the activity of cardiac sympathetic nerves. Previous studies have shown that HMR predicts cardiac death in patients with heart failure. The measure is obtained by injecting MIBG into a vein, then using imaging to assess uptake in the heart and upper mediastinum (centre of the thoracic cavity).

Professor Nakajima noted that while MIBG imaging is approved in the US and Japan for clinical practice, and in Europe for clinical research, it is less commonly used outside Japan due to its cost. A typical MIBG tracer costs €350 in Japan compared to €1,900-3,400 in the US.3 He said: "While the costs of the scan may be high, it would be value for money if unnecessary device implantations were avoided."
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Notes to editor

Sources of funding: None.

Disclosures: K. Nakajima has collaborative research work with FUJIFILM Toyama Chemical, supplier of 123I-MIBG in Japan.

References and notes

1 The abstract 'Machine learning model for predicting sudden cardiac death and heart failure death using 123I-metaiodobenzylguanidine' will be presented during the session Moderated Posters 4 on Monday 13 May at 15:30 to 16:30 WEST in the Moderated Poster Area.

2 Ponikowski P, Voors AA, Anker SD, et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J. 2016;37:2129-2200. doi: 10.1093/eurheartj/ehw128.

3 Travin MI, Matsunari I, Thomas GS, et al. How do we establish cardiac sympathetic nervous system imaging with 123I-mIBG in clinical practice? Perspectives and lessons from Japan and the US. J Nucl Cardiol. 2018 Sep 3. doi: 10.1007/s12350-018-1394-5.

About the International Conference on Nuclear Cardiology and Cardiac CT (ICNC)

The International Conference on Nuclear Cardiology and Cardiac CT (ICNC) is held every two years and is co-organised by the European Association of Cardiovascular Imaging (EACVI) of the European Society of Cardiology (ESC), the American Society of Nuclear Cardiology (ASNC), and the European Association of Nuclear Medicine (EANM).

About the American Society of Nuclear Cardiology (ASNC)

ASNC, located in Fairfax (Virginia, USA) is committed to excellence in imaging. Representing over 4,500 nuclear cardiologists, radiologists, technologists, scientists and academics all over the world. ASNC is the leader in the development of established standards of care and guidelines in nuclear cardiovascular imaging. ASNC continuously supports the advancement of the profession through education, research, and advocacy.

About the European Association of Cardiovascular Imaging (EACVI)

The European Association of Cardiovascular Imaging (EACVI) - a branch of the ESC - is the world leading network of Cardiovascular Imaging (CVI) experts, gathering four imaging modalities under one entity (Echocardiography, Cardiovascular Magnetic Resonance, Nuclear Cardiology and Cardiac Computed Tomography). Its aim is to promote excellence in clinical diagnosis, research, technical development, and education in cardiovascular imaging. The EACVI welcomes over 11,000 professionals including cardiologists, sonographers, nurses, basic scientists and allied professionals.

About the European Society of Cardiology

The European Society of Cardiology brings together health care professionals from more than 150 countries, working to advance cardiovascular medicine and help people lead longer, healthier lives.

About the European Association of Nuclear Medicine (EANM)

The European Association of Nuclear Medicine (EANM) is the largest organisation dedicated to nuclear medicine and multimodality imaging in Europe, with a strong focus on interdisciplinary. This is especially reflected in the educational offers of the European School of Multimodality Imaging and Therapy (ESMIT) and the programme of the EANM Annual Congress.

Information for journalists attending ICNC 2019

ICNC 2019 will be held 12 to 14 May at the Lisbon Congress Centre (CCL) in Lisbon, Portugal. Explore the scientific programme.
  • To register on-site please bring a valid press card or appropriate letter of assignment with proof of three recent published articles (cardiology or health-related, or referring to a previous ESC Event).
  • Press registration is not available to industry or its public relations representatives, event management, marketing or communications representatives.


European Society of Cardiology

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