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A new non-invasive diagnostic tool in coronary artery disease: artificial intelligence as an essential element of predictive, preventive, and personalized medicine

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Abstract

Background

Known coronary artery disease (CAD) risk scores (e.g., Framingham) estimate the CAD-related event risk rather than presence/absence of CAD. Artificial intelligence (AI) is rarely used in this context.

Aims

This study aims to evaluate the diagnostic power of AI (memetic pattern-based algorithm (MPA)) in CAD and to expand its applicability to a broader patient population.

Methods and results

Nine hundred eighty-seven patients of the Ludwigshafen Risk and Cardiovascular Health Study (LURIC) were divided into a training (n = 493) and a test population (n = 494). They were evaluated by the Basel MPA. The “training population” was further used to expand and optimize the Basel MPA, and after modifications, a final validation was carried out on the “test population.” The results were compared with the Framingham Risk Score (FRS) using receiver operating curves (ROC; area-under-the-curve (AUC)). Of the 987 LURIC patients, 71% were male, age 62 ± 11 years and 68% had documented CAD. AUC of Framingham and BASEL MPA to diagnose CAD in “LURIC training” were 0.69 and 0.80, respectively. AUC of the optimized MPA in the training and test cohort were 0.88 and 0.87, respectively. The positive predictive values (PPV) of the optimized MPA for exclusion of CAD in “training” and “test” were 98 and 95%, respectively. The PPV of MPA for identification of CAD was 93 and 94%, respectively.

Conclusions

The successful use of the MPA approach has been demonstrated in a broad-risk spectrum of patients undergoing CAD evaluation, as an element of predictive, preventive, personalized medicine, and may be used instead of further non-invasive diagnostic procedures.

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Abbreviations

AI:

Artificial intelligence

CAD:

Coronary artery disease

FRS:

Framingham Risk Score

PROCAM:

Prospective Cardiovascular Münster Study

LURIC:

Ludwigshafen Risk and Cardiovascular Health Study

MPA:

Memetic pattern-based algorithm

PPPM:

Predictive, preventive, and personalized medicine

MCV:

Mean corpuscular volume of red blood cells

MCHC:

Mean corpuscular hemoglobin concentration of red blood cells

INR:

International normalized ratio (anticoagulation)

GGT:

Gamma-glutamyl transferase

ALAT:

Alanine aminotransferase

ASAT:

Aspartate aminotransferase

LDL:

Low-density lipoprotein

HDL:

High-density lipoprotein

H+:

Healthy patients/individuals (coronary artery disease excluded)

CAD+:

Patients with coronary artery disease

TP:

True-positive result

FP:

False-positive result

AUC:

Area under the curve

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Acknowledgments

The MPA modeling process, used in this study has received the 2018 Swiss Biolabs Award for its implementation in a prototype “Cardioexplorer.” The jury consisted of Universities (University Hospital Basel, School of Life sciences Biotechnet Switzerland), industry partners (Sensile Medical, Roche Diagnostics, Omya International AG, Ava Science, Bühlmann Laboratories), and Associations (Swiss Biolabs Association, Economic Development Agency Olten, Basel Area).

Funding

The study was in part funded by the Swiss Heart Foundation.

Author information

Authors and Affiliations

  1. Cardiology Department, University Hospital, University of Basel, Petersgraben 4, 4031, Basel, Switzerland

    Michael J. Zellweger

  2. Software and Modeling, Exploris AG, Modeling, Exploris AG, Zürich, Switzerland

    Andrew Tsirkin & Vasily Vasilchenko

  3. Exploris AG, Zürich, Switzerland

    Michael Failer & Peter Ruff

  4. DACH Gesellschaft Prävention von Herz-Kreislauf-Erkrankungen e.V., Hamburg, Germany

    Alexander Dressel

  5. Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    Marcus E. Kleber & Winfried März

  6. Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria

    Winfried März

  7. Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Germany

    Winfried März

  8. Synlab Academy, Synlab Holding Deutschland GmbH, Augsburg, Germany

    Winfried März

Authors
  1. Michael J. Zellweger
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  2. Andrew Tsirkin
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  3. Vasily Vasilchenko
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  4. Michael Failer
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  6. Marcus E. Kleber
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  7. Peter Ruff
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  8. Winfried März
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Corresponding author

Correspondence to Michael J. Zellweger.

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Conflict of interest

Michael J. Zellweger is an advisory board member of Exploris AG. Peter Ruff is part owner, board member, and head of Exploris AG, which is a privately owned Swiss research company focusing on development of novel diagnostic solutions. Andrew Tsirkin is head in the modeling and development team of Exploris AG. Vasily Vasilchenko is a data analyst of Exploris AG. Michael Failer is head for regulatory affairs of Exploris AG. Alexander Dressel and Marcus E. Kleber declare that they have no conflict of interest. Winfried März is an advisory board member of Exploris AG.

Ethical approval

All procedures performed in this study involving human participants were in accordance with the ethical standards of the locally appointed ethics committees (Basel: Ethikkommission beider Basel; reference number: 67/08; LURIC: Ethics Committee at the “Ärztekammer Rheinland-Pfalz”; reference number: 837.255.97 (1394)). The study is also in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Consent

Informed consent has been obtained from all of the patients.

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Zellweger, M.J., Tsirkin, A., Vasilchenko, V. et al. A new non-invasive diagnostic tool in coronary artery disease: artificial intelligence as an essential element of predictive, preventive, and personalized medicine. EPMA Journal 9, 235–247 (2018). https://doi.org/10.1007/s13167-018-0142-x

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  • DOI: https://doi.org/10.1007/s13167-018-0142-x

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