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Pulse Wave Velocity and Machine Learning to Predict Cardiovascular Outcomes in Prediabetic and Diabetic Populations

  • Systems-Level Quality Improvement
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Abstract

Few studies have addressed the predictive value of arterial stiffness determined by pulse wave velocity (PWV) in a high-risk population with no prevalent cardiovascular disease and with obesity, hypertension, hyperglycemia, and preserved kidney function. This longitudinal, retrospective study enrolled 88 high-risk patients and had a follow-up time of 12.4 years. We collected clinical and laboratory data, as well as information on arterial stiffness parameters using arterial tonometry and measurements from ambulatory blood pressure monitoring. We considered nonfatal, incident cardiovascular events as the primary outcome. Given the small size of our dataset, we used survival analysis (i.e., Cox proportional hazards model) combined with a machine learning-based algorithm/penalization method to evaluate the data. Our predictive model, calculated with Cox regression and least absolute shrinkage and selection operator (LASSO), included body mass index, diabetes mellitus, gender (male), and PWV. We recorded 16 nonfatal cardiovascular events (5 myocardial infarctions, 5 episodes of heart failure, and 6 strokes). The adjusted hazard ratio for PWV was 1.199 (95% confidence interval: 1.09–1.37, p < 0.001). Arterial stiffness was a predictor of cardiovascular disease development, as determined by PWV in a high-risk population. Thus, in obese, hypertensive, hyperglycemic patients with preserved kidney function, PWV can serve as a prognostic factor for major adverse cardiac events.

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Data availability

According to the contract signed with Mostoles University Hospital, which provided the database, we cannot provide our database to any other researcher. Furthermore, we must destroy the database once our investigation has been concluded.

Abbreviations

BP:

Blood pressure

PWV:

Pulse wave velocity

CV:

Cardiovascular

CVD:

Cardiovascular disease

AIx:

Augmentation index of aortic pressure

ABPM:

Ambulatory blood pressure monitoring

MS:

Metabolic syndrome

T2DM:

Type 2 diabetes mellitus

HbA1c:

Glycated hemoglobin

EPV:

Events per variables

LASSO:

Least absolute shrinkage and selection operator

ACR:

Albumin/creatinine ratio

MACE:

Major adverse cardiovascular event

MSE:

Mean square error

GFR:

Glomerular filtration rate

BMI:

Body mass index

RF:

Random forest

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Acknowledgments

The authors would like to thank Blanca San Jose Montano, the Health Science Librarian-Documentalist of our institution, for her great support, suggestions, and encouragement in the development of this manuscript.

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Authors and Affiliations

  1. Department of Internal Medicine, Mostoles University Hospital, Rey Juan Carlos University, Móstoles, Spain

    Rafael Garcia-Carretero & Luis Vigil-Medina

  2. Department of Signal Theory and Communications and Telematics Systems and Computing, Rey Juan Carlos University, Móstoles, Spain

    Oscar Barquero-Perez & Javier Ramos-Lopez

Authors
  1. Rafael Garcia-Carretero
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  4. Javier Ramos-Lopez
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Contributions

Dr. Garcia-Carretero designed and conceived the study, pre-processed the database, and made substantial contributions to the analyses and interpretation of data. He wrote the first draft of this paper. Dr. Vigil-Medina contributed to the recruitment of patients involved in this study. Dr. Barquero-Perez and Dr. Ramos-Lopez, as thesis advisors, critically reviewed the final manuscript.

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Correspondence to Rafael Garcia-Carretero.

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Ethical approval

All procedures involving human participants were conducted in accordance with the ethical standards of the responsible institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Our research was approved by our Ethics and Research Committee.

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Given that our research was retrospective and was approved by our Research Committee, the need to obtain consent was waived.

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We obtained a consent for publication from our own institution and our Ethics and Research Committee.

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All authors declare that they have no conflicts of interest.

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Garcia-Carretero, R., Vigil-Medina, L., Barquero-Perez, O. et al. Pulse Wave Velocity and Machine Learning to Predict Cardiovascular Outcomes in Prediabetic and Diabetic Populations. J Med Syst 44, 16 (2020). https://doi.org/10.1007/s10916-019-1479-y

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