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Low-Cost Office-Based Cardiovascular Risk Stratification Using Machine Learning and Focused Carotid Ultrasound in an Asian-Indian Cohort

  • Image & Signal Processing
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Abstract

This study developed an office-based cardiovascular risk calculator using a machine learning (ML) algorithm that utilized a focused carotid ultrasound. The design of this study was divided into three steps. The first step involved collecting 18 office-based biomarkers consisting of six clinical risk factors (age, sex, body mass index, systolic blood pressure, diastolic blood pressure, and smoking) and 12 carotid ultrasound image-based phenotypes. The second step consisted of the design of an ML-based cardiovascular risk calculator-called “AtheroEdge Composite Risk Score 2.0” (AECRS2.0ML) for risk stratification, considering chronic kidney disease (CKD) as the surrogate endpoint of cardiovascular disease. The last step consisted of comparing AECRS2.0ML against the currently utilized office-based CVD calculators, namely the Framingham risk score (FRS) and the World Health Organization (WHO) risk scores. A cohort of 379 Asian-Indian patients with type-2 diabetes mellitus, hypertension, and chronic kidney disease (stage 1 to 5) were recruited for this cross-sectional study. From this retrospective cohort, 758 ultrasound scan images were acquired from the far walls of the left and right common carotid arteries [mean age = 55 ± 10.8 years, 67.28% males, 91.82% diabetic, 86.54% hypertensive, and 83.11% with CKD]. The mean office-based cardiovascular risk estimates using FRS and WHO calculators were 26% and 19%, respectively. AECRS2.0ML demonstrated a better risk stratification ability having a higher area-under-the-curve against FRS and WHO by ~30% (0.871 vs. 0.669) and ~ 20% (0.871 vs. 0.727), respectively. The office-based machine-learning cardiovascular risk-stratification tool (AECRS2.0ML) shows superior performance compared to currently available conventional cardiovascular risk calculators.

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References

  1. D. Prabhakaran, P. Jeemon, and A. Roy, "Cardiovascular diseases in India," Circulation, vol. 133, pp. 1605-1620, 2016.

    PubMed  Google Scholar 

  2. R. B. D’agostino, R. S. Vasan, M. J. Pencina, P. A. Wolf, M. Cobain, J. M. Massaro, et al., "General cardiovascular risk profile for use in primary care: the Framingham Heart Study," Circulation, vol. 117, pp. 743-753, 2008.

    PubMed  Google Scholar 

  3. D. C. Goff, D. M. Lloyd-Jones, G. Bennett, S. Coady, R. B. D’agostino, R. Gibbons, et al., "2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines," Journal of the American College of Cardiology, vol. 63, pp. 2935-2959, 2014.

    PubMed  Google Scholar 

  4. J. Hippisley-Cox, C. Coupland, and P. Brindle, "Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study," BMJ, vol. 357, pp. 1-12, 2017.

    Google Scholar 

  5. R. Conroy, K. Pyörälä, A. E. Fitzgerald, S. Sans, A. Menotti, G. De Backer, et al., "Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project," European heart journal, vol. 24, pp. 987-1003, 2003.

    CAS  PubMed  Google Scholar 

  6. P. Ueda, M. Woodward, Y. Lu, K. Hajifathalian, R. Al-Wotayan, C. A. Aguilar-Salinas, et al., "Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries: a pooled analysis of prospective cohorts and health surveys," The lancet Diabetes & endocrinology, vol. 5, pp. 196-213, 2017.

    Google Scholar 

  7. Sepanlou, S. G., alekzadeh, R., Poustchi, H., Sharafkhah, M., Ghodsi, S., Malekzadeh, F., et al., The clinical performance of an office-based risk scoring system for fatal cardiovascular diseases in North-East of Iran, PloS one, vol. 10, 2015.

  8. E. Y. Yang and V. Nambi, "Ultrasound imaging of carotid intima-media thickness: an office-based tool to assist physicians in cardiovascular risk assessment," Current atherosclerosis reports, vol. 13, p. 431, 2011.

    CAS  PubMed  Google Scholar 

  9. S. Mendis, L. H. Lindholm, G. Mancia, J. Whitworth, M. Alderman, S. Lim, et al., "World Health Organization (WHO) and International Society of Hypertension (ISH) risk prediction charts: assessment of cardiovascular risk for prevention and control of cardiovascular disease in low and middle-income countries," Journal of hypertension, vol. 25, pp. 1578-1582, 2007.

    CAS  PubMed  Google Scholar 

  10. W. H. Organization, WHO/ISH cardiovascular risk prediction charts, Prevention of cardiovascular disease: guideline for assessment and management of cardiovascular risk [cited 2011 May 12]. Geneva: WHO, 2007.

  11. T. A. Gaziano, C. R. Young, G. Fitzmaurice, S. Atwood, and J. M. Gaziano, "Laboratory-based versus non-laboratory-based method for assessment of cardiovascular disease risk: the NHANES I Follow-up Study cohort," The Lancet, vol. 371, pp. 923-931, 2008.

    Google Scholar 

  12. Pandya, A., Weinstein, M. C., and Gaziano, T. A., A comparative assessment of non-laboratory-based versus commonly used laboratory-based cardiovascular disease risk scores in the NHANES III population, PloS one, vol. 6, 2011.

  13. T. A. Gaziano, A. Pandya, K. Steyn, N. Levitt, W. Mollentze, G. Joubert, et al., "Comparative assessment of absolute cardiovascular disease risk characterization from non-laboratory-based risk assessment in South African populations," BMC medicine, vol. 11, p. 170, 2013.

    PubMed  PubMed Central  Google Scholar 

  14. H. T. Jørstad, E. B. Colkesen, S. M. Boekholdt, J. G. Tijssen, N. J. Wareham, K.-T. Khaw, et al., "Estimated 10-year cardiovascular mortality seriously underestimates overall cardiovascular risk," Heart, vol. 102, p. 63, 2016.

    PubMed  Google Scholar 

  15. J. S. Taggar and G. Y. Lip, "The QRISK was less likely to overestimate cardiovascular risk than the Framingham or ASSIGN equations," ACP journal club, vol. 148, pp. 25-25, 2008.

    PubMed  Google Scholar 

  16. R. L. Coleman, R. J. Stevens, R. Retnakaran, and R. R. Holman, "Framingham, SCORE, and DECODE Risk Equations Do Not Provide Reliable Cardiovascular Risk Estimates in Type 2 Diabetes," Diabetes Care, vol. 30, p. 1292, 2007.

    PubMed  Google Scholar 

  17. N. R. Cook, N. P. Paynter, C. B. Eaton, J. E. Manson, L. W. Martin, J. G. Robinson, et al., "Comparison of the Framingham and Reynolds Risk scores for global cardiovascular risk prediction in the multiethnic Women's Health Initiative," Circulation, vol. 125, pp. 1748-S11, 2012.

    PubMed  PubMed Central  Google Scholar 

  18. T. A. Gaziano, S. Abrahams-Gessel, S. Alam, D. Alam, M. Ali, G. Bloomfield, et al., "Comparison of nonblood-based and blood-based total CV risk scores in global populations," Global heart, vol. 11, pp. 37-46. e2, 2016.

    PubMed  Google Scholar 

  19. V. Viswanathan, A. Jamthikar, D. Gupta, N. Shanu, A. Puvvula, N. Khanna, et al., "Low-cost preventive screening using carotid ultrasound in patients with diabetes," Frontiers in bioscience (Landmark edition), vol. 25, p. 1132, 2020.

    Google Scholar 

  20. A. Jamthikar, D. Gupta, N. N. Khanna, T. Araki, L. Saba, A. Nicolaides, et al., "A Special Report on Changing Trends in Preventive Stroke/Cardiovascular Risk Assessment Via B-Mode Ultrasonography," Current atherosclerosis reports, vol. 21, p. 25, 2019.

    PubMed  Google Scholar 

  21. E. Cuadrado-Godia, A. D. Jamthikar, D. Gupta, N. N. Khanna, T. Araki, M. Maniruzzaman, et al., "Ranking of stroke and cardiovascular risk factors for an optimal risk calculator design: Logistic regression approach," Computers in biology and medicine, vol. 108, pp. 182-195, 2019.

    PubMed  Google Scholar 

  22. N. N. Khanna, A. D. Jamthikar, T. Araki, D. Gupta, M. Piga, L. Saba, et al., "Nonlinear model for the carotid artery disease 10-year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: A Japanese diabetes cohort study," Echocardiography, vol. 36, pp. 345-361, 2019.

    PubMed  Google Scholar 

  23. Puvvula, A., Jamthikar, A. D., Gupta, D., Khanna, N. N., Porcu, M., Saba, L., et al., "Morphological Carotid Plaque Area Is Associated With Glomerular Filtration Rate: A Study of South Asian Indian Patients With Diabetes and Chronic Kidney Disease," Angiology, p. 3319720910660, Mar 17 2020.

  24. E. Cuadrado-Godia, M. Maniruzzaman, T. Araki, A. Puvvula, M. J. Rahman, L. Saba, et al., "Morphologic TPA (mTPA) and composite risk score for moderate carotid atherosclerotic plaque is strongly associated with HbA1c in diabetes cohort," Computers in biology and medicine, vol. 101, pp. 128-145, 2018.

    CAS  PubMed  Google Scholar 

  25. V. Kotsis, A. D. Jamthikar, T. Araki, D. Gupta, J. R. Laird, A. A. Giannopoulos, et al., "Echolucency-based phenotype in carotid atherosclerosis disease for risk stratification of diabetes patients," Diabetes research and clinical practice, vol. 143, pp. 322-331, 2018.

    PubMed  Google Scholar 

  26. M. Amato, P. Montorsi, A. Ravani, E. Oldani, S. Galli, P. M. Ravagnani, et al., "Carotid intima-media thickness by B-mode ultrasound as surrogate of coronary atherosclerosis: correlation with quantitative coronary angiography and coronary intravascular ultrasound findings," European Heart Journal, vol. 28, pp. 2094-2101, 2007.

    PubMed  Google Scholar 

  27. M. L. Bots, "Carotid intima-media thickness as a surrogate marker for cardiovascular disease in intervention studies," Current medical research and opinion, vol. 22, pp. 2181-2190, 2006.

    PubMed  Google Scholar 

  28. Stein, J. H. and Johnson, H. M., Carotid intima-media thickness, plaques, and cardiovascular disease risk: implications for preventive cardiology guidelines, ed: Journal of the American College of Cardiology, 2010.

  29. M. Cooney, M.-T. Cooney, V. Maher, B. Khan, T. Leong, and I. Graham, "Improvement in the estimation of cardiovascular risk by carotid intima-medial thickness: a report from the Dublin Cardiohealth station study," Preventive medicine reports, vol. 2, pp. 725-729, 2015.

    PubMed  PubMed Central  Google Scholar 

  30. N. N. Khanna, A. D. Jamthikar, D. Gupta, A. Nicolaides, T. Araki, L. Saba, et al., "Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: A diabetic study," Computers in biology and medicine, vol. 105, pp. 125-143, 2019.

    PubMed  Google Scholar 

  31. Viswanathan, V., Jamthikar, A. D., Gupta, D., Puvvula, A., Khanna, N. N., Saba, L., et al., Integration of eGFR biomarker in image-based CV/Stroke risk calculator: a south Asian-Indian diabetes cohort with moderate chronic kidney disease, Int Angiol, Mar 26 2020.

  32. N. N. Khanna, A. D. Jamthikar, D. Gupta, T. Araki, M. Piga, L. Saba, et al., "Effect of carotid image-based phenotypes on cardiovascular risk calculator: AECRS1. 0," Medical & biological engineering & computing, vol. 57, pp. 1553-1566, 2019.

    Google Scholar 

  33. A. Jamthikar, D. Gupta, N. N. Khanna, L. Saba, T. Araki, K. Viskovic, et al., "A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes," Cardiovascular diagnosis and therapy, vol. 9, p. 420, 2019.

    PubMed  PubMed Central  Google Scholar 

  34. Jamthikar, A., Gupta, D., Mantella, L. E., Saba, L., Laird, J. R., Johri, A. M., et al., Multiclass Machine Learning vs. Conventional Calculators for Stroke/CVD Risk Assessment using Carotid Plaque Predictors with Coronary Angiography Scores as Gold Standard: A 500 Participants study, International Journal of Cardiovascular Imaging, vol. In Press, 2020.

  35. A. Jamthikar, D. Gupta, L. Saba, N. N. Khanna, T. Araki, K. Viskovic, et al., "Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models," Cardiovascular Diagnosis and Therapy, vol. 10, pp. 919-938, 2020.

    PubMed  PubMed Central  Google Scholar 

  36. I. A. Kakadiaris, M. Vrigkas, A. A. Yen, T. Kuznetsova, M. Budoff, and M. Naghavi, "Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA," Journal of the American Heart Association, vol. 7, p. e009476, 2018.

    PubMed  PubMed Central  Google Scholar 

  37. S. F. Weng, J. Reps, J. Kai, J. M. Garibaldi, and N. Qureshi, "Can machine-learning improve cardiovascular risk prediction using routine clinical data?," PloS one, vol. 12, p. e0174944, 2017.

  38. Davis, A., Billick, K., Horton, K., Jankowski, M., Knoll, P., Marshall, J., et al., Artificial Intelligence and Echocardiography: A Primer for Cardiac Sonographers, Journal of the American Society of Echocardiography vol. In Press, 2020.

  39. B. A. Goldstein, A. M. Navar, and R. E. Carter, "Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges," European heart journal, vol. 38, pp. 1805-1814, 2016.

    PubMed Central  Google Scholar 

  40. A. M. Alaa, T. Bolton, E. Di Angelantonio, J. H. Rudd, and M. van Der Schaar, "Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants," PloS one, vol. 14, p. e0213653, 2019.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. J. H. Stein, C. E. Korcarz, R. T. Hurst, E. Lonn, C. B. Kendall, E. R. Mohler, et al., "Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: a consensus statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force endorsed by the Society for Vascular Medicine," Journal of the American Society of Echocardiography, vol. 21, pp. 93-111, 2008.

    PubMed  Google Scholar 

  42. F. Molinari, C. S. Pattichis, G. Zeng, L. Saba, U. R. Acharya, R. Sanfilippo, et al., "Completely automated multiresolution edge snapper—a new technique for an accurate carotid ultrasound IMT measurement: clinical validation and benchmarking on a multi-institutional database," IEEE Transactions on image processing, vol. 21, pp. 1211-1222, 2012.

    PubMed  Google Scholar 

  43. F. Molinari, G. Zeng, and J. S. Suri, "Intima-media thickness: setting a standard for a completely automated method of ultrasound measurement," IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 57, pp. 1112-1124, 2010.

    PubMed  Google Scholar 

  44. F. Molinari, K. M. Meiburger, G. Zeng, L. Saba, U. Rajendra Acharya, L. Famiglietti, et al., "Automated carotid IMT measurement and its validation in low contrast ultrasound database of 885 patient Indian population epidemiological study: results of AtheroEdge™ Software," International angiology : a journal of the International Union of Angiology, vol. 31, pp. 42-53, 2012.

    CAS  Google Scholar 

  45. L. Saba, G. Mallarini, R. Sanfilippo, G. Zeng, R. Montisci, and J. Suri, "Intima Media Thickness Variability (IMTV) and its association with cerebrovascular events: a novel marker of carotid therosclerosis?," Cardiovascular diagnosis and therapy, vol. 2, p. 10, 2012.

    PubMed  PubMed Central  Google Scholar 

  46. L. Saba, K. M. Meiburger, F. Molinari, G. Ledda, M. Anzidei, U. R. Acharya, et al., "Carotid IMT variability (IMTV) and its validation in symptomatic versus asymptomatic Italian population: can this be a useful index for studying symptomaticity?," Echocardiography, vol. 29, pp. 1111-1119, 2012.

    PubMed  Google Scholar 

  47. Saba, L., Jamthikar, A., Gupta, D., Khanna, N., Viskovic, K., Suri, H., et al., Global perspective on carotid intima-media thickness and plaque: should the current measurement guidelines be revisited?, International angiology: a journal of the International Union of Angiology, 2019.

  48. L. Saba, F. Molinari, K. Meiburger, M. Piga, G. Zeng, U. A. Rajendra, et al., "What is the correct distance measurement metric when measuring carotid ultrasound intima-media thickness automatically?," International angiology: a journal of the International Union of Angiology, vol. 31, pp. 483-489, 2012.

    CAS  Google Scholar 

  49. Molinari, F., Meiburger, K. M., Saba, L., Acharya, U. R., Famiglietti, L., Georgiou, N., et al., "Automated Carotid IMT Measurement and Its Validation in Low Contrast Ultrasound Database of 885 Patient Indian Population Epidemiological Study: Results of AtheroEdge® Software," in Multi-Modality Atherosclerosis Imaging and Diagnosis, ed: Springer, pp. 209-219, 2014.

  50. F. Molinari, K. M. Meiburger, L. Saba, G. Zeng, U. R. Acharya, M. Ledda, et al., "Fully automated dual-snake formulation for carotid intima-media thickness measurement: a new approach," Journal of Ultrasound in Medicine, vol. 31, pp. 1123-1136, 2012.

    PubMed  Google Scholar 

  51. P. Lucatelli, E. Raz, L. Saba, G. M. Argiolas, R. Montisci, M. Wintermark, et al., "Relationship between leukoaraiosis, carotid intima-media thickness and intima-media thickness variability: Preliminary results," European radiology, vol. 26, pp. 4423-4431, 2016.

    PubMed  Google Scholar 

  52. Cuadrado-Godia, E., Srivastava, S. K., Saba, L., Araki, T., Suri, H. S., Giannopolulos, A., et al., "Geometric Total Plaque Area Is an Equally Powerful Phenotype Compared With Carotid Intima-Media Thickness for Stroke Risk Assessment: A Deep Learning Approach," Journal for Vascular Ultrasound, p. 1544316718806421, 2018.

  53. J. D. Spence, M. Eliasziw, M. DiCicco, D. G. Hackam, R. Galil, and T. Lohmann, "Carotid plaque area: a tool for targeting and evaluating vascular preventive therapy," Stroke, vol. 33, pp. 2916-22, 2002.

    PubMed  Google Scholar 

  54. J. D. Spence and K. Solo, "Resistant atherosclerosis: The need for monitoring of plaque burden," Stroke, vol. 48, pp. 1624-1629, 2017.

    CAS  PubMed  Google Scholar 

  55. Mathiesen Ellisiv, B., Johnsen Stein, H., Wilsgaard, T., Bønaa Kaare, H., Løchen, M.-L., and Njølstad, I., Carotid Plaque Area and Intima-Media Thickness in Prediction of First-Ever Ischemic Stroke, Stroke, vol. 42, pp. 972-978, 2011/04/01 2011.

  56. S. Alsulaimani, H. Gardener, M. S. Elkind, K. Cheung, R. L. Sacco, and T. Rundek, "Elevated homocysteine and carotid plaque area and densitometry in the Northern Manhattan Study," Stroke, vol. 44, pp. 457-61, 2013.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Y. Inaba, J. A. Chen, and S. R. Bergmann, "Carotid plaque, compared with carotid intima-media thickness, more accurately predicts coronary artery disease events: a meta-analysis," Atherosclerosis, vol. 220, pp. 128-33, Jan 2012.

    CAS  PubMed  Google Scholar 

  58. K. N. Colledanchise, L. E. Mantella, M. Bullen, M.-F. Hétu, J. G. Abunassar, and A. M. Johri, "Combined femoral and carotid plaque burden identifies obstructive coronary artery disease in women," Journal of the American Society of Echocardiography, vol. 33, pp. 90-100, 2020.

    PubMed  Google Scholar 

  59. J. H. Stein and M. C. Tattersall, "Carotid intima-media thickness and cardiovascular disease risk prediction," Journal of the American College of Cardiology, vol. 63, pp. 2301-2302, 2014.

    PubMed  Google Scholar 

  60. L. Saba, S. K. Banchhor, T. Araki, H. S. Suri, N. D. Londhe, J. R. Laird, et al., "Intra-and Inter-operator Reproducibility Analysis of Automated Cloud-based Carotid Intima Media Thickness Ultrasound Measurement," Journal of Clinical & Diagnostic Research, vol. 12, pp. KC01-KC11, 2018.

    Google Scholar 

  61. F. Molinari, G. Krishnamurthi, U. R. Acharya, S. V. Sree, G. Zeng, L. Saba, et al., "Hypothesis validation of far-wall brightness in carotid-artery ultrasound for feature-based IMT measurement using a combination of level-set segmentation and registration," IEEE Transactions on Instrumentation and measurement, vol. 61, pp. 1054-1063, 2012.

    Google Scholar 

  62. F. Molinari, G. Zeng, and J. S. Suri, "Greedy technique and its validation for fusion of two segmentation paradigms leads to an accurate intima–media thickness measure in plaque carotid arterial ultrasound," Journal for Vascular Ultrasound, vol. 34, pp. 63-73, 2010.

    Google Scholar 

  63. L. Saba, R. Montisci, F. Molinari, N. Tallapally, G. Zeng, G. Mallarini, et al., "Comparison between manual and automated analysis for the quantification of carotid wall by using sonography. A validation study with CT," European journal of radiology, vol. 81, pp. 911-918, 2012.

    PubMed  Google Scholar 

  64. L. Saba, S. K. Banchhor, H. S. Suri, N. D. Londhe, T. Araki, N. Ikeda, et al., "Accurate cloud-based smart IMT measurement, its validation and stroke risk stratification in carotid ultrasound: A web-based point-of-care tool for multicenter clinical trial," Computers in biology and medicine, vol. 75, pp. 217-234, 2016.

    PubMed  Google Scholar 

  65. Suri, J. S., Turk, M., Jamthikar, A., Gupta, D., Khanna, N., Araki, T., et al., Performance evaluation of AECRS1. 0 using stroke risk calculators, in European Journal of Neurology, pp. 280-281, 2019.

  66. T. Araki, N. Ikeda, D. Shukla, P. K. Jain, N. D. Londhe, V. K. Shrivastava, et al., "PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology," Computer methods and programs in biomedicine, vol. 128, pp. 137-158, 2016.

    PubMed  Google Scholar 

  67. A. S. Go, G. M. Chertow, D. Fan, C. E. McCulloch, and C.-y. Hsu, "Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization," New England Journal of Medicine, vol. 351, pp. 1296-1305, 2004.

    CAS  PubMed  Google Scholar 

  68. Y. Luo, X. Wang, K. Matsushita, C. Wang, X. Zhao, B. Hu, et al., "Associations Between Estimated Glomerular Filtration Rate and Stroke Outcomes in Diabetic Versus Nondiabetic Patients," Stroke, vol. 45, pp. 2887-2893, 2014.

    PubMed  Google Scholar 

  69. J. S. Lees, C. E. Welsh, C. A. Celis-Morales, D. Mackay, J. Lewsey, S. R. Gray, et al., "Glomerular filtration rate by differing measures, albuminuria and prediction of cardiovascular disease, mortality and end-stage kidney disease," Nature medicine, vol. 25, pp. 1753-1760, 2019.

    CAS  PubMed  PubMed Central  Google Scholar 

  70. M. Lee, J. L. Saver, K.-H. Chang, H.-W. Liao, S.-C. Chang, and B. Ovbiagele, "Low glomerular filtration rate and risk of stroke: meta-analysis," Bmj, vol. 341, p. c4249, 2010.

    PubMed  PubMed Central  Google Scholar 

  71. G. Eknoyan, N. Lameire, K. Eckardt, B. Kasiske, D. Wheeler, A. Levin, et al., "KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease," Kidney Int, vol. 3, pp. 5-14, 2013.

    Google Scholar 

  72. A. J. Collins, S. Li, D. T. Gilbertson, J. Liu, S. C. Chen, and C. A. Herzog, "Chronic kidney disease and cardiovascular disease in the Medicare population," Kidney Int Suppl, pp. S24-31, Nov 2003.

  73. U. R. Acharya, S. V. Sree, R. Ribeiro, G. Krishnamurthi, R. T. Marinho, J. Sanches, et al., "Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm," Medical physics, vol. 39, pp. 4255-4264, 2012.

    PubMed  Google Scholar 

  74. U. R. Acharya, M. R. K. Mookiah, S. V. Sree, D. Afonso, J. Sanches, S. Shafique, et al., "Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment," Medical & biological engineering & computing, vol. 51, pp. 513-523, 2013.

    Google Scholar 

  75. L. Saba, N. Dey, A. S. Ashour, S. Samanta, S. S. Nath, S. Chakraborty, et al., "Automated stratification of liver disease in ultrasound: An online accurate feature classification paradigm," Comput Methods Programs Biomed, vol. 130, pp. 118-34, Jul 2016.

    PubMed  Google Scholar 

  76. M. Maniruzzaman, N. Kumar, M. M. Abedin, M. S. Islam, H. S. Suri, A. S. El-Baz, et al., "Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm," Computer methods and programs in biomedicine, vol. 152, pp. 23-34, 2017.

    PubMed  Google Scholar 

  77. T. F. Lüscher, "Predictors as well as surrogate and hard endpoints in cardiovascular disease," European Heart Journal, vol. 36, pp. 2197-2199, 2015.

    PubMed  Google Scholar 

  78. J. H. Revkin, C. L. Shear, H. G. Pouleur, S. W. Ryder, and D. G. Orloff, "Biomarkers in the prevention and treatment of atherosclerosis: need, validation, and future," Pharmacol Rev, vol. 59, pp. 40-53, Mar 2007.

    CAS  PubMed  Google Scholar 

  79. M. L. Bots, G. W. Evans, C. H. Tegeler, and R. Meijer, "Carotid intima-media thickness measurements: relations with atherosclerosis, risk of cardiovascular disease and application in randomized controlled trials," Chinese medical journal, vol. 129, p. 215, 2016.

    PubMed  PubMed Central  Google Scholar 

  80. J. B. Thompson, M. Blaha, J. R. Resar, R. S. Blumenthal, and M. Y. Desai, "Strategies to reverse atherosclerosis: an imaging perspective," Curr Treat Options Cardiovasc Med, vol. 10, pp. 283-93, Aug 2008.

    PubMed  Google Scholar 

  81. J.-P. Boissel, J.-P. Collet, P. Moleur, and M. Haugh, "Surrogate endpoints: a basis for a rational approach," European journal of clinical pharmacology, vol. 43, pp. 235-244, 1992.

    CAS  PubMed  Google Scholar 

  82. T. E. Yap, S. I. Balendra, M. T. Almonte, and M. F. Cordeiro, "Retinal correlates of neurological disorders," Therapeutic advances in chronic disease, vol. 10, pp. 2040622319882205-2040622319882205, 2019.

    PubMed  PubMed Central  Google Scholar 

  83. Karamitsos, T. D., Arvanitaki, A., Karvounis, H., Neubauer, S., and Ferreira, V. M., Myocardial tissue characterization and fibrosis by imaging, JACC: Cardiovascular Imaging, 2019.

  84. E. Y. Chen, S. K. Joshi, A. Tran, and V. Prasad, "Estimation of study time reduction using surrogate end points rather than overall survival in oncology clinical trials," JAMA internal medicine, vol. 179, pp. 642-647, 2019.

    PubMed  PubMed Central  Google Scholar 

  85. P. Libby, P. M. Ridker, and G. K. Hansson, "Progress and challenges in translating the biology of atherosclerosis," Nature, vol. 473, pp. 317-325, 2011.

    CAS  PubMed  Google Scholar 

  86. V. Cachofeiro, M. Goicochea, S. G. De Vinuesa, P. Oubiña, V. Lahera, and J. Luño, "Oxidative stress and inflammation, a link between chronic kidney disease and cardiovascular disease: New strategies to prevent cardiovascular risk in chronic kidney disease," Kidney International, vol. 74, pp. S4-S9, 2008.

    Google Scholar 

  87. T. Munzel, T. Heitzer, and D. G. Harrison, "The physiology and pathophysiology of the nitric oxide/superoxide system," Herz, vol. 22, pp. 158-72, Jun 1997.

    CAS  PubMed  Google Scholar 

  88. A. Recio-Mayoral, D. Banerjee, C. Streather, and J. C. Kaski, "Endothelial dysfunction, inflammation and atherosclerosis in chronic kidney disease–a cross-sectional study of predialysis, dialysis and kidney-transplantation patients," Atherosclerosis, vol. 216, pp. 446-451, 2011.

    CAS  PubMed  Google Scholar 

  89. P. Libby, P. M. Ridker, and A. Maseri, "Inflammation and atherosclerosis," Circulation, vol. 105, pp. 1135-1143, 2002.

    CAS  PubMed  Google Scholar 

  90. P. Libby, "Vascular biology of atherosclerosis: overview and state of the art," The American journal of cardiology, vol. 91, pp. 3-6, 2003.

    Google Scholar 

  91. A. Jamthikar, D. Gupta, E. Cuadrado-Godia, A. Puvvula, N. N. Khanna, L. Saba, et al., "Ultrasound-based stroke/cardiovascular risk stratification using Framingham Risk Score and ASCVD Risk Score based on “Integrated Vascular Age” instead of “Chronological Age”: a multi-ethnic study of Asian Indian, Caucasian, and Japanese cohorts," Cardiovascular Diagnosis and Therapy, vol. 10, pp. 940-954, 2020.

    Google Scholar 

  92. Viswanathan, V., Jamthikar, A. D., Gupta, D., Puvvula, A., Khanna, N. N., Saba, L., et al., Does the Carotid Bulb offer a better 10-year CVD/Stroke Risk Assessment compared with the Common Carotid Artery?: A 1516 Ultrasound Scan Study, Angiology, vol. In Press, 2020.

  93. Suri, J. S., Imaging based symptomatic classification and cardiovascular stroke risk score estimation, ed: Google Patents, 2011.

  94. T. Araki, P. K. Jain, H. S. Suri, N. D. Londhe, N. Ikeda, A. El-Baz, et al., "Stroke risk stratification and its validation using ultrasonic Echolucent Carotid Wall plaque morphology: a machine learning paradigm," Computers in biology and medicine, vol. 80, pp. 77-96, 2017.

    PubMed  Google Scholar 

  95. Banchhor, S. K., Londhe, N. D., Araki, T., Saba, L., Radeva, P., Khanna, N. N., et al., Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review, Computers in Biology and Medicine, vol. 101, pp. 184-198, 2018/10/01/ 2018.

  96. N. Garg, S. K. Muduli, A. Kapoor, S. Tewari, S. Kumar, R. Khanna, et al., "Comparison of different cardiovascular risk score calculators for cardiovascular risk prediction and guideline recommended statin uses," Indian Heart Journal, vol. 69, pp. 458-463, 2017.

    PubMed  PubMed Central  Google Scholar 

  97. K. Zarkogianni, M. Athanasiou, A. C. Thanopoulou, and K. S. Nikita, "Comparison of Machine Learning Approaches Toward Assessing the Risk of Developing Cardiovascular Disease as a Long-Term Diabetes Complication," IEEE Journal of Biomedical and Health Informatics, vol. 22, pp. 1637-1647, 2018.

    PubMed  Google Scholar 

  98. Skandha, S., Gupta, S., Saba, L., Koppula, V., and Suri, J. S., Ultrasound-based Carotid Plaque Tissue Risk Stratification using 3-D Optimized Artificial Intelligence Paradigm: a Cardiovascular/Stroke Application: Atheromatic 2.0, Computers Biology and Medicine, vol. In Press, 2020

  99. Janosi, A., Steinbrunn, W., Pfisterer, M., Detrano, R., (1988). Heart Disease Data Set. Available: https://archive.ics.uci.edu/ml/datasets/Heart+Disease

  100. Kaggle. (27 May). Pima Indians Diabetes Database: Predict the onset of diabetes based on diagnostic measures. Available: https://www.kaggle.com/uciml/pima-indians-diabetes-database

  101. P. Nordet, S. Mendis, A. Dueñas, R. de la Noval, N. Armas, I. L. de la Noval, et al., "Total cardiovascular risk assessment and management using two prediction tools, with and without blood cholesterol," MEDICC review, vol. 15, pp. 36-40, 2013.

    PubMed  Google Scholar 

  102. P. Joseph, S. Yusuf, S. F. Lee, Q. Ibrahim, K. Teo, S. Rangarajan, et al., "Prognostic validation of a non-laboratory and a laboratory based cardiovascular disease risk score in multiple regions of the world," Heart, vol. 104, pp. 581-587, 2018.

    CAS  PubMed  Google Scholar 

  103. M. Ostermann, A. Zarbock, S. Goldstein, K. Kashani, E. Macedo, R. Murugan, et al., "Recommendations on Acute Kidney Injury Biomarkers From the Acute Disease Quality Initiative Consensus Conference: A Consensus Statement," JAMA Network Open, vol. 3, pp. e2019209-e2019209, 2020.

    PubMed  Google Scholar 

  104. Jamthikar, A. D., Gupta, D., Puvvula, A., Johri, A. M., Khanna, N. N., Saba, L., et al., Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging, Rheumatology international, pp. 1-19, 2020.

  105. Khanna, N. N., Jamthikar, A. D., Gupta, D., Piga, M., Saba, L., Carcassi, C., et al., Rheumatoid Arthritis: Atherosclerosis Imaging and Cardiovascular Risk Assessment Using Machine and Deep Learning–Based Tissue Characterization, Current Atherosclerosis Reports, vol. 21, p. 7, 2019/01/25 2019.

  106. Viswanathan, V., Jamthikar, A. D., Gupta, D., Shanu, N., Puvvula, A., Khanna, N. N., et al., Low-cost preventive screening using carotid ultrasound in patients with diabetes, Front Biosci (Landmark Ed), vol. 25, pp. 1132-1171, Mar 1 2020.

  107. Saba, L., Biswas, M., Kuppili, V., Godia, E. C., Suri, H. S., Edla, D. R., et al., The present and future of deep learning in radiology, European journal of radiology, 2019.

  108. M. Biswas, L. Saba, S. Chakrabartty, N. N. Khanna, H. Song, H. S. Suri, et al., "Two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in carotid ultrasound: A screening tool for cardiovascular/stroke risk assessment," Computers in Biology and Medicine, vol. 123, p. 103847, 2020.

    CAS  PubMed  Google Scholar 

  109. L. Saba, M. Biswas, H. S. Suri, K. Viskovic, J. R. Laird, E. Cuadrado-Godia, et al., "Ultrasound-based carotid stenosis measurement and risk stratification in diabetic cohort: a deep learning paradigm," Cardiovasc Diagn Ther, vol. 9, pp. 439-461, Oct 2019.

    PubMed  PubMed Central  Google Scholar 

  110. E. Cuadrado-Godia, P. Dwivedi, S. Sharma, A. O. Santiago, J. R. Gonzalez, M. Balcells, et al., "Cerebral small vessel disease: A review focusing on pathophysiology, biomarkers, and machine learning strategies," Journal of stroke, vol. 20, p. 302, 2018.

    PubMed  PubMed Central  Google Scholar 

  111. Saba, L., Banchhor, S. K., Araki, T., Viskovic, K., Londhe, N. D., Laird, J. R., et al., Intra-and inter-operator reproducibility of automated cloud-based carotid lumen diameter ultrasound measurement, Indian Heart Journal, 2018.

  112. L. Saba, J. C. Than, N. M. Noor, O. M. Rijal, R. M. Kassim, A. Yunus, et al., "Inter-observer variability analysis of automatic lung delineation in normal and disease patients," Journal of medical systems, vol. 40, p. 142, 2016.

    PubMed  Google Scholar 

  113. Saba, L., Sanches, J. M., Pedro, L. M., and Suri, J. S., Multi-modality atherosclerosis imaging and diagnosis: Springer, 2014.

  114. Rikin Trivedi, L. S., Suri, J. S., (2015). 3D Imaging Technologies in Atherosclerosis.

  115. M. Biswas, V. Kuppili, T. Araki, D. R. Edla, E. C. Godia, L. Saba, et al., "Deep learning strategy for accurate carotid intima-media thickness measurement: An ultrasound study on Japanese diabetic cohort," Comput Biol Med, vol. 98, pp. 100-117, May 12 2018.

    PubMed  Google Scholar 

  116. V. K. Shrivastava, N. D. Londhe, R. S. Sonawane, and J. S. Suri, "Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: a first comparative study of its kind," Computer methods and programs in biomedicine, vol. 126, pp. 98-109, 2016.

    PubMed  Google Scholar 

  117. U. R. Acharya, G. Swapna, S. V. Sree, F. Molinari, S. Gupta, R. H. Bardales, et al., "A review on ultrasound-based thyroid cancer tissue characterization and automated classification," Technology in cancer research & treatment, vol. 13, pp. 289-301, 2014.

    CAS  Google Scholar 

  118. S. Parikh, M. Patel, H. Tiwari, D. Bala, and B. Joshi, "Assessment of cardiovascular disease risk by using Framingham risk equation amongst the residents of Ahmedabad city," Natl J Community Med, vol. 4, pp. 392-7, 2013.

    Google Scholar 

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Acknowledgments

The authors thank Professor Vijay Nambi, MD, PhD from Michael E DeBakey Veterans Affairs Hospital and Baylor College of Medicine, TX, the USA, for his valuable input and discussions. ADJ would like to thank the Ministry of Human Resource and Development, Government of India, for providing financial support to conduct part of his PhD dissertation at VNIT, Nagpur, India.

Funding

No funding received from any organization.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, MH, Nagpur, India

    Ankush D. Jamthikar & Deep Gupta

  2. Department of Medicine, Division of Cardiology, Queen’s University, Kingston, Ontario, Canada

    Amer M. Johri

  3. Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, Ontario, Canada

    Laura E. Mantella

  4. Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy

    Luca Saba

  5. Ohio Health Heart and Vascular, Columbus, OH, USA

    Raghu Kolluri

  6. Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA

    Aditya M. Sharma

  7. MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India

    Vijay Viswanathan

  8. Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Nicosia, Cyprus

    Andrew Nicolaides

  9. Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA

    Jasjit S. Suri

  10. American Institute of Medical and Biological Engineering, Washington, DC, USA

    Jasjit S. Suri

  11. American Insitute of Ultrasound in Medicine, Laurel, MD, USA

    Jasjit S. Suri

  12. Asia Pacific Vascular Society, New Delhi, India

    Jasjit S. Suri

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  1. Ankush D. Jamthikar
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  2. Deep Gupta
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  5. Luca Saba
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  7. Aditya M. Sharma
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  8. Vijay Viswanathan
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  10. Jasjit S. Suri
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Correspondence to Jasjit S. Suri.

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Conflict of Interest

Dr. Jasjit Suri is affiliated with AtheroPoint™, focused on stroke and cardiovascular imaging. All other authors have not conflict of interest.

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All the study participants were approved by the ethics committee and the institutional review board of MV Diabetes Hospital, Chennai, India.

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Jamthikar, A.D., Gupta, D., Johri, A.M. et al. Low-Cost Office-Based Cardiovascular Risk Stratification Using Machine Learning and Focused Carotid Ultrasound in an Asian-Indian Cohort. J Med Syst 44, 208 (2020). https://doi.org/10.1007/s10916-020-01675-7

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