Advances in research on the use of wearable devices in cardiovascular diseases_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

MA Xin , LI Huizhen , LI Yongnan ,  WU Xiangyang

Department of Cardiac Surgery, Lanzhou University Second Hospital, Lanzhou, 730030, P. R. China;

Corresponding author：

WU Xiangyang, Email: wuxyok@163.com

Keywords：

Wearable devices; cardiovascular disease; portability; review

DOI：

10.7507/1007-4848.202401006

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective Wearable devices refer to a class of monitoring devices that can be tightly integrated with the human body and are designed to continuously monitor individual's activity without impeding or restricting the user's normal activities in the process. With the rapid advancement of chips, sensors, and artificial intelligence technologies, such devices have been widely used for patients with cardiovascular diseases who require continuous health monitoring. These patients require continuous monitoring of a number of physiological indicators to assess disease progression, treatment efficacy, and recovery in the early stages of the disease, during the treatment, and in the recovery period. Traditional monitoring methods require patients to go to the doctor on a regular basis with the help of fixed devices and analysis by doctors, which not only increases the financial burden of patients, but also consumes medical resources and time. However, wearable devices can collect data in real time and transmit it directly to doctors via the network, thus providing an efficient and cost-effective monitoring solution for patients. In this paper, we will review the applications, advantages and challenges of wearable devices in the treatment of cardiovascular diseases, as well as the outlook for their future applications.

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2.	The Writing Committee of the Report on Cardiovascular Health and Diseases in China, Hu SS. Report on cardiovascular health and diseases in China 2021: An updated summary. J Geriatr Cardiol, 2023, 20(6): 399-430.
3.	Song Y, Min J, Yu Y, et al. Wireless battery-free wearable sweat sensor powered by human motion. Sci Adv, 2020, 6(40): eaay9842.
4.	Coyle S, Lau KT, Moyna N, et al. BIOTEX: Biosensing textiles for personalised healthcare management. IEEE Trans Inf Technol Biomed, 2010, 14(2): 364-370.
5.	Bonato P. Wearable sensors and systems. From enabling technology to clinical applications. IEEE Eng Med Biol Mag, 2010, 29(3): 25-36.
6.	Zhu G, Zhou YS, Bai P, et al. A shape-adaptive thin-film-based approach for 50% high-efficiency energy generation through micro-grating sliding electrification. Adv Mater, 2014, 26(23): 3788-3796.
7.	Xie Y, Wang S, Niu S, et al. Grating-structured freestanding triboelectric-layer nanogenerator for harvesting mechanical energy at 85% total conversion efficiency. Adv Mater, 2014, 26(38): 6599-6607.
8.	Tang W, Jiang T, Fan FR, et al. Liquid‐metal electrode for high‐performance triboelectric nanogenerator at an instantaneous energy conversion efficiency of 70.6%. Adv Funct, 2015, 25(24): 3718-3725.
9.	Bandodkar AJ, Lee SP, Huang I, et al. Sweat-activated biocompatible batteries for epidermal electronic and microfluidic systems. Nat Electron, 2020, 3(9): 554-562.
10.	Talkhooncheh AH, Yu Y, Agarwal A, et al. A biofuel-cell-based energy harvester with 86% peak efficiency and 0.25 V minimum input voltage using source-adaptive MPPT. IEEE J Solid-State Circuits, 2021, 56(3): 715-728.
11.	Yu Y, Nassar J, Xu C, et al. Biofuel-powered soft electronic skin with multiplexed and wireless sensing for human-machine interfaces. Sci Robot, 2020, 5(41): eaaz7946.
12.	Stuart T, Hanna J, Gutruf P. Wearable devices for continuous monitoring of biosignals: Challenges and opportunities. APL Bioeng, 2022, 6(2): 021502.
13.	Williams GJ, Al-Baraikan A, Rademakers FE, et al. Wearable technology and the cardiovascular system: The future of patient assessment. Lancet Digital Health, 2023, 5(7): e467-e476.
14.	Hill J. ABC of clinical electrocardiography: Exercise tolerance testing. BMJ, 2002, 324(7345): 1084-1087.
15.	Hu H, Huang H, Li M, et al. A wearable cardiac ultrasound imager. Nature, 2023, 613(7945): 667-675.
16.	Pellikka PA, She L, Holly TA, et al. Variability in ejection fraction measured by echocardiography, gated single-photon emission computed tomography, and cardiac magnetic resonance in patients with coronary artery disease and left ventricular dysfunction. JAMA Netw Open, 2018, 1(4): e181456.
17.	Coravos A, Doerr M, Goldsack J, et al. Modernizing and designing evaluation frameworks for connected sensor technologies in medicine. npj Digital Medicine, 2020, 3(1): 37.
18.	Bayoumy K, Gaber M, Elshafeey A, et al. Smart wearable devices in cardiovascular care: Where we are and how to move forward. Nat Rev Cardiol, 2021, 18(8): 581-599.
19.	Matheny ME, Whicher D, Thadaney Israni S. Artificial intelligence in health care: A report from the National Academy of Medicine. JAMA. 2020, 323(6): 509-510.
20.	Haibe-Kains B, Adam GA, Hosny A, et al. Transparency and reproducibility in artificial intelligence. Nature, 2020, 586(7829): E14-E16.
21.	Seo K, Yamamoto Y, Kirillova A, et al. Improved cardiac performance and decreased arrhythmia in hypertrophic cardiomyopathy with non-β-blocking R-enantiomer carvedilol. Circulation. 2023, 148(21): 1691-1704.
22.	Jensen MT. Resting heart rate and relation to disease and longevity: Past, present and future. Scand J Clin Lab Invest, 2019, 79(1-2): 108-116.
23.	Zheng Y, Leung B, Sy S, et al. A clip-free eyeglasses-based wearable monitoring device for measuring photoplethysmograhic signals. Annu Int Conf IEEE Eng Med Biol Soc, 2012, 2012: 5022-5025.
24.	Shcherbina A, Mattsson CM, Waggott D, et al. Accuracy in wrist-worn, sensor-based measurements of heart rate and energy expenditure in a diverse Cohort. J Pers Med, 2017, 7(2): 3.
25.	Ouyang H, Tian J, Sun G, et al. Self-powered pulse sensor for antidiastole of cardiovascular disease. Adv Mater, 2017, 29(40).
26.	Tison GH, Sanchez JM, Ballinger B, et al. Passive detection of atrial fibrillation using a commercially available smartwatch. JAMA Cardiol, 2018, 3(5): 409-416.
27.	Krivoshei L, Weber S, Burkard T, et al. Smart detection of atrial fibrillation. Europace, 2017, 19(5):753-757.
28.	Bumgarner JM, Lambert CT, Hussein AA, et al. Smartwatch algorithm for automated detection of atrial fibrillation. J Am Coll Cardiol, 2018, 71(21): 2381-2388.
29.	Brasier N, Raichle CJ, Dörr M, et al. Detection of atrial fibrillation with a smartphone camera: First prospective, international, two-centre, clinical validation study (DETECT AF PRO). EP Europace, 2019, 21(1): 41-47.
30.	Guo Y, Wang H, Zhang H, et al. Mobile photoplethysmographic technology to detect atrial fibrillation. J Am Coll Cardiol, 2019, 74(19): 2365-2375.
31.	Golbus JR, Gosch K, Birmingham MC, et al. Association between wearable device measured activity and patient-reported outcomes for heart failure. JACC: Heart Failure, 2023, 11(11): 1521-1530.
32.	Kurihara M, Maniwa Y, Ohta S. Chaos-based quantitative health evaluation and disease state estimation by acceleration plethysmogram. J Japan Socie Fuzzy Theo Intelli Informat, 2003, 15(5): 565-576.
33.	Elgendi M, Fletcher R, Liang Y, et al. The use of photoplethysmography for assessing hypertension. NPJ Digit Med. 2019, 2: 60.
34.	Schutte AE, Kollias A, Stergiou GS. Blood pressure and its variability: Classic and novel measurement techniques. Nat Rev Cardiol, 2022, 19(10): 643-654.
35.	Stergiou GS, Kollias A, Protogerou AD. Evidence on blood pressure measurement methodology and clinical implementation. J Am Coll Cardiol, 2017, 70(5): 587-589.
36.	Parati G, Ochoa JE, Lombardi C, et al. Assessment and management of blood-pressure variability. Nat Rev Cardiol, 2013, 10(3): 143-155.
37.	Mukkamala R, Stergiou GS, Avolio AP. Cuffless blood pressure measurement. Annu Rev Biomed Eng, 2022, 24: 203-230.
38.	Lee C, Sik Shin H, Lee M. Relations between ac-dc components and optical path length in photoplethysmography. J Biomed Opt, 2011, 16(7): 077012.
39.	Vybornova A, Polychronopoulou E, Wurzner-Ghajarzadeh A, et al. Blood pressure from the optical Aktiia Bracelet: A 1-month validation study using an extended ISO81060-2 protocol adapted for a cuffless wrist device. Blood Press Monit, 2021, 26(4): 305-311.
40.	Mukkamala R, Yavarimanesh M, Natarajan K, et al. Evaluation of the accuracy of cuffless blood pressure measurement devices: Challenges and proposals. Hypertension, 2021, 78(5): 1161-1167.
41.	McMurray JJ, Pfeffer MA. Heart failure. Lancet, 2005, 365(9474): 1877-1889.
42.	Khunti K, Baker R, Grimshaw G. Diagnosis of patients with chronic heart failure in primary care: Usefulness of history, examination, and investigations. Br J Gen Pract, 2000, 50(450): 50-54.
43.	Thomas JT, Kelly RF, Thomas SJ, et al. Utility of history, physical examination, electrocardiogram, and chest radiograph for differentiating normal from decreased systolic function in patients with heart failure. Am J Med, 2002, 112(6): 437-445.
44.	Davie AP, Francis CM, Caruana L, et al. Assessing diagnosis in heart failure: Which features are any use? QJM, 1997, 90(5): 335-339.
45.	Greene SJ, Butler J, Albert NM, et al. Medical therapy for heart failure with reduced ejection fraction. J Am Colle Cardiol, 2018, 72(4): 351-366.
46.	Walsh JT, Charlesworth A, Andrews R, et al. Relation of daily activity levels in patients with chronic heart failure to long-term prognosis. Am J Cardiol, 1997, 79(10): 1364-1369.
47.	Waring T, Gross K, Soucier R, et al. Measured physical activity and 30-day rehospitalization in heart failure patients. J Cardiopulm Rehabil Prev, 2017, 37(2): 124-129.
48.	Deka P, Pozehl B, Norman JF, et al. Feasibility of using the Fitbit® Charge HR in validating self-reported exercise diaries in a community setting in patients with heart failure. Eur J Cardiovasc Nurs, 2018, 17(7): 605-611.
49.	Wolf MS, King J, Wilson EA, et al. Usability of FDA-approved medication guides. J Gen Intern Med, 2012, 27(12): 1714-1720.
50.	Chen Y, Sloan FA, Yashkin AP. Adherence to diabetes guidelines for screening, physical activity and medication and onset of complications and death. J Diabetes Complications. 2015, 29(8): 1228-1233.
51.	Sharma A, Mentz RJ, Granger BB, et al. Utilizing mobile technologies to improve physical activity and medication adherence in patients with heart failure and diabetes mellitus: Rationale and design of the TARGET-HF-DM trial. Am Heart J, 2019, 211: 22-33.
52.	Shamaki GR, Markson F, Soji-Ayoade D, et al. Peripheral artery disease: A comprehensive updated review. Curr Probl Cardiol. 2022, 47(11): 101082.
53.	Arain FA, Cooper LT Jr. Peripheral arterial disease: Diagnosis and management. Mayo Clin Proc. 2008, 83(8): 944-949.
54.	Aboyans V, Ricco JB, Bartelink MEL, et al. 2017 ESC guidelines on the diagnosis and treatment of peripheral arterial diseases, in collaboration with the European Society for Vascular Surgery (ESVS): Document covering atherosclerotic disease of extracranial carotid and vertebral, mesenteric, renal, upper and lower extremity arteriesEndorsed by: The European Stroke Organization (ESO) The Task Force for the Diagnosis and Treatment of Peripheral Arterial Diseases of the European Society of Cardiology (ESC) and of the European Society for Vascular Surgery (ESVS). Eur Heart J, 2018, 39(9): 763-816.
55.	Herráiz-Adillo Á, Cavero-Redondo I, Álvarez-Bueno C, et al. Factors affecting the validity of the oscillometric Ankle Brachial Index to detect peripheral arterial disease. Int Angiol, 2017, 36(6): 536-544.
56.	Watson EL, Patel B, Katsogridakis E, et al. Selecting portable ankle/toe brachial pressure index systems for a peripheral arterial disease population screening programme: A systematic review, clinical evaluation exercise, and consensus process. Eur J Vasc Endovasc Surg, 2022, 64(6): 693-702.
57.	Jin C, Li J, Liu F, et al. Life's essential 8 and 10-year and lifetime risk of atherosclerotic cardiovascular disease in China. Am J Prev Med, 2023, 64(6): 927-935.
58.	Sulague RM, Suan NNM, Mendoza MF, et al. The associations between exercise and lipid biomarkers. Prog Cardiovasc Dis, 2022, 75: 59-68.
59.	Lavie CJ, Kachur S, Sui X. Impact of fitness and changes in fitness on lipids and survival. Prog Cardiovasc Dis, 2019, 62(5): 431-435.
60.	Ghorbanzadeh O, Blaschke T, Gholamnia K, et al. Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens, 2019, 11(2): 196.
61.	Ben-Shlomo Y, Spears M, Boustred C, et al. Aortic pulse wave velocity improves cardiovascular event prediction: An individual participant meta-analysis of prospective observational data from 17, 635 subjects. J Am Coll Cardiol, 2014, 63(7): 636-646.
62.	Vlachopoulos C, Aznaouridis K, Stefanadis C. Prediction of cardiovascular events and all-cause mortality with arterial stiffness. J Am Coll Cardiol, 2010, 55(13): 1318-1327.
63.	Li Y, Xu Y, Ma Z, et al. An XGBoost-based model for assessment of aortic stiffness from wrist photoplethysmogram. Comput Methods Programs Biomed, 2022, 226: 107128.

1. Roth GA, Johnson C, Abajobir A, et al. Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol. 2017, 70(1): 1-25.
2. The Writing Committee of the Report on Cardiovascular Health and Diseases in China, Hu SS. Report on cardiovascular health and diseases in China 2021: An updated summary. J Geriatr Cardiol, 2023, 20(6): 399-430.
3. Song Y, Min J, Yu Y, et al. Wireless battery-free wearable sweat sensor powered by human motion. Sci Adv, 2020, 6(40): eaay9842.
4. Coyle S, Lau KT, Moyna N, et al. BIOTEX: Biosensing textiles for personalised healthcare management. IEEE Trans Inf Technol Biomed, 2010, 14(2): 364-370.
5. Bonato P. Wearable sensors and systems. From enabling technology to clinical applications. IEEE Eng Med Biol Mag, 2010, 29(3): 25-36.
6. Zhu G, Zhou YS, Bai P, et al. A shape-adaptive thin-film-based approach for 50% high-efficiency energy generation through micro-grating sliding electrification. Adv Mater, 2014, 26(23): 3788-3796.
7. Xie Y, Wang S, Niu S, et al. Grating-structured freestanding triboelectric-layer nanogenerator for harvesting mechanical energy at 85% total conversion efficiency. Adv Mater, 2014, 26(38): 6599-6607.
8. Tang W, Jiang T, Fan FR, et al. Liquid‐metal electrode for high‐performance triboelectric nanogenerator at an instantaneous energy conversion efficiency of 70.6%. Adv Funct, 2015, 25(24): 3718-3725.
9. Bandodkar AJ, Lee SP, Huang I, et al. Sweat-activated biocompatible batteries for epidermal electronic and microfluidic systems. Nat Electron, 2020, 3(9): 554-562.
10. Talkhooncheh AH, Yu Y, Agarwal A, et al. A biofuel-cell-based energy harvester with 86% peak efficiency and 0.25 V minimum input voltage using source-adaptive MPPT. IEEE J Solid-State Circuits, 2021, 56(3): 715-728.
11. Yu Y, Nassar J, Xu C, et al. Biofuel-powered soft electronic skin with multiplexed and wireless sensing for human-machine interfaces. Sci Robot, 2020, 5(41): eaaz7946.
12. Stuart T, Hanna J, Gutruf P. Wearable devices for continuous monitoring of biosignals: Challenges and opportunities. APL Bioeng, 2022, 6(2): 021502.
13. Williams GJ, Al-Baraikan A, Rademakers FE, et al. Wearable technology and the cardiovascular system: The future of patient assessment. Lancet Digital Health, 2023, 5(7): e467-e476.
14. Hill J. ABC of clinical electrocardiography: Exercise tolerance testing. BMJ, 2002, 324(7345): 1084-1087.
15. Hu H, Huang H, Li M, et al. A wearable cardiac ultrasound imager. Nature, 2023, 613(7945): 667-675.
16. Pellikka PA, She L, Holly TA, et al. Variability in ejection fraction measured by echocardiography, gated single-photon emission computed tomography, and cardiac magnetic resonance in patients with coronary artery disease and left ventricular dysfunction. JAMA Netw Open, 2018, 1(4): e181456.
17. Coravos A, Doerr M, Goldsack J, et al. Modernizing and designing evaluation frameworks for connected sensor technologies in medicine. npj Digital Medicine, 2020, 3(1): 37.
18. Bayoumy K, Gaber M, Elshafeey A, et al. Smart wearable devices in cardiovascular care: Where we are and how to move forward. Nat Rev Cardiol, 2021, 18(8): 581-599.
19. Matheny ME, Whicher D, Thadaney Israni S. Artificial intelligence in health care: A report from the National Academy of Medicine. JAMA. 2020, 323(6): 509-510.
20. Haibe-Kains B, Adam GA, Hosny A, et al. Transparency and reproducibility in artificial intelligence. Nature, 2020, 586(7829): E14-E16.
21. Seo K, Yamamoto Y, Kirillova A, et al. Improved cardiac performance and decreased arrhythmia in hypertrophic cardiomyopathy with non-β-blocking R-enantiomer carvedilol. Circulation. 2023, 148(21): 1691-1704.
22. Jensen MT. Resting heart rate and relation to disease and longevity: Past, present and future. Scand J Clin Lab Invest, 2019, 79(1-2): 108-116.
23. Zheng Y, Leung B, Sy S, et al. A clip-free eyeglasses-based wearable monitoring device for measuring photoplethysmograhic signals. Annu Int Conf IEEE Eng Med Biol Soc, 2012, 2012: 5022-5025.
24. Shcherbina A, Mattsson CM, Waggott D, et al. Accuracy in wrist-worn, sensor-based measurements of heart rate and energy expenditure in a diverse Cohort. J Pers Med, 2017, 7(2): 3.
25. Ouyang H, Tian J, Sun G, et al. Self-powered pulse sensor for antidiastole of cardiovascular disease. Adv Mater, 2017, 29(40).
26. Tison GH, Sanchez JM, Ballinger B, et al. Passive detection of atrial fibrillation using a commercially available smartwatch. JAMA Cardiol, 2018, 3(5): 409-416.
27. Krivoshei L, Weber S, Burkard T, et al. Smart detection of atrial fibrillation. Europace, 2017, 19(5):753-757.
28. Bumgarner JM, Lambert CT, Hussein AA, et al. Smartwatch algorithm for automated detection of atrial fibrillation. J Am Coll Cardiol, 2018, 71(21): 2381-2388.
29. Brasier N, Raichle CJ, Dörr M, et al. Detection of atrial fibrillation with a smartphone camera: First prospective, international, two-centre, clinical validation study (DETECT AF PRO). EP Europace, 2019, 21(1): 41-47.
30. Guo Y, Wang H, Zhang H, et al. Mobile photoplethysmographic technology to detect atrial fibrillation. J Am Coll Cardiol, 2019, 74(19): 2365-2375.
31. Golbus JR, Gosch K, Birmingham MC, et al. Association between wearable device measured activity and patient-reported outcomes for heart failure. JACC: Heart Failure, 2023, 11(11): 1521-1530.
32. Kurihara M, Maniwa Y, Ohta S. Chaos-based quantitative health evaluation and disease state estimation by acceleration plethysmogram. J Japan Socie Fuzzy Theo Intelli Informat, 2003, 15(5): 565-576.
33. Elgendi M, Fletcher R, Liang Y, et al. The use of photoplethysmography for assessing hypertension. NPJ Digit Med. 2019, 2: 60.
34. Schutte AE, Kollias A, Stergiou GS. Blood pressure and its variability: Classic and novel measurement techniques. Nat Rev Cardiol, 2022, 19(10): 643-654.
35. Stergiou GS, Kollias A, Protogerou AD. Evidence on blood pressure measurement methodology and clinical implementation. J Am Coll Cardiol, 2017, 70(5): 587-589.
36. Parati G, Ochoa JE, Lombardi C, et al. Assessment and management of blood-pressure variability. Nat Rev Cardiol, 2013, 10(3): 143-155.
37. Mukkamala R, Stergiou GS, Avolio AP. Cuffless blood pressure measurement. Annu Rev Biomed Eng, 2022, 24: 203-230.
38. Lee C, Sik Shin H, Lee M. Relations between ac-dc components and optical path length in photoplethysmography. J Biomed Opt, 2011, 16(7): 077012.
39. Vybornova A, Polychronopoulou E, Wurzner-Ghajarzadeh A, et al. Blood pressure from the optical Aktiia Bracelet: A 1-month validation study using an extended ISO81060-2 protocol adapted for a cuffless wrist device. Blood Press Monit, 2021, 26(4): 305-311.
40. Mukkamala R, Yavarimanesh M, Natarajan K, et al. Evaluation of the accuracy of cuffless blood pressure measurement devices: Challenges and proposals. Hypertension, 2021, 78(5): 1161-1167.
41. McMurray JJ, Pfeffer MA. Heart failure. Lancet, 2005, 365(9474): 1877-1889.
42. Khunti K, Baker R, Grimshaw G. Diagnosis of patients with chronic heart failure in primary care: Usefulness of history, examination, and investigations. Br J Gen Pract, 2000, 50(450): 50-54.
43. Thomas JT, Kelly RF, Thomas SJ, et al. Utility of history, physical examination, electrocardiogram, and chest radiograph for differentiating normal from decreased systolic function in patients with heart failure. Am J Med, 2002, 112(6): 437-445.
44. Davie AP, Francis CM, Caruana L, et al. Assessing diagnosis in heart failure: Which features are any use? QJM, 1997, 90(5): 335-339.
45. Greene SJ, Butler J, Albert NM, et al. Medical therapy for heart failure with reduced ejection fraction. J Am Colle Cardiol, 2018, 72(4): 351-366.
46. Walsh JT, Charlesworth A, Andrews R, et al. Relation of daily activity levels in patients with chronic heart failure to long-term prognosis. Am J Cardiol, 1997, 79(10): 1364-1369.
47. Waring T, Gross K, Soucier R, et al. Measured physical activity and 30-day rehospitalization in heart failure patients. J Cardiopulm Rehabil Prev, 2017, 37(2): 124-129.
48. Deka P, Pozehl B, Norman JF, et al. Feasibility of using the Fitbit® Charge HR in validating self-reported exercise diaries in a community setting in patients with heart failure. Eur J Cardiovasc Nurs, 2018, 17(7): 605-611.
49. Wolf MS, King J, Wilson EA, et al. Usability of FDA-approved medication guides. J Gen Intern Med, 2012, 27(12): 1714-1720.
50. Chen Y, Sloan FA, Yashkin AP. Adherence to diabetes guidelines for screening, physical activity and medication and onset of complications and death. J Diabetes Complications. 2015, 29(8): 1228-1233.
51. Sharma A, Mentz RJ, Granger BB, et al. Utilizing mobile technologies to improve physical activity and medication adherence in patients with heart failure and diabetes mellitus: Rationale and design of the TARGET-HF-DM trial. Am Heart J, 2019, 211: 22-33.
52. Shamaki GR, Markson F, Soji-Ayoade D, et al. Peripheral artery disease: A comprehensive updated review. Curr Probl Cardiol. 2022, 47(11): 101082.
53. Arain FA, Cooper LT Jr. Peripheral arterial disease: Diagnosis and management. Mayo Clin Proc. 2008, 83(8): 944-949.
54. Aboyans V, Ricco JB, Bartelink MEL, et al. 2017 ESC guidelines on the diagnosis and treatment of peripheral arterial diseases, in collaboration with the European Society for Vascular Surgery (ESVS): Document covering atherosclerotic disease of extracranial carotid and vertebral, mesenteric, renal, upper and lower extremity arteriesEndorsed by: The European Stroke Organization (ESO) The Task Force for the Diagnosis and Treatment of Peripheral Arterial Diseases of the European Society of Cardiology (ESC) and of the European Society for Vascular Surgery (ESVS). Eur Heart J, 2018, 39(9): 763-816.
55. Herráiz-Adillo Á, Cavero-Redondo I, Álvarez-Bueno C, et al. Factors affecting the validity of the oscillometric Ankle Brachial Index to detect peripheral arterial disease. Int Angiol, 2017, 36(6): 536-544.
56. Watson EL, Patel B, Katsogridakis E, et al. Selecting portable ankle/toe brachial pressure index systems for a peripheral arterial disease population screening programme: A systematic review, clinical evaluation exercise, and consensus process. Eur J Vasc Endovasc Surg, 2022, 64(6): 693-702.
57. Jin C, Li J, Liu F, et al. Life's essential 8 and 10-year and lifetime risk of atherosclerotic cardiovascular disease in China. Am J Prev Med, 2023, 64(6): 927-935.
58. Sulague RM, Suan NNM, Mendoza MF, et al. The associations between exercise and lipid biomarkers. Prog Cardiovasc Dis, 2022, 75: 59-68.
59. Lavie CJ, Kachur S, Sui X. Impact of fitness and changes in fitness on lipids and survival. Prog Cardiovasc Dis, 2019, 62(5): 431-435.
60. Ghorbanzadeh O, Blaschke T, Gholamnia K, et al. Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens, 2019, 11(2): 196.
61. Ben-Shlomo Y, Spears M, Boustred C, et al. Aortic pulse wave velocity improves cardiovascular event prediction: An individual participant meta-analysis of prospective observational data from 17, 635 subjects. J Am Coll Cardiol, 2014, 63(7): 636-646.
62. Vlachopoulos C, Aznaouridis K, Stefanadis C. Prediction of cardiovascular events and all-cause mortality with arterial stiffness. J Am Coll Cardiol, 2010, 55(13): 1318-1327.
63. Li Y, Xu Y, Ma Z, et al. An XGBoost-based model for assessment of aortic stiffness from wrist photoplethysmogram. Comput Methods Programs Biomed, 2022, 226: 107128.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

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