Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis | BMC Public Health
8 min read
Collaborators GS. Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021;20(10):795–820.
Google Scholar
Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, Cooney M-T, Corrà U, Cosyns B, Deaton C, et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts)Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart J. 2016;37(29):2315–81.
Google Scholar
Liu S, Li Y, Zeng X, Wang H, Yin P, Wang L, Liu Y, Liu J, Qi J, Ran S, et al. Burden of Cardiovascular Diseases in China, 1990–2016: Findings From the 2016 Global Burden of Disease Study. JAMA Cardiol. 2019;4(4):342–52.
Google Scholar
China TJTFfGotAaMoCRi: Guidelines for Risk Assessment and management of cardiovascular diseases in China. Chi J Prev Med 2019;(01):13–35.
Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, Braun LT, de Ferranti S, Faiella-Tommasino J, Forman DE, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;139(25):e1082–143.
Google Scholar
D’Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–53.
Google Scholar
Goff DC, Lloyd-Jones DM, Bennett G, Coady S, D’Agostino RB, Gibbons R, Greenland P, Lackland DT, Levy D, O’Donnell CJ, 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. Circulation. 2014;129(25 Suppl 2):S49–73.
Google Scholar
Yang X, Li J, Hu D, Chen J, Li Y, Huang J, Liu X, Liu F, Cao J, Shen C, et al. Predicting the 10-Year Risks of Atherosclerotic Cardiovascular Disease in Chinese Population: The China-PAR Project (Prediction for ASCVD Risk in China). Circulation. 2016;134(19):1430–40.
Google Scholar
Zeng NM, Zheng XW, Peng H, Jiao Y, Li HM, Zhang MZ, Wang AL, Zhang YH. Validation of the China-PAR Equations for Cardio-cerebrovascular Risk Prediction in the Inner Mongolian Population. Biomed Environ Sci. 2018;31(6):463–6.
Google Scholar
Tang X, Zhang D, He L, Wu N, Si Y, Cao Y, Huang S, Li N, Li J, Dou H, et al. Performance of atherosclerotic cardiovascular risk prediction models in a rural Northern Chinese population: Results from the Fangshan Cohort Study. Am Heart J. 2019;211:34–44.
Google Scholar
Ko DT, Sivaswamy A, Sud M, Kotrri G, Azizi P, Koh M, Austin PC, Lee DS, Roifman I, Thanassoulis G, et al. Calibration and discrimination of the Framingham Risk Score and the Pooled Cohort Equations. CMAJ. 2020;192(17):E442–9.
Google Scholar
Xianghui ZYY, Jia H, et al. Evaluation of screening indices for metabolic syndrome in adult Kazakh in Xinjiang. Chinese Journal of Hypertension. 2017;25(01):55–60.
guo S-x, Jingyu Z, Yihua Z, know Xo, Heng G, strong Ci, Feng S, na L, liu J-m, RuiDongSheng: Epidemiological Characteristics and Analysis on Hypertension of Kazakstans and Hans in Xinjiang. J Shihezi Univ (Natural Science) 2008;26(05):538–540.
Jia HHG, Yusong D, et al. Epidemiological study on overweight and obesity among rural adult residents in Hazakh, Uygur and Han populations in Xinjiang. Chi J Epidemiol. 2013;34(12):1164–8.
Jiang Y, Ma R, Guo H, Zhang X, Wang X, Wang K, Hu Y, Keerman M, Yan Y, Ma J, et al. External validation of three atherosclerotic cardiovascular disease risk equations in rural areas of Xinjiang, China. BMC Public Health. 2020;20(1):1471.
Google Scholar
Barbieri A, Legrand C. Joint longitudinal and time-to-event cure models for the assessment of being cured. Stat Methods Med Res. 2020;29(4):1256–70.
Google Scholar
Chun M, Clarke R, Cairns BJ, Clifton D, Bennett D, Chen Y, Guo Y, Pei P, Lv J, Yu C, et al. Stroke risk prediction using machine learning: a prospective cohort study of 0.5 million Chinese adults. J Am Med Inform Assoc. 2021;28(8):1719–27.
Google Scholar
Sung JM, Cho I-J, Sung D, Kim S, Kim HC, Chae M-H, Kavousi M, Rueda-Ochoa OL, Ikram MA, Franco OH, et al. Development and verification of prediction models for preventing cardiovascular diseases. PLoS ONE. 2019;14(9):e0222809.
Google Scholar
Moncada-Torres A, van Maaren MC, Hendriks MP, Siesling S, Geleijnse G. Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival. Sci Rep. 2021;11(1):6968.
Google Scholar
de Vries BCS, Hegeman JH, Nijmeijer W, Geerdink J, Seifert C, Groothuis-Oudshoorn CGM. Comparing three machine learning approaches to design a risk assessment tool for future fractures: predicting a subsequent major osteoporotic fracture in fracture patients with osteopenia and osteoporosis. Osteoporos Int. 2021;32(3):437–49.
Google Scholar
Herrin J, Abraham NS, Yao X, Noseworthy PA, Inselman J, Shah ND, Ngufor C. Comparative Effectiveness of Machine Learning Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment. JAMA Netw Open. 2021;4(5):e2110703.
Google Scholar
Yang L, Wu H, Jin X, Zheng P, Hu S, Xu X, Yu W, Yan J. Study of cardiovascular disease prediction model based on random forest in eastern China. Sci Rep. 2020;10(1):5245.
Google Scholar
Jiang Y, Zhang X, Ma R, Wang X, Liu J, Keerman M, Yan Y, Ma J, Song Y, Zhang J, et al. Cardiovascular Disease Prediction by Machine Learning Algorithms Based on Cytokines in Kazakhs of China. Clin Epidemiol. 2021;13:417–28.
Google Scholar
Qian X, Li Y, Zhang X, Guo H, He J, Wang X, Yan Y, Ma J, Ma R, Guo S. A Cardiovascular Disease Prediction Model Based on Routine Physical Examination Indicators Using Machine Learning Methods: A Cohort Study. Front Cardiovasc Med. 2022;9:854287.
Google Scholar
Hypertension CCftRoGfPaTo AH, Chinese Medical Association Committee of Cardiovascular Diseases CMDA, Hypertension Professional Committee, Exchange HBoCAftPoIH, Society HBoCG: Chinese guidelines for the management of hypertension Writing Group of 2018. Chi J Cardiovasc Med 2019;24(1):24-56
Cigarette smoking among adults–United States, 1992, and changes in the definition of current cigarette smoking. MMWR Morb Mortal Wkly Rep 1994;43(19):342–346.
Sun K, Ren M, Liu D, Wang C, Yang C, Yan L. Alcohol consumption and risk of metabolic syndrome: a meta-analysis of prospective studies. Clin Nutr. 2014;33(4):596–602.
Google Scholar
Wang X, Yu W. 2012 Guidelines for the Management of Diabetes – American Diabetes Association. Int J Endocrinol Metab. 2012;03:211–4.
Fatty Liver and Alcoholic Liver Disease Group HS, Chinese Medical Association: Guidelines for Management of Non-alcoholic Fatty Liver Disease. Chi J Liver Dis (Electronic Version) 2010;2(04):43–48.
Crichton N. Cox proportional hazards model. J Clin Nurs. 2002;11(6):723.
Google Scholar
Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16(4):385–95.
Google Scholar
Ambale-Venkatesh B, Yang X, Wu CO, Liu K, Hundley WG, McClelland R, Gomes AS, Folsom AR, Shea S, Guallar E, et al. Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis. Circ Res. 2017;121(9):1092–101.
Google Scholar
Pencina MJ, D’Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med. 2004;23(13):2109–23.
Google Scholar
Pencina MJ, D’Agostino RB, D’Agostino RB, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157-72 ; discussion 207-12.
Google Scholar
Rufibach K. Use of Brier score to assess binary predictions. J Clin Epidemiol. 2010;63(8):938–9.
Google Scholar
Lemeshow S, Hosmer DW. A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol. 1982;115(1):92–106.
Google Scholar
Zhang Z-Q, He G, Luo Z-W, Cheng C-C, Wang P, Li J, Zhu M-G, Ming L, He T-S, Ouyang Y-L, et al. Individual mortality risk predictive system of patients with acute-on-chronic liver failure based on a random survival forest model. Chin Med J (Engl). 2021;134(14):1701–8.
Google Scholar
Zhang X, Tang F, Ji J, Han W, Lu P. Risk Prediction of Dyslipidemia for Chinese Han Adults Using Random Forest Survival Model. Clin Epidemiol. 2019;11:1047–55.
Google Scholar
Zhuang X-D, Tian T, Liao L-Z, Dong Y-H, Zhou H-J, Zhang S-Z, Chen W-Y, Du Z-M, Wang X-Q, Liao X-X. Deep Phenotyping and Prediction of Long-term Cardiovascular Disease: Optimized by Machine Learning. Can J Cardiol. 2022;38(6):774–82.
Google Scholar
Liu Y, Kang P, Xu J, An S. Application of conditional inference forest in time-to-event data analysis. J South Med Univ. 2020;40(04):475–82.
Guo H, Liu J, Zhang J, Ma R, Ding Y, Zhang M, He J, Xu S, Li S, Yan Y, et al. The Prevalence of Metabolic Syndrome Using Three Different Diagnostic Criteria among Low Earning Nomadic Kazakhs in the Far Northwest of China: New Cut-Off Points of Waist Circumference to Diagnose MetS and Its Implications. PLoS ONE. 2016;11(2):e0148976.
Google Scholar
Izumi Y, Matsumoto K, Ozawa Y, Kasamaki Y, Shinndo A, Ohta M, Jumabay M, Nakayama T, Yokoyama E, Shimabukuro H, et al. Effect of age at menopause on blood pressure in postmenopausal women. Am J Hypertens. 2007;20(10):1045–50.
Google Scholar
Miao F, Cai Y-P, Zhang Y-X, Li Y, Zhang Y-T. Risk Prediction of One-Year Mortality in Patients with Cardiac Arrhythmias Using Random Survival Forest. Comput Math Methods Med. 2015;2015:303250.
Google Scholar
Ma L-J, Li P. li F-J, Chen J-Y, Zhu L-H: Survey and analysis of women’s health care and health needs of women’s diseases in Hotan area of Xinjiang Province. Xinjiang Med Sci. 2021;51(4):438–42.
Chai L-P, Mao L, Li X-J, Wang X-T, Li W-X, Rui D-S: Spatial equity analysis of health resources supply level in specialized public health institutions in Xinjiang. Health service Management in China, 2022;39(10):765–768+796.
Morvan L, Carlier T, Jamet B, Bailly C, Bodet-Milin C, Moreau P, Kraeber-Bodéré F, Mateus D. Leveraging RSF and PET images for prognosis of multiple myeloma at diagnosis. Int J Comput Assist Radiol Surg. 2020;15(1):129–39.
Google Scholar
Segar MW, Vaduganathan M, Patel KV, McGuire DK, Butler J, Fonarow GC, Basit M, Kannan V, Grodin JL, Everett B, et al. Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score. Diabetes Care. 2019;42(12):2298–306.
Google Scholar
Miao F, Cai Y-P, Zhang Y-T, Li C-Y: Is random survival forest an alternative to cox proportional model on predicting cardiovascular disease? In: 6th European Conference of the International Federation for Medical and Biological Engineering, MBEC 2014. Edited by Lacković I, Vasic D: Springer, Cham; 2014: 740–743.
Langlois MR, Nordestgaard BG, Langsted A, Chapman MJ, Aakre KM, Baum H, Borén J, Bruckert E, Catapano A, Cobbaert C, et al. Quantifying atherogenic lipoproteins for lipid-lowering strategies: consensus-based recommendations from EAS and EFLM. Clin Chem Lab Med. 2020;58(4):496–517.
Google Scholar
Thanassoulis G, Williams K, Ye K, Brook R, Couture P, Lawler PR, de Graaf J, Furberg CD, Sniderman A. Relations of change in plasma levels of LDL-C, non-HDL-C and apoB with risk reduction from statin therapy: a meta-analysis of randomized trials. J Am Heart Assoc. 2014;3(2):e000759.
Google Scholar
link
