311 related articles for article (PubMed ID: 18342687)
1. Laboratory-based versus non-laboratory-based method for assessment of cardiovascular disease risk: the NHANES I Follow-up Study cohort.
Gaziano TA; Young CR; Fitzmaurice G; Atwood S; Gaziano JM
Lancet; 2008 Mar; 371(9616):923-31. PubMed ID: 18342687
[TBL] [Abstract][Full Text] [Related]
2. Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.
Crider K; Williams J; Qi YP; Gutman J; Yeung L; Mai C; Finkelstain J; Mehta S; Pons-Duran C; Menéndez C; Moraleda C; Rogers L; Daniels K; Green P
Cochrane Database Syst Rev; 2022 Feb; 2(2022):. PubMed ID: 36321557
[TBL] [Abstract][Full Text] [Related]
3. A consultation-based method is equal to SCORE and an extensive laboratory-based method in predicting risk of future cardiovascular disease.
Petersson U; Ostgren CJ; Brudin L; Nilsson PM
Eur J Cardiovasc Prev Rehabil; 2009 Oct; 16(5):536-40. PubMed ID: 19357517
[TBL] [Abstract][Full Text] [Related]
4. A novel risk score to predict cardiovascular disease risk in national populations (Globorisk): a pooled analysis of prospective cohorts and health examination surveys.
Hajifathalian K; Ueda P; Lu Y; Woodward M; Ahmadvand A; Aguilar-Salinas CA; Azizi F; Cifkova R; Di Cesare M; Eriksen L; Farzadfar F; Ikeda N; Khalili D; Khang YH; Lanska V; León-Muñoz L; Magliano D; Msyamboza KP; Oh K; Rodríguez-Artalejo F; Rojas-Martinez R; Shaw JE; Stevens GA; Tolstrup J; Zhou B; Salomon JA; Ezzati M; Danaei G
Lancet Diabetes Endocrinol; 2015 May; 3(5):339-55. PubMed ID: 25819778
[TBL] [Abstract][Full Text] [Related]
5. Development and validation of QDiabetes-2018 risk prediction algorithm to estimate future risk of type 2 diabetes: cohort study.
Hippisley-Cox J; Coupland C
BMJ; 2017 Nov; 359():j5019. PubMed ID: 29158232
[No Abstract] [Full Text] [Related]
6. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study.
Hippisley-Cox J; Coupland C; Brindle P
BMJ; 2017 May; 357():j2099. PubMed ID: 28536104
[No Abstract] [Full Text] [Related]
7. Assessment of absolute risk of death after myocardial infarction by use of multiple-risk-factor assessment equations: GISSI-Prevenzione mortality risk chart.
Marchioli R; Avanzini F; Barzi F; Chieffo C; Di Castelnuovo A; Franzosi MG; Geraci E; Maggioni AP; Marfisi RM; Mininni N; Nicolosi GL; Santini M; Schweiger C; Tavazzi L; Tognoni G; Valagussa F;
Eur Heart J; 2001 Nov; 22(22):2085-103. PubMed ID: 11686666
[TBL] [Abstract][Full Text] [Related]
8. Influence of smoking combined with another risk factor on the risk of mortality from coronary heart disease and stroke: pooled analysis of 10 Japanese cohort studies.
Nakamura K; Nakagawa H; Sakurai M; Murakami Y; Irie F; Fujiyoshi A; Okamura T; Miura K; Ueshima H;
Cerebrovasc Dis; 2012; 33(5):480-91. PubMed ID: 22517421
[TBL] [Abstract][Full Text] [Related]
9. CKD and cardiovascular disease in screened high-risk volunteer and general populations: the Kidney Early Evaluation Program (KEEP) and National Health and Nutrition Examination Survey (NHANES) 1999-2004.
McCullough PA; Li S; Jurkovitz CT; Stevens LA; Wang C; Collins AJ; Chen SC; Norris KC; McFarlane SI; Johnson B; Shlipak MG; Obialo CI; Brown WW; Vassalotti JA; Whaley-Connell AT;
Am J Kidney Dis; 2008 Apr; 51(4 Suppl 2):S38-45. PubMed ID: 18359407
[TBL] [Abstract][Full Text] [Related]
10. 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.
Ueda P; Woodward M; Lu Y; Hajifathalian K; Al-Wotayan R; Aguilar-Salinas CA; Ahmadvand A; Azizi F; Bentham J; Cifkova R; Di Cesare M; Eriksen L; Farzadfar F; Ferguson TS; Ikeda N; Khalili D; Khang YH; Lanska V; León-Muñoz L; Magliano DJ; Margozzini P; Msyamboza KP; Mutungi G; Oh K; Oum S; Rodríguez-Artalejo F; Rojas-Martinez R; Valdivia G; Wilks R; Shaw JE; Stevens GA; Tolstrup JS; Zhou B; Salomon JA; Ezzati M; Danaei G
Lancet Diabetes Endocrinol; 2017 Mar; 5(3):196-213. PubMed ID: 28126460
[TBL] [Abstract][Full Text] [Related]
11. Red cell distribution width and risk of cardiovascular mortality: Insights from National Health and Nutrition Examination Survey (NHANES)-III.
Shah N; Pahuja M; Pant S; Handa A; Agarwal V; Patel N; Dusaj R
Int J Cardiol; 2017 Apr; 232():105-110. PubMed ID: 28117138
[TBL] [Abstract][Full Text] [Related]
12. Sex Differences in the Prevalence of, and Trends in, Cardiovascular Risk Factors, Treatment, and Control in the United States, 2001 to 2016.
Peters SAE; Muntner P; Woodward M
Circulation; 2019 Feb; 139(8):1025-1035. PubMed ID: 30779652
[TBL] [Abstract][Full Text] [Related]
13. Cardiovascular risk assessment using pulse pressure in the first national health and nutrition examination survey (NHANES I).
Domanski M; Norman J; Wolz M; Mitchell G; Pfeffer M
Hypertension; 2001 Oct; 38(4):793-7. PubMed ID: 11641288
[TBL] [Abstract][Full Text] [Related]
14. Prediction rule for cardiovascular events and mortality in peripheral arterial disease patients: data from the prospective Second Manifestations of ARTerial disease (SMART) cohort study.
Sprengers RW; Janssen KJ; Moll FL; Verhaar MC; van der Graaf Y;
J Vasc Surg; 2009 Dec; 50(6):1369-76. PubMed ID: 19837547
[TBL] [Abstract][Full Text] [Related]
15. Anthropometric measures in cardiovascular disease prediction: comparison of laboratory-based versus non-laboratory-based model.
Dhana K; Ikram MA; Hofman A; Franco OH; Kavousi M
Heart; 2015 Mar; 101(5):377-83. PubMed ID: 25502814
[TBL] [Abstract][Full Text] [Related]
16. Association of Isolated Diastolic Hypertension as Defined by the 2017 ACC/AHA Blood Pressure Guideline With Incident Cardiovascular Outcomes.
McEvoy JW; Daya N; Rahman F; Hoogeveen RC; Blumenthal RS; Shah AM; Ballantyne CM; Coresh J; Selvin E
JAMA; 2020 Jan; 323(4):329-338. PubMed ID: 31990314
[TBL] [Abstract][Full Text] [Related]
17. Effects of a gluten-reduced or gluten-free diet for the primary prevention of cardiovascular disease.
Schmucker C; Eisele-Metzger A; Meerpohl JJ; Lehane C; Kuellenberg de Gaudry D; Lohner S; Schwingshackl L
Cochrane Database Syst Rev; 2022 Feb; 2(2):CD013556. PubMed ID: 35199850
[TBL] [Abstract][Full Text] [Related]
18. A comparison of laboratory-based and office-based Framingham risk scores to predict 10-year risk of cardiovascular diseases: a population-based study.
Dehghan A; Ahmadnia Motlagh S; Khezri R; Rezaei F; Aune D
J Transl Med; 2023 Oct; 21(1):687. PubMed ID: 37789412
[TBL] [Abstract][Full Text] [Related]
19. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning.
Dinh A; Miertschin S; Young A; Mohanty SD
BMC Med Inform Decis Mak; 2019 Nov; 19(1):211. PubMed ID: 31694707
[TBL] [Abstract][Full Text] [Related]
20. Short-term predictive ability of selected cardiovascular risk prediction models in a rural Bangladeshi population: a case-cohort study.
Fatema K; Rahman B; Zwar NA; Milton AH; Ali L
BMC Cardiovasc Disord; 2016 May; 16(1):105. PubMed ID: 27386836
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]