Literature DB >> 2383409

The prediction of midlife coronary heart disease and hypertension in young adults: the Johns Hopkins multiple risk equations.

T A Pearson1, A Z LaCroix, L A Mead, K Y Liang.   

Abstract

Educating medical students about the identification of risk factors for coronary disease and hypertension should be enhanced by exercises in which medical students identify their own risk factors and visualize the impact of current risk status on future risk of disease. A cohort of 1,130 former Johns Hopkins medical students were examined in medical school and followed annually from 1948 to 1964 to identify youthful factors associated with the development of coronary heart disease and hypertension in midlife. In the ensuing years through 1984, 51 cases of coronary heart disease and 114 cases of hypertension developed. Multiple risk equations using Cox proportional hazards regression were developed to predict these endpoints. Incidence of coronary heart disease was predicted best by an equation containing age, serum cholesterol at baseline, cigarette smoking at baseline, and paternal history of coronary disease. Hypertension was predicted best by an equation containing age, systolic blood pressure at baseline, paternal history of hypertension, and Quetelet index. These equations were applied to a class of present-day medical students to demonstrate the considerable variability in 30-year risk of coronary disease or hypertension. Thus, coronary heart disease and hypertension in midlife can be predicted by factors identified in youth. The Johns Hopkins multiple risk equations may be valuable as tools in preventive cardiology education to illustrate risk assessment and the importance of risk factor interventions.

Entities:  

Mesh:

Year:  1990        PMID: 2383409

Source DB:  PubMed          Journal:  Am J Prev Med        ISSN: 0749-3797            Impact factor:   5.043


  8 in total

1.  Prediction model and assessment of probability of incident hypertension: the Rural Chinese Cohort Study.

Authors:  Bingyuan Wang; Yu Liu; Xizhuo Sun; Zhaoxia Yin; Honghui Li; Yongcheng Ren; Yang Zhao; Ruiyuan Zhang; Ming Zhang; Dongsheng Hu
Journal:  J Hum Hypertens       Date:  2020-02-27       Impact factor: 3.012

Review 2.  Antilipidemic Drug Therapy Today and in the Future.

Authors:  Werner Kramer
Journal:  Handb Exp Pharmacol       Date:  2016

3.  Developing and validating a new precise risk-prediction model for new-onset hypertension: The Jichi Genki hypertension prediction model (JG model).

Authors:  Hiroshi Kanegae; Takamitsu Oikawa; Kenji Suzuki; Yukie Okawara; Kazuomi Kario
Journal:  J Clin Hypertens (Greenwich)       Date:  2018-03-31       Impact factor: 3.738

Review 4.  Recent development of risk-prediction models for incident hypertension: An updated systematic review.

Authors:  Dongdong Sun; Jielin Liu; Lei Xiao; Ya Liu; Zuoguang Wang; Chuang Li; Yongxin Jin; Qiong Zhao; Shaojun Wen
Journal:  PLoS One       Date:  2017-10-30       Impact factor: 3.240

5.  Summarising and synthesising regression coefficients through systematic review and meta-analysis for improving hypertension prediction using metamodelling: protocol.

Authors:  Mohammad Ziaul Islam Chowdhury; Iffat Naeem; Hude Quan; Alexander A Leung; Khokan C Sikdar; Maeve O'Beirne; Tanvir C Turin
Journal:  BMJ Open       Date:  2020-04-09       Impact factor: 2.692

6.  Validation of the Framingham hypertension risk score in a middle eastern population: Tehran lipid and glucose study (TLGS).

Authors:  Fatemeh Koohi; Ewout W Steyerberg; Leila Cheraghi; Alireza Abdshah; Fereidoun Azizi; Davood Khalili
Journal:  BMC Public Health       Date:  2021-04-24       Impact factor: 3.295

7.  Prediction of hypertension using traditional regression and machine learning models: A systematic review and meta-analysis.

Authors:  Mohammad Ziaul Islam Chowdhury; Iffat Naeem; Hude Quan; Alexander A Leung; Khokan C Sikdar; Maeve O'Beirne; Tanvir C Turin
Journal:  PLoS One       Date:  2022-04-07       Impact factor: 3.240

Review 8.  Risk models to predict hypertension: a systematic review.

Authors:  Justin B Echouffo-Tcheugui; G David Batty; Mika Kivimäki; Andre P Kengne
Journal:  PLoS One       Date:  2013-07-05       Impact factor: 3.240

  8 in total

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