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

Patients with optimum ( 140/90 mm Hg) over time. We aimed to establish the best frequency of BP screening by assessing the rates and determinants of progression to hypertension. We assessed repeated BP measurements in individuals without hypertension (BP<140/90 mm Hg) from the Framingham Study (4200 men, 5645 women; mean age 52 years) who attended clinic examinations during 1978-94. The incidence of hypertension (or use of antihypertensive treatment) and its determinants were studied. A stepwise increase in hypertension incidence occurred across the three non-hypertensive BP categories; 5.3% (95% CI 4.4-6.3%) of participants with optimum BP, 17.6% (15.2-20.3%) with normal, and 37.3% (33.3-41.5%) with high normal BP aged below age 65 years progressed to hypertension over 4 years. Corresponding 4-year rates of progression for patients 65 years and older were 16.0% (12.0-20.9), 25.5% (20.4-31.4), and 49.5% (42.6-56.4), respectively. Obesity and weight gain also contributed to progression; a 5% weight gain on follow-up was associated with 20-30% increased odds of hypertension. High normal BP and normal BP frequently progress to hypertension over a period of 4 years, especially in older adults. These findings support recommendations for monitoring individuals with high normal BP once a year, and monitoring those with normal BP every 2 years, and they emphasise the importance of weight control as a measure for primary prevention of hypertension. Show more

To access the calibration of a predictive model in a survival analysis setting, several authors have extended the Hosmer-Lemeshow goodness-of-fit test to survival data. Grønnesby and Borgan developed a test under the proportional hazards assumption, and Nam and D'Agostino developed a nonparametric test that is applicable in a more general survival setting for data with limited censoring. We analyze the performance of the two tests and show that the Grønnesby-Borgan test attains appropriate size in a variety of settings, whereas the Nam-D'Agostino method has a higher than nominal Type 1 error when there is more than trivial censoring. Both tests are sensitive to small cell sizes. We develop a modification of the Nam-D'Agostino test to allow for higher censoring rates. We show that this modified Nam-D'Agostino test has appropriate control of Type 1 error and comparable power to the Grønnesby-Borgan test and is applicable to settings other than proportional hazards. We also discuss the application to small cell sizes. Show more

Cardiovascular disease (CVD) is among the leading causes of death and disability worldwide. Since its beginning, the Framingham study has been a leader in identifying CVD risk factors. Clinical trials have demonstrated that when the modifiable risk factors are treated and corrected, the chances of CVD occurring can be reduced. The Framingham study also recognized that CVD risk factors are multifactorial and interact over time to produce CVD. In response, Framingham investigators developed the Framingham Risk Functions (also called Framingham Risk Scores) to evaluate the chance or likelihood of developing CVD in individuals. These functions are multivariate functions (algorithms) that combine the information in CVD risk factors such as sex, age, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking behavior, and diabetes status to produce an estimate (or risk) of developing CVD or a component of CVD (such as coronary heart disease, stroke, peripheral vascular disease, or heart failure) over a fixed time, for example, the next 10 years. These estimates of CVD risk are often major inputs in recommending drug treatments such as cholesterol-lowering drugs. Show more