Diabetes research and clinical practice · 2010
Development of a coronary heart disease risk prediction model for type 1 diabetes: the Pittsburgh CHD in Type 1 Diabetes Risk Model.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zgibor Janice C, Ruppert Kristine, Orchard Trevor J, Soedamah-Muthu Sabita S, Fuller John, Chaturvedi Nish, Roberts Mark S
Аннотация
AIM: To create a coronary heart disease (CHD) risk prediction model specific to type 1 diabetes.
METHODS: Development of the model used data from the Pittsburgh Epidemiology of Diabetes Complications Study (EDC). EDC subjects had type 1 diabetes diagnosed between 1950 and 1980, received their first study exam between 1986 and 1988, and have been followed biennially since. The final cohort for model development consisted of 603 subjects and 46 incident events. Hard CHD was defined as CHD death, fatal/non-fatal MI or Q-waves. Baseline CHD risk factors were tested bivariately and introduced into a Weibull model. The prediction model was externally validated in the EURODIAB Prospective Complications Study.
RESULTS: In males, predictors were higher white blood cell count, micro- or macroalbuminuira, lower HDLc and longer diabetes duration. In females, larger waist/hip ratio, higher non-HDLc, higher systolic blood pressure, use of blood pressure medication, and longer diabetes duration were included. Models were robust to internal and external validation procedures.
CONCLUSIONS: CHD risk prediction models for hard CHD in those with type 1 diabetes should include risk factors not considered by existing models. Using models specifically developed for predicting CHD in type 1 diabetes may allow for more targeted prevention strategies.
METHODS: Development of the model used data from the Pittsburgh Epidemiology of Diabetes Complications Study (EDC). EDC subjects had type 1 diabetes diagnosed between 1950 and 1980, received their first study exam between 1986 and 1988, and have been followed biennially since. The final cohort for model development consisted of 603 subjects and 46 incident events. Hard CHD was defined as CHD death, fatal/non-fatal MI or Q-waves. Baseline CHD risk factors were tested bivariately and introduced into a Weibull model. The prediction model was externally validated in the EURODIAB Prospective Complications Study.
RESULTS: In males, predictors were higher white blood cell count, micro- or macroalbuminuira, lower HDLc and longer diabetes duration. In females, larger waist/hip ratio, higher non-HDLc, higher systolic blood pressure, use of blood pressure medication, and longer diabetes duration were included. Models were robust to internal and external validation procedures.
CONCLUSIONS: CHD risk prediction models for hard CHD in those with type 1 diabetes should include risk factors not considered by existing models. Using models specifically developed for predicting CHD in type 1 diabetes may allow for more targeted prevention strategies.