Prediction of First Cardiovascular Disease Event in Type 1 Diabetes Mellitus: The Steno Type 1 Risk Engine

Dorte Vistisen, Gregers Stig Andersen, Christian Stevns Hansen, Adam Hulman, Jan Erik Henriksen, Henning Beck-Nielsen, Marit Eika Jørgensen

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

BACKGROUND: Patients with type 1 diabetes mellitus are at increased risk of developing cardiovascular disease (CVD), but they are currently undertreated. There are no risk scores used on a regular basis in clinical practice for assessing the risk of CVD in type 1 diabetes mellitus.

METHODS AND RESULTS: From 4306 clinically diagnosed adult patients with type 1 diabetes mellitus, we developed a prediction model for estimating the risk of first fatal or nonfatal CVD event (ischemic heart disease, ischemic stroke, heart failure, and peripheral artery disease). Detailed clinical data including lifestyle factors were linked to event data from validated national registers. The risk prediction model was developed by using a 2-stage approach. First, a nonparametric, data-driven approach was used to identify potentially informative risk factors and interactions (random forest and survival tree analysis). Second, based on results from the first step, Poisson regression analysis was used to derive the final model. The final CVD prediction model was externally validated in a different population of 2119 patients with type 1 diabetes mellitus. During a median follow-up of 6.8 years (interquartile range, 2.9-10.9) a total of 793 (18.4%) patients developed CVD. The final prediction model included age, sex, diabetes duration, systolic blood pressure, low-density lipoprotein cholesterol, hemoglobin A1c, albuminuria, glomerular filtration rate, smoking, and exercise. Discrimination was excellent for a 5-year CVD event with a C-statistic of 0.826 (95% confidence interval, 0.807-0.845) in the derivation data and a C-statistic of 0.803 (95% confidence interval, 0.767-0.839) in the validation data. The Hosmer-Lemeshow test showed good calibration (P>0.05) in both cohorts.

CONCLUSIONS: This high-performing CVD risk model allows for the implementation of decision rules in a clinical setting.

OriginalsprogEngelsk
TidsskriftCirculation Research
Vol/bind133
Udgave nummer11
Sider (fra-til)1058-1066
ISSN0009-7330
DOI
StatusUdgivet - 2016

Fingeraftryk

Confidence Intervals
Albuminuria
Survival Analysis
Glomerular Filtration Rate
LDL Cholesterol
Smoking
Regression Analysis
Exercise
Population

Citer dette

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title = "Prediction of First Cardiovascular Disease Event in Type 1 Diabetes Mellitus: The Steno Type 1 Risk Engine",
abstract = "BACKGROUND: Patients with type 1 diabetes mellitus are at increased risk of developing cardiovascular disease (CVD), but they are currently undertreated. There are no risk scores used on a regular basis in clinical practice for assessing the risk of CVD in type 1 diabetes mellitus.METHODS AND RESULTS: From 4306 clinically diagnosed adult patients with type 1 diabetes mellitus, we developed a prediction model for estimating the risk of first fatal or nonfatal CVD event (ischemic heart disease, ischemic stroke, heart failure, and peripheral artery disease). Detailed clinical data including lifestyle factors were linked to event data from validated national registers. The risk prediction model was developed by using a 2-stage approach. First, a nonparametric, data-driven approach was used to identify potentially informative risk factors and interactions (random forest and survival tree analysis). Second, based on results from the first step, Poisson regression analysis was used to derive the final model. The final CVD prediction model was externally validated in a different population of 2119 patients with type 1 diabetes mellitus. During a median follow-up of 6.8 years (interquartile range, 2.9-10.9) a total of 793 (18.4{\%}) patients developed CVD. The final prediction model included age, sex, diabetes duration, systolic blood pressure, low-density lipoprotein cholesterol, hemoglobin A1c, albuminuria, glomerular filtration rate, smoking, and exercise. Discrimination was excellent for a 5-year CVD event with a C-statistic of 0.826 (95{\%} confidence interval, 0.807-0.845) in the derivation data and a C-statistic of 0.803 (95{\%} confidence interval, 0.767-0.839) in the validation data. The Hosmer-Lemeshow test showed good calibration (P>0.05) in both cohorts.CONCLUSIONS: This high-performing CVD risk model allows for the implementation of decision rules in a clinical setting.",
author = "Dorte Vistisen and Andersen, {Gregers Stig} and Hansen, {Christian Stevns} and Adam Hulman and Henriksen, {Jan Erik} and Henning Beck-Nielsen and J{\o}rgensen, {Marit Eika}",
note = "{\circledC} 2016 American Heart Association, Inc.",
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journal = "Circulation Research",
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Prediction of First Cardiovascular Disease Event in Type 1 Diabetes Mellitus : The Steno Type 1 Risk Engine. / Vistisen, Dorte; Andersen, Gregers Stig; Hansen, Christian Stevns; Hulman, Adam; Henriksen, Jan Erik; Beck-Nielsen, Henning; Jørgensen, Marit Eika.

I: Circulation Research, Bind 133, Nr. 11, 2016, s. 1058-1066.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Prediction of First Cardiovascular Disease Event in Type 1 Diabetes Mellitus

T2 - The Steno Type 1 Risk Engine

AU - Vistisen, Dorte

AU - Andersen, Gregers Stig

AU - Hansen, Christian Stevns

AU - Hulman, Adam

AU - Henriksen, Jan Erik

AU - Beck-Nielsen, Henning

AU - Jørgensen, Marit Eika

N1 - © 2016 American Heart Association, Inc.

PY - 2016

Y1 - 2016

N2 - BACKGROUND: Patients with type 1 diabetes mellitus are at increased risk of developing cardiovascular disease (CVD), but they are currently undertreated. There are no risk scores used on a regular basis in clinical practice for assessing the risk of CVD in type 1 diabetes mellitus.METHODS AND RESULTS: From 4306 clinically diagnosed adult patients with type 1 diabetes mellitus, we developed a prediction model for estimating the risk of first fatal or nonfatal CVD event (ischemic heart disease, ischemic stroke, heart failure, and peripheral artery disease). Detailed clinical data including lifestyle factors were linked to event data from validated national registers. The risk prediction model was developed by using a 2-stage approach. First, a nonparametric, data-driven approach was used to identify potentially informative risk factors and interactions (random forest and survival tree analysis). Second, based on results from the first step, Poisson regression analysis was used to derive the final model. The final CVD prediction model was externally validated in a different population of 2119 patients with type 1 diabetes mellitus. During a median follow-up of 6.8 years (interquartile range, 2.9-10.9) a total of 793 (18.4%) patients developed CVD. The final prediction model included age, sex, diabetes duration, systolic blood pressure, low-density lipoprotein cholesterol, hemoglobin A1c, albuminuria, glomerular filtration rate, smoking, and exercise. Discrimination was excellent for a 5-year CVD event with a C-statistic of 0.826 (95% confidence interval, 0.807-0.845) in the derivation data and a C-statistic of 0.803 (95% confidence interval, 0.767-0.839) in the validation data. The Hosmer-Lemeshow test showed good calibration (P>0.05) in both cohorts.CONCLUSIONS: This high-performing CVD risk model allows for the implementation of decision rules in a clinical setting.

AB - BACKGROUND: Patients with type 1 diabetes mellitus are at increased risk of developing cardiovascular disease (CVD), but they are currently undertreated. There are no risk scores used on a regular basis in clinical practice for assessing the risk of CVD in type 1 diabetes mellitus.METHODS AND RESULTS: From 4306 clinically diagnosed adult patients with type 1 diabetes mellitus, we developed a prediction model for estimating the risk of first fatal or nonfatal CVD event (ischemic heart disease, ischemic stroke, heart failure, and peripheral artery disease). Detailed clinical data including lifestyle factors were linked to event data from validated national registers. The risk prediction model was developed by using a 2-stage approach. First, a nonparametric, data-driven approach was used to identify potentially informative risk factors and interactions (random forest and survival tree analysis). Second, based on results from the first step, Poisson regression analysis was used to derive the final model. The final CVD prediction model was externally validated in a different population of 2119 patients with type 1 diabetes mellitus. During a median follow-up of 6.8 years (interquartile range, 2.9-10.9) a total of 793 (18.4%) patients developed CVD. The final prediction model included age, sex, diabetes duration, systolic blood pressure, low-density lipoprotein cholesterol, hemoglobin A1c, albuminuria, glomerular filtration rate, smoking, and exercise. Discrimination was excellent for a 5-year CVD event with a C-statistic of 0.826 (95% confidence interval, 0.807-0.845) in the derivation data and a C-statistic of 0.803 (95% confidence interval, 0.767-0.839) in the validation data. The Hosmer-Lemeshow test showed good calibration (P>0.05) in both cohorts.CONCLUSIONS: This high-performing CVD risk model allows for the implementation of decision rules in a clinical setting.

U2 - 10.1161/CIRCULATIONAHA.115.018844

DO - 10.1161/CIRCULATIONAHA.115.018844

M3 - Journal article

C2 - 26888765

VL - 133

SP - 1058

EP - 1066

JO - Circulation Research

JF - Circulation Research

SN - 0009-7330

IS - 11

ER -