FINRISK 3.0: A new formula for cardiovascular risk prediction with LDL and 30-year risk projections

Abstract Background The FINRISK calculator has been in use in Finland since 2007 to predict 10-year risk of acute coronary heart disease (CHD), stroke, or either of them (i.e. cardiovascular disease, CVD). This study describes the development of the updated version (3.0) of the risk equation formula. Methods In the version 3.0, the risks of CHD, stroke, or CVD were modelled with Fine-Gray sub distribution hazard models separately for men and women. Low-density lipoprotein (LDL) cholesterol was added as an alternative for total cholesterol (TC). Other variables (smoking, systolic blood pressure, high-density lipoprotein (HDL) cholesterol, diabetes, and family history) remained the same. To enhance understanding on the importance of risk factors in young adults, risk equations were developed to give 30-year risks in addition to 10-year risks. Data from FINRISK 1992, 1997, 2002, 2007, and 2012 studies were used. Results Both models, including either TC or LDL, had practically equal predictive ability for the 10-year-risk of CVD. The AUC values were 0.791 (TC model) and 0.790 (LDL model) for men and 0.811 (both models) for women. Calibration plots indicated that the prediction accuracy was not affected by replacing TC with LDL. The average risks of diseases for 40-year-old men and women were calculated for 10 and 30 years from the TC-models. For CHD, 10- and 30-year risks for men were 2.0% and 11.5%, respectively. For women the corresponding figures were 0.4% and 3.6%. For stroke, 10- and 30-year risks for men were 0.7% and 5.1% and for women 0.4% and 4.1%, respectively. For CVD, the probabilities for men were 2.6% and 16.2% and for women 0.8% and 7.5%. Conclusions Concerning predictive ability, TC can be replaced by LDL enhancing the use of the FINRISK calculator in clinical work where both or either of them are measured. Adding 30-year risk predictions to the calculator is justified to underscore the impact of risk factors already at younger age. Key messages • In the risk equation formula, total cholesterol can be replaced by LDL which is frequently measured in clinical practice and is an important modifiable lipid fraction when evaluating CVD risk. • Updating the risk equation formula of the FINRISK calculator and adding 30-year risk projections enhance cardiovascular risk prediction and screening of patients at younger age.

Authors

Institutions

Publication Details

Journal
European Journal of Public Health
Published
2025-10-01
DOI
https://doi.org/10.1093/eurpub/ckaf161.018
Primary Topic
Lipoproteins and Cardiovascular Health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

FINRISK 3.0: A new formula for cardiovascular risk prediction with LDL and 30-year risk projections

Jaakko Reinikainen, Hanna K Tolonen, Jukka Kontto, Tiina Laatikainen et al.
European Journal of Public Health
Lipoproteins and Cardiovascular Health
article

FINRISK 3.0: A new formula for cardiovascular risk prediction with LDL and 30-year risk projections

Jaakko Reinikainen, Hanna K Tolonen, Jukka Kontto, Tiina Laatikainen, Erkki Vartiainen, HM Elonheimo, Katri Sääksjärvi, Markku Peltonen, Pekka Jousilahti
article en

Abstract

Abstract Background The FINRISK calculator has been in use in Finland since 2007 to predict 10-year risk of acute coronary heart disease (CHD), stroke, or either of them (i.e. cardiovascular disease, CVD). This study describes the development of the updated version (3.0) of the risk equation formula. Methods In the version 3.0, the risks of CHD, stroke, or CVD were modelled with Fine-Gray sub distribution hazard models separately for men and women. Low-density lipoprotein (LDL) cholesterol was added as an alternative for total cholesterol (TC). Other variables (smoking, systolic blood pressure, high-density lipoprotein (HDL) cholesterol, diabetes, and family history) remained the same. To enhance understanding on the importance of risk factors in young adults, risk equations were developed to give 30-year risks in addition to 10-year risks. Data from FINRISK 1992, 1997, 2002, 2007, and 2012 studies were used. Results Both models, including either TC or LDL, had practically equal predictive ability for the 10-year-risk of CVD. The AUC values were 0.791 (TC model) and 0.790 (LDL model) for men and 0.811 (both models) for women. Calibration plots indicated that the prediction accuracy was not affected by replacing TC with LDL. The average risks of diseases for 40-year-old men and women were calculated for 10 and 30 years from the TC-models. For CHD, 10- and 30-year risks for men were 2.0% and 11.5%, respectively. For women the corresponding figures were 0.4% and 3.6%. For stroke, 10- and 30-year risks for men were 0.7% and 5.1% and for women 0.4% and 4.1%, respectively. For CVD, the probabilities for men were 2.6% and 16.2% and for women 0.8% and 7.5%. Conclusions Concerning predictive ability, TC can be replaced by LDL enhancing the use of the FINRISK calculator in clinical work where both or either of them are measured. Adding 30-year risk predictions to the calculator is justified to underscore the impact of risk factors already at younger age. Key messages • In the risk equation formula, total cholesterol can be replaced by LDL which is frequently measured in clinical practice and is an important modifiable lipid fraction when evaluating CVD risk. • Updating the risk equation formula of the FINRISK calculator and adding 30-year risk projections enhance cardiovascular risk prediction and screening of patients at younger age.

European Journal of Public HealthVol. 35(Supplement_4)
University of Eastern Finland (FI), Pain Management Institute (US), Public Health Institute (US)
Openalex Percentile: Top 51%
Lipoproteins and Cardiovascular Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.