TY - JOUR T1 - Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies. JF - Lancet Y1 - 2011 A1 - Wormser, David A1 - Kaptoge, Stephen A1 - Di Angelantonio, Emanuele A1 - Wood, Angela M A1 - Pennells, Lisa A1 - Thompson, Alex A1 - Sarwar, Nadeem A1 - Kizer, Jorge R A1 - Lawlor, Debbie A A1 - Nordestgaard, Børge G A1 - Ridker, Paul A1 - Salomaa, Veikko A1 - Stevens, June A1 - Woodward, Mark A1 - Sattar, Naveed A1 - Collins, Rory A1 - Thompson, Simon G A1 - Whitlock, Gary A1 - Danesh, John KW - Age Factors KW - Blood Pressure KW - Body Mass Index KW - Cardiovascular Diseases KW - Cholesterol KW - Cholesterol, HDL KW - Diabetes Mellitus KW - Female KW - Humans KW - Male KW - Middle Aged KW - Obesity, Abdominal KW - Proportional Hazards Models KW - Prospective Studies KW - Risk Assessment KW - Sex Factors KW - Smoking KW - Systole KW - Waist Circumference KW - Waist-Hip Ratio AB -

BACKGROUND: Guidelines differ about the value of assessment of adiposity measures for cardiovascular disease risk prediction when information is available for other risk factors. We studied the separate and combined associations of body-mass index (BMI), waist circumference, and waist-to-hip ratio with risk of first-onset cardiovascular disease.

METHODS: We used individual records from 58 cohorts to calculate hazard ratios (HRs) per 1 SD higher baseline values (4.56 kg/m(2) higher BMI, 12.6 cm higher waist circumference, and 0.083 higher waist-to-hip ratio) and measures of risk discrimination and reclassification. Serial adiposity assessments were used to calculate regression dilution ratios.

RESULTS: Individual records were available for 221,934 people in 17 countries (14,297 incident cardiovascular disease outcomes; 1.87 million person-years at risk). Serial adiposity assessments were made in up to 63,821 people (mean interval 5.7 years [SD 3.9]). In people with BMI of 20 kg/m(2) or higher, HRs for cardiovascular disease were 1.23 (95% CI 1.17-1.29) with BMI, 1.27 (1.20-1.33) with waist circumference, and 1.25 (1.19-1.31) with waist-to-hip ratio, after adjustment for age, sex, and smoking status. After further adjustment for baseline systolic blood pressure, history of diabetes, and total and HDL cholesterol, corresponding HRs were 1.07 (1.03-1.11) with BMI, 1.10 (1.05-1.14) with waist circumference, and 1.12 (1.08-1.15) with waist-to-hip ratio. Addition of information on BMI, waist circumference, or waist-to-hip ratio to a cardiovascular disease risk prediction model containing conventional risk factors did not importantly improve risk discrimination (C-index changes of -0.0001, -0.0001, and 0.0008, respectively), nor classification of participants to categories of predicted 10-year risk (net reclassification improvement -0.19%, -0.05%, and -0.05%, respectively). Findings were similar when adiposity measures were considered in combination. Reproducibility was greater for BMI (regression dilution ratio 0.95, 95% CI 0.93-0.97) than for waist circumference (0.86, 0.83-0.89) or waist-to-hip ratio (0.63, 0.57-0.70).

INTERPRETATION: BMI, waist circumference, and waist-to-hip ratio, whether assessed singly or in combination, do not importantly improve cardiovascular disease risk prediction in people in developed countries when additional information is available for systolic blood pressure, history of diabetes, and lipids.

FUNDING: British Heart Foundation and UK Medical Research Council.

VL - 377 IS - 9771 U1 - http://www.ncbi.nlm.nih.gov/pubmed/21397319?dopt=Abstract ER - TY - JOUR T1 - Lipid-related markers and cardiovascular disease prediction. JF - JAMA Y1 - 2012 A1 - Di Angelantonio, Emanuele A1 - Gao, Pei A1 - Pennells, Lisa A1 - Kaptoge, Stephen A1 - Caslake, Muriel A1 - Thompson, Alexander A1 - Butterworth, Adam S A1 - Sarwar, Nadeem A1 - Wormser, David A1 - Saleheen, Danish A1 - Ballantyne, Christie M A1 - Psaty, Bruce M A1 - Sundström, Johan A1 - Ridker, Paul M A1 - Nagel, Dorothea A1 - Gillum, Richard F A1 - Ford, Ian A1 - Ducimetiere, Pierre A1 - Kiechl, Stefan A1 - Koenig, Wolfgang A1 - Dullaart, Robin P F A1 - Assmann, Gerd A1 - D'Agostino, Ralph B A1 - Dagenais, Gilles R A1 - Cooper, Jackie A A1 - Kromhout, Daan A1 - Onat, Altan A1 - Tipping, Robert W A1 - Gómez-de-la-Cámara, Agustín A1 - Rosengren, Annika A1 - Sutherland, Susan E A1 - Gallacher, John A1 - Fowkes, F Gerry R A1 - Casiglia, Edoardo A1 - Hofman, Albert A1 - Salomaa, Veikko A1 - Barrett-Connor, Elizabeth A1 - Clarke, Robert A1 - Brunner, Eric A1 - Jukema, J Wouter A1 - Simons, Leon A A1 - Sandhu, Manjinder A1 - Wareham, Nicholas J A1 - Khaw, Kay-Tee A1 - Kauhanen, Jussi A1 - Salonen, Jukka T A1 - Howard, William J A1 - Nordestgaard, Børge G A1 - Wood, Angela M A1 - Thompson, Simon G A1 - Boekholdt, S Matthijs A1 - Sattar, Naveed A1 - Packard, Chris A1 - Gudnason, Vilmundur A1 - Danesh, John KW - Aged KW - Biomarkers KW - Cardiovascular Diseases KW - Cholesterol, HDL KW - Cohort Studies KW - Female KW - Humans KW - Lipoproteins KW - Male KW - Middle Aged KW - Risk Assessment AB -

CONTEXT: The value of assessing various emerging lipid-related markers for prediction of first cardiovascular events is debated.

OBJECTIVE: To determine whether adding information on apolipoprotein B and apolipoprotein A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 to total cholesterol and high-density lipoprotein cholesterol (HDL-C) improves cardiovascular disease (CVD) risk prediction.

DESIGN, SETTING, AND PARTICIPANTS: Individual records were available for 165,544 participants without baseline CVD in 37 prospective cohorts (calendar years of recruitment: 1968-2007) with up to 15,126 incident fatal or nonfatal CVD outcomes (10,132 CHD and 4994 stroke outcomes) during a median follow-up of 10.4 years (interquartile range, 7.6-14 years).

MAIN OUTCOME MEASURES: Discrimination of CVD outcomes and reclassification of participants across predicted 10-year risk categories of low (<10%), intermediate (10%-<20%), and high (≥20%) risk.

RESULTS: The addition of information on various lipid-related markers to total cholesterol, HDL-C, and other conventional risk factors yielded improvement in the model's discrimination: C-index change, 0.0006 (95% CI, 0.0002-0.0009) for the combination of apolipoprotein B and A-I; 0.0016 (95% CI, 0.0009-0.0023) for lipoprotein(a); and 0.0018 (95% CI, 0.0010-0.0026) for lipoprotein-associated phospholipase A2 mass. Net reclassification improvements were less than 1% with the addition of each of these markers to risk scores containing conventional risk factors. We estimated that for 100,000 adults aged 40 years or older, 15,436 would be initially classified at intermediate risk using conventional risk factors alone. Additional testing with a combination of apolipoprotein B and A-I would reclassify 1.1%; lipoprotein(a), 4.1%; and lipoprotein-associated phospholipase A2 mass, 2.7% of people to a 20% or higher predicted CVD risk category and, therefore, in need of statin treatment under Adult Treatment Panel III guidelines.

CONCLUSION: In a study of individuals without known CVD, the addition of information on the combination of apolipoprotein B and A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 mass to risk scores containing total cholesterol and HDL-C led to slight improvement in CVD prediction.

VL - 307 IS - 23 U1 - http://www.ncbi.nlm.nih.gov/pubmed/22797450?dopt=Abstract ER - TY - JOUR T1 - Association of Cardiometabolic Multimorbidity With Mortality. JF - JAMA Y1 - 2015 A1 - Di Angelantonio, Emanuele A1 - Kaptoge, Stephen A1 - Wormser, David A1 - Willeit, Peter A1 - Butterworth, Adam S A1 - Bansal, Narinder A1 - O'Keeffe, Linda M A1 - Gao, Pei A1 - Wood, Angela M A1 - Burgess, Stephen A1 - Freitag, Daniel F A1 - Pennells, Lisa A1 - Peters, Sanne A A1 - Hart, Carole L A1 - Håheim, Lise Lund A1 - Gillum, Richard F A1 - Nordestgaard, Børge G A1 - Psaty, Bruce M A1 - Yeap, Bu B A1 - Knuiman, Matthew W A1 - Nietert, Paul J A1 - Kauhanen, Jussi A1 - Salonen, Jukka T A1 - Kuller, Lewis H A1 - Simons, Leon A A1 - van der Schouw, Yvonne T A1 - Barrett-Connor, Elizabeth A1 - Selmer, Randi A1 - Crespo, Carlos J A1 - Rodriguez, Beatriz A1 - Verschuren, W M Monique A1 - Salomaa, Veikko A1 - Svärdsudd, Kurt A1 - van der Harst, Pim A1 - Björkelund, Cecilia A1 - Wilhelmsen, Lars A1 - Wallace, Robert B A1 - Brenner, Hermann A1 - Amouyel, Philippe A1 - Barr, Elizabeth L M A1 - Iso, Hiroyasu A1 - Onat, Altan A1 - Trevisan, Maurizio A1 - D'Agostino, Ralph B A1 - Cooper, Cyrus A1 - Kavousi, Maryam A1 - Welin, Lennart A1 - Roussel, Ronan A1 - Hu, Frank B A1 - Sato, Shinichi A1 - Davidson, Karina W A1 - Howard, Barbara V A1 - Leening, Maarten J G A1 - Leening, Maarten A1 - Rosengren, Annika A1 - Dörr, Marcus A1 - Deeg, Dorly J H A1 - Kiechl, Stefan A1 - Stehouwer, Coen D A A1 - Nissinen, Aulikki A1 - Giampaoli, Simona A1 - Donfrancesco, Chiara A1 - Kromhout, Daan A1 - Price, Jackie F A1 - Peters, Annette A1 - Meade, Tom W A1 - Casiglia, Edoardo A1 - Lawlor, Debbie A A1 - Gallacher, John A1 - Nagel, Dorothea A1 - Franco, Oscar H A1 - Assmann, Gerd A1 - Dagenais, Gilles R A1 - Jukema, J Wouter A1 - Sundström, Johan A1 - Woodward, Mark A1 - Brunner, Eric J A1 - Khaw, Kay-Tee A1 - Wareham, Nicholas J A1 - Whitsel, Eric A A1 - Njølstad, Inger A1 - Hedblad, Bo A1 - Wassertheil-Smoller, Sylvia A1 - Engström, Gunnar A1 - Rosamond, Wayne D A1 - Selvin, Elizabeth A1 - Sattar, Naveed A1 - Thompson, Simon G A1 - Danesh, John KW - Adult KW - Aged KW - Comorbidity KW - Diabetes Mellitus KW - Female KW - Humans KW - Life Expectancy KW - Male KW - Middle Aged KW - Mortality KW - Myocardial Infarction KW - Risk Factors KW - Stroke AB -

IMPORTANCE: The prevalence of cardiometabolic multimorbidity is increasing.

OBJECTIVE: To estimate reductions in life expectancy associated with cardiometabolic multimorbidity.

DESIGN, SETTING, AND PARTICIPANTS: Age- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689,300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128,843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499,808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates.

EXPOSURES: A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI).

MAIN OUTCOMES AND MEASURES: All-cause mortality and estimated reductions in life expectancy.

RESULTS: In participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy.

CONCLUSIONS AND RELEVANCE: Mortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity.

VL - 314 IS - 1 U1 - http://www.ncbi.nlm.nih.gov/pubmed/26151266?dopt=Abstract ER - TY - JOUR T1 - Natriuretic peptides and integrated risk assessment for cardiovascular disease: an individual-participant-data meta-analysis. JF - Lancet Diabetes Endocrinol Y1 - 2016 A1 - Willeit, Peter A1 - Kaptoge, Stephen A1 - Welsh, Paul A1 - Butterworth, Adam A1 - Chowdhury, Rajiv A1 - Spackman, Sarah A1 - Pennells, Lisa A1 - Gao, Pei A1 - Burgess, Stephen A1 - Freitag, Daniel A1 - Sweeting, Michael A1 - Wood, Angela A1 - Cook, Nancy A1 - Judd, Suzanne A1 - Trompet, Stella A1 - Nambi, Vijay A1 - Olsen, Michael A1 - Everett, Brendan A1 - Kee, Frank A1 - Arnlöv, Johan A1 - Salomaa, Veikko A1 - Levy, Daniel A1 - Kauhanen, Jussi A1 - Laukkanen, Jari A1 - Kavousi, Maryam A1 - Ninomiya, Toshiharu A1 - Casas, Juan-Pablo A1 - Daniels, Lori A1 - Lind, Lars A1 - Kistorp, Caroline A1 - Rosenberg, Jens A1 - Mueller, Thomas A1 - Rubattu, Speranza A1 - Panagiotakos, Demosthenes A1 - Franco, Oscar A1 - de Lemos, James A1 - Luchner, Andreas A1 - Kizer, Jorge A1 - Kiechl, Stefan A1 - Salonen, Jukka A1 - Goya Wannamethee, S A1 - de Boer, Rudolf A1 - Nordestgaard, Børge A1 - Andersson, Jonas A1 - Jørgensen, Torben A1 - Melander, Olle A1 - Ballantyne, Christie A1 - DeFilippi, Christopher A1 - Ridker, Paul A1 - Cushman, Mary A1 - Rosamond, Wayne A1 - Thompson, Simon A1 - Gudnason, Vilmundur A1 - Sattar, Naveed A1 - Danesh, John A1 - Di Angelantonio, Emanuele KW - Aged KW - Biomarkers KW - Cardiovascular Diseases KW - Female KW - Humans KW - Male KW - Middle Aged KW - Natriuretic Peptide, Brain KW - Peptide Fragments KW - Prospective Studies KW - Risk Assessment AB -

BACKGROUND: Guidelines for primary prevention of cardiovascular diseases focus on prediction of coronary heart disease and stroke. We assessed whether or not measurement of N-terminal-pro-B-type natriuretic peptide (NT-proBNP) concentration could enable a more integrated approach than at present by predicting heart failure and enhancing coronary heart disease and stroke risk assessment.

METHODS: In this individual-participant-data meta-analysis, we generated and harmonised individual-participant data from relevant prospective studies via both de-novo NT-proBNP concentration measurement of stored samples and collection of data from studies identified through a systematic search of the literature (PubMed, Scientific Citation Index Expanded, and Embase) for articles published up to Sept 4, 2014, using search terms related to natriuretic peptide family members and the primary outcomes, with no language restrictions. We calculated risk ratios and measures of risk discrimination and reclassification across predicted 10 year risk categories (ie, <5%, 5% to <7·5%, and ≥7·5%), adding assessment of NT-proBNP concentration to that of conventional risk factors (ie, age, sex, smoking status, systolic blood pressure, history of diabetes, and total and HDL cholesterol concentrations). Primary outcomes were the combination of coronary heart disease and stroke, and the combination of coronary heart disease, stroke, and heart failure.

FINDINGS: We recorded 5500 coronary heart disease, 4002 stroke, and 2212 heart failure outcomes among 95 617 participants without a history of cardiovascular disease in 40 prospective studies. Risk ratios (for a comparison of the top third vs bottom third of NT-proBNP concentrations, adjusted for conventional risk factors) were 1·76 (95% CI 1·56-1·98) for the combination of coronary heart disease and stroke and 2·00 (1·77-2·26) for the combination of coronary heart disease, stroke, and heart failure. Addition of information about NT-proBNP concentration to a model containing conventional risk factors was associated with a C-index increase of 0·012 (0·010-0·014) and a net reclassification improvement of 0·027 (0·019-0·036) for the combination of coronary heart disease and stroke and a C-index increase of 0·019 (0·016-0·022) and a net reclassification improvement of 0·028 (0·019-0·038) for the combination of coronary heart disease, stroke, and heart failure.

INTERPRETATION: In people without baseline cardiovascular disease, NT-proBNP concentration assessment strongly predicted first-onset heart failure and augmented coronary heart disease and stroke prediction, suggesting that NT-proBNP concentration assessment could be used to integrate heart failure into cardiovascular disease primary prevention.

FUNDING: British Heart Foundation, Austrian Science Fund, UK Medical Research Council, National Institute for Health Research, European Research Council, and European Commission Framework Programme 7.

VL - 4 IS - 10 ER - TY - JOUR T1 - REPEATED MEASUREMENTS OF BLOOD PRESSURE AND CHOLESTEROL IMPROVES CARDIOVASCULAR DISEASE RISK PREDICTION: AN INDIVIDUAL-PARTICIPANT-DATA META-ANALYSIS. JF - Am J Epidemiol Y1 - 2017 A1 - Paige, Ellie A1 - Barrett, Jessica A1 - Pennells, Lisa A1 - Sweeting, Michael A1 - Willeit, Peter A1 - Di Angelantonio, Emanuele A1 - Gudnason, Vilmundur A1 - Nordestgaard, Børge G A1 - Psaty, Bruce M A1 - Goldbourt, Uri A1 - Best, Lyle G A1 - Assmann, Gerd A1 - Salonen, Jukka T A1 - Nietert, Paul J A1 - Verschuren, Wm Monique A1 - Brunner, Eric J A1 - Kronmal, Richard A A1 - Salomaa, Veikko A1 - Bakker, Stephan Jl A1 - Dagenais, Gilles R A1 - Sato, Shinichi A1 - Jansson, Jan-Håkan A1 - Willeit, Johann A1 - Onat, Altan A1 - de la Cámara, Agustin Gómez A1 - Roussel, Ronan A1 - Völzke, Henry A1 - Dankner, Rachel A1 - Tipping, Robert W A1 - Meade, Tom W A1 - Donfrancesco, Chiara A1 - Kuller, Lewis H A1 - Peters, Annette A1 - Gallacher, John A1 - Kromhout, Daan A1 - Iso, Hiroyasu A1 - Knuiman, Matthew A1 - Casiglia, Edoardo A1 - Kavousi, Maryam A1 - Palmieri, Luigi A1 - Sundström, Johan A1 - Davis, Barry R A1 - Njølstad, Inger A1 - Couper, David A1 - Danesh, John A1 - Thompson, Simon G A1 - Wood, Angela AB -

The added value of incorporating information from repeated measurements of blood pressure and cholesterol for cardiovascular disease (CVD) risk prediction has not been rigorously assessed. We used data from the Emerging Risk Factors Collaboration on 191,445 adults (38 cohorts from across 17 countries with data from 1962-2014) with > 1 million measurements of systolic blood pressure, total cholesterol and high-density lipoprotein cholesterol; over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative means of repeated measurements and summary measures from longitudinal modelling of the repeated measurements were compared to models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analysed across studies. Compared to the single time point model, the cumulative means and the longitudinal models increased the C-index by 0.0040 (95% CI: 0.0023, 0.0057) and 0.0023 (0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared to the single time point model, overall net reclassification improvements were 0.0369 (0.0303, 0.0436) for the cumulative means model and 0.0177 (0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.

ER - TY - JOUR T1 - Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies. JF - Eur Heart J Y1 - 2018 A1 - Pennells, Lisa A1 - Kaptoge, Stephen A1 - Wood, Angela A1 - Sweeting, Mike A1 - Zhao, Xiaohui A1 - White, Ian A1 - Burgess, Stephen A1 - Willeit, Peter A1 - Bolton, Thomas A1 - Moons, Karel G M A1 - van der Schouw, Yvonne T A1 - Selmer, Randi A1 - Khaw, Kay-Tee A1 - Gudnason, Vilmundur A1 - Assmann, Gerd A1 - Amouyel, Philippe A1 - Salomaa, Veikko A1 - Kivimaki, Mika A1 - Nordestgaard, Børge G A1 - Blaha, Michael J A1 - Kuller, Lewis H A1 - Brenner, Hermann A1 - Gillum, Richard F A1 - Meisinger, Christa A1 - Ford, Ian A1 - Knuiman, Matthew W A1 - Rosengren, Annika A1 - Lawlor, Debbie A A1 - Völzke, Henry A1 - Cooper, Cyrus A1 - Marín Ibañez, Alejandro A1 - Casiglia, Edoardo A1 - Kauhanen, Jussi A1 - Cooper, Jackie A A1 - Rodriguez, Beatriz A1 - Sundström, Johan A1 - Barrett-Connor, Elizabeth A1 - Dankner, Rachel A1 - Nietert, Paul J A1 - Davidson, Karina W A1 - Wallace, Robert B A1 - Blazer, Dan G A1 - Björkelund, Cecilia A1 - Donfrancesco, Chiara A1 - Krumholz, Harlan M A1 - Nissinen, Aulikki A1 - Davis, Barry R A1 - Coady, Sean A1 - Whincup, Peter H A1 - Jørgensen, Torben A1 - Ducimetiere, Pierre A1 - Trevisan, Maurizio A1 - Engström, Gunnar A1 - Crespo, Carlos J A1 - Meade, Tom W A1 - Visser, Marjolein A1 - Kromhout, Daan A1 - Kiechl, Stefan A1 - Daimon, Makoto A1 - Price, Jackie F A1 - Gómez de la Cámara, Agustin A1 - Wouter Jukema, J A1 - Lamarche, Benoît A1 - Onat, Altan A1 - Simons, Leon A A1 - Kavousi, Maryam A1 - Ben-Shlomo, Yoav A1 - Gallacher, John A1 - Dekker, Jacqueline M A1 - Arima, Hisatomi A1 - Shara, Nawar A1 - Tipping, Robert W A1 - Roussel, Ronan A1 - Brunner, Eric J A1 - Koenig, Wolfgang A1 - Sakurai, Masaru A1 - Pavlovic, Jelena A1 - Gansevoort, Ron T A1 - Nagel, Dorothea A1 - Goldbourt, Uri A1 - Barr, Elizabeth L M A1 - Palmieri, Luigi A1 - Njølstad, Inger A1 - Sato, Shinichi A1 - Monique Verschuren, W M A1 - Varghese, Cherian V A1 - Graham, Ian A1 - Onuma, Oyere A1 - Greenland, Philip A1 - Woodward, Mark A1 - Ezzati, Majid A1 - Psaty, Bruce M A1 - Sattar, Naveed A1 - Jackson, Rod A1 - Ridker, Paul M A1 - Cook, Nancy R A1 - D'Agostino, Ralph B A1 - Thompson, Simon G A1 - Danesh, John A1 - Di Angelantonio, Emanuele AB -

Aims: There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after 'recalibration', a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied.

Methods and results: Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at 'high' 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10%, 52%, and 41%, respectively, whereas RRS under-predicted by 10%. Original versions of algorithms classified 29-39% of individuals aged ≥40 years as high risk. By contrast, recalibration reduced this proportion to 22-24% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44-51 such individuals using original algorithms, in contrast to 37-39 individuals with recalibrated algorithms.

Conclusion: Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need.

ER -