Assessing risk prediction models using individual participant data from multiple studies.

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous

Quantifying the contribution of established risk factors to cardiovascular mortality differences between Russia and Norway.

Surprisingly few attempts have been made to quantify the simultaneous contribution of well-established risk factors to CVD mortality differences between countries. We aimed to develop and critically appraise an approach to doing so, applying it to the substantial CVD mortality gap between Russia and Norway using survey data in three cities and mortality risks from the Emerging Risk Factor Collaboration. We estimated the absolute and relative differences in CVD mortality at ages 40-69 years between countries attributable to the risk factors, under the counterfactual that the age- and sex-specific risk factor profile in Russia was as in Norway, and vice-versa. Under the counterfactual that Russia had the Norwegian risk factor profile, the absolute age-standardized CVD mortality gap would decline by 33.3% (95% CI 25.1-40.1) among men and 22.1% (10.4-31.3) among women. In relative terms, the mortality rate ratio (Russia/Norway) would decline from 9-10 to 7-8. Under the counterfactual that Norway had the Russian risk factor profile, the mortality gap reduced less. Well-established CVD risk factors account for a third of the male and around a quarter of the female CVD mortality gap between Russia and Norway. However, these estimates are based on widely held epidemiological assumptions that deserve further scrutiny

Validation and public health modelling of risk prediction models for kidney cancer using the UK Biobank

Funder: Kidney Cancer UKFunder: Yorkshire Cancer ResearchFunder: Mark Foundation For Cancer Research; Id: http://dx.doi.org/10.13039/100014599Funder: The Rosetrees TrustFunder: Urology Foundation; Id: http://dx.doi.org/10.13039/501100007568Objectives: To externally validate risk models for the detection of kidney cancer, as early detection of kidney cancer improves survival and stratifying the population using risk models could enable an individually tailored screening programme. Methods: We validated the performance of 30 existing phenotypic models predicting the risk of kidney cancer in the UK Biobank cohort (n = 450 687). We compared the discrimination and calibration of models for men, women, and a mixed‐sex cohort. Population level data were used to estimate model performance in a screening scenario for a range of risk thresholds (6‐year risk: 0.1–1.0%). Results: In all, 10 models had reasonable discrimination (area under the receiver‐operating characteristic curve >0.60), although some had poor calibration. Modelling demonstrated similar performance of the best models over a range of thresholds. The models showed an improvement in ability to identify cases compared to age‐ and sex‐based screening. All the models performed less well in women than men. Conclusions: The present study is the first comprehensive external validation of risk models for kidney cancer. The best‐performing models are better at identifying individuals at high risk of kidney cancer than age and sex alone; however, the benefits are relatively small. Feasibility studies are required to determine applicability to a screening programme

Cardiovascular Risk Factors Associated With Venous Thromboembolism

Importance It is uncertain to what extent established cardiovascular risk factors are associated with venous thromboembolism (VTE). Objective To estimate associations between major cardiovascular risk factors and VTE, i.e., deep-vein thrombosis (DVT) and pulmonary embolism (PE). Design Analysis of individual-participant data from the Emerging Risk Factors Collaboration (ERFC; 731,728 participants; 75 cohorts; latest date of follow-up 2015), and UK Biobank (UKBB; 421,537 participants; latest date of follow-up 2016). Setting Approximately population-based prospective cohort studies. Participants Individuals without cardiovascular disease at baseline. Exposures A panel of several established cardiovascular risk factors. Main Outcomes and Measures Hazard ratios (HRs) per 1-SD higher risk factor levels (or presence/absence). Incident fatal outcomes in ERFC (n=1041 VTE, n=25,131 CHD) and incident fatal/non-fatal outcomes in UKBB (n=2321 VTE, n=3385 CHD). HRs were adjusted for age, sex, smoking status, diabetes mellitus, and body-mass index. Results Adjusted HRs for VTE were: 2.67 (2.45-2.91) in ERFC and 1.81 (1.71-1.92) in UKBB per decade older age; 1.38 (1.20-1.58) in ERFC and 1.23 (1.08-1.40) in UKBB with smoking; 1.43 (1.35-1.50) in ERFC and 1.37 (1.32-1.41) in UKBB per 1-SD higher body-mass index; and 0.75 (0.61-0.93) in ERFC and 0.82 (0.71-0.94) in UKBB with current alcohol consumption. For the preceding factors, there were similar HRs for pulmonary embolism versus deep vein thrombosis in UKBB (except adiposity was more strongly associated with PE; P<0.01), and similar HRs for unprovoked versus provoked VTE. Apart from adiposity, these risk factors were less strongly associated with VTE than coronary heart disease. We noted inconsistent associations with diabetes and blood pressure for VTEs across ERFC and UKBB, and had limited ability to study lipid and inflammation markers. Conclusions and Relevance Older age, smoking, adiposity, and lower alcohol consumption were consistently associated with higher VTE risk.A study website (http://www.phpc.cam.ac.uk/ceu/erfc/list-of-studies/) includes a list that investigators have provided of funding agencies that have supported individual cohorts in the ERFC contributing to the present consortium. This research has been conducted using the UK Biobank resource (application 26865)

Prioritising cardiovascular disease risk assessment to high risk individuals based on primary care records.

OBJECTIVE: To provide quantitative evidence for systematically prioritising individuals for full formal cardiovascular disease (CVD) risk assessment using primary care records with a novel tool (eHEART) with age- and sex- specific risk thresholds. METHODS AND ANALYSIS: eHEART was derived using landmark Cox models for incident CVD with repeated measures of conventional CVD risk predictors in 1,642,498 individuals from the Clinical Practice Research Datalink. Using 119,137 individuals from UK Biobank, we modelled the implications of initiating guideline-recommended statin therapy using eHEART with age- and sex-specific prioritisation thresholds corresponding to 5% false negative rates to prioritise adults aged 40-69 years in a population in England for invitation to a formal CVD risk assessment. RESULTS: Formal CVD risk assessment on all adults would identify 76% and 49% of future CVD events amongst men and women respectively, and 93 (95% CI: 90, 95) men and 279 (95% CI: 259, 297) women would need to be screened (NNS) to prevent one CVD event. In contrast, if eHEART was first used to prioritise individuals for formal CVD risk assessment, we would identify 73% and 47% of future events amongst men and women respectively, and a NNS of 75 (95% CI: 72, 77) men and 162 (95% CI: 150, 172) women. Replacing the age- and sex-specific prioritisation thresholds with a 10% threshold identify around 10% less events. CONCLUSIONS: The use of prioritisation tools with age- and sex-specific thresholds could lead to more efficient CVD assessment programmes with only small reductions in effectiveness at preventing new CVD events

Improving 10-year cardiovascular risk prediction in apparently healthy people : flexible addition of risk modifiers on top of SCORE2

AIMS: In clinical practice, factors associated with cardiovascular disease (CVD) like albuminuria, education level, or coronary artery calcium (CAC) are often known, but not incorporated in cardiovascular risk prediction models. The aims of the current study were to evaluate a methodology for the flexible addition of risk modifying characteristics on top of SCORE2 and to quantify the added value of several clinically relevant risk modifying characteristics. METHODS AND RESULTS: Individuals without previous CVD or DM were included from the UK Biobank; Atherosclerosis Risk in Communities (ARIC); Multi-Ethnic Study of Atherosclerosis (MESA); European Prospective Investigation into Cancer, The Netherlands (EPIC-NL); and Heinz Nixdorf Recall (HNR) studies (n = 409 757) in whom 16 166 CVD events and 19 149 non-cardiovascular deaths were observed over exactly 10.0 years of follow-up. The effect of each possible risk modifying characteristic was derived using competing risk-adjusted Fine and Gray models. The risk modifying characteristics were applied to individual predictions with a flexible method using the population prevalence and the subdistribution hazard ratio (SHR) of the relevant predictor. Risk modifying characteristics that increased discrimination most were CAC percentile with 0.0198 [95% confidence interval (CI) 0.0115; 0.0281] and hs-Troponin-T with 0.0100 (95% CI 0.0063; 0.0137). External validation was performed in the Clinical Practice Research Datalink (CPRD) cohort (UK, n = 518 015, 12 675 CVD events). Adjustment of SCORE2-predicted risks with both single and multiple risk modifiers did not negatively affect calibration and led to a modest increase in discrimination [0.740 (95% CI 0.736-0.745) vs. unimproved SCORE2 risk C-index 0.737 (95% CI 0.732-0.741)]. CONCLUSION: The current paper presents a method on how to integrate possible risk modifying characteristics that are not included in existing CVD risk models for the prediction of CVD event risk in apparently healthy people. This flexible methodology improves the accuracy of predicted risks and increases applicability of prediction models for individuals with additional risk known modifiers

Natriuretic peptides and integrated risk assessment for cardiovascular disease. an individual-participant-data meta-analysis

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

Association between depressive symptoms and incident cardiovascular diseases

Importance: It is uncertain whether depressive symptoms are independently associated with subsequent risk of cardiovascular diseases (CVD). Objective: To characterize the association between depressive symptoms and CVD incidence across the spectrum of lower mood. Design, setting and participants: A pooled analysis of individual-participant-data from the Emerging Risk Factors Collaboration (ERFC; 162,036 participants; 21 cohorts; baseline surveys, 1960-2008; latest follow-up, March 2020) and UK Biobank (UKB; 401,219 participants; baseline surveys, 2006-2010; latest follow-up, March 2020). Eligible participants had information about self-reported depressive symptoms and no CVD history at baseline. Exposure: Depressive symptoms were recorded using validated instruments. ERFC scores were harmonized across studies to a scale representative of the Centre for Epidemiological Studies Depression scale (CES-D; range 0-60; ≥16 indicates possible depressive disorder). UKB recorded the Patient Health Questionnaire-2 (PHQ-2; range 0-6; ≥3 indicates possible depressive disorder). Main Outcomes and Measures: Primary outcomes were incident fatal/nonfatal coronary heart disease (CHD), stroke and CVD (composite of CHD and stroke). Hazard ratios (HRs) per 1-SD higher log-CES-D or PHQ-2 adjusted for age, sex, smoking and diabetes were reported. Results: Among 162,036 participants from the ERFC, 73% were female, mean (SD) age at baseline was 63 (9) years, and 5,078 CHD and 3,932 stroke events were recorded (median follow-up, 9.5-years). Associations with CHD, stroke and CVD were log-linear. HRs (95%CI) per 1SD higher depression score for CHD, stroke and CVD respectively were 1.07 (1.03-1.11), 1.05 (1.01-1.10), and 1.06 (1.04-1.08). This reflects, 36 versus 29 CHD events, 28 versus 25 stroke events, and 63 versus 54 CVD events per 1000 individuals over 10 years in the highest versus lowest quintile of CES-D (geometric mean CES-D score, 19 versus 1). Among 401,219 participants from the UKB, 55% were female, mean baseline age was 56 (8) years, and 4607 CHD and 3253 stroke events were recorded (median follow-up, 8.1-years). HRs per 1SD higher depression score for CHD, stroke and CVD respectively were 1.11 (1.08-1.14), 1.10 (1.06-1.14) and 1.10 (1.08-1.13). This reflects, 21 versus 14 CHD events, 15 versus 10 stroke events, and 36 versus 25 CVD events per 1000 individuals over 10 years in those with PHQ2 ≥4 versus 0. The magnitude and statistical significance of the HRs were not materially changed after adjustment for additional risk factors. Conclusions and Relevance: In a pooled analysis of 563,255 participants in 22 cohorts, baseline depressive symptoms were associated with CVD incidence, including at symptom levels below the threshold indicative of a depressive disorder. However, the magnitude of associations was modest.Lisa Pennells, Stephen Kaptoge and Sarah Spackman are funded by a British Heart Foundation Programme Grant (RG/18/13/33946). Steven Bell was funded by the National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024). Tom Bolton is funded by the National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024). Angela Wood is supported by a BHF-Turing Cardiovascular Data Science Award and by the EC-Innovative Medicines Initiative (BigData@Heart). John Danesh holds a British Heart Foundation Professorship and a National Institute for Health Research Senior Investigator Award.* *The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care

The Bangladesh Risk of Acute Vascular Events (BRAVE) Study: objectives and design.

During recent decades, Bangladesh has experienced a rapid epidemiological transition from communicable to non-communicable diseases. Coronary heart disease (CHD), with myocardial infarction (MI) as its main manifestation, is a major cause of death in the country. However, there is limited reliable evidence about its determinants in this population. The Bangladesh Risk of Acute Vascular Events (BRAVE) study is an epidemiological bioresource established to examine environmental, genetic, lifestyle and biochemical determinants of CHD among the Bangladeshi population. By early 2015, the ongoing BRAVE study had recruited over 5000 confirmed first-ever MI cases, and over 5000 controls "frequency-matched" by age and sex. For each participant, information has been recorded on demographic factors, lifestyle, socioeconomic, clinical, and anthropometric characteristics. A 12-lead electrocardiogram has been recorded. Biological samples have been collected and stored, including extracted DNA, plasma, serum and whole blood. Additionally, for the 3000 cases and 3000 controls initially recruited, genotyping has been done using the CardioMetabochip+ and the Exome+ arrays. The mean age (standard deviation) of MI cases is 53 (10) years, with 88 % of cases being male and 46 % aged 50 years or younger. The median interval between reported onset of symptoms and hospital admission is 5 h. Initial analyses indicate that Bangladeshis are genetically distinct from major non-South Asian ethnicities, as well as distinct from other South Asian ethnicities. The BRAVE study is well-placed to serve as a powerful resource to investigate current and future hypotheses relating to environmental, biochemical and genetic causes of CHD in an important but under-studied South Asian population.The Gates Cambridge Trust has supported Dr Chowdhury. Epidemiological fieldwork in BRAVE has been supported by grants to investigators at the Cardiovascular Epidemiology Unit, University of Cambridge. The Cardiovascular Epidemiology Unit is underpinned by programme grants from the British Heart Foundation (RG/13/13/30194), the UK Medical Research Council (MR/L003120/1), and the UK National Institute of Health Research Cambridge Biomedical Research Centre. BRAVE has received support for genetic assays from the European Research Council (ERC-2010-AdG-20100317), European Commission Framework 7 (Grant Agreement number: 279233), and the Cambridge British Heart Foundation Centre for Excellence in Cardiovascular Science; We would like to acknowledge the contributions of the following individuals: Cardiology Research Group in Bangladesh Mohammad Afzalur Rahman, Mohammad Abdul Kader Akanda, M Atahar Ali, Mir Jamal Uddin, SM Siddiqur Rahman, Amal Kumar Choudhury, Md. Mamunur Rashid, Nazir Ahmed Chowdhury, Mohammad Abdullahel Baqui, Kajal Kumar Karmoker, Mohammad Golam Azam; Setting up/implementation of fieldwork in Bangladesh Abbas Bhuiya, Susmita Chowdhury, Kamrun Nahar, Neelima Das, Proshon Roy, Sumona Ferdous, Taposh Kumar Biswas, Abu Sadat Mohammad Sayed Sharif, Ranjit Shingha, Rose Jinnath Tomas, Babulal Parshei, Mabubur Rahman, Mohammad Emon Hossain, Akhirunnesa Mily, AK Mottashir Ahmed, Sati Chowdhury, Sushila Roy, Dipak Kanti Chowdhury, Swapan Kumar Roy; Epidemiological/statistical support in Cambridge Stephen Kaptoge, Simon Thompson, Angela Wood, Narinder Bansal, Anna Ramond, Clare Oliver-Williams, Marinka Steur, Linda O’Keeffe, Eleni Sofianopoulou, Setor Kunutsor, Donal Gorman, Oscar H Franco, Malcolm Legget, Pinal Patel, Marc Suhrcke, Sylvaine Bruggraber, Jonathan Powell; Data management Matthew Walker, Steve Ellis, Shawkat Jahangir, Habibur Rahman, Rifat Hasan Shammi, Shafqat Ullah, Mohammad Abdul Matin and Administration Beth Collins, Hannah Lombardi, Binder Kaur, Rachel Henry, Marilena Papanikolaou, Robert Smith, Abdul Wazed, Robert Williams, Julie Jenkins, Keith Hoddy.This is the final published version of the article. It was originally published in the European Journal of Epidemiology (Chowdhury R, et al., European Journal of Epidemiology, 2015, doi:10.1007/s10654-015-0037-2). The final version is available at http://dx.doi.org/10.1007/s10654-015-0037-

Estimating individual lifetime risk of incident cardiovascular events in adults with type 2 diabetes: an update and geographical calibration of the DIAbetes Lifetime perspective model (DIAL2)

Background: The 2021 ESC cardiovascular disease (CVD) prevention guidelines recommend the use of (lifetime) risk prediction models to aid decisions regarding intensified preventive treatment options in adults with type 2 diabetes, e.g. the DIAbetes Lifetime perspective model (DIAL model). The aim of this study was to update the DIAL-model using contemporary and representative registry data (DIAL2) and to systematically calibrate the model for use in other European countries. Methods and Results: The DIAL2 model was derived in 467,856 people with type 2 diabetes without a history of CVD from the Swedish National Diabetes Register, with a median follow-up of 7.3 years (IQR 4.0-10.6 years) and comprising 63,824 CVD (including fatal CVD, nonfatal stroke and nonfatal myocardial infarction) events and 66,048 non-CVD mortality events. The model was systematically recalibrated to Europe’s low and moderate risk region using contemporary incidence data and mean risk factor distributions. The recalibrated DIAL2 model was externally validated in 218,267 individuals with type 2 diabetes from the Scottish Care Information – Diabetes (SCID) and Clinical Practice Research Datalink (CPRD). In these individuals, 43,074 CVD events and 27,115 non-CVD fatal events were observed. The DIAL2 model discriminated well, with C-indices of 0.732 (95%CI 0.726-0.739) in CPRD and 0.700 (95%CI 0.691-0.709) in SCID. Interpretation: The recalibrated DIAL2 model provides a useful tool for the prediction of CVD-free life expectancy and lifetime CVD risk for people with type 2 diabetes without previous CVD in the European low and moderate risk regions. These long-term individualized measures of CVD risk are well suited for shared decision making in clinical practice as recommended by the 2021 CVD ESC prevention guidelines