Covariate-adjusted measures of discrimination for survival data

MOTIVATION: Discrimination statistics describe the ability of a survival model to assign higher risks to individuals who experience earlier events: examples are Harrell's C-index and Royston and Sauerbrei's D, which we call the D-index. Prognostic covariates whose distributions are controlled by the study design (e.g. age and sex) influence discrimination and can make it difficult to compare model discrimination between studies. Although covariate adjustment is a standard procedure for quantifying disease-risk factor associations, there are no covariate adjustment methods for discrimination statistics in censored survival data. OBJECTIVE: To develop extensions of the C-index and D-index that describe the prognostic ability of a model adjusted for one or more covariate(s). METHOD: We define a covariate-adjusted C-index and D-index for censored survival data, propose several estimators, and investigate their performance in simulation studies and in data from a large individual participant data meta-analysis, the Emerging Risk Factors Collaboration. RESULTS: The proposed methods perform well in simulations. In the Emerging Risk Factors Collaboration data, the age-adjusted C-index and D-index were substantially smaller than unadjusted values. The study-specific standard deviation of baseline age was strongly associated with the unadjusted C-index and D-index but not significantly associated with the age-adjusted indices. CONCLUSIONS: The proposed estimators improve meta-analysis comparisons, are easy to implement and give a more meaningful clinical interpretation.This work was supported by the Medical Research Council Grant G0700463 and Unit Programme U105260558

Mild-to-Moderate Kidney Dysfunction and Cardiovascular Disease : Observational and Mendelian Randomization Analyses

Funding Information: The Emerging Risk Factors Collaboration (ERFC) coordinating center was underpinned by program grants from the British Heart Foundation (BHF; SP/09/002; RG/13/13/30194; RG/18/13/33946), BHF Centre of Research Excellence (RE/18/1/34212), the UK Medical Research Council (MR/L003120/1), and the National Institute for Health and Care Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014), with project-specific support received from the UK NIHR, British United Provident Association UK Foundation, and an unrestricted educational grant from GlaxoSmithKline. This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the Department of Health and Social Care (England), the Chief Scientist Office of the Scottish Government Health and Social Care Directorates, the Health and Social Care Research and Development Division (Welsh Government), the Public Health Agency (Northern Ireland), the BHF, and the Wellcome Trust. A variety of funding sources have supported recruitment, follow-up, and laboratory measurements in the studies contributing data to the ERFC, which are listed on the ERFC website ( www.phpc.cam.ac.uk/ceu/erfc/list-of-studies ). EPIC-CVD (European Prospective Investigation into Cancer and Nutrition–Cardiovascular Disease Study) was funded by the European Research Council (268834) and the European Commission Framework Programme 7 (HEALTH-F2-2012-279233). The coordination of EPIC is financially supported by International Agency for Research on Cancer (IARC) and also by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC). The national cohorts are supported by: Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l’Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), German Institute of Human Nutrition PotsdamRehbruecke (DIfE), Federal Ministry of Education and Research (BMBF) (Germany); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS) - Instituto de Salud Carlos III (ISCIII), Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology - ICO (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C8221/A29017 to EPIC-Oxford), Medical Research Council, United Kingdom (1000143 to EPIC-Norfolk; MR/M012190/1 to EPIC-Oxford). The establishment of the EPIC-InterAct subcohort (used in the EPIC-CVD study) and conduct of biochemical assays was supported by the EU Sixth Framework Programme (FP6) (grant LSHM_CT_2006_037197 to the InterAct project) and the Medical Research Council Epidemiology Unit (grants MC_UU_12015/1 and MC_UU_12015/5). This research is based on data from the Million Veteran Program, Office of Research and Development, and Veterans Health Administration and was supported by award I01-BX004821 (principal investigators, Drs Peter W.F. Wilson and Kelly Cho) and I01-BX003360 (principal investigators, Dr Adriana M. Hung). Dr Damrauer is supported by IK2-CX001780. Dr Hung is supported by CX001897. Dr Tsao is supported by BX003362-01 from VA Office of Research and Development. Dr Robinson-Cohen is supported by R01DK122075. Dr Sun was funded by a BHF Programme Grant (RG/18/13/33946). Dr Arnold was funded by a BHF Programme Grant (RG/18/13/33946). Dr Kaptoge is funded by a BHF Chair award (CH/12/2/29428). Dr Mason is funded by the EU/EFPIA Innovative Medicines Initiative Joint Undertaking BigData@Heart grant 116074. Dr Bolton was funded by the NIHR BTRU in Donor Health and Genomics (NIHR BTRU-2014-10024). Dr Allara is funded by a BHF Programme Grant (RG/18/13/33946). Prof Inouye is supported by the Munz Chair of Cardiovascular Prediction and Prevention and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). Prof Inouye was also supported by the UK Economic and Social Research 878 Council (ES/T013192/1). Prof Danesh holds a British Heart Foundation Professorship and a NIHR Senior Investigator Award. Prof Wood is part of the BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No 116074. Prof Wood was supported by the BHF-Turing Cardiovascular Data Science Award (BCDSA\100005). Prof Di Angelantonio holds a NIHR Senior Investigator Award. Publisher Copyright: © 2022 The Authors.Background: End-stage renal disease is associated with a high risk of cardiovascular events. It is unknown, however, whether mild-to-moderate kidney dysfunction is causally related to coronary heart disease (CHD) and stroke. Methods: Observational analyses were conducted using individual-level data from 4 population data sources (Emerging Risk Factors Collaboration, EPIC-CVD [European Prospective Investigation into Cancer and Nutrition-Cardiovascular Disease Study], Million Veteran Program, and UK Biobank), comprising 648 135 participants with no history of cardiovascular disease or diabetes at baseline, yielding 42 858 and 15 693 incident CHD and stroke events, respectively, during 6.8 million person-years of follow-up. Using a genetic risk score of 218 variants for estimated glomerular filtration rate (eGFR), we conducted Mendelian randomization analyses involving 413 718 participants (25 917 CHD and 8622 strokes) in EPIC-CVD, Million Veteran Program, and UK Biobank. Results: There were U-shaped observational associations of creatinine-based eGFR with CHD and stroke, with higher risk in participants with eGFR values 105 mL·min-1·1.73 m-2, compared with those with eGFR between 60 and 105 mL·min-1·1.73 m-2. Mendelian randomization analyses for CHD showed an association among participants with eGFR 105 mL·min-1·1.73 m-2. Results were not materially different after adjustment for factors associated with the eGFR genetic risk score, such as lipoprotein(a), triglycerides, hemoglobin A1c, and blood pressure. Mendelian randomization results for stroke were nonsignificant but broadly similar to those for CHD. Conclusions: In people without manifest cardiovascular disease or diabetes, mild-to-moderate kidney dysfunction is causally related to risk of CHD, highlighting the potential value of preventive approaches that preserve and modulate kidney function.Peer reviewe

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

Vascular function and cardiovascular risk factors in women with severe flushing

Background: Seventy per cent of postmenopausal women suffer from hot flushes causing significant morbidity in 25%. Oestrogen replacement provides symptom relief, but its use has declined following safety issues and there is, as yet, no good alternative. Pathophysiology is poorly understood, but one proposed mechanism is altered peripheral vascular reactivity. It has recently been suggested that the presence of flushing may be a marker of underlying cardiovascular risk. <p/>Aim: To measure (i) peripheral vascular reactivity in subcutaneous vessels (ii) routine and novel cardiovascular risk factors in postmenopausal women who flush, and compare results to a matched group of women who do not flush. <p/>Methods: Thirty-two postmenopausal women with at least 20 flushes/week and 14 nonflushing postmenopausal women were recruited. Cutaneous microvascular perfusion was measured using laser Doppler imaging, and endothelial function was assessed by iontophoresis (administration of vasoactive agents through the skin by an electric current) of acetylcholine [Ach] (endothelial-dependent) and sodium nitroprusside [SNP] (endothelial independent). Blood samples for risk factors were taken following vascular assessment. <p/>Results: Both study groups were well matched demographically. The response of the subcutaneous vessels was greater in women who flushed than those who did not, following administration of both the endothelium-dependent and independent vasodilators, (ACh, P ≤ 0·001, SNP, P = 0·001, 2-way anova). By contrast, levels of High Density Lipoprotein (HDL)-cholesterol and ApoA1 were significantly lower in the flushing women compared with the control women (P = 0·02 and 0·002, respectively), and levels of inter-cellular adhesion molecule-1 (ICAM-1) were higher (P = 0·03), findings robust to adjustment for confounders, suggesting an adverse cardiovascular risk profile. <p/>Conclusion: These results confirm a better vascular response in women but paradoxically, such women appear to have worse (not better) cardiovascular disease risk factors in particular lower HDL-cholesterol but also higher non-HDL-c to HDL-c ratio and increased ICAM-1. Further studies are needed to assess vascular risk factors in women who flush

Rosuvastatin for primary prevention in patients with European systematic coronary risk evaluation risk ≥5% or Framingham risk >20%: post hoc analyses of the JUPITER trial requested by European health authorities

Aims: On the basis of the JUPITER trial, European health authorities recently approved the use of rosuvastatin to reduce first major cardiovascular events among ‘high' global risk primary prevention patients defined either by Framingham risk score >20% or European systematic coronary risk evaluation (SCORE) ≥5%. However, as these are post hoc analyses, data describing these subgroups have not previously been available to the clinical community. Methods and results: We randomized 17 802 apparently healthy men aged ≥50 and women ≥60 with low-density lipoprotein cholesterol (LDL-C) 20% or SCORE risk ≥5%. During 1.8-year median follow-up (maximum 5 years) of patients with Framingham risk >20%, the rate of myocardial infarction/stroke/cardiovascular death was 9.4 and 18.2 per 1000 person-years in rosuvastatin and placebo-allocated patients, respectively [hazard ratio (HR): 0.50, 95% confidence interval (CI): 0.27–0.93, P = 0.028]. Among patients with SCORE risk ≥5%, the corresponding rates were 6.9 and 12.0 using a model extrapolating risk for age ≥65 years (HR: 0.57, 95% CI: 0.43–0.78, P = 0.0003) and rates were 5.9 and 12.7 when risk for age was capped at 65 years (HR: 0.47, 95% CI: 0.32–0.68, P 20% or SCORE risk ≥5%), but LDL-C levels not requiring pharmacologic treatment, rosuvastatin 20 mg significantly reduced major cardiovascular events. ClinicalTrial.gov Identifier: NCT0023968

Marginal role for 53 common genetic variants in cardiovascular disease prediction.

OBJECTIVE: We investigated discrimination and calibration of cardiovascular disease (CVD) risk scores when genotypic was added to phenotypic information. The potential of genetic information for those at intermediate risk by a phenotype-based risk score was assessed. METHODS: Data were from seven prospective studies including 11 851 individuals initially free of CVD or diabetes, with 1444 incident CVD events over 10 years' follow-up. We calculated a score from 53 CVD-related single nucleotide polymorphisms and an established CVD risk equation 'QRISK-2' comprising phenotypic measures. The area under the receiver operating characteristic curve (AUROC), detection rate for given false-positive rate (FPR) and net reclassification improvement (NRI) index were estimated for gene scores alone and in addition to the QRISK-2 CVD risk score. We also evaluated use of genetic information only for those at intermediate risk according to QRISK-2. RESULTS: The AUROC was 0.635 for QRISK-2 alone and 0.623 with addition of the gene score. The detection rate for 5% FPR improved from 11.9% to 12.0% when the gene score was added. For a 10-year CVD risk cut-off point of 10%, the NRI was 0.25% when the gene score was added to QRISK-2. Applying the genetic risk score only to those with QRISK-2 risk of 10%-<20% and prescribing statins where risk exceeded 20% suggested that genetic information could prevent one additional event for every 462 people screened. CONCLUSION: The gene score produced minimal incremental population-wide utility over phenotypic risk prediction of CVD. Tailored prediction using genetic information for those at intermediate risk may have clinical utility

The age-specific quantitative effects of metabolic risk factors on cardiovascular diseases and diabetes: a pooled analysis.

BACKGROUND: The effects of systolic blood pressure (SBP), serum total cholesterol (TC), fasting plasma glucose (FPG), and body mass index (BMI) on the risk of cardiovascular diseases (CVD) have been established in epidemiological studies, but consistent estimates of effect sizes by age and sex are not available. METHODS: We reviewed large cohort pooling projects, evaluating effects of baseline or usual exposure to metabolic risks on ischemic heart disease (IHD), hypertensive heart disease (HHD), stroke, diabetes, and, as relevant selected other CVDs, after adjusting for important confounders. We pooled all data to estimate relative risks (RRs) for each risk factor and examined effect modification by age or other factors, using random effects models. RESULTS: Across all risk factors, an average of 123 cohorts provided data on 1.4 million individuals and 52,000 CVD events. Each metabolic risk factor was robustly related to CVD. At the baseline age of 55-64 years, the RR for 10 mmHg higher SBP was largest for HHD (2.16; 95% CI 2.09-2.24), followed by effects on both stroke subtypes (1.66; 1.39-1.98 for hemorrhagic stroke and 1.63; 1.57-1.69 for ischemic stroke). In the same age group, RRs for 1 mmol/L higher TC were 1.44 (1.29-1.61) for IHD and 1.20 (1.15-1.25) for ischemic stroke. The RRs for 5 kg/m(2) higher BMI for ages 55-64 ranged from 2.32 (2.04-2.63) for diabetes, to 1.44 (1.40-1.48) for IHD. For 1 mmol/L higher FPG, RRs in this age group were 1.18 (1.08-1.29) for IHD and 1.14 (1.01-1.29) for total stroke. For all risk factors, proportional effects declined with age, were generally consistent by sex, and differed by region in only a few age groups for certain risk factor-disease pairs. CONCLUSION: Our results provide robust, comparable and precise estimates of the effects of major metabolic risk factors on CVD and diabetes by age group

The age-specific quantitative effects of metabolic risk factors on cardiovascular diseases and diabetes: A pooled analysis

BACKGROUND: The effects of systolic blood pressure (SBP), serum total cholesterol (TC), fasting plasma glucose (FPG), and body mass index (BMI) on the risk of cardiovascular diseases (CVD) have been established in epidemiological studies, but consistent estimates of effect sizes by age and sex are not available. METHODS: We reviewed large cohort pooling projects, evaluating effects of baseline or usual exposure to metabolic risks on ischemic heart disease (IHD), hypertensive heart disease (HHD), stroke, diabetes, and, as relevant selected other CVDs, after adjusting for important confounders. We pooled all data to estimate relative risks (RRs) for each risk factor and examined effect modification by age or other factors, using random effects models. RESULTS: Across all risk factors, an average of 123 cohorts provided data on 1.4 million individuals and 52,000 CVD events. Each metabolic risk factor was robustly related to CVD. At the baseline age of 55-64 years, the RR for 10 mmHg higher SBP was largest for HHD (2.16; 95% CI 2.09-2.24), followed by effects on both stroke subtypes (1.66; 1.39-1.98 for hemorrhagic stroke and 1.63; 1.57-1.69 for ischemic stroke). In the same age group, RRs for 1 mmol/L higher TC were 1.44 (1.29-1.61) for IHD and 1.20 (1.15-1.25) for ischemic stroke. The RRs for 5 kg/m(2) higher BMI for ages 55-64 ranged from 2.32 (2.04-2.63) for diabetes, to 1.44 (1.40-1.48) for IHD. For 1 mmol/L higher FPG, RRs in this age group were 1.18 (1.08-1.29) for IHD and 1.14 (1.01-1.29) for total stroke. For all risk factors, proportional effects declined with age, were generally consistent by sex, and differed by region in only a few age groups for certain risk factor-disease pairs. CONCLUSION: Our results provide robust, comparable and precise estimates of the effects of major metabolic risk factors on CVD and diabetes by age group.Peer reviewe