Improving Coronary Heart Disease Detection Using K-Means Clustering Techniques

The Framingham Heart Study has contributed importantly to understanding of the causes of coronary heart disease (CHD), stroke, and other cardiovascular diseases. Framingham research has helped define the quantitative and additive nature of these causes or, as they are now called, “cardiovascular risk factors.”1 The National Cholesterol Education Program (NCEP)2 3 has made extensive use of Framingham data in developing its strategy for preventing CHD by controlling high cholesterol levels. The NCEP guidelines2 3 adjust the intensity of cholesterol-lowering therapy with absolute risk as determined by summation of risk factors. The National High Blood Pressure Education Program (NHBPEP) has set forth a parallel approach for blood pressure control. In contrast to the NCEP,2 however, earlier NHBPEP reports issued through the Joint National Committee4 did not match the intensity of therapy to absolute risk for CHD. “Normalization” of blood pressure is the essential goal of therapy regardless of risk status. Blood pressure–lowering therapy is carried out as much for prevention of stroke and other cardiovascular complications as for reduction of CHD risk. Nonetheless, risk assessment could be important for making decisions about type and intensity of therapy for hypertension. Thus, the most recent Joint National Committee report5 gives more attention to risk stratification for adjustment of therapy for hypertension. Although Framingham data have already been influential in the development of national guidelines for risk factor management, the opportunity may exist for both cholesterol and blood pressure programs to draw more extensively from Framingham results when formulating improved risk assessment guidelines and recommending more specific strategies for risk factor modification. The American Heart Association has previously used Framingham risk factor data to prepare charts for estimating CHD risk. Framingham investigators of the National Heart, Lung, and Blood Institute prepared the original charts and have now revised …

Establishing new approaches for the prevention and treatment of stroke relies on identifying modifiable risk factors that contribute to the development of this complex disease. Mendelian randomization (MR) studies, analogous to naturally occurring randomized trials, can assess causality of potentially modifiable biomarkers and offer new insights into biological pathways. Stroke is the second leading cause of death worldwide and the chief determinant of long-term disability. Stroke is a heterogeneous disease arising from several distinct underlying pathologies and is typically classified as ischemic or hemorrhagic, and further subclassified using imaging data. Ischemic stroke (IS), including its 3 main subtypes: small vessel disease, large vessel disease, and cardioembolic stroke, accounts for ≈80% of stroke and is the result of an interrupted blood supply, leading to localized areas of ischemia in the brain. Small vessel disease may be a consequence of nonatherosclerotic, as well as atherosclerotic, mechanisms that result in an occlusion of the small perforating arteries, whereas large vessel disease results from occlusions or emboli from plaque rupture in larger vessels, such as a carotid artery. Cardioembolic stroke arises typically from emboli from the heart. By contrast, hemorrhagic stroke is a consequence of intracerebral hemorrhage (bleeding into the brain) or subarachnoid hemorrhage (bleeding into the subarachnoid space). These diverse stroke subtypes have distinct underlying pathologies reflecting different risk factor distributions. MR studies, using genetic variants as instrumental variables, afford a powerful approach to assessing causality of risk factors and avoid biases inherent in observational studies, including confounding and reverse causation. This review considers the contribution of MR studies to stroke epidemiology and their relevance to understanding risk factors and new therapeutic targets for stroke. Meta-analyses of large prospective studies have enhanced our knowledge of classical and emerging risk factors for stroke.1–4 Classical risk factors for stroke include nonmodifiable characteristics, …

Background: Coronary heart disease (CHD) is a leading cause of death for breast cancer survivors. While cardiotoxic breast cancer treatments are linked with cardiovascular injury and an increased risk of CHD, current risk prediction models for CHD do not consider the effect of cancer treatments and likely underestimate CHD and mortality risk in this population. Methods: We conducted support vector machine, decision tree, and logistic regression models on electronic health record data to evaluate the independent and joint effects of cardiovascular health (CVH) and cancer treatments on post-treatment CHD and all-cause mortality occurring within 10 years among 1934 women diagnosed with breast cancer in 2006 and 2007 at our medical center. CVH was classified as poor, intermediate, or ideal according to 5 factors: smoking, body mass index, blood pressure, glucose/hemoglobin A1c, and cholesterol. We considered anthracyclines and hormone therapies as cardiotoxic cancer treatments in these analyses. Results: Women with ideal CVH scores had a lower risk of CHD (23.7%) and death (14.3%) compared to those with poor CVH scores (61.9% for CHD, 32.5% for death). Women exposed to cancer treatments had a higher risk of CHD (58.9%) and death (37.5%) compared to women who were not exposed (29.1% for CHD, 15.4% for death). Meanwhile, the joint effects of poor CVH and receipt of cardiotoxic cancer treatments conferred a much higher risk of CHD (75.9%) and death (39.5%) compared to women with ideal CVH who did not receive treatments (20.8% for CHD, 11.2% for death). All approaches ( Figure 1 ) demonstrated the highest area under the curve (AUC) for models considering the joint effects of CVH and cancer treatments (range: 0.70-0.75), followed by the independent effects of CVH (range: 0.65-0.66) and the independent effects of cancer treatments (range: 0.59-0.63). Conclusions: Cancer treatment data should be considered along with traditional cardiovascular risk factor data in the prediction of CHD and mortality among breast cancer survivors.

BackgroundSubclinical alterations in left ventricular (LV) structure, diastolic function, and metabolic disturbances are associated with coronary heart disease (CHD) risk, but their relationships remained unclear. Large‐scale longitudinal metabolomic profiling of LV measures is lacking.MethodsUsing untargeted metabolomics, we quantified 563 fasting plasma metabolites from 1799 American Indian individuals attending 2 exams (~5.5 years apart). We examined associations between metabolites and measures of LV structure (LV mass index, relative wall thickness), and diastolic function (peak early filling velocity to peak late filling velocity, isovolumic relaxation time, and deceleration time) using generalized estimating equation model. Findings were then replicated in an independent biracial cohort. Frailty Cox proportional hazards models were used to examine whether LV‐related metabolites are associated with the risk of CHD over a 20‐year follow‐up. Pathway enrichment analysis was performed to identify relevant metabolic pathways.ResultsWe identified 173 metabolites (47 named; q<0.05) associated with LV structure or diastolic function in the SHFS (Strong Heart Family Study), and some metabolites were confirmed in the biracial cohort. Three metabolites were additionally associated with incident CHD. Aspartic acid and palmitoleic acid were associated with lower LV mass index and peak early filling velocity to peak late filling velocity ratio and lower CHD risk (hazard ratios [HRs], 0.75 [95% CI, 0.56–0.99] to 0.81 [95% CI, 0.67–0.99]), whereas isothreonic acid was associated with higher relative wall thickness and higher CHD risk (HR, 1.15 [95% CI, 1.01–1.32]). LV‐related metabolites were enriched in arginine biosynthesis, alanine–aspartate–glutamate metabolism, and starch and sucrose metabolism.ConclusionsWe identified metabolomic markers of LV structure and diastolic function, several of which that were independently associated with CHD risk, providing insight into metabolic pathways underlying LV subclinical changes and CHD.

Introduction: Fat infiltration into muscle reduces muscle density and increases the risk for metabolic dysfunction, mobility disability and mortality. We assessed abdominal muscle density and muscle area in relation to incident coronary heart disease (CHD) events. Methods: Adults (mean age 64.6 ± 9.6) from the Multi-Ethnic Study of Atherosclerosis body composition ancillary study had computed tomography (CT)-derived measures of muscle density, muscle area and visceral fat at the L2-L4 spinal column levels between 2002-05 and were followed for incident events. Using sex-stratified Cox proportional hazards regression, associations of muscle density and muscle area (in the same model) with CHD events were assessed using restricted cubic splines with adjustment for confounders including visceral adiposity and BMI. Results: After 10.3±2.9 years of follow up, 924 males had 70 CHD (7.6%) events and 945 females had 44 CHD (4.7%) events. Muscle density was higher in males (44.5±4.9 HU) than females (40.1±5.2 HU), as was muscle area (116.7±23.9 vs. 80.4±23.9 cm 2 , respectively). Among males, and in mutually adjusted models, each incremental SD of muscle density was inversely associated with CHD (HR=0.67; 95%CI=0.49-0.92), while each SD of muscle area was associated with higher risk of CHD (HR=1.75; 95%CI=1.34-2.28; Figure). Overall patterns were similar but less strong among females for density (HR=0.79; 95%CI=0.50-1.22) and area (HR=1.17; 95%CI=0.79-1.73). Conclusions: Greater muscle density is associated with lower CHD risk, while greater muscle size is associated with increased CHD risk. These associations were independent of traditional CHD risk factors and measures of visceral and total body adiposity. Quantity and quality of skeletal muscle may represent distinct aspects of aging and physical functioning that are important in heart health.

Coronary Heart Disease Risk in Blood or Marrow Transplant Survivors

BackgroundWe investigated the association between income-education groups and incident coronary heart disease (CHD) in a national prospective cohort study.MethodsThe REasons for Geographic And Racial Differences in Stroke study recruited 30,239 black and white community-dwelling adults between 2003 and 2007 and collected participant-reported and in-home physiologic variables at baseline, with expert adjudicated CHD endpoints during follow-up. Mutually exclusive income-education groups were: low income (annual household income <$35,000)/low education (< high school), low income/high education, high income/low education, and high income/high education. Cox models estimated hazard ratios (HR) for incident CHD for each exposure group, examining differences by age group.ResultsAt baseline, 24,461 participants free of CHD experienced 809 incident CHD events through December 31, 2011 (median follow-up 6.0 years; interquartile range 4.5–7.3 years). Those with low income/low education had the highest incidence of CHD (10.1 [95 % CI 8.4–12.1]/1000 person-years). After full adjustment, those with low income/low education had higher risk of incident CHD (HR 1.42 [95 % CI: 1.14–1.76]) than those with high income/high education, but findings varied by age. Among those aged <65 years, compared with those reporting high income/high education, risk of incident CHD was significantly higher for those reporting low income/low education and low income/high education (adjusted HR 2.07 [95 % CI 1.42–3.01] and 1.69 [95 % CI 1.30–2.20], respectively). Those aged ≥65 years, risk of incident CHD was similar across income-education groups after full adjustment.ConclusionFor younger individuals, low income, regardless of education, was associated with higher risk of CHD, but not observed for ≥65 years. Findings suggest that for younger participants, education attainment may not overcome the disadvantage conferred by low income in terms of CHD risk, whereas among those ≥65 years, the independent effects of income and education are less pronounced.

Among adults, skipping meals is associated with excess body weight, hypertension, insulin resistance, and elevated fasting lipid concentrations. However, it remains unknown whether specific eating habits regardless of dietary composition influence coronary heart disease (CHD) risk. The objective of this study was to prospectively examine eating habits and risk of CHD. Eating habits, including breakfast eating, were assessed in 1992 in 26 902 American men 45 to 82 years of age from the Health Professionals Follow-up Study who were free of cardiovascular disease and cancer. During 16 years of follow-up, 1527 incident CHD cases were diagnosed. Cox proportional hazards models were used to estimate relative risks and 95% confidence intervals for CHD, adjusted for demographic, diet, lifestyle, and other CHD risk factors. Men who skipped breakfast had a 27% higher risk of CHD compared with men who did not (relative risk, 1.27; 95% confidence interval, 1.06-1.53). Compared with men who did not eat late at night, those who ate late at night had a 55% higher CHD risk (relative risk, 1.55; 95% confidence interval, 1.05-2.29). These associations were mediated by body mass index, hypertension, hypercholesterolemia, and diabetes mellitus. No association was observed between eating frequency (times per day) and risk of CHD. Eating breakfast was associated with significantly lower CHD risk in this cohort of male health professionals.

Coronary heart disease (CHD) and stroke are common complications of type 2 diabetes mellitus (T2DM). We aimed to explore the differences in the risks of CHD and stroke between Chinese women and men with T2DM and their association with metabolic syndrome (MS). This study included 1514 patients with T2DM. The Asian Guidelines of ATPIII (2005) were used for MS diagnosis, and the UKPDS risk engine was used to evaluate the 10-year CHD and stroke risks. Women had lower CHD risk (15.3% versus 26.3%), fatal CHD risk (11.8% versus 19.0%), stroke risk (8.4% versus 10.3%), and fatal stroke risk (1.4% versus 1.6%) compared with men with T2DM (p < 0.05–0.001). The CHD risk (28.4% versus 22.6%, p < 0.001) was significantly higher in men with MS than in those without MS. The CHD (16.2% versus 11.0%, p < 0.001) and stroke risks (8.9% versus 5.8%, p < 0.001) were higher in women with MS than in those without MS. In conclusion, our findings indicated that Chinese women with T2DM are less susceptible to CHD and stroke than men. Further, MS increases the risk of both these events, highlighting the need for comprehensive metabolic control in T2DM.

Although 36% of US men are normal weight (BMI <25 kg/m(2)), the health benefits of greater leanness in normal-weight individuals are seldom acknowledged. To assess the optimal body weight with respect to minimizing coronary heart disease (CHD) risk, we applied Cox proportional hazard analyses of 20,525 nonsmoking, nondiabetic, normal-weight men followed prospectively for 7.7 years, including 20,301 who provided follow-up questionnaires. Two-hundred and forty two men reported coronary artery bypass graph (CABG) or percutaneous transluminal coronary angioplasty (PTCA) and 82 reported physician-diagnosed incident myocardial infarction (267 total). The National Death Index identified 40 additional ischemic heart disease deaths. In these normal-weight men, each kg/m(2) decrement in baseline BMI was associated with 11.2% lower risk for total CHD (P = 0.005), 13.2% lower risk for nonfatal CHD (P = 0.002), 19.0% lower risk for nonfatal myocardial infarction (P = 0.01), and 12.2% lower risk for PTCA or CABG (P = 0.007). Compared to men with BMI between 22.5 and 25 kg/m(2), those <22.5 kg/m(2) had 24.1% lower total CHD risk (P = 0.01), 27.9% lower nonfatal CHD risk (P = 0.01), 37.8% lower nonfatal myocardial infarction risk (P = 0.05), and 27.8% lower PTCA or CABG risk (P = 0.02). In nonabdominally obese men (waist circumference <102 cm), CHD risk declined linearly with declining waist circumference. CHD risk was unrelated to change in waist circumference between 18 years old and baseline except as it contributed to baseline circumference. These results suggest that the optimal BMI for minimizing CHD risk lies somewhere <22.5 kg/m(2), as suggested from our previous analyses of incident diabetes, hypertension, and hypercholesterolemia in these men.

We examined the association between soy isoflavone intake and risk of cardiovascular disease (CVD) outcomes in adults. We searched the online databases for relevant studies published up to September 2021. In total, 13 publications were included in the systematic review and 12 in the meta-analysis. We found that a high intake of soy isoflavones was significantly associated with a lower risk of coronary heart disease (CHD) among whole populations (Pooled RR: 0.92, 95% CI: 0.85–0.99, I2 = 41.0%, Pheterogeneity = 0.10) and a lower risk of overall CVD (Pooled RR: 0.91, 95% CI: 0.84–0.98, I2 = 30.7%, Pheterogeneity = 0.19) and CHD (Pooled RR: 0.89, 95% CI: 0.83–0.96, I2 = 14.4%, Pheterogeneity = 0.32) among Western population. In the linear dose-response analysis, a 3 mg/day increase in soy isoflavone intake was associated with 16% and 14% lower risks of overall CVD and CHD, respectively, among Western population. In conclusion, we found that soy isoflavone intake was associated with a lower risk of overall CVD and CHD in adults, particularly among Western population.

Observational studies of hormone therapy (HT) in postmenopausal women have found considerably lower rates of coronary heart disease (CHD) in those taking either estrogen alone or a combination of estrogen and progestin. Nevertheless, data from the Women's Health Initiative suggest a possible increase in risk of CHD for women taking estrogen plus progestin compared with those receiving placebo. Estrogen alone reportedly had no effect. The present investigators prospectively studied CHD in relation to HT in postmenopausal women participating in the Nurses' Health Study and followed from 1976 to 2000. Information was obtained on 121,700 female nurses ranging in age from 30 to 55 years. Proportional hazards models were used to determine the multivariable adjusted relative risk (RR). The age-adjusted RR of major CHD (coronary death and nonfatal myocardial infarction) in women taking estrogen alone was 0.57 for current users compared with women who never had HT. The RR for combined HT was 0.49. After adjusting for a wide range of possible confounding factors, the respective RR values were 0.71 and 0.68. In both instances, the higher RR figures resulted from controlling for major CHD risk factors. HT correlated strongly and inversely with CHD when women began therapy near the time of menopause. The risk of CHD was somewhat higher when treatment-either estrogen alone or combined HT-began 10 years or longer after menopause. Estrogen alone was not related to the risk of CHD in women starting HT at age 60 years or older. There was some indication of a reduced risk for women taking combined HT. No substantial differences in the relation of HT to CHD risk were apparent across strata of either major CHD risk factors or the use of cholesterol-lowering drugs. Sensitivity analyses failed to show that a reduced risk of CHD for current users of HT was a result of incomplete capture of coronary events occurring shortly after the start of HT. Postmenopausal women in this large study exhibited an approximately 30% lower risk of CHD when taking estrogen alone or combined with progestin. Coronary risk may depend in part on when HT begins in relation to the onset of menopause or age. Current data do not permit a general recommendation for long-term HT with the goal of preventing chronic disease.

We are pleased that our paper on job strain and coronary heart disease (CHD) risk prediction1 has attracted the interest of IJE readers. Given that stress has been the subject of observational research for several decades,2 surprisingly few attempts have been made to explore the predictive value of stress in clinical practice. The purpose of our paper was to address this gap in knowledge. We examined whether information on work stress would improve identification of patients at high risk of developing CHD if we already knew their standard risk factors, such as raised blood pressure, lipid levels, smoking habits and the presence of diabetes. Determining a patient's risk of CHD is important as this helps a physician to decide whether there is a need to recommend lifestyle change or prescribe medication (e.g. statins). Our findings for job strain,3 the most commonly used operationalization of work stress, suggest this additional information is unlikely to enhance the predictive capacity of Framingham risk prediction algorithm (a summary of conventional risk factor levels). In his letter to the Editor, Bozorgmanesh raises important points regarding risk prediction, both in general and specific to our paper. He notes that choice of the reference method that represents current clinical practice is critical because if the best available method is not selected, the value of the new measure (here work stress) may be overestimated. The Framingham risk algorithm is a widely used tool to assess a patient's 10-year risk of CHD based on standard coronary risk factors—this was the model used in our paper. Bozorgmanesh noted that we did not specify whether it was the most recent revision4 or an earlier, less well-calibrated, version.5 We are grateful for the opportunity to clarify that we used the most recent algorithm,4 thus any concern that we used a comparator model that would have resulted in an overestimation of the value of adding job strain is unfounded. Traditional C-statistics have been found to be insensitive for detecting improvement in risk prediction;6 Net Reclassification Index (NRI) analysis is seen as a more informative alternative.7 Bozorgmanesh correctly notes that there has recently been some further developments in the NRI, but to date most clinical studies still apply the standard NRI.7 This was also the case in our paper, mainly to enable comparison with other studies that have tested new risk markers to improve CHD prediction.8,9 In this analysis, we divided the predicted risk level into four categories: <4, 4–5.9, 6–9.9 and ≥10%. An alternative approach would have been to use cut-points that are of more direct relevance to clinical decisions on risk prevention, that is, very low (risk <5%), low (5–9.9%), intermediate (10–19.9%) and high (≥20%);10 the latter indicative of a need to consider pharmacotherapy. Unfortunately, the small number of participants in the highest category in our study prevented us from doing so. This brings us to the final point made by Bozorgmanesh: the use of a 2-step analysis strategy. That is, using conventional risk prediction models first and, instead of then examining the entire population, testing whether the new measure (here job strain) improves risk stratification within the intermediate category only (people with an estimated risk of 10–20%). This group is particularly problematic for clinicians because, while some group members are truly at low risk of CHD (no interventions needed), others are at high risk and would thus benefit from medication, such as statin therapy or behaviour modification. Further research is needed to examine whether information on work stress could be useful to improve risk stratification among those who are, based on conventional risk models, at intermediate risk. Following our null findings, one might ask whether further research on this issue is likely to be worthwhile. We believe that our findings on job strain are not the final word in this field of research. Indeed, we have subsequently tested other life stress markers and found, among working populations, inclusion of information on long working hours to improve CHD risk prediction. Employees who work very long days are at increased risk of CHD and the net reclassification improvement on inclusion of this information is ~5%.11 While this finding needs to be replicated in other cohorts before firm conclusions can be drawn, it indicates a possible role for stress markers in improved CHD risk prediction in clinical practice.

BackgroundHispanics are a heterogeneous population with differences in the prevalence of cardiovascular disease (CVD) and its related risk factors among ethnic sub-groups. This study evaluated the association of genetic admixture and CVD in self-identified Hispanic women from the Women's Health Initiative (WHI). MethodsData came from the WHI Observational Study and the Clinical Trial Components conducted among postmenopausal women. The CVD outcomes included coronary heart disease (CHD) and stroke. The proportions of European (EUR), sub-Saharan African (AFR), and Amerindian (AMI) admixture were estimated using 92 ancestry-informative markers. Cox regression models were used to assess the relationship between genetic admixture and CVD adjusting for age, lifestyle risk factors, known risk factors, and neighborhood socioeconomic status. ResultsAmong 5195 participants EUR ancestry was associated with a lower CHD risk after adjusting for age (HR 0.41, p = 0.02), and in the fully adjusted model (HR 0.40, p = 0.03). AFR ancestry was associated with a higher CHD risk after adjusting for age (HR 2.91, p = 0.03), but it only showed a trend in in the fully adjusted model (HR 2.46, p = 0.10). AMI ancestry was not statistically significantly associated with CHD and none of the genetic admixture proportions were statistically significantly associated with stroke (p > 0.05). ConclusionEUR ancestry was associated with a lower risk of CHD in Hispanic women. This highlights the need to account for genetic admixture in future CVD studies to consider different heritage groups to understand the role that genetic, neighborhood socioeconomic status, and environmental factors contribute to CVD health disparities in Hispanic women.

Introduction Health promotion (HP) aims to enhance good health while preventing ill-health at three levels of activity; primary (preventative), secondary (diagnostic) and tertiary (management).1 It can range from simple provision of health education to ongoing support, but the effectiveness of HP is ultimately dependent on its ability to influence change. HP as part of the Community Pharmacy Contract (CPC) aims to increase public knowledge and target ‘hard-to-reach’ individuals by focusing mainly on primary and tertiary HP. The CPC does not include screening programmes (secondary HP) as a service. Coronary heart disease (CHD) is a significant cause of morbidity and mortality in the UK. While there is evidence to support the effectiveness of some community pharmacy HP strategies in CHD, there is paucity of research in relation to screening services.2 Against this background, Alliance Pharmacy introduced a free CHD risk screening programme to provide tailored HP advice as part of a participant–pharmacist consultation. The aim of this study is to report on the CHD risk levels of participants and to provide a qualitative indication of consultation outcomes. Methods Case records for 12 733 people who accessed a free CHD risk screening service between August 2004 and April 2006 offered at 217 community pharmacies were obtained. The service involved initial self-completion of the Healthy Heart Assessment (HHA) form and measurement of height, weight, body mass index, blood pressure, total cholesterol and highdensity lipoprotein levels by pharmacists to calculate CHD risk.3 Action taken by pharmacists (lifestyle advice, statin recommendation or general practitioner (GP) referral) and qualitative statements of advice were recorded, and a copy provided to the participants. The service did not include follow-up of participants. All participants consented to taking part in evaluations of the service. Ethical committee scrutiny was not required for this service development evaluation. Results Case records for 10 035 participants (3658 male) were evaluable; 5730 (57%) were at low CHD risk (<15%); 3636 (36%) at moderate-to-high CHD risk (≥15%); and 669 (7%) had existing heart disease. A significantly higher proportion of male (48% versus 30% female) participants were at moderate- to-high risk of CHD (chi-square test; P < 0.005). A range of outcomes resulted from consultations. Lifestyle advice was provided irrespective of participants’ CHD risk or existing disease. In the moderate-to-high-risk group, of which 52% received prescribed medication, lifestyle advice was recorded for 62%, 16% were referred and 34% were advised to have a re-assessment. Statin recommendations were made in 1% of all cases. There was evidence of supportive and motivational statements in the advice recorded. Discussion Pharmacists were able to identify individuals’ level of CHD risk and provide them with bespoke advice. Identification of at-risk participants did not automatically result in referrals or statin recommendation. One-third of those accessing the screening service had moderate-to-high risk of CHD, a significantly higher proportion of whom were men. It is not known whether these individuals had been previously exposed to HP but presumably by accessing this service they may have contemplated change. As effectiveness of HP advice will depend among other factors on ability to influence change, future consultations may need to explore patients’ attitude towards change in relation to the Trans Theoretical Model4 to better tailor HP advice. The high uptake of the service by those at moderate-to-high CHD risk indicates a need for this type of screening programme in community pharmacy, perhaps specifically to reach men who access medical services less.