Treatment drop-in in a contemporary cohort used to derive cardiovascular risk prediction equations.

BACKGROUND: No routinely recommended cardiovascular disease (CVD) risk prediction equations have adjusted for CVD preventive medications initiated during follow-up (treatment drop-in) in their derivation cohorts. This will lead to underestimation of risk when equations are applied in clinical practice if treatment drop-in is common. We aimed to quantify the treatment drop-in in a large contemporary national cohort to determine whether equations are likely to require adjustment. METHODS: Eight de-identified individual-level national health administrative datasets in Aotearoa New Zealand were linked to establish a cohort of almost all New Zealanders without CVD and aged 30-74 years in 2006. Individuals dispensing blood-pressure-lowering and/or lipid-lowering medications between 1 July 2006 and 31 December 2006 (baseline dispensing), and in each 6-month period during 12 years' follow-up to 31 December 2018 (follow-up dispensing), were identified. Person-years of treatment drop-in were determined. RESULTS: A total of 1 399 348 (80%) out of the 1 746 695 individuals in the cohort were not dispensed CVD medications at baseline. Blood-pressure-lowering and/or lipid-lowering treatment drop-in accounted for 14% of follow-up time in the group untreated at baseline and increased significantly with increasing predicted baseline 5-year CVD risk (12%, 31%, 34% and 37% in <5%, 5-9%, 10-14% and ≥15% risk groups, respectively) and with increasing age (8% in 30-44 year-olds to 30% in 60-74 year-olds). CONCLUSIONS: CVD preventive treatment drop-in accounted for approximately one-third of follow-up time among participants typically eligible for preventive treatment (≥5% 5-year predicted risk). Equations derived from cohorts with long-term follow-up that do not adjust for treatment drop-in effect will underestimate CVD risk in higher risk individuals and lead to undertreatment. Future CVD risk prediction studies need to address this potential flaw.

Risk of cardiovascular disease in cancer survivors: A cohort study of 446,384 New Zealand primary care patients.

BACKGROUND: Given advances in the management of cancer, it is increasingly important for clinicians to appropriately manage the risk of cardiovascular disease (CVD) among cancer survivors. It is unclear whether CVD risk is increased among cancer survivors overall, and there is inconsistency in evidence to date about CVD incidence and mortality by cancer type. METHODS: Patients aged 30-74 years entered an open cohort study at the time of first CVD risk assessment, between 2004 and 2018, in primary care in New Zealand. Patients with established CVD or cancer within 2 years prior to study entry were excluded. Cancer diagnosis (1995-2016) was determined from a national cancer registry. Cause-specific hazard models were used to examine the association between history of cancer and two outcomes: (1) CVD-related hospitalization and/or death and (2) CVD death. RESULTS: The study included 446,384 patients, of whom 14,263 (3.2%) were cancer survivors. Risk of CVD hospitalization and/or death was increased among cancer survivors compared with patients without cancer at cohort entry (multivariable-adjusted hazard ratio, mHR, 1.11, 95% CI 1.05-1.18), more so for CVD death (1.31, 1.14-1.52). Risk of CVD hospitalization and/or death was increased in patients with myeloma (2.66, 1.60-4.42), lung cancer (2.19, 1.48-3.24) and non-Hodgkin lymphoma (1.90, 1.42-2.54), but not for some cancers (e.g., colorectal, 0.87, 0.71-1.06). Risk of CVD death was increased in several cancer types including melanoma (1.73, 1.25-2.38) and breast cancer (1.56, 1.16-2.11). CONCLUSION: CVD risk management needs to be prioritized among cancer survivors overall, and particularly in those with myeloma, lung cancer and non-Hodgkin lymphoma given consistent evidence of increased risk.

Performance of cardiovascular disease risk prediction equations in more than 14 000 survivors of cancer in New Zealand primary care: a validation study.

BACKGROUND: People with cancer have an increased risk of cardiovascular disease. Risk prediction equations developed in New Zealand accurately predict 5-year cardiovascular disease risk in a general primary care population in the country. We assessed the performance of these equations for survivors of cancer in New Zealand. METHODS: For this validation study, patients aged 30-74 years from the PREDICT open cohort study, which was used to develop the New Zealand cardiovascular disease risk prediction equations, were included in the analysis if they had a primary diagnosis of invasive cancer at least 2 years before the date of the first cardiovascular disease risk assessment. The risk prediction equations are sex-specific and include the following predictors: age, ethnicity, socioeconomic deprivation index, family history of cardiovascular disease, smoking status, history of atrial fibrillation and diabetes, systolic blood pressure, total cholesterol to HDL cholesterol ratio, and preventive pharmacotherapy (blood-pressure-lowering, lipid-lowering, and antithrombotic drugs). Calibration was assessed by comparing the mean predicted 5-year cardiovascular disease risk, estimated using the risk prediction equations, with the observed risk across deciles of risk, for men and women, and according to the three clinical 5-year cardiovascular disease risk groups in New Zealand guidelines (<5%, 5% to <15%, and ≥15%). Discrimination was assessed by Harrell's C statistic. FINDINGS: 14 263 patients were included in the study. The mean age was 61 years (SD 9) for men and 60 years (SD 8) for women, with a median follow-up of 5·8 years for men and 5·7 years for women. The observed cardiovascular disease risk was underpredicted by a maximum of 2·5% in male and 3·2% in female decile groups. When patients were grouped according to clinical risk groups, observed cardiovascular disease risk was underpredicted by less than 2% in the lower risk groups and overpredicted by 2·2% for men and 3·3% for women in the highest risk group. Harrell's C statistics were 0·67 (SE 0·01) for men and 0·73 (0·01) for women. INTERPRETATION: The New Zealand cardiovascular disease risk prediction equations reasonably predicted the observed 5-year cardiovascular disease risk in survivors of cancer in the country, in whom risk prediction was considered clinically appropriate. Prediction could be improved by adding cancer-specific variables and considering competing risks. Our findings suggest that the equations are reasonable clinical tools for use in survivors of cancer in New Zealand. FUNDING: Auckland Medical Research Foundation, Health Research Council of New Zealand.

Effects of Optimism on Work Satisfaction Among Nurses: A Mediation Model Through Work-Family Conflict.

Background: Nurses are suffering from various stressors which adversely impact their work satisfaction and mental health. Research is scarce on optimism, one of the positive psychological resource which may reduce work-family conflict and improve work satisfaction. Objectives: This study aims to assess work satisfaction among Chinese nurses and to observe and illustrate the relationships among optimism, work-family conflict, and work satisfaction. Methods: This study was designed as a cross-sectional study with stratified sampling. From September 2019 to December 2020, a self-administered WeChat questionnaire was collected from 768 nurses online in China to evaluate the nurses' work satisfaction, optimism, and work-family conflict. Spearman correlation and hierarchical multiple regression analysis were applied to examine associated factors of work satisfaction. A structural equation model was employed to test the mediating effect of work-family conflict in the relationship between optimism and work satisfaction. Results: Optimism were observed to have a positive correlation with work satisfaction while the correlation between work-family conflict and work satisfaction was observed to be negative. Optimism and work-family conflict explained 4.8 and 9.2% of the incremental variances of work satisfaction, respectively. Work-family conflict served as a mediator in the relationship between optimism and work satisfaction. Conclusions: Nurses in China experienced high levels of work satisfaction. Optimism could increase the chance of higher work satisfaction while work-family conflict increased the risk of lower work satisfaction. Psychological interventions and improvement of working conditions are essential to relieve work-family conflicts and enhance work satisfaction.

Rationale, design and population description of the CREDENCE study: cardiovascular risk equations for diabetes patients from New Zealand and Chinese electronic health records.

The cardiovascular risk equations for diabetes patients from New Zealand and Chinese electronic health records (CREDENCE) study is a unique prospectively designed investigation of cardiovascular risk in two large contemporary cohorts of people with type 2 diabetes from New Zealand (NZ) and China. The study was designed to derive equivalent cardiovascular risk prediction equations in a developed and a developing country, using the same epidemiological and statistical methodology. Two similar cohorts of people with type 2 diabetes were identified from large general population studies in China and New Zealand, which had been generated from longitudinal electronic health record systems. The CREDENCE study aims to determine whether cardiovascular risk prediction equations derived in patients with type 2 diabetes in a developed country are applicable in a developing country, and vice versa, by deriving and validating equivalent diabetes-specific cardiovascular risk prediction models from the two countries. Baseline data in CREDENCE was collected from October 2004 in New Zealand and from January 2010 in China. In the first stage of CREDENCE, a total of 93,207 patients (46,649 from NZ and 46,558 from China) were followed until December 31st 2018. Median follow-up was 7.0 years (New Zealand) and 5.7 years (China). There were 5926 (7.7% fatal) CVD events in the New Zealand cohort and 3650 (8.8% fatal) in the Chinese cohort. The research results have implications for policy makers, clinicians and the public and will facilitate personalised management of cardiovascular risk in people with type 2 diabetes worldwide.

Cardiovascular risk prediction in type 2 diabetes before and after widespread screening: a derivation and validation study.

BACKGROUND: Until recently, most patients with diabetes worldwide have been diagnosed when symptomatic and have high cardiovascular risk, meaning most should be prescribed cardiovascular preventive medications. However, in New Zealand, a world-first national programme led to approximately 90% of eligible adults being screened for diabetes by 2016, up from 50% in 2012, identifying many asymptomatic patients with recent-onset diabetes. We hypothesised that cardiovascular risk prediction equations derived before widespread screening would now significantly overestimate risk in screen-detected patients. METHODS: New Zealanders aged 30-74 years with type 2 diabetes and without known cardiovascular disease, heart failure, or substantial renal impairment were identified from the 400 000-person PREDICT primary care cohort study between Oct 27, 2004, and Dec 30, 2016, covering the period before and after widespread screening. Sex-specific equations estimating 5-year risk of cardiovascular disease were developed using Cox regression models, with 18 prespecified predictors, including diabetes-related and renal function measures. Equation performance was compared with an equivalent equation derived in the New Zealand Diabetes Cohort Study (NZDCS), which recruited between 2000 and 2006, before widespread screening. FINDINGS: 46 652 participants were included in the PREDICT-1° Diabetes subcohort, of whom 4114 experienced first cardiovascular events during follow-up (median 5·2 years, IQR 3·3-7·4). 14 829 (31·8%) were not taking oral hypoglycaemic medications or insulin at baseline. Median 5-year cardiovascular risk estimated by the new equations was 4·0% (IQR 2·3-6·8) in women and 7·1% (4·5-11·2) in men. The older NZDCS equation overestimated median cardiovascular risk by three times in women (median 14·2% [9·7-20·0]) and two times in men (17·1% [4·5-20·0]). Model and discrimination performance measures for PREDICT-1° Diabetse equations were also significantly better than for the NZDCS equation (eg, for women: R2=32% [95% CI 29-34], Harrell's C=0·73 [0·72-0·74], Royston's D=1·410 [1·330-1·490] vs R2=24% [21-26], C=0·69 [0·67-0·70], and D=1·147 [1·107-1·187]). INTERPRETATION: International treatment guidelines still consider most people with diabetes to be at high cardiovascular risk; however, we show that recent widespread diabetes screening has radically changed the cardiovascular risk profile of people with diabetes in New Zealand. Many of these patients have normal renal function, are not dispensed glucose-lowering medications, and have low cardiovascular risk. These findings have clear international implications as increased diabetes screening is inevitable due to increasing obesity, simpler screening tests, and the introduction of new-generation glucose-lowering medications that prevent cardiovascular events. Cardiovascular risk prediction equations derived from contemporary diabetes populations, with multiple diabetes-related and renal function predictors, will be required to better differentiate between low-risk and high-risk patients in this increasingly heterogeneous population and to inform appropriate non-pharmacological management and cost-effective targeting of expensive new medications. FUNDING: Health Research Council of New Zealand, Heart Foundation of New Zealand, and Healthier Lives National Science Challenge.

Carotid Pulse Wave Analysis: Future Direction of Hemodynamic and Cardiovascular Risk Assessment.

Evaluation of the hemodynamic function of the cardiovascular system via measurement of the mechanical properties of the large arteries may provide a substantial improvement over present techniques. Practitioners are familiar with the problem of low reproducibility of conventional sphygmomanometry, which exhibits reasonable accuracy but low precision owing to its marked variability over time and in different circumstances (e.g., the white coat effect). Arterial wall stiffness is a consequence of atherosclerosis developing over time; thus, it has little short-term variability and is thus preferable to be used as a prognostic marker. In particular, arterial stiffness can be evaluated at the carotid artery using noninvasive approaches based on wearable sensor technologies for pulse wave analysis. These enable the assessment of central pressures and pulse waveform parameters that are expected to replace peripheral blood pressure measurement using the inflatable cuff. In this study, we discuss this simple and inexpensive technique, which has been shown to be reliable with the clinical and epidemiological evidence for its use as a biomarker of cardiovascular risk.

Risk of major bleeding by ethnicity and socioeconomic deprivation among 488,107 people in primary care: a cohort study.

BACKGROUND: Antithrombotic medications (antiplatelets and anticoagulants) reduce the risk of cardiovascular disease (CVD), but with the disadvantage of increasing bleeding risk. Ethnicity and socioeconomic deprivation are independent predictors of major bleeds among patients without CVD, but it is unclear whether they are also predictors of major bleeds among patients with CVD or atrial fibrillation (AF) after adjustment for clinical variables. METHODS: Prospective cohort study of 488,107 people in New Zealand Primary Care (including 64,420 Maori, the indigenous people of New Zealand) aged 30-79 years who had their CVD risk assessed between 2007 and 2016. Participants were divided into three mutually exclusive subgroups: (1) AF with or without CVD (n = 15,212), (2) CVD and no AF (n = 43,790), (3) no CVD or AF (n = 429,105). Adjusted hazards ratios (adjHRs) were estimated from Cox proportional hazards models predicting major bleeding risk for each of the three subgroups to determine whether ethnicity and socioeconomic deprivation are independent predictors of major bleeds in different cardiovascular risk groups. RESULTS: In all three subgroups (AF, CVD, no CVD/AF), Maori (adjHR 1.63 [1.39-1.91], 1.24 [1.09-1.42], 1.57 [95% CI 1.45-1.70], respectively), Pacific people (adjHR 1.90 [1.58-2.28], 1.30 [1.12-1.51], 1.62 [95% CI 1.49-1.75], respectively) and Chinese people (adjHR 1.53 [1.08-2.16], 1.15 [0.90-1.47], 1.13 [95% CI 1.01-1.26], respectively) were at increased risk of a major bleed compared to Europeans, although for Chinese people the effect did not reach statistical significance in the CVD subgroup. Compared to Europeans, Maori and Pacific peoples were generally at increased risk of all bleed types (gastrointestinal, intracranial and other bleeds). An increased risk of intracranial bleeds was observed among Chinese and Other Asian people and, in the CVD and no CVD/AF subgroups, among Indian people. Increasing socioeconomic deprivation was also associated with increased risk of a major bleed in all three subgroups (adjHR 1.07 [1.02-1.12], 1.07 [1.03-1.10], 1.10 [95% CI 1.08-1.12], respectively, for each increase in socioeconomic deprivation quintile). CONCLUSION: Ethnicity and socioeconomic status should be considered in bleeding risk assessments to guide the use of antithrombotic medication for the management of AF and CVD.

Performance of CVD risk equations for older patients assessed in general practice: a cohort study.

AIMS: To investigate how well the New Zealand PREDICT-CVD risk equations, derived in people aged 30-74 years and US Pooled Cohort Equations (PCEs) derived in people aged 40-79 years, perform for older people. METHODS: The PREDICT cohort study automatically recruits participants when clinicians use PREDICT software to conduct a CVD risk assessment. We identified patients aged 70 years and over, without prior CVD, renal disease or heart failure who had been risk assessed between 2004 and 2016. Equation performance was assessed in five-year age bands using calibration graphs and standard discrimination metrics. RESULTS: 40,161 patients (median 73 years; IQR 71-77) experienced 5,948 CVD events during 185,150 person-years follow-up. PREDICT-CVD equations were well calibrated in 70-74 year olds but underestimated events for women from 75 years and men from 80 years. Discrimination metrics were also poor for these age groups. Recalibrated PCEs overestimated CVD risk in both sexes and had poor discrimination from age 70 years for men and from age 75 years for women. CONCLUSIONS: While PREDICT-CVD equations performed better than PCEs, neither performed well. Multimorbidity and competing risks are likely to contribute to the poor performance and new CVD risk equations need to include these factors.

How do cardiovascular risk prediction equations developed among 30-74 year olds perform in older age groups? A validation study in 125 000 people aged 75-89 years.

BACKGROUND: Cardiovascular disease (CVD) risk prediction equations are being used to guide risk management among increasingly older individuals. We examined the performance of recent equations, derived from a 2006 cohort including almost all New Zealanders aged 30-74 years, among older people. METHODS: All New Zealanders aged 75-89 years in contact with state-funded health services in 2006 without prior CVD or heart failure and with complete predictor data were identified by anonymised individual-level linkage of eight national administrative health datasets. Baseline 5-year CVD risk was estimated using sex-specific New Zealand risk equations, and CVD hospitalisations or deaths occurring between 2007 and 2011 inclusive were ascertained. Performance was assessed with calibration plots and standard metrics. RESULTS: Among 124 358 New Zealanders aged 75-89 years old, 30 152 CVD events were recorded during follow-up. Sex-specific equations derived from 30-74 year olds slightly underestimated CVD risk among women and slightly overestimated risk among men aged 75-89 years. Discrimination metrics were poor in both sexes and the risk equations explained only 9.4% of the variation in time to CVD event among women and 6.0% for men. In the 5-year age bands, progressively worsening underprediction in women, overprediction in men and poorer performance metrics were observed with increasing age. CONCLUSION: Entire-population CVD risk equations developed among 30-74 year olds do not perform well among older people. Existing risk algorithms developed from primarily middle-aged or early-retirement cohorts should be used with caution in those aged ≥75 years until carefully validated in narrow age bands to avoid masking poorer performance in older age groups.

Personalized Prediction of Cardiovascular Benefits and Bleeding Harms From Aspirin for Primary Prevention: A Benefit-Harm Analysis.

Background: Whether the benefits of aspirin for the primary prevention of cardiovascular disease (CVD) outweigh its bleeding harms in some patients is unclear. Objective: To identify persons without CVD for whom aspirin would probably result in a net benefit. Design: Individualized benefit-harm analysis based on sex-specific risk scores and estimates of the proportional effect of aspirin on CVD and major bleeding from a 2019 meta-analysis. Setting: New Zealand primary care. Participants: 245 028 persons (43.6% women) aged 30 to 79 years without established CVD who had their CVD risk assessed between 2012 and 2016. Measurements: The net effect of aspirin was calculated for each participant by subtracting the number of CVD events likely to be prevented (CVD risk score × proportional effect of aspirin on CVD risk) from the number of major bleeds likely to be caused (major bleed risk score × proportional effect of aspirin on major bleeding risk) over 5 years. Results: 2.5% of women and 12.1% of men were likely to have a net benefit from aspirin treatment for 5 years if 1 CVD event was assumed to be equivalent in severity to 1 major bleed, increasing to 21.4% of women and 40.7% of men if 1 CVD event was assumed to be equivalent to 2 major bleeds. Net benefit subgroups had higher baseline CVD risk, higher levels of most established CVD risk factors, and lower levels of bleeding-specific risk factors than net harm subgroups. Limitations: Risk scores and effect estimates were uncertain. Effects of aspirin on cancer outcomes were not considered. Applicability to non-New Zealand populations was not assessed. Conclusion: For some persons without CVD, aspirin is likely to result in net benefit. Primary Funding Source: Health Research Council of New Zealand.

Predicting Bleeding Risk to Guide Aspirin Use for the Primary Prevention of Cardiovascular Disease: A Cohort Study.

Background: Many prognostic models for cardiovascular risk can be used to estimate aspirin's absolute benefits, but few bleeding risk models are available to estimate its likely harms. Objective: To develop prognostic bleeding risk models among persons in whom aspirin might be considered for the primary prevention of cardiovascular disease (CVD). Design: Prospective cohort study. Setting: New Zealand primary care. Participants: The study cohort comprised 385 191 persons aged 30 to 79 years whose CVD risk was assessed between 2007 and 2016. Those with indications for or contraindications to aspirin and those who were already receiving antiplatelet or anticoagulant therapy were excluded. Measurements: For each sex, Cox proportional hazards models were developed to predict major bleeding risk; participants were censored at the earliest of the date on which they first met an exclusion criterion, date of death, or study end date (30 June 2017). The main models included the following predictors: demographic characteristics (age, ethnicity, and socioeconomic deprivation), clinical measurements (systolic blood pressure and ratio of total-high-density lipoprotein cholesterol), family history of premature CVD, medical history (smoking, diabetes, bleeding, peptic ulcer disease, cancer, chronic liver disease, chronic pancreatitis, or alcohol-related conditions), and medication use (nonsteroidal anti-inflammatory agents, corticosteroids, and selective serotonin reuptake inhibitors). Results: During 1 619 846 person-years of follow-up, 4442 persons had major bleeding events (of which 313 [7%] were fatal). The main models predicted a median 5-year bleeding risk of 1.0% (interquartile range, 0.8% to 1.5%) in women and 1.1% (interquartile range, 0.7% to 1.6%) in men. Plots of predicted-against-observed event rates showed good calibration throughout the risk range. Limitation: Hemoglobin level, platelet count, and body mass index were excluded from the main models because of high numbers of missing values, and the models were not externally validated in non-New Zealand populations. Conclusion: Prognostic bleeding risk models were developed that can be used to estimate the absolute bleeding harms of aspirin among persons in whom aspirin is being considered for the primary prevention of CVD. Primary Funding Source: The Health Research Council of New Zealand.

Development and validation of alternative cardiovascular risk prediction equations for population health planning: a routine health data linkage study of 1.7 million New Zealanders.

Background: Cardiovascular disease (CVD) risk prediction equations are primarily used in clinical settings to inform individual risk management decisions. We sought to develop and validate alternative equations derived solely from linked routinely collected national health data that could be applied countrywide to inform population health planning. Methods: Individual-level linkage of eight administrative health datasets identified all New Zealand residents aged 30-74 years in contact with publicly funded health services during 2006 with no previous hospitalizations for CVD or heart failure, and with complete data on eight pre-specified predictors. The linked health datasets encompassed demographic characteristics, hospitalizations, outpatient visits, primary care enrolment, primary care reimbursement, community laboratory requests, community pharmaceutical dispensing and mortality. Sex-specific Cox models were developed to estimate the risk of CVD death or hospitalization within 5 years and included sex, age, ethnicity, level of deprivation, diabetes, previous hospitalization for atrial fibrillation and baseline preventive pharmacotherapy (blood-pressure-lowering, lipid-lowering and antiplatelet/anticoagulant medications) as predictors. Calibration and discrimination were assessed in the whole cohort, in 15-year age bands, in different ethnic groups, in quintiles of deprivation, according to baseline dispensing of pharmacotherapy, and in regional sub-populations. Results: First CVD events occurred in 62 031 of the 1 746 695 people during 8 526 024 person-years of follow-up (mean = 4.8 years). Median 5-year CVD risk was 1.1% in women and 2.6% in men. In both sexes, the risk equations were well calibrated throughout the risk range and had good risk discrimination in the national, regional and ethnic populations, within 15-year age bands, in deprivation quintiles and according to baseline medication dispensing. Conclusions: Robust policy-focused CVD risk equations can be developed solely from administrative health data to inform population health planning, and will complement CVD primary prevention at the individual level using clinical risk tools. Similar policy-focused equations could be replicated in countries and regions with linked administrative health datasets.

Performance of a Framingham cardiovascular risk model among Indians and Europeans in New Zealand and the role of body mass index and social deprivation.

Objectives: To evaluate a Framingham 5-year cardiovascular disease (CVD) risk score in Indians and Europeans in New Zealand, and determine whether body mass index (BMI) and socioeconomic deprivation were independent predictors of CVD risk. Methods: We included Indians and Europeans, aged 30-74 years without prior CVD undergoing risk assessment in New Zealand primary care during 2002-2015 (n=256 446). Risk profiles included standard Framingham predictors (age, sex, systolic blood pressure, total cholesterol/high-density lipoprotein ratio, smoking and diabetes) and were linked with national CVD hospitalisations and mortality datasets. Discrimination was measured by the area under the receiver operating characteristics curve (AUC) and calibration examined graphically. We used Cox regression to study the impact of BMI and deprivation on the risk of CVD with and without adjustment for the Framingham score. Results: During follow-up, 8105 and 1156 CVD events occurred in Europeans and Indians, respectively. Higher AUCs of 0.76 were found in Indian men (95% CI 0.74 to 0.78) and women (95% CI 0.73 to 0.78) compared with 0.74 (95% CI 0.73 to 0.74) in European men and 0.72 (95% CI 0.71 to 0.73) in European women. Framingham was best calibrated in Indian men, and overestimated risk in Indian women and in Europeans. BMI and deprivation were positively associated with CVD, also after adjustment for the Framingham risk score, although the BMI association was attenuated. Conclusions: The Framingham risk model performed reasonably well in Indian men, but overestimated risk in Indian women and in Europeans. BMI and socioeconomic deprivation could be useful predictors in addition to a Framingham score.

Is general practice identification of prior cardiovascular disease at the time of CVD risk assessment accurate and does it matter?

AIMS: To determine the accuracy of general practice recording of prior cardiovascular disease (CVD) at the time of CVD risk assessment and whether recording impacts on CVD management. METHODS: Prior CVD status entered at the time of a first CVD risk assessment from 2002-2015 was compared to prior ischaemic CVD hospitalisations from national datasets using anonymous linkage with an encrypted National Health Index identifier. Clinical factors associated with inaccurate recording of prior events were identified using multivariable logistic regression. The impact of recording accuracy was assessed by the dispensing of CVD preventive medications in the six months after first CVD risk assessment. RESULTS: Among 454,369 people aged 35-74 years who had CVD risk assessments, 30,924 (6.8%) had previously been admitted with ischaemic CVD. Of these people, only 61% were recorded as having prior CVD during risk assessment, with better recording for coronary and stroke events than for peripheral vascular procedures. Inaccurate primary care recording was more likely for younger people (<55 years), women, Maori, Pacific, Indian and Asian ethnic groups whereas smokers and people with diabetes were more likely to have prior CVD correctly identified. Over more than a decade, the odds of inaccurate recording during risk assessment increased [OR 1.09 (95% CIs 1.08-1.10)]. If prior CVD was entered at the time of risk assessment then dispensing of blood pressure-lowering, lipid-lowering, antiplatelet/anticoagulant medications, separately or together, was higher (86%, 85%, 83% and 69%, respectively) than if not recorded (70%, 60%, 60% and 43%). CONCLUSIONS: Overall, 39% of people with prior CVD hospitalisations were not recorded as having prior CVD when their CVD risk was first assessed in general practice. This was associated with inequities in evidence-based risk management. System-based measures are required for robust data sharing at the time of clinical decision making.

Cardiovascular disease risk prediction equations in 400 000 primary care patients in New Zealand: a derivation and validation study.

BACKGROUND: Most cardiovascular disease risk prediction equations in use today were derived from cohorts established last century and with participants at higher risk but less socioeconomically and ethnically diverse than patients they are now applied to. We recruited a nationally representative cohort in New Zealand to develop equations relevant to patients in contemporary primary care and compared the performance of these new equations to equations that are recommended in the USA. METHODS: The PREDICT study automatically recruits participants in routine primary care when general practitioners in New Zealand use PREDICT software to assess their patients' risk profiles for cardiovascular disease, which are prospectively linked to national ICD-coded hospitalisation and mortality databases. The study population included male and female patients in primary care who had no prior cardiovascular disease, renal disease, or congestive heart failure. New equations predicting total cardiovascular disease risk were developed using Cox regression models, which included clinical predictors plus an area-based deprivation index and self-identified ethnicity. Calibration and discrimination performance of the equations were assessed and compared with 2013 American College of Cardiology/American Heart Association Pooled Cohort Equations (PCEs). The additional predictors included in new PREDICT equations were also appended to the PCEs to determine whether they were independent predictors in the equations from the USA. FINDINGS: Outcome events were derived for 401 752 people aged 30-74 years at the time of their first PREDICT risk assessment between Aug 27, 2002, and Oct 12, 2015, representing about 90% of the eligible population. The mean follow-up was 4·2 years, and a third of participants were followed for 5 years or more. 15 386 (4%) people had cardiovascular disease events (1507 [10%] were fatal, and 8549 [56%] met the PCEs definition of hard atherosclerotic cardiovascular disease) during 1 685 521 person-years follow-up. The median 5-year risk of total cardiovascular disease events predicted by the new equations was 2·3% in women and 3·2% in men. Multivariable adjusted risk increased by about 10% per quintile of socioeconomic deprivation. Maori, Pacific, and Indian patients were at 13-48% higher risk of cardiovascular disease than Europeans, and Chinese or other Asians were at 25-33% lower risk of cardiovascular disease than Europeans. The PCEs overestimated of hard atherosclerotic cardiovascular disease by about 40% in men and by 60% in women, and the additional predictors in the new equations were also independent predictors in the PCEs. The new equations were significantly better than PCEs on all performance metrics. INTERPRETATION: We constructed a large prospective cohort study representing typical patients in primary care in New Zealand who were recommended for cardiovascular disease risk assessment. Most patients are now at low risk of cardiovascular disease, which explains why the PCEs based mainly on old cohorts substantially overestimate risk. Although the PCEs and many other equations will need to be recalibrated to mitigate overtreatment of the healthy majority, they also need new predictors that include measures of socioeconomic deprivation and multiple ethnicities to identify vulnerable high-risk subpopulations that might otherwise be undertreated. FUNDING: Health Research Council of New Zealand, Heart Foundation of New Zealand, and Healthier Lives National Science Challenge.

Can traditional risk factors explain the higher risk of cardiovascular disease in South Asians compared to Europeans in Norway and New Zealand? Two cohort studies.

OBJECTIVES: The objective was to prospectively examine potential differences in the risk of first cardiovascular disease (CVD) events between South Asians and Europeans living in Norway and New Zealand, and to investigate whether traditional risk factors could explain any differences. METHODS: We included participants (30-74 years) without prior CVD in a Norwegian (n=16 606) and a New Zealand (n=129 449) cohort. Ethnicity and cardiovascular risk factor information was linked with hospital registry data and cause of death registries to identify subsequent CVD events. We used Cox proportional hazards regression to investigate the relationship between risk factors and subsequent CVD for South Asians and Europeans, and to calculate age-adjusted HRs for CVD in South Asians versus Europeans in the two cohorts separately. We sequentially added the major CVD risk factors (blood pressure, lipids, diabetes and smoking) to study their explanatory role in observed ethnic CVD risk differences. RESULTS: South Asians had higher total cholesterol (TC)/high-density lipoprotein (HDL) ratio and more diabetes at baseline than Europeans, but lower blood pressure and smoking levels. South Asians had increased age-adjusted risk of CVD compared with Europeans (87%-92% higher in the Norwegian cohort and 42%-75% higher in the New Zealand cohort) and remained with significantly increased risk after adjusting for all major CVD risk factors. Adjusted HRs for South Asians versus Europeans in the Norwegian cohort were 1.57 (95% CI 1.19 to 2.07) in men and 1.76 (95% CI 1.09 to 2.82) in women. Corresponding figures for the New Zealand cohort were 1.64 (95% CI 1.43 to 1.88) in men and 1.39 (95% CI 1.11 to 1.73) in women. CONCLUSION: Differences in TC/HDL ratio and diabetes appear to explain some of the excess risk of CVD in South Asians compared with Europeans. Preventing dyslipidaemia and diabetes in South Asians may therefore help reduce their excess risk of CVD.

New Zealand Diabetes Cohort Study cardiovascular risk score for people with Type 2 diabetes: validation in the PREDICT cohort.

INTRODUCTION: New Zealand (NZ) guidelines recommend treating people for cardiovascular disease (CVD) risk on the basis of five-year absolute risk using a NZ adaptation of the Framingham risk equation. A diabetes-specific Diabetes Cohort Study (DCS) CVD predictive risk model has been developed and validated using NZ Get Checked data. AIM: To revalidate the DCS model with an independent cohort of people routinely assessed using PREDICT, a web-based CVD risk assessment and management programme. METHODS: People with Type 2 diabetes without pre-existing CVD were identified amongst people who had a PREDICT risk assessment between 2002 and 2005. From this group we identified those with sufficient data to allow estimation of CVD risk with the DCS models. We compared the DCS models with the NZ Framingham risk equation in terms of discrimination, calibration, and reclassification implications. RESULTS: Of 3044 people in our study cohort, 1829 people had complete data and therefore had CVD risks calculated. Of this group, 12.8% (235) had a cardiovascular event during the five-year follow-up. The DCS models had better discrimination than the currently used equation, with C-statistics being 0.68 for the two DCS models and 0.65 for the NZ Framingham model. DISCUSSION: The DCS models were superior to the NZ Framingham equation at discriminating people with diabetes who will have a cardiovascular event. The adoption of a DCS model would lead to a small increase in the number of people with diabetes who are treated with medication, but potentially more CVD events would be avoided.

Markedly different clustering of CVD risk factors in New Zealand Indian and European people but similar risk scores (PREDICT-14).

OBJECTIVE: To compare the cardiovascular disease (CVD) risk profiles of Indian and European patients from routine primary care assessments in the northern region of New Zealand. METHOD: Anonymous CVD risk profiles were extracted from PREDICT (a web-based decision support program) for Indian and European patients aged 35-74 years. Linear regression models were used to obtain mean differences adjusted for age, gender and deprivation. RESULTS: At recruitment, Indian participants (n=8,830) were younger than Europeans (n=47,091), in keeping with national guidelines that recommend earlier CVD risk assessment for Indians. Compared with Europeans, a greater proportion of Indian participants lived in areas of higher deprivation and had a two to four-fold greater burden of diabetes in all age groups. Indian participants had a significantly lower proportion of smokers and a lower mean systolic blood pressure. The respective cardiovascular risk factor profiles lead to similar age-adjusted Framingham five-year CVD risk scores. CONCLUSIONS AND IMPLICATIONS: National data sources indicate that there are higher rates of hospitalisations and deaths from CVD in Indians compared with Europeans. Our study found similar predicted CVD risk in these two populations despite markedly different clustering of risk factors, suggesting that the Framingham risk equation may underestimate risk in Indians. There is a need for better ethnicity coding to identify all South Asian ethnicities.