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. Māori, 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.

Reference: The Lancet, Volume 391, No. 10133, p1897–1907, 12 May 2018

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Cancer risk associated with chronic diseases and disease markers: prospective cohort study

Objectives To assess the independent and joint associations of major chronic diseases and disease markers with cancer risk and to explore the benefit of physical activity in reducing the cancer risk associated with chronic diseases and disease markers.

Design Prospective cohort study.

Setting Standard medical screening program in Taiwan.

Participants 405 878 participants, for whom cardiovascular disease markers (blood pressure, total cholesterol, and heart rate), diabetes, chronic kidney disease markers (proteinuria and glomerular filtration rate), pulmonary disease, and gouty arthritis marker (uric acid) were measured or diagnosed according to standard methods, were followed for an average of 8.7 years.

Main outcome measures Cancer incidence and cancer mortality.

Results A statistically significantly increased risk of incident cancer was observed for the eight diseases and markers individually (except blood pressure and pulmonary disease), with adjusted hazard ratios ranging from 1.07 to 1.44. All eight diseases and markers were statistically significantly associated with risk of cancer death, with adjusted hazard ratios ranging from 1.12 to 1.70. Chronic disease risk scores summarizing the eight diseases and markers were positively associated with cancer risk in a dose-response manner, with the highest scores associated with a 2.21-fold (95% confidence interval 1.77-fold to 2.75-fold) and 4.00-fold (2.84-fold to 5.63-fold) higher cancer incidence and cancer mortality, respectively. High chronic disease risk scores were associated with substantial years of life lost, and the highest scores were associated with 13.3 years of life lost in men and 15.9 years of life lost in women. The population attributable fractions of cancer incidence or cancer mortality from the eight chronic diseases and markers together were comparable to those from five major lifestyle factors combined (cancer incidence: 20.5% v 24.8%; cancer mortality: 38.9% v 39.7%). Among physically active (versus inactive) participants, the increased cancer risk associated with chronic diseases and markers was attenuated by 48% for cancer incidence and 27% for cancer mortality.

Conclusions Chronic disease is an overlooked risk factor for cancer, as important as five major lifestyle factors combined. In this study, chronic diseases contributed to more than one fifth of the risk for incident cancer and more than one third of the risk for cancer death. Physical activity is associated with a nearly 40% reduction in the cancer risk associated with chronic diseases.

Reference: BMJ 2018;360:k134

Development and validation of QMortality risk prediction algorithm to estimate short term risk of death and assess frailty: cohort study

Objectives To derive and validate a risk prediction equation to estimate the short term risk of death, and to develop a classification method for frailty based on risk of death and risk of unplanned hospital admission.

Design Prospective open cohort study.

Participants Routinely collected data from 1436 general practices contributing data to QResearch in England between 2012 and 2016. 1079 practices were used to develop the scores and a separate set of 357 practices to validate the scores. 1.47 million patients aged 65-100 years were in the derivation cohort and 0.50 million patients in the validation cohort.

Methods Cox proportional hazards models in the derivation cohort were used to derive separate risk equations in men and women for evaluation of the risk of death at one year. Risk factors considered were age, sex, ethnicity, deprivation, smoking status, alcohol intake, body mass index, medical conditions, specific drugs, social factors, and results of recent investigations. Measures of calibration and discrimination were determined in the validation cohort for men and women separately and for each age and ethnic group. The new mortality equation was used in conjunction with the existing QAdmissions equation (which predicts risk of unplanned hospital admission) to classify patients into frailty groups.

Main outcome measure The primary outcome was all cause mortality.

Results During follow-up 180 132 deaths were identified in the derivation cohort arising from 4.39 million person years of observation. The final model included terms for age, body mass index, Townsend score, ethnic group, smoking status, alcohol intake, unplanned hospital admissions in the past 12 months, atrial fibrillation, antipsychotics, cancer, asthma or chronic obstructive pulmonary disease, living in a care home, congestive heart failure, corticosteroids, cardiovascular disease, dementia, epilepsy, learning disability, leg ulcer, chronic liver disease or pancreatitis, Parkinson’s disease, poor mobility, rheumatoid arthritis, chronic kidney disease, type 1 diabetes, type 2 diabetes, venous thromboembolism, anaemia, abnormal liver function test result, high platelet count, visited doctor in the past year with either appetite loss, unexpected weight loss, or breathlessness. The model had good calibration and high levels of explained variation and discrimination. In women, the equation explained 55.6% of the variation in time to death (R2), and had very good discrimination—the D statistic was 2.29, and Harrell’s C statistic value was 0.85. The corresponding values for men were 53.1%, 2.18, and 0.84. By combining predicted risks of mortality and unplanned hospital admissions, 2.7% of patients (n=13 665) were classified as severely frail, 9.4% (n=46 770) as moderately frail, 43.1% (n=215 253) as mildly frail, and 44.8% (n=223 790) as fit.

Conclusions We have developed new equations to predict the short term risk of death in men and women aged 65 or more, taking account of demographic, social, and clinical variables. The equations had good performance on a separate validation cohort. The QMortality equations can be used in conjunction with the QAdmissions equations, to classify patients into four frailty groups (known as QFrailty categories) to enable patients to be identified for further assessment or interventions.

Reference: BMJ 2017;358:j4208

Quadrivalent HPV Vaccination and the Risk of Adverse Pregnancy Outcomes

BACKGROUND

The quadrivalent human papillomavirus (HPV) vaccine is recommended for all girls and women 9 to 26 years of age. Some women will have inadvertent exposure to vaccination during early pregnancy, but few data exist regarding the safety of the quadrivalent HPV vaccine in this context.

METHODS

We assessed a cohort that included all the women in Denmark who had a pregnancy ending between October 1, 2006, and November 30, 2013. Using nationwide registers, we linked information on vaccination, adverse pregnancy outcomes, and potential confounders among women in the cohort. Women who had vaccine exposure during the prespecified time windows were matched for propensity score in a 1:4 ratio with women who did not have vaccine exposure during the same time windows. Outcomes included spontaneous abortion, stillbirth, major birth defect, small size for gestational age, low birth weight, and preterm birth.

RESULTS

In matched analyses, exposure to the quadrivalent HPV vaccine was not associated with significantly higher risks than no exposure for major birth defect (65 cases among 1665 exposed pregnancies and 220 cases among 6660 unexposed pregnancies; prevalence odds ratio, 1.19; 95% confidence interval [CI], 0.90 to 1.58), spontaneous abortion (20 cases among 463 exposed pregnancies and 131 cases among 1852 unexposed pregnancies; hazard ratio, 0.71; 95% CI, 0.45 to 1.14), preterm birth (116 cases among 1774 exposed pregnancies and 407 cases among 7096 unexposed pregnancies; prevalence odds ratio, 1.15; 95% CI, 0.93 to 1.42), low birth weight (76 cases among 1768 exposed pregnancies and 277 cases among 7072 unexposed pregnancies; prevalence odds ratio, 1.10; 95% CI, 0.85 to 1.43), small size for gestational age (171 cases among 1768 exposed pregnancies and 783 cases among 7072 unexposed pregnancies; prevalence odds ratio, 0.86; 95% CI, 0.72 to 1.02), or stillbirth (2 cases among 501 exposed pregnancies and 4 cases among 2004 unexposed pregnancies; hazard ratio, 2.43; 95% CI, 0.45 to 13.21).

CONCLUSIONS

Quadrivalent HPV vaccination during pregnancy was not associated with a significantly higher risk of adverse pregnancy outcomes than no such exposure. (Funded by the Novo Nordisk Foundation and the Danish Medical Research Council.)

Non-steroidal anti-inflammatory drugs and risk of heart failure in four European countries: nested case-control study

Objectives To investigate the cardiovascular safety of non-steroidal anti-inflammatory drugs (NSAIDs) and estimate the risk of hospital admission for heart failure with use of individual NSAIDs.

Design Nested case-control study.

Setting Five population based healthcare databases from four European countries (the Netherlands, Italy, Germany, and the United Kingdom).

Participants Adult individuals (age ≥18 years) who started NSAID treatment in 2000-10. Overall, 92 163 hospital admissions for heart failure were identified and matched with 8 246 403 controls (matched via risk set sampling according to age, sex, year of cohort entry).

Main outcome measure Association between risk of hospital admission for heart failure and use of 27 individual NSAIDs, including 23 traditional NSAIDs and four selective COX 2 inhibitors. Associations were assessed by multivariable conditional logistic regression models. The dose-response relation between NSAID use and heart failure risk was also assessed.

Results Current use of any NSAID (use in preceding 14 days) was found to be associated with a 19% increase of risk of hospital admission for heart failure (adjusted odds ratio 1.19; 95% confidence interval 1.17 to 1.22), compared with past use of any NSAIDs (use >183 days in the past). Risk of admission for heart failure increased for seven traditional NSAIDs (diclofenac, ibuprofen, indomethacin, ketorolac, naproxen, nimesulide, and piroxicam) and two COX 2 inhibitors (etoricoxib and rofecoxib). Odds ratios ranged from 1.16 (95% confidence interval 1.07 to 1.27) for naproxen to 1.83 (1.66 to 2.02) for ketorolac. Risk of heart failure doubled for diclofenac, etoricoxib, indomethacin, piroxicam, and rofecoxib used at very high doses (≥2 defined daily dose equivalents), although some confidence intervals were wide. Even medium doses (0.9-1.2 defined daily dose equivalents) of indomethacin and etoricoxib were associated with increased risk. There was no evidence that celecoxib increased the risk of admission for heart failure at commonly used doses.

Conclusions The risk of hospital admission for heart failure associated with current use of NSAIDs appears to vary between individual NSAIDs, and this effect is dose dependent. This risk is associated with the use of a large number of individual NSAIDs reported by this study, which could help to inform both clinicians and health regulators.

BMJ 2016;354:i4857

Atrial fibrillation and risks of cardiovascular disease, renal disease, and death: systematic review and meta-analysis

Objective To quantify the association between atrial fibrillation and cardiovascular disease, renal disease, and death.

Design Systematic review and meta-analysis.

Data sources Medline and Embase.

Eligibility criteria Cohort studies examining the association between atrial fibrillation and cardiovascular disease, renal disease, and death. Two reviewers independently extracted study characteristics and the relative risk of outcomes associated with atrial fibrillation: specifically, all cause mortality, cardiovascular mortality, major cardiovascular events, any stroke, ischaemic stroke, haemorrhagic stroke, ischaemic heart disease, sudden cardiac death, congestive heart failure, chronic kidney disease, and peripheral arterial disease. Estimates were pooled with inverse variance weighted random effects meta-analysis.

Results 104 eligible cohort studies involving 9 686 513 participants (587 867 with atrial fibrillation) were identified. Atrial fibrillation was associated with an increased risk of all cause mortality (relative risk 1.46, 95% confidence interval 1.39 to 1.54), cardiovascular mortality (2.03, 1.79 to 2.30), major cardiovascular events (1.96, 1.53 to 2.51), stroke (2.42, 2.17 to 2.71), ischaemic stroke (2.33, 1.84 to 2.94), ischaemic heart disease (1.61, 1.38 to 1.87), sudden cardiac death (1.88, 1.36 to 2.60), heart failure (4.99, 3.04 to 8.22), chronic kidney disease (1.64, 1.41 to 1.91), and peripheral arterial disease (1.31, 1.19 to 1.45) but not haemorrhagic stroke (2.00, 0.67 to 5.96). Among the outcomes examined, the highest absolute risk increase was for heart failure. Associations between atrial fibrillation and included outcomes were broadly consistent across subgroups and in sensitivity analyses.

Conclusions Atrial fibrillation is associated with an increased risk of death and an increased risk of cardiovascular and renal disease. Interventions aimed at reducing outcomes beyond stroke are warranted in patients with atrial fibrillation.

BMJ 2016;354:i4482

External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study

Objective To perform an external validation and direct comparison of published prognostic models for early prediction of the risk of gestational diabetes mellitus, including predictors applicable in the first trimester of pregnancy.

Design External validation of all published prognostic models in large scale, prospective, multicentre cohort study.

Setting 31 independent midwifery practices and six hospitals in the Netherlands.

Participants Women recruited in their first trimester (<14 weeks) of pregnancy between December 2012 and January 2014, at their initial prenatal visit. Women with pre-existing diabetes mellitus of any type were excluded.

Main outcome measures Discrimination of the prognostic models was assessed by the C statistic, and calibration assessed by calibration plots.

Results 3723 women were included for analysis, of whom 181 (4.9%) developed gestational diabetes mellitus in pregnancy. 12 prognostic models for the disorder could be validated in the cohort. C statistics ranged from 0.67 to 0.78. Calibration plots showed that eight of the 12 models were well calibrated. The four models with the highest C statistics included almost all of the following predictors: maternal age, maternal body mass index, history of gestational diabetes mellitus, ethnicity, and family history of diabetes. Prognostic models had a similar performance in a subgroup of nulliparous women only. Decision curve analysis showed that the use of these four models always had a positive net benefit.

Conclusions In this external validation study, most of the published prognostic models for gestational diabetes mellitus show acceptable discrimination and calibration. The four models with the highest discriminative abilities in this study cohort, which also perform well in a subgroup of nulliparous women, are easy models to apply in clinical practice and therefore deserve further evaluation regarding their clinical impact.

BMJ 2016;354:i4338