Katrina. K. Poppe

Aims: Hypertensive disorders of pregnancy (HDP) are established markers of cardiovascular disease (CVD) risk yet are not routinely incorporated into primary prevention. We aimed to (1) assess how HDP is recorded across primary care and national hospitalization data in New Zealand and (2) estimate the association between a history of HDP and incident CVD over 5 years. Methods and results: This cohort study used linked health records from the ProCare Primary Health Organisation and national datasets. HDP was identified from Read-coded and free-text general practice records and ICD-10-AM hospital data. Two analyses were undertaken: (1) comparison of HDP recording between primary care and hospital discharge data among women aged 18–64 years; and (2) estimation of 5-year incident CVD risk among women aged 30–64 years without prior CVD. Cox proportional hazards models estimated adjusted hazard ratios (aHRs), incorporating the linear predictor from a national primary prevention CVD risk equation. Among 172 328 women aged 18–64 years, a history of HDP was recorded in 4.7% (population-level prevalence), with 91% of this recorded in hospital discharge data only. Over 5 years, 2391 women experienced incident CVD events. A recorded history of HDP was associated with a 72% higher CVD risk (aHR = 1.72, 95% confidence interval: 1.49–1.98). Women with HDP experienced cardiovascular events at younger ages than those without (median 50.2 vs. 57.3 years). Conclusion: HDP is inadequately recorded in primary care in New Zealand. When documented, it identifies women at significantly higher and earlier CVD risk. Improving HDP documentation in primary care may enable earlier and more equitable CVD prevention.

Background: Multimorbidity adds complexity to medication management. National guidelines in New Zealand (NZ) recommend dual therapy with blood-pressure- and lipid-lowering agents for most patients at five-year cardiovascular disease (CVD) risk ≥15% (high risk), considered at 5-14% (intermediate risk), and not generally recommended at <5% (low risk). Aim: To examine the association between multimorbidity and dispensing of dual therapy across risk strata. Design and setting: A cohort study among 430,286 patients aged 30-79 years without prior CVD, enrolled in the NZ Primary Health Organisation ProCare in 2014. Method: Multivariable Poisson regression estimated relative risks (RR) for dual therapy across CVD risk groups at cohort entry. Two-year maintenance of dual therapy was then compared by multimorbidity within each risk stratum. Results: Baseline dual therapy receipt was 1.5%, 31% and 40% in low, intermediate and high risk groups respectively. Multimorbidity was associated with greater use of dual therapy across risk groups (low risk: RR 2.90 [95% CI 2.77-3.03]; intermediate risk: RR 1.35 [95% CI 1.32-1.38]; high risk: RR 1.15 [95% CI 1.10-1.22]). Multimorbidity was also associated with higher maintenance in the low (88% vs 84%) and intermediate (90% vs 86%) risk groups (both p<0.001) and was high, irrespective of multimorbidity, in high risk (89% vs 88%; p=0.415). Conclusion: Patients with multimorbidity were more likely than others to be commenced on and maintain CVD prevention. However, while dispensing increased with increasing CVD risk, a large evidence-practice gap in dual therapy remained for those at high CVD risk. Keywords: General practice; Multimorbidity; Cardiovascular preventive medication

Aims: Most countries lack the clinical cohort data required to create cardiovascular disease (CVD) risk prediction models and rely on models developed elsewhere. However, individual-person linkage of health administrative databases enables the creation of local models. Objectives: This study aimed to explore whether the addition of routine biochemical tests could improve the performance of ‘administrative data-based’ models. Methods and results: Estimated glomerular filtration rate, haemoglobin A1c, total cholesterol/high-density lipoprotein cholesterol ratio, and triglyceride tests performed on one-third of New Zealand adults were identified and linked to national administrative health datasets (11 564 665 tests). Sex-specific Cox models estimating 5-year CVD in people aged 30–74 without prior CVD were developed, internally and temporally validated, and compared, including and excluding the four biochemical predictors. 805 817 individuals were included, of whom 64% had all four examined test results available. 1.8% of women and 3.4% of men experienced a CVD event during 4.7 years mean follow-up. All biochemical predictors except triglyceride level in men were statistically significant independent predictors of CVD risk. The addition of these predictors improved global model performance metrics over models without biochemical predictors and identified 34% of men and 28% of women in the cohort who accounted for 75% of all CVD events, respectively. Conclusion: Biochemical predictors improved the performance of administrative data-based CVD risk prediction models, illustrating the untapped potential of widely available databases. Similar models could be developed in many countries and healthcare organizations if health administrative databases were linked. These models could be remotely applied across populations to inform CVD prevention policy and practices. Condensed Novel CVD risk prediction models have recently been developed in New Zealand and Denmark from predictors available in health administrative data, but their performance is limited by their parsimony. Biochemical predictors are now linkable to New Zealand administrative datasets and we show that they improve the predictive ability of previous ‘administrative data-based’ models. These new models were remotely deployed across a large New Zealand region, identifying 27% of individuals who accounted for 74% of subsequent CVD events. Similar models could be created in many countries and healthcare organizations to aid population health planning efforts, investigate equity gaps, and pre-screen populations who experience difficulties accessing traditional health services.

Background/Objective: Migraine is the second most disabling condition worldwide affecting over one billion people. We aimed to investigate the frequency of recorded migraine utilising general practice and hospital admission data, comparing this with population-based prevalence estimates for Aotearoa New Zealand (NZ). Methods: From the electronic health record of de-identified data for 374,071 men and women aged 18 and over enrolled as of 2014 in NZ general practices, any entry of recorded migraine from a patient’s visit to primary care or hospital facility recorded using Read codes, Read terms, or free text entries was extracted. Via an encrypted National Health Identifier (NHI), individual patient linkage identified migraine coded in the national hospitalisation dataset, enabling exploration by gender, age group, ethnicity and NZ deprivation index quintile. Results: Compared to population prevalence (15%), 4.8% of this cohort had recorded migraine (79% in general practice only; 14% in hospital records only; 7.5% in both primary and secondary care). Women (6.7%) had higher recording than men (2.5%), with the highest age-specific recording at 45–54 years (women 7.4%, men 2.7%). By gender and ethnicity, migraine recording was highest for European women (7.9%). Within quintile 5 communities (most deprived), recording was significantly lower for females ( p < 0.0001) and males ( p = 0.03) than in quintiles 1–4. Conclusions: This study is the first to estimate migraine in NZ using linked primary and secondary care data. As well as highlighting age-, gender-, and ethnicity-specific differences, the underestimate of diagnosis compared to self-reported survey data is likely due to health care access, under-reporting, under-diagnosis, under-recording, and knowledge gaps about migraine symptoms and therapies.

Background Cardiovascular risk is underassessed in women. Many women undergo screening mammography in midlife when the risk of cardiovascular disease rises. Mammographic features such as breast arterial calcification and tissue density are associated with cardiovascular risk. We developed and tested a deep learning algorithm for cardiovascular risk prediction based on routine mammography images. Methods Lifepool is a cohort of women with at least one screening mammogram linked to hospitalisation and death databases. A deep learning model based on DeepSurv architecture was developed to predict major cardiovascular events from mammography images. Model performance was compared against standard risk prediction models using the concordance index, comparative to the Harrells C-statistic. Results There were 49 196 women included, with a median follow-up of 8.8 years (IQR 7.7–10.6), among whom 3392 experienced a first major cardiovascular event. The DeepSurv model using mammography features and participant age had a concordance index of 0.72 (95% CI 0.71 to 0.73), with similar performance to modern models containing age and clinical variables including the New Zealand ‘PREDICT’ tool and the American Heart Association ‘PREVENT’ equations. Conclusions A deep learning algorithm based on only mammographic features and age predicted cardiovascular risk with performance comparable to traditional cardiovascular risk equations. Risk assessments based on mammography may be a novel opportunity for improving cardiovascular risk screening in women.

Objectives: This study examined whether incorporating free-text entries into structured general practice records improves the detection of long-term conditions (LTCs) and multimorbidity (MM) in New Zealand (NZ) general practices. Methods: Data from 374 071 deidentified individuals in general practices were analysed to identify 61 LTCs. Structured data were extracted using Read codes from a national master list, and clinical raters independently identified condition-related free-text, including synonyms, negation terms and common misspellings in randomised samples. Keywords were categorised and refined through ten iterative tests. Programmatic text classification was developed and assessed against gold-standard clinician ratings, using sensitivity, specificity, positive predictive value (PPV) and F ₁ -score. Results: A quarter of general practitioner classifications contained either unrecognised Read codes or consisted of free-text only. Clinician inter-rater reliability was high (kappa ≥0.9). Compared with clinical gold standard, text classification yielded an average sensitivity of 88%, specificity of 99% and PPV of 95%, with an F ₁ -score range of 82%–95%. Incorporating free text increased LTC prevalence from 42.1% to 46.3%, reducing misclassification of MM diagnoses by identifying 12 626 additional patients with MM and 15 972 additional patients with at least one LTC. Discussion: In the course of workflow, general practitioners face barriers to accurate LTC coding or may simply annotate with text-based descriptions. Programmatic text classification has demonstrated high performance and identified many more patients receiving LTC care. Conclusions: Combining structured and unstructured data optimises MM detection in NZ general practices and has the potential to improve case management, follow-up care and allocation of healthcare resources.

Introduction: The recommended assessment intervals for primary prevention of cardiovascular disease (CVD) differ in major international guidelines. We aimed to provide empirical evidence on the optimal frequency of CVD risk assessment to inform future guidelines. Methods: We estimated the expected time to cross the 10-year CVD risk treatment threshold of 10% using extended two-stage landmarking for more than 2 million people using UK primary care electronic health records between April 2004 and May 2019 from the Clinical Practice Research Datalink GOLD Database (CPRD GOLD), which was linked to hospital admissions data from the Hospital Episodes Statistics (HES) dataset and national mortality records from the Office for National Statistics (ONS). We grouped people based on their sex, initial risk level and age, and computed various percentiles of the expected crossing times per group. Based on the percentiles, optimal assessment intervals were identified and their performance was evaluated comparing to the current recommended intervals in the UK. Results: Our results showed that the expected crossing times for people with lower initial risk were much longer than those with higher initial risk. Within each initial risk group, expected time to crossing the risk treatment thresholds was shorter in people aged ≥65 years. Based on the median expected crossing times, our recommended intervals for women with initial 10-year risk of 7.5%–10%, 5%–7.5%, 2.5%–5% or<2.5% are 3 (1 if ≥65 years old), 7 (4), 10 (6) and 10 (10) years, respectively; intervals for men are 2 (1), 5 (5), 9 (9) and 10 (10) years. These intervals outperformed the 5-yearly risk reassessment for all individuals currently recommended in the UK. Conclusions: Our evidence suggests that CVD risk assessment intervals for primary prevention should be stratified by sex, initial risk level and age. For the UK population, our method found risk assessment intervals that reduce the number of assessments required while shortening the waiting time to the next assessment for those most in need.

Background Cardiovascular disease (CVD) preventive medications are recommended for patients at high short-term CVD risk. As most younger people with multiple raised CVD risk factors levels have low short-term risk, they could be falsely reassured to take no action. Heart age—the chronological age of a hypothetical person with the same short-term absolute CVD risk as the patient being assessed, but with an ‘ideal’ risk profile—is a complementary relative CVD risk metric developed to encourage these younger patients to make long-term lifestyle changes. However, clinicians sometimes use heart age to inform medication decisions. We assessed the appropriateness of this practice by comparing heart age and short-term CVD risk. Methods New Zealand primary care patients are recruited to the PREDICT cohort when their CVD risk is assessed. PREDICT is an ongoing prospective study in one-third of New Zealand general practices, designed to derive CVD risk prediction algorithms. Five-year CVD risk was calculated for 35–74-year-old PREDICT participants using published equations. Heart age was calculated using non-smoking, systolic blood pressure of 120 mm Hg and total cholesterol/high-density lipoprotein ratio of 3.5, as the ‘ideal’ risk profile. CVD risk and heart age gaps (difference between chronological age and heart age) were compared. Results Among 371 676 PREDICT participants, 5-year CVD risk increased with age, approximately doubling every 10 years, whereas heart age gaps decreased with increasing age, approximately halving between 35 and 44-year olds and 65–74-year olds. There were 5–40-year heart age gap differences between groups with similar 5-year CVD risks, but different ages. Conclusion Short-term CVD risk and heart age are not interchangeable risk metrics. Short-term risk increases with increasing age whereas heart age gaps generally decline, with major differences between younger and older people with similar short-term risk. If heart age is used to inform medication decisions rather than encourage long-term lifestyle changes, older people at high short-term risk could be undertreated and younger people at low short-term risk could be unnecessarily medicated.

Background and Aims: Frailty is increasingly prevalent in people presenting with acute coronary syndrome (ACS). This high-risk group is typically excluded from trials of interventions in ACS, and there is uncertainty about the risks and benefits of invasive management. Methods: Patients with an ACS diagnosis between 2010 and 2015 in England were identified from Hospital Episode Statistics, with linked Office for National Statistics mortality data. Frailty was defined by the Hospital Frailty Risk Score. Causal inference analysis used regional variation in revascularization as an instrumental variable to estimate average treatment effects of revascularization on cardiovascular mortality up to 5 years in people presenting with ACS and low-, intermediate-, or high-risk frailty. Results: The analysis included 565 378 ACS patients, of whom 11.6% (n = 65 522) were at intermediate risk and 4.7% (n = 26 504) were at high risk of frailty. Intermediate and high frailty risks were associated with reduced likelihood of echocardiography, invasive angiography, or revascularization and increased likelihood of mortality and major adverse cardiovascular events compared with low frailty risk. Cardiovascular death at 5 years was 78.6%, 77.3%, and 75.7% in people at low, intermediate, and high frailty risk, respectively. Instrumental variable analysis suggested that revascularization resulted in a higher absolute reduction in cardiovascular mortality in high and intermediate frail risk patients compared with low risk at 1-year post-ACS. Conclusions: Frailty is common in people presenting with ACS, where cardiovascular causes are the principal mode of death. Revascularization is associated with short- and long-term survival benefits in people at intermediate and high risk of frailty after adjustment for measured and unmeasured confounders.