Comparing secondary prevention for patients with coronary heart disease and stroke attending Australian general practices: a cross-sectional study using nationwide electronic database.

OBJECTIVES: To compare secondary prevention care for patients with coronary heart disease (CHD) and stroke, exploring particularly the influences due to frequency and regularity of primary care visits. SETTING: Secondary prevention for patients (≥18 years) in the National Prescription Service administrative electronic health record database collated from 458 Australian general practice sites across all states and territories. DESIGN: Retrospective cross-sectional and panel study. Patient and care-level characteristics were compared for differing CHD/stroke diagnoses. Associations between the type of cardiovascular diagnosis and medication prescription as well as risk factor assessment were examined using multivariable logistic regression. PARTICIPANTS: Patients with three or more general practice encounters within 2 years of their latest visit during 2016-2020. OUTCOME MEASURES: Proportions and odds ratios (ORs) for (1) prescription of antihypertensives, antilipidaemics and antiplatelets and (2) assessment of blood pressure (BP) and low-density lipoprotein cholesterol (LDL-C) in patients with stroke only compared against those with CHD only and those with both conditions. RESULTS: There were 111 892 patients with CHD only, 27 863 with stroke only and 9791 with both conditions. Relative to patients with CHD, patients with stroke were underprescribed antihypertensives (70.8% vs 82.8%), antilipidaemics (63.1% vs 78.7%) and antiplatelets (42.2% vs 45.7%). With sociodemographic factors, comorbidities and level of care considered as covariates, the odds of non-prescription of any recommended secondary prevention medications were higher in patients with stroke only (adjusted OR 1.37; 95% CI (1.31, 1.44)) compared with patients with CHD only. Patients with stroke only were also more likely to have neither BP nor LDL-C monitored (adjusted OR 1.26; 95% CI (1.18, 1.34)). Frequent and regular general practitioner encounters were independently associated with the prescription of secondary prevention medications (p<0.001). CONCLUSIONS: Secondary prevention management is suboptimal in cardiovascular disease patients and worse post-stroke compared with post-CHD. More frequent and regular primary care encounters were associated with improved secondary prevention.

Factors influencing blood pressure control in patients with atrial fibrillation and hypertension in Australian primary care.

OBJECTIVE: This study explored factors that may influence blood pressure (BP) control in patients with atrial fibrillation (AF) with hypertension. METHODS: Cross-sectional retrospective analysis of the MedicineInsight database which includes de-identified electronic health records from general practices (GPs) across Australia. BP control was assessed in patients with diagnosed AF and hypertension (controlled BP defined as <140/90 mm Hg). We explored BP control, factors influencing BP control and likelihood of receiving guideline-recommended treatment. RESULTS: 34 815 patients with AF and hypertension were included; mean age was 76.9 (10.2 SD) years and 46.2% were female. 38.0% had uncontrolled BP. Women (OR 0.72; 95% CI 0.68, 0.76; p<0.001) and adults ≥75 years (OR 0.78; 95% CI 0.70, 0.86; p<0.001) were less likely to have controlled BP. Greater continuity of care (CoC; that is, visits with the same clinician) and having frequent GP visits were associated with higher odds of controlled BP (model 1: CoC, OR 1.29; 95% CI 1.20, 1.40, p<0.001; GP visits, OR 1.71; 95% CI 1.58, 1.85, p<0.001) and a greater likelihood of being prescribed ≥2 types of BP-lowering medicines (model 2: CoC, OR 1.12; 95% CI 1.03, 1.23; p=0.011; GP visits, OR 1.80; 95% CI 1.63, 1.98; p<0.001). CONCLUSIONS: Uncontrolled BP was more likely in women and adults ≥75 years. Patients who had frequent GP visits with the same clinician were more likely to have BP controlled and receive guideline-recommended antihypertensive treatment. This suggests that targeting these primary care factors could potentially improve BP control and subsequently reduce stroke risk in patients with AF.

Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: a systematic review and meta-analysis.

BACKGROUND: Cardiovascular disease (CVD) risk prediction is important for guiding the intensity of therapy in CVD prevention. Whilst current risk prediction algorithms use traditional statistical approaches, machine learning (ML) presents an alternative method that may improve risk prediction accuracy. This systematic review and meta-analysis aimed to investigate whether ML algorithms demonstrate greater performance compared with traditional risk scores in CVD risk prognostication. METHODS AND RESULTS: MEDLINE, EMBASE, CENTRAL, and SCOPUS Web of Science Core collections were searched for studies comparing ML models to traditional risk scores for CVD risk prediction between the years 2000 and 2021. We included studies that assessed both ML and traditional risk scores in adult (≥18 year old) primary prevention populations. We assessed the risk of bias using the Prediction Model Risk of Bias Assessment Tool (PROBAST) tool. Only studies that provided a measure of discrimination [i.e. C-statistics with 95% confidence intervals (CIs)] were included in the meta-analysis. A total of 16 studies were included in the review and meta-analysis (3302 515 individuals). All study designs were retrospective cohort studies. Out of 16 studies, 3 externally validated their models, and 11 reported calibration metrics. A total of 11 studies demonstrated a high risk of bias. The summary C-statistics (95% CI) of the top-performing ML models and traditional risk scores were 0.773 (95% CI: 0.740-0.806) and 0.759 (95% CI: 0.726-0.792), respectively. The difference in C-statistic was 0.0139 (95% CI: 0.0139-0.140), P < 0.0001. CONCLUSION: ML models outperformed traditional risk scores in the discrimination of CVD risk prognostication. Integration of ML algorithms into electronic healthcare systems in primary care could improve identification of patients at high risk of subsequent CVD events and hence increase opportunities for CVD prevention. It is uncertain whether they can be implemented in clinical settings. Future implementation research is needed to examine how ML models may be utilized for primary prevention.This review was registered with PROSPERO (CRD42020220811).