Keywords: Chronic Disease , Longitudinal Study, Cardiometabolic Health, Real-World Evidence

Background:

The chronic disease management (CDM) programme in general practice, introduced in 2020, aims improve long-term clinical outcomes through structured, protocol-driven care. While widely implemented, there is limited published evidence on real-world patient trajectories under CDM in Irish primary care. This study evaluates clinical trends in a single practice over the initial years of CDM implementation.

Research questions:

We aimed to examine:
(1) participation rates and delivery under CDM,
(2) within-patient trends in clinical parameters across visits

Method:

We are conducting a longitudinal observational study using routinely collected health records from a single practice participating in the programme. All eligible patients with ≥4 independent CDM visit between June 2020 and May 2023 were invited. Metrics were grouped by visit rank (1–4) to assess trends in blood pressure, cholesterol, HbA1c, BMI, and eGFR. Values were averaged per visit; targets and change scores were calculated per patient. The initial 3 years of data have been collected and analysed; 2024 and 2025 data are being collected now.

Results:

Based on initial data, 956 patients enrolled in CDM (95% uptake), 43% were aged 70–79 and the average number of chronic disease diagnoses was 1.7 per patient. In patients with ≥4 CDM visits, mean systolic blood pressure declined from 142 mmHg at baseline, to 132 mmHg at visit 4, and with an increasing proportion achieving target control. LDL cholesterol and HbA1c (T2DM patients) showed modest improvements, while BMI remained stable. Prescribing patterns suggest appropriate use of statins and diabetes medications but require further analysis.

Conclusions:

Structured CDM participation was associated with modest improvements in blood pressure, lipids, and glycaemic control. Findings support the value of sustained engagement, with further follow-up needed to assess long-term benefit and prescribing impact.

Points for discussion:

Why do some risk factors (like blood pressure) improve, while others (like weight or smoking) don’t?

What’s the best way to use GP prescribing data to understand how treatments are working in the real world?

We often focus on people who improve — but how should we track and support those who start within target but slowly worsen?