An 11-item questionnaire to screen for AF, HF, and CAD in primary care
Proactive screening for symptoms: A simple method to improve early detection of unrecognized cardiovascular disease in primary care. Results from the Lifelines Cohort Study
Introduction and methods
Proactive screening has been shown to uncover unrecognized CVD in patients at risk, which may lead to prevention of CV events or prevention of progression to more severe CVD. For example, a trial in primary care patients detected more AF cases with screening compared to usual care . Screening may be even more beneficial in patients with COPD and T2DM, as shown by studies detecting HF in primary care patients with COPD and T2DM [2, 3], and CAD in diabetes patients and long-term smokers with and without COPD [4, 5]. However, primary care patients with COPD and T2DM are generally not screened for CVD symptoms during check-ups.
The present analysis evaluated the diagnostic value of symptoms for detection of unrecognized AF, HF and CAD in a general adult population and validated this diagnostic model in patients with COPD and/or T2DM. To this end, data was used from the population-based Lifelines cohort study , consisting of baseline questionnaire items on symptoms and health-related quality of life of participants (n=100311) aged ≥40 years and living in the Netherlands. Three multivariable analyses were performed for outcomes of unrecognized AF, unrecognized HF and unrecognized CAD. Unrecognized AF was diagnosed if AF was present on baseline ECG of participants but previously diagnosed AF was absent. Unrecognized HF was diagnosed if participants did not self-report HF at baseline, but did report newly recognized HF in the first follow-up questionnaire. Unrecognized CAD was diagnosed if participants did not report prior MI, PCI, or coronary artery bypass grafting (CABG) at baseline, but did report new MI, PCI, or CABG in the first follow-up questionnaire, or had evidence of prior MI on their baseline ECG. Predictors and respective scores from the three separate models were also combined into one diagnostic model, from which a corresponding 11-item questionnaire was derived for the simultaneous screening of all three outcomes.
- Unrecognized CVD was diagnosed in 1.3% of the participants (n=1325), of which 0.1% had unrecognized AF (n=131), 0.6% had unrecognized HF (n=599), and 0.7% had unrecognized CAD (n=687).
- In addition to age, sex and BMI, independent predictors for unrecognized AF were palpitations (c-statistic 0.899 [95%CI: 0.871-0.927]), for unrecognized HF they were palpitations, chest pain, dyspnea, exercise intolerance, health-related stress and expected health worsening (c-statistic 0.818 [95%CI: 0.800-0.836]), and for unrecognized CAD they were current smoking, chest pain, exercise intolerance and claudication (c-statistic 0.710 [95%CI: 0.690-0.730]).
- The combined diagnostic model had an area under the curve (AUC) of 0.752 (95%CI: 0.737–0.766) for the detection of the composite endpoint of unrecognized AF, HF, and/or CAD in participants from the total population.
- The combined diagnostic model was validated in participants with COPD and/or T2DM and resulted in an AUC of 0.753 (95%CI: 0.729-0.776). The AUC was 0.757 (95%CI: 0.734-0.781) if those with COPD were not given points for dyspnea.
This study showed that a single diagnostic model consisting of demographics and symptoms had substantial discriminative value for uncovering unrecognized AF, HF and CAD using data from a population-based cohort study in the Netherlands. Focusing on multiple CVDs during screening is important as symptoms may overlap between different CVDs. The developed questionnaire can help with pre-selecting people who should or should not undergo further screening for CVD. The questionnaire should first be externally validated and assessment of the added value to existing screening methods and patient outcomes is necessary, before the questionnaire can be implemented into practice.