Patients attending for the first time to a tertiary hypertension unit represent a sample of the general population of hypertensive patients from a region. Even though it is a biased sample, the longitudinal analysis with time of the data of these patients represents in part real life data and may show trends in adherence to guidelines by their primary care physicians and by patients themselves. Our tertiary hypertension unit has a computerized medical record with more than hundred structured items recorded at each visit. We analyzed changes in patients and treatment characteristics referred to our center over 15 years.Design and method:
We extracted the data of the first visit of all patients attending for the first time our unit between 07/2000 and 06/2015 from our Clinical DataWarehouse. We analyzed patients’ characteristics and treatment evolution between three periods of 5 years. We then analyzed treatment determinants among periods using multivariate analyses, taking into account all patients characteristics.Results:
A total of 17856 patients were included in the study. The prevalence of comorbidities (diabetes, dyslipidemia, renal and heart failure, coronary artery disease, obesity) declared by patients increased with time whereas age remained stable over the three periods. The prevalence of smokers (current or past) decreased with time. Patients declared more frequently regular physical activity with time. Familial history of cardiovascular events strongly decreased. The number of patients treated with at least one antihypertensive treatment strongly increased as well as the number of antihypertensive classes per patient. The prescription rate of angiotensin receptor blockers and calcium-channel blockers increased whereas that of loop diuretics and beta blockers decreased with time. In a multivariate analysis, each anti-hypertensive class was associated with almost all recorded patients’ characteristics.Conclusions:
Patients’ characteristics and treatment strategies strongly evolved over the last 15 years in Paris area. We showed that treatment strategy in real life was based on many patients’ characteristics. Therefore, clinicians personalized their treatment decision by taking into account all patients’ characteristics. This study offers the opportunity to provide real life algorithms for personalized treatment strategy.