Balance of benefits and harms of different blood pressure targets in people with multiple chronic conditions: a quantitative benefit-harm assessment

Abstract

ObjectiveRecent studies suggest that a systolic blood pressure (SBP) target of 120 mm Hg is appropriate for people with hypertension, but this is debated particularly in people with multiple chronic conditions (MCC). We aimed to quantitatively determine whether benefits of a lower SBP target justify increased risks of harm in people with MCC, considering patient-valued outcomes and their relative importance.DesignHighly stratified quantitative benefit-harm assessment based on various input data identified as the most valid and applicable from a systematic review of evidence and based on weights from a patient preference survey.SettingOutpatient care.ParticipantsHypertensive patients, grouped by age, gender, prior history of stroke, chronic heart failure, chronic kidney disease and type 2 diabetes mellitus.InterventionsSBP target of 120 versus 140 mm Hg for patients without history of stroke.Primary and secondary outcome measuresProbability that the benefits of a SBP target of 120 mm Hg outweigh the harms compared with 140 mm Hg over 5 years (primary) with thresholds >0.6 (120 mm Hg better), <0.4 (140 mm Hg better) and 0.4 to 0.6 (unclear), number of prevented clinical events (secondary), calculated with the Gail/National Cancer Institute approach.ResultsConsidering individual patient preferences had a substantial impact on the benefit-harm balance. With average preferences, 120 mm Hg was the better target compared with 140 mm Hg for many subgroups of patients without prior stroke, especially in patients over 75. For women below 65 with chronic kidney disease and without diabetes and prior stroke, 140 mm Hg was better. The analyses did not include mild adverse effects, and apply only to patients who tolerate antihypertensive treatment.ConclusionsFor most patients, a lower SBP target was beneficial, but this depended also on individual preferences, implying individual decision-making is important. Our modelling allows for individualised treatment targets based on patient preferences, age, gender and co-morbidities.