Predicting out-of-office blood pressure in the clinic for the diagnosis of hypertension in primary care: An economic evaluation
Sheppard JP., McManus RJ.
© 2018 Lippincott Williams and Wilkins. All rights reserved. Clinical guidelines in the United States and United Kingdom recommend that individuals with suspected hypertension should have ambulatory blood pressure (BP) monitoring to confirm the diagnosis. This approach reduces misdiagnosis because of white coat hypertension but will not identify people with masked hypertension who may benefit from treatment. The Predicting Out-of-Office Blood Pressure (PROOF-BP) algorithm predicts masked and white coat hypertension based on patient characteristics and clinic BP, improving the accuracy of diagnosis while limiting subsequent ambulatory BP monitoring. This study assessed the cost-effectiveness of using this tool in diagnosing hypertension in primary care. A Markov cost.utility cohort model was developed to compare diagnostic strategies: The PROOF-BP approach, including those with clinic BP .130/80 mm Hg who receive ambulatory BP monitoring as guided by the algorithm, compared with current standard diagnostic strategies including those with clinic BP .140/90 mm Hg combined with further monitoring (ambulatory BP monitoring as reference, clinic, and home monitoring also assessed). The model adopted a lifetime horizon with a 3-month time cycle, taking a UK Health Service/Personal Social Services perspective. The PROOF-BP algorithm was cost-effective in screening all patients with clinic BP .130/80 mm Hg compared with current strategies that only screen those with clinic BP .140/90 mm Hg, provided healthcare providers were willing to pay up to 20 000 ($26 000)/quality-adjusted life year gained. Deterministic and probabilistic sensitivity analyses supported the base-case findings. The PROOF-BP algorithm seems to be cost-effective compared with the conventional BP diagnostic options in primary care. Its use in clinical practice is likely to lead to reduced cardiovascular disease, death, and disability.