Journal
HYPERTENSION
Volume 67, Issue 5, Pages 941-950Publisher
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1161/HYPERTENSIONAHA.115.07108
Keywords
ambulatory blood pressure monitoring; hypertension; masked hypertension; white coat hypertension
Categories
Funding
- Medical Research Council (MRC) Strategic Skills Post-doctoral Fellowship [MR/K022032/1]
- National Institute for Health Research (NIHR) Programme [RP-PG-1209-10051]
- NIHR
- NIHR University College London Hospitals Biomedical Research Centre
- NIHR School for Primary Care Research
- Theme Leader of the NIHR Oxford Biomedical Research Centre
- NIHR CLAHRC Oxford
- Medical Research Council [MR/K022032/1, MC_PC_13090] Funding Source: researchfish
- National Institute for Health Research [NIHR-RP-02-12-015, NF-SI-0514-10011, IS-SPC-0514-10043, NF-SI-0509-10222, CDF/01/017, NF-SI-0611-10273, RP-PG-1209-10051, NF-SI-0514-10121] Funding Source: researchfish
- MRC [MC_PC_13090, MR/K022032/1] Funding Source: UKRI
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Patients often have lower (white coat effect) or higher (masked effect) ambulatory/ home blood pressure readings compared with clinic measurements, resulting in misdiagnosis of hypertension. The present study assessed whether blood pressure and patient characteristics from a single clinic visit can accurately predict the difference between ambulatory/ home and clinic blood pressure readings (the home-clinic difference). A linear regression model predicting the homeclinic blood pressure difference was derived in 2 data sets measuring automated clinic and ambulatory/ home blood pressure (n=991) using candidate predictors identified from a literature review. The model was validated in 4 further data sets (n=1172) using area under the receiver operator characteristic curve analysis. A masked effect was associated with male sex, a positive clinic blood pressure change (difference between consecutive measurements during a single visit), and a diagnosis of hypertension. Increasing age, clinic blood pressure level, and pulse pressure were associated with a white coat effect. The model showed good calibration across data sets (Pearson correlation, 0.48-0.80) and performed well-predicting ambulatory hypertension (area under the receiver operator characteristic curve, 0.75; 95% confidence interval, 0.72-0.79 [systolic]; 0.87; 0.85-0.89 [diastolic]). Used as a triaging tool for ambulatory monitoring, the model improved classification of a patient's blood pressure status compared with other guideline recommended approaches (93% [92% to 95%] classified correctly; United States, 73% [70% to 75%]; Canada, 74% [71% to 77%]; United Kingdom, 78% [76% to 81%]). This study demonstrates that patient characteristics from a single clinic visit can accurately predict a patient's ambulatory blood pressure. Usage of this prediction tool for triaging of ambulatory monitoring could result in more accurate diagnosis of hypertension and hence more appropriate treatment.
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