Seasonal Blood Pressure Variation: A Neglected Confounder in Clinical Hypertension Research and Practice
Bibliographic record
Abstract
For more than a century blood pressure (BP) measured by the doctor in the office has been recognized as an indisputable predictor of cardiovascular morbidity and mortality.1,2 However, BP is known to show dynamic variability in response to the physical and emotional challenges of routine daily activities, which may confound any investigation involving BP changes.3 Many factors are known to influence BP readings,1 including the physical and emotional challenges of routine daily activities, the setting (office, home, work), the observer (doctor, nurse, self-measurement, automated unattended), the device (auscultatory, automated), the patient’s posture (seated, standing, lying), the time of measurement (morning, evening, nighttime sleep), the measurement schedule (number of readings and those averaged), talking, exercise, meals, smoking, coffee, alcohol, and time of antihypertensive drug intake. In recognizing these confounding factors, international recommendations have standardized the methodologies for BP evaluation in the office and out-of-the-office to minimize their influence and improve the accuracy of BP evaluation.4,5 In the American Journal of Hypertension Gepts et al.6 have added to this list of confounders by demonstrating how seasonal variations in BP can influence the interpretation of interventions aiming at improving BP control. In this study, when seasonality was not accounted for, a significant negative association of the study intervention on BP control was observed. However, when BP seasonality was taken into account, they showed that the intervention had no effect on BP control. It is surprising that this major confounder has been systematically ignored for so long. A recent review and meta-analysis of 47 published trials which examined seasonal changes in BP7 showed that the seasonal BP variation is a global phenomenon affecting all age groups, both sexes, and normotensive and hypertensive subjects. Meta-analysis of these trials showed an average seasonal BP decline in the high-temperature season of 6/3 mm Hg (systolic/diastolic) for office and home BP and 3.5/2 mm Hg for daytime ambulatory BP, and a small rise in nighttime ambulatory BP (1.3/0.5 mm Hg).7 Importantly, these changes were greater in treated hypertensive patients and in older individuals.7 Thus, these findings suggest that the magnitude of the seasonal BP change is comparable to the reduction that might be expected from an effective antihypertensive drug, particularly in elderly patients. The 2017 American College of Cardiology/American Heart Association2 as well as the 2018 European Society of Cardiology/European Society of Hypertension guidelines1 for hypertension do not mention the seasonal BP variation phenomenon, and no recommendations are provided for guiding the practicing physicians on its potential implications in treating hypertension. The European Society of Hypertension Working Group on BP Monitoring and Cardiovascular Variability recently published a consensus statement on the seasonal variation in BP,8 in which the evidence on the epidemiology, pathophysiology, relevance, and magnitude of the seasonal BP variation was presented together with recommendations for practicing physicians.8 The potential impact of the seasonal BP variation on the interpretation of clinical hypertension research was also discussed,8 leading to the conclusion that failure to take this factor into account may have distorted the findings of numerous published clinical hypertension trials and epidemiological surveys (Table 1). Consequences of not adjusting the results of clinical hypertension trials for the seasonal BP variation Abbreviation: BP, blood pressure. Consequences of not adjusting the results of clinical hypertension trials for the seasonal BP variation Abbreviation: BP, blood pressure. In conclusion, the article by Gepts et al.6 clearly demonstrated that the environmental temperature must be taken into account in clinical hypertension research when evaluating or comparing BP measurements taken on different occasions throughout different seasons. Clinical trials using office or out-of-office BP measurements as efficacy measures, which last long enough to be influenced by ambient temperature change, need to be adjusted for the seasonal variation in BP. Such an adjustment should be proportional to the change in environmental temperature and should be based on BP changes observed at the same time interval without intervention and assessed using the same BP measurement methodology. The authors declared no conflict of interest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.022 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".