Simultaneously measured inter-arm blood pressure difference is not associated with pulse wave velocity in a clinical dataset of at-risk hypertensive patients.

Recent analysis of systolic inter-arm differences in blood pressure from the INTERPRESS-IPD Collaboration suggest an association with increased all-cause mortality, cardiovascular mortality and cardiovascular events. Previous studies have demonstrated associations with other risk parameters. We aimed to reproduce these associations in a cohort of 199 treated, at-risk hypertensive patients with pulse wave velocity (PWV) as a surrogate marker of cardiovascular (CV) damage. Simultaneously measured inter-arm blood pressure (BP) differences, 24 hour ambulatory BP and PWV were measured in 199 treated patients from a tertiary hospital hypertension outpatient clinic. Associations between systolic inter-arm BP difference and PWV were analyzed with uni- and multi-variate regression models. Out of 199 participants, 90 showed an inter-arm BP difference of more than 5 mmHg. The inter-arm difference was not associated with PWV. Furthermore, neither observed single BP measurements nor 24 hour ambulatory BP was associated with inter-arm BP differences. In our clinical patient cohort we failed to observe an association between inter-arm BP differences and PWV. Mode of assessment, study design and the sample characteristics of this treated, hypertensive cohort may have contributed to the negative findings. The limited sample size of the study poses a challenge to the detection of smaller effects in our study.

Automatic data extraction from 24 hour blood pressure measurement reports of a large multicenter clinical trial.

BACKGROUND AND OBJECTIVES: Ambulatory blood pressure monitoring (ABPM) is usually reported in descriptive values such as circadian averages and standard deviations. Making use of the original, individual blood pressure measurements may be advantageous, particularly for research purposes, as this increases the flexibility of the analytical process, enables alternative statistical analyses and provide novel insights. Here we describe the development of a new multistep, hierarchical data extraction algorithm to collect raw data from .pdf reports and text files as part of a large multi-center clinical study. METHODS: Original reports were saved in a nested file system, from which they were automatically extracted, read and saved into databases with custom made programs written in Python 3. Data were further processed, cleaned and relevant descriptive statistics such as averages and standard deviations calculated according to a variety of definitions of day- and night-time. Additionally, data control mechanisms for manual review of the data and programmatic auto-detection of extraction errors was implemented as part of the project. RESULTS: The developed algorithm extracted 97% of the data automatically, the missing data consisted mostly of reports that were saved incorrectly or not formatted in the specified way. Manual checks comparing samples of the extracted data to original reports indicated a high level of accuracy of the extracted data, no errors introduced due to flaws in the extraction software were detected in the extracted dataset. CONCLUSIONS: The developed multistep, hierarchical data extraction algorithm facilitated collection from different file formats and paired with database cleaning and data processing steps led to an effective and accurate assembly of raw ABPM data for further and adjustable analyses. Manual work was minimized while data quality was ensured with standardized, reproducible procedures.