Objective: Blood pressure (BP) is routinely monitored by healthcare providers and during drug development. For improvement in accuracy, several oscillometric devices perform multiple sequential inflations, rather than relying on a single inflation, to determine a mean systolic and diastolic blood pressure (SBP, DBP). Blood pressure is known to vary physiologically throughout a day, and over the course of days and weeks, due to intrinsic and extrinsic factors. Little data has been published describing the variability of SBP and DBP recorded minutes apart using oscillometric methods (i.e. within session variability). Design and method: We investigated the stability of within session SBP and DBP across 4 datasets by evaluating differences between 3 consecutive inflations recorded 1-2 minutes apart using a clinically validated oscillometric BP monitor that passed the ANSI/AAMI/ISO 2013, ESH-IP 2002 and BHS 1993 protocols (Table 1). These datasets included patients with controlled or uncontrolled hypertension. Results: Data were available from over 10.000 measurement pairs. The differences between inflation 1 and 2 were within ±10, 20 and 30 mmHg for 78.1, 94.4 and 97.9% of the SBP measurements, respectively. For the DBP, 90.7, 95.5 and 97.3 % of the differences between inflation 1 and 2 were within ±10, 15 and 20 mmHg, respectively. A similar trend was seen between inflation 2 and 3. Table 1: Categorical Analysis of BP changes between consecutive inflations. Conclusions: Our data demonstrate substantial variability between consecutive oscillometric BP measurements performed a few minutes apart. Over 20% of the time, consecutive inflations show an absolute change in SBP of at least 10 mmHg. It is unclear whether this represents true physiologic changes in BP or is partly artefactual due to failure of a device's algorithm to determine BP accurately from the oscillometric waveform. These data suggest that criteria for within session variability for automated oscillometric blood pressure devices should be established, including a threshold for repeating the session after 10 minutes of rest.
Background Sites participating in clinical trials may not have the expertise and infrastructure to accurately measure cardiac intervals on 12-lead ECGs and rely heavily on the automated ECG device generated results for clinical decision-making. Methods Using a dataset of over 260,000 ECGs collected in clinical oncology studies, we investigated the mean difference and the rate of false negative results between the digital ECG machine QTc and QRS measurements compared to those obtained by a centralized ECG core lab. Results The mean differences between the core lab and the automated algorithm QTcF and QRS measurements were + 1.8 ± 16.0 ms and − 1.0 ± 8.8 ms, respectively. Among the ECGs with a centralized QTcF value > 450 or > 470 ms, 39.5% and 47.8% respectively had a device reported QTcF value ≤ 450 ms or ≤ 470 ms. Among the ECGs with a centrally measured QTcF > 500 ms, 55.8% had a device reported value ≤ 500 ms. Automated QTcF measurements failed to detect a QTcF increase > 60 ms for 53.9% of the ECGs identified by the core lab. Automated measurements also failed to detect QRS prolongation, though to a lesser extent than failures to detect QTc prolongation. Among the ECGs with a centrally measured QRS > 110 or 120 ms, 7.9% and 7.3% respectively had a device reported QRS value ≤ 110 ms or ≤ 120 ms. Conclusion Relying on automated measurements from ECG devices for patient inclusion and treatment (dis)continuation decisions poses a potential risk to patients participating in oncology studies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.