Evidence map›Paper›PMID 38988353›Full record

ArticleFrontiers in medicine2024

Optimizing time-in-target-range assessment for blood pressure: insights from a large-scale study with continual cuffless monitoring.

Naomi D L Fisher, Tiago P Almeida, David Perruchoud, Jay Shah, Josep Sola

Abstract read
In one paragraph

Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Naomi D L FisherBrigham and Women's Hospital, Boston, MA, United States.
Tiago P AlmeidaAktiia SA, Neuchâtel, Switzerland.
David PerruchoudAktiia SA, Neuchâtel, Switzerland.
Jay ShahAktiia SA, Neuchâtel, Switzerland.
Josep SolaAktiia SA, Neuchâtel, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Blood pressure (BP) time-in-target-range (TTR) is an emerging predictor of cardiovascular risk. Conventional BP methods are fundamentally unable to provide an optimal assessment of TTR, using irregular measurements separated by lengthy intervals. We investigated the optimal duration and frequency for reliable, practical TTR assessment in clinical settings using continual monitoring. Methods: This retrospective study analyzed 2.3 million BP readings from 5,189 European home users (55 ± 11 years, 82% male, BMI 28.0 ± 5.8) using a cuffless BP monitor (Aktiia SA). Systolic BP (SBP) data over 15 consecutive days were assessed (29 ± 11 readings/subject/24-h; 434 + 132 readings/subject/15-day). Subjects were classified into risk-related TTR groups based on 15-day SBP data (24-h, target 90-125 mmHg; ≥6 daytime readings). Various measurement frequencies and durations (1-14 days; 24-h/daytime; 2, 4 or ≥ 6 readings/day) were compared to this reference. Two specific configurations paralleling ambulatory ("One-Day-24 h") and home ("One-Week-Daytime") BP monitoring were selected for detailed analysis. Results: The reference TTR classified 63.0% of the subjects as high risk, 19.0% intermediate, and 18.0% low. "One-Day-24 h" schedule inaccurately classified 26% of subjects compared to the reference TTR, and "One-Week-Daytime" schedule inaccurately classified 45%. Classification accuracy with both schedules was high for subjects with very low or very high reference TTR, but poor otherwise. Accuracy of ≥90% in TTR classification only occurred with 7 days of continual 24-h monitoring. Discussion: For the first time, with the benefit of a cuffless device that measures BP with sufficient frequency and duration, practical use of TTR is enabled as a potentially enhanced metric to manage hypertension.

Indexed as

Blood pressurecardiovascular riskcuffless blood pressure monitorhypertension managementtime in target range

Identifiers

PMID38988353
PMCPMC11233795

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.