Evidence map›Paper›PMID 37138759›Full record

ArticleFrontiers in medicine2023

Accuracy of non-invasive cuffless blood pressure in the intensive care unit: Promises and challenges.

Sondre Heimark, Kasper Gade Bøtker-Rasmussen, Alexey Stepanov, Øyvind Gløersen Haga, Victor Gonzalez, Trine M Seeberg, Fadl Elmula M Fadl Elmula, Bård Waldum-Grevbo

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Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.2field-weighted citation impact, top 13% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

5 citing papers in PubMed, 7 citations in OpenAlex.

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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

8 authors at 3 institutions in 1 country.

Sondre HeimarkDepartment of Nephrology, Oslo University Hospital, Ullevål, Oslo, Norway.
Kasper Gade Bøtker-RasmussenAidee Health AS, Oslo, Norway.
Alexey StepanovAidee Health AS, Oslo, Norway.
Øyvind Gløersen HagaDepartment of Smart Sensors and Microsystems, SINTEF Digital, Oslo, Norway.
Victor GonzalezDepartment of Smart Sensors and Microsystems, SINTEF Digital, Oslo, Norway.
Trine M SeebergAidee Health AS, Oslo, Norway.
Fadl Elmula M Fadl ElmulaCardiorenal Research Centre, Oslo University Hospital, Ullevål, Oslo, Norway.
Bård Waldum-GrevboDepartment of Nephrology, Oslo University Hospital, Ullevål, Oslo, Norway.
SINTEF DigitalOslo University Hospital · NOUniversity of Oslo · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Continuous non-invasive cuffless blood pressure (BP) monitoring may reduce adverse outcomes in hospitalized patients if accuracy is approved. We aimed to investigate accuracy of two different BP prediction models in critically ill intensive care unit (ICU) patients, using a prototype cuffless BP device based on electrocardiogram and photoplethysmography signals. We compared a pulse arrival time (PAT)-based BP model (generalized PAT-based model) derived from a general population cohort to more complex and individualized models (complex individualized models) utilizing other features of the BP sensor signals. Methods: Patients admitted to an ICU with indication of invasive BP monitoring were included. The first half of each patient's data was used to train a subject-specific machine learning model (complex individualized models). The second half was used to estimate BP and test accuracy of both the generalized PAT-based model and the complex individualized models. A total of 7,327 measurements of 15 s epochs were included in pairwise comparisons across 25 patients. Results: The generalized PAT-based model achieved a mean absolute error (SD of errors) of 7.6 (7.2) mmHg, 3.3 (3.1) mmHg and 4.6 (4.4) mmHg for systolic BP, diastolic BP and mean arterial pressure (MAP) respectively. Corresponding results for the complex individualized model were 6.5 (6.7) mmHg, 3.1 (3.0) mmHg and 4.0 (4.0) mmHg. Percentage of absolute errors within 10 mmHg for the generalized model were 77.6, 96.2, and 89.6% for systolic BP, diastolic BP and MAP, respectively. Corresponding results for the individualized model were 83.8, 96.2, and 94.2%. Accuracy was significantly improved when comparing the complex individualized models to the generalized PAT-based model in systolic BP and MAP, but not diastolic BP. Conclusion: A generalized PAT-based model, developed from a different population was not able to accurately track BP changes in critically ill ICU patients. Individually fitted models utilizing other cuffless BP sensor signals significantly improved accuracy, indicating that cuffless BP can be measured non-invasively, but the challenge toward generalizable models remains for future research to resolve.

Indexed as

blood pressurecufflessintensive care unitmachine learningpulse arrival time

Identifiers

PMID37138759
PMCPMC10150697
OpenAlexW4366086201

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.