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

doi:10.3389/fmed.2023.1154041

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

Front Med (Lausanne). 2023 Apr 17:10:1154041. doi: 10.3389/fmed.2023.1154041. eCollection 2023.

Authors

Sondre Heimark^{1

2}, Kasper Gade Bøtker-Rasmussen^{3

4}, Alexey Stepanov³, Øyvind Gløersen Haga⁴, Victor Gonzalez⁴, Trine M Seeberg^{3

4}, Fadl Elmula M Fadl Elmula⁵, Bård Waldum-Grevbo¹

Affiliations

¹ Department of Nephrology, Oslo University Hospital, Ullevål, Oslo, Norway.
² Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
³ Aidee Health AS, Oslo, Norway.
⁴ Department of Smart Sensors and Microsystems, SINTEF Digital, Oslo, Norway.
⁵ Cardiorenal Research Centre, Oslo University Hospital, Ullevål, Oslo, Norway.

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.

Keywords: blood pressure; cuffless; intensive care unit; machine learning; pulse arrival time.