AI Article Synopsis

  • The study aimed to improve the accuracy of continuous, cuffless blood pressure (BP) monitoring in critically ill ICU patients by comparing two BP prediction models: a generalized model based on pulse arrival time (PAT) and more complex individualized models.
  • The researchers trained machine learning models on patient data and tested their accuracy across over 7,300 measurements, finding that the complex individualized models performed better in estimating systolic BP and mean arterial pressure (MAP) compared to the generalized model.
  • Ultimately, while both models showed promise, the complex individualized approach proved to be more accurate, particularly for systolic BP and MAP, thereby suggesting its potential for better clinical outcomes in hospitalized patients.

Article 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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10150697PMC
http://dx.doi.org/10.3389/fmed.2023.1154041DOI Listing

Publication Analysis

Top Keywords

pat-based model
24
complex individualized
24
generalized pat-based
20
individualized models
20
mmhg mmhg
16
model
10
non-invasive cuffless
8
cuffless blood
8
blood pressure
8
intensive care
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!