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Cited by 16Non-Invasive Vital Sign MonitoringHemodynamic Monitoring and TherapyCardiovascular Health and Disease Prevention

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Clinical Study of Continuous Non-Invasive Blood Pressure Monitoring in Neonates

Open access
Rao, Anoop, Eskandar-Afshari, Fatima, Weiner, Ya’el, Billman, Elle, McMillin, Alexandra, Sella, Noa, Roxlo, Thomas, Liu, Junjun, Leong, Weyland, Helfenbein, Eric, Walendowski, Alan, Muir, Arthur, Joseph, Alexandria, Verma, Archana, Ramamoorthy, Chandra, Honkanen, Anita, Green, Gabrielle, Drake, Keith, Govindan, Rathinaswamy B., Rhine, William, Quan, Xina·2023·Sensors
DOI 10.3390/s23073690PMC PMC10098632
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Abstract

The continuous monitoring of arterial blood pressure (BP) is vital for assessing and treating cardiovascular instability in a sick infant. Currently, invasive catheters are inserted into an artery to monitor critically-ill infants. Catheterization requires skill, is time consuming, prone to complications, and often painful. Herein, we report on the feasibility and accuracy of a non-invasive, wearable device that is easy to place and operate and continuously monitors BP without the need for external calibration. The device uses capacitive sensors to acquire pulse waveform measurements from the wrist and/or foot of preterm and term infants. Systolic, diastolic, and mean arterial pressures are inferred from the recorded pulse waveform data using algorithms trained using artificial neural network (ANN) techniques. The sensor-derived, continuous, non-invasive BP data were compared with corresponding invasive arterial line (IAL) data from 81 infants with a wide variety of pathologies to conclude that inferred BP values meet FDA-level accuracy requirements for these critically ill, yet normotensive term and preterm infants.

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Crossref · 10.3390/s23073690

Cites

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Figures

Figure 1 (a) Boppli sensor schematic: illustration of the sensor and wearable, continuous, non-invasive BP device for neonates (cross-sectional at the top; aerial view at the bottom); (b) Boppli sensor placement around the foot of an infant.
Figure 1 (a) Boppli sensor schematic: illustration of the sensor and wearable, continuous, non-invasive BP device for neonates (cross-sectional at the top; aerial view at the bottom); (b) Boppli sensor placement around the foot of an infant.CC BY · PubMed Central
Figure 2 Overview of the working of the sensor and steps to infer blood pressure.
Figure 2 Overview of the working of the sensor and steps to infer blood pressure.CC BY · PubMed Central
Figure 3 Bland–Altman plots for SBP, DBP, and MAP: (a) values averaged over each individual for 81 test patients; and (b) all data values without averaging. Bland–Altman plots represent a scatter plot of average versus difference of BP readings constructed for SBP, DBP, and MAP. Points are color coded by patient weight. Red dotted lines indicate 2 * SD of the calculated values. Green dotted lines indicate targets based on FDA guidelines for accuracy of MAE ≤ 5 mmHg and 2 * SD ≤ 2 * 8 mmHg. The figures were generated using JMP software version 16.2.0.
Figure 3 Bland–Altman plots for SBP, DBP, and MAP: (a) values averaged over each individual for 81 test patients; and (b) all data values without averaging. Bland–Altman plots represent a scatter plot of average versus difference of BP readings constructed for SBP, DBP, and MAP. Points are color coded by patient weight. Red dotted lines indicate 2 * SD of the calculated values. Green dotted lines indicate targets based on FDA guidelines for accuracy of MAE ≤ 5 mmHg and 2 * SD ≤ 2 * 8 mmHg. The figures were generated using JMP software version 16.2.0.CC BY · PubMed Central
Figure 4 Predicted BP values vs. ground truth values. The blue line represents an unconstrained linear fit to the data. The green line is a linear fit constrained by a zero-intercept. The grey line is the identity (ID) fit where the predicted values equal the ground truth values. The figures were generated using JMP software version 16.2.0.
Figure 4 Predicted BP values vs. ground truth values. The blue line represents an unconstrained linear fit to the data. The green line is a linear fit constrained by a zero-intercept. The grey line is the identity (ID) fit where the predicted values equal the ground truth values. The figures were generated using JMP software version 16.2.0.CC BY · PubMed Central
Figure 5 Effect of gestational age (GA) on efficacy. Groups categorized by prematurity are compared through mean diamonds (green) and Tukey–Kramer circles (black). EPT (<28 wks GA), MPT (28–37 wks GA), FT (≥38 wks GA). Overlap between diamonds and circles indicates there is no significant difference between groups. Mean diamonds are contained within the FDA guidelines for accuracy (dotted green lines). Targets for 95% confidence levels are indicated with dashed green lines. Points are color-coded by patient weight.
Figure 5 Effect of gestational age (GA) on efficacy. Groups categorized by prematurity are compared through mean diamonds (green) and Tukey–Kramer circles (black). EPT (<28 wks GA), MPT (28–37 wks GA), FT (≥38 wks GA). Overlap between diamonds and circles indicates there is no significant difference between groups. Mean diamonds are contained within the FDA guidelines for accuracy (dotted green lines). Targets for 95% confidence levels are indicated with dashed green lines. Points are color-coded by patient weight.CC BY · PubMed Central
Figure 6 Effect of sex on efficacy. Female and male patient subsets are compared through mean diamonds (green) and Tukey–Kramer circles (black). Minimal overlap between diamonds and circles indicates there is a significant difference between the two groups. Mean diamonds are contained within the FDA guidelines for accuracy (dotted green lines). Targets for 95% confidence levels are indicated with dashed green lines. Points are color-coded by patient weight.
Figure 6 Effect of sex on efficacy. Female and male patient subsets are compared through mean diamonds (green) and Tukey–Kramer circles (black). Minimal overlap between diamonds and circles indicates there is a significant difference between the two groups. Mean diamonds are contained within the FDA guidelines for accuracy (dotted green lines). Targets for 95% confidence levels are indicated with dashed green lines. Points are color-coded by patient weight.CC BY · PubMed Central

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Research works printed but not linked: 1

  1. Tareerath, 2018, Comparison of the Incidences of Cuff-Related Trauma after Non-Invasive Arterial Blood Pressure Measurement with and without Padding in Patients Undergoing Elective Surgery, J. Med. Assoc. Thail. Chotmaihet Thangphaet, 101, 1

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Metadata

Authors
Rao, A., Eskandar-Afshari, F., Weiner, Y., Billman, E., McMillin, A., Sella, N., Roxlo, T., Liu, J., Leong, W., Helfenbein, E., Walendowski, A., Muir, A., Joseph, A., Verma, A., Ramamoorthy, C., Honkanen, A., Green, G., Drake, K., Govindan, R. B., Rhine, W., & Quan, X.
Affiliations
  • Stanford Medicine (US)
  • University of Southern California (US)
  • Children's Hospital Los Angeles (US)
  • Stanford University (US)
  • PyrAmes Inc., Cupertino, CA 95014, USA
  • Philips Healthcare, Sunnyvale, CA 94085, USA
  • Children's National (US)
Corresponding author
Rao, Anoop
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2023-04-02
Volume
23
Issue
7
Pages
3690
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s23073690
PMC
PMC10098632
OpenAlex
W4362578520
Cited by
14
References
20
Citations by year
2026: 2 · 2025: 10 · 2024: 1 · 2023: 1
FWCI
1.11
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • NIH SBIR (1R43HD101175, P50FD006430)
  • Food and Drug Administration (1R43HD101175, P50FD006430)
  • Southwest Pediatric Device Consortium (1R43HD101175, P50FD006430)
  • Stanford’s Maternal and Child Health Research Institute (1R43HD101175, P50FD006430)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
  • Health Sciences › Medicine › Surgery › Hemodynamic Monitoring and Therapy
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Disease Prevention
Keywords
non-invasive blood pressure monitoring, neonates, preterm infants, capacitive sensors, pulse waveform, artificial neural networks, wearable devices, cardiovascular instability, intensive care
MeSH terms
Blood Pressure, Blood Pressure Determination, Humans, Infant, Infant, Newborn, Infant, Premature, Wrist, Arterial Pressure

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