Predicting Blood Pressure Decreases During Intervals of Non-invasive Blood Pressure Measurement Using Pulse Wave Transit Time

Q: What is the clinical evidence from this study?

Study design: Observational. Population: Intraoperative blood pressure fluctuations during non-cardiac surgery under general anesthesia (n=61). Intervention: Pulse wave transit time (PWTT), heart rate, and perfusion index algorithm vs. Invasive arterial mean arterial pressure (aMAP). Primary outcome: Sensitivity and specificity for predicting ≥ 20% fluctuations of invasive arterial mean arterial pressure (aMAP) during 5-minute noninvasive blood pressure intervals.

Q: What does this research mean for the field?

A noninvasive algorithm incorporating pulse wave transit time, heart rate, and perfusion index predicts ≥20% mean arterial pressure fluctuations within 5 minutes with 78% sensitivity and 77% specificity. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Daisuke Toyoda; Saori Kai; Satoko Isonishi; Yasumasa Sakamoto; Yuichi Maki; Yoshifumi Kotake

doi:10.21203/rs.3.rs-11323749/v1

October 27, 2026Open Access

Predicting Blood Pressure Decreases During Intervals of Non-invasive Blood Pressure Measurement Using Pulse Wave Transit Time

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Key result

Noninvasive algorithm predicts rapid blood pressure fluctuations with ~78% sensitivity.

n=61

Why the study?

This study aimed to develop and validate an algorithm using pulse wave transit time from electrocardiogram and pulse oximetry data to predict mean blood pressure fluctuations of 20% or greater during the 5 minutes interval between noninvasive blood pressure measurements during general anesthesia.

Does a noninvasive algorithm combining pulse wave transit time, heart rate, and perfusion index accurately predict ≥20% fluctuations in invasive mean arterial pressure during 5-minute NIBP intervals in patients undergoing non-cardiac surgery under general anesthesia?

Study Design

Type

Observational (n=61)

Multicenter

No

Structured PICO

Does a noninvasive algorithm combining pulse wave transit time, heart rate, and perfusion index accurately predict ≥20% fluctuations in invasive mean arterial pressure during 5-minute NIBP intervals in patients undergoing non-cardiac surgery under general anesthesia?

P

Population

61 adults undergoing scheduled non-cardiac surgery under general anesthesia for ≥3 hours requiring invasive arterial pressure monitoring, without preoperative arrhythmias or lateral/prone positioning, evaluated intraoperatively across derivation (n=31) and validation (n=30) phases.

E

Exposure

Noninvasive mean arterial pressure prediction algorithm (pMAP) calculated every 8 heartbeats using pulse wave transit time (PWTT) from ECG and finger pulse oximetry, combined with heart rate and perfusion index (PI), referenced to 5-minute intermittent noninvasive blood pressure (NIBP) measurements.

C

Comparator

Direct invasive radial arterial mean blood pressure (aMAP) recorded every 8 heartbeats.

O

Outcome

Sensitivity, specificity, positive predictive value, and negative predictive value of pMAP (using a 16% fluctuation threshold) for predicting ≥20% fluctuations in invasive aMAP during the 5-minute interval between NIBP measurements.surrogate

Limitations

Requires a sinus rhythm and assumes that the positions of the ECG electrodes and pulse oximeter sensor remain unchanged; not verified in patients with arrhythmias or position changes.
Evaluated blood pressure fluctuations without distinguishing between decreases and increases.
While pMAP predictions are based on NIBP measurements, IBP was used as a reference for evaluation, reflecting both measurement method bias and pMAP estimation bias.
The current system does not include a function to automatically trigger NIBP measurements based on the pMAP.
Targeted patients clinically indicated for invasive arterial pressure monitoring with cardiovascular comorbidities, whereas the program is primarily intended for patients not indicated for IBP.
Requires sinus rhythm and fixed positions of ECG electrodes and pulse oximeter sensor; unverified in patients with arrhythmias or requiring position changes
Evaluated blood pressure fluctuations without distinguishing between blood pressure decreases and increases
Used IBP as the reference standard while calibrating pMAP to NIBP, confounding measurement method bias with pMAP estimation bias
The tested system lacked an active function to automatically trigger additional NIBP measurements based on pMAP
Enrolled patients with clinical indications for invasive arterial monitoring, limiting generalizability to lower-risk patients monitored solely with NIBP

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Implication

Continuous noninvasive monitoring of pulse wave transit time, heart rate, and perfusion index can predict ≥20% intraoperative mean arterial pressure fluctuations between routine 5-minute cuff inflations with 78% sensitivity and 77% specificity, potentially enabling earlier detection of intraoperative hypotension.

Abstract

Background To develop and validate an algorithm using pulse wave transit time (PWTT) obtained from electrocardiogram and pulse oximetry data to predict mean blood pressure fluctuations of 20% or greater occurring during the 5 minutes interval between noninvasive blood pressure measurements during general anesthesia. Methods Data were prospectively collected from 61 non-cardiac surgery patients requiring invasive arterial pressure monitoring. These subjects were divided into derivation phase and validation phase. In the derivation phase, three algorithms to predict MAP (predicted MAP, pMAP) were evaluated: one using PWTT every 64 heart beats, one using PWTT, heart rate and perfusion index every 64 heart beats, and one using PWTT, heart rate and perfusion index every 8 heart beats. The best performing algorithm was validated in the subsequent validation phase. The primary endpoint was sensitivity and specificity for predicting ≥ 20% fluctuations of aMAP. The secondary endpoint was the bias±precision of pMAP values compared to aMAP. Results In the derivation phase involving 31 cases, the algorithm using PWTT, heart rate, and perfusion index every 8 beats demonstrated highest sensitivity and second-best specificity and selected for validation. In the validation phase involving 30 cases, the algorithm predicted a 20% change in aMAP within 5 minutes with a sensitivity of 78% and specificity of 77%. The bias ± precision (1 SD) between pMAP and aMAP of all the measurement pair was of 0.4 ± 4.6 mmHg. Conclusion Estimated mean arterial pressure using non-invasive parameters including PWTT predicted ≥ 20% fluctuations in invasive mean arterial pressure within 5 minutes with sensitivity and specificity exceeding 75%. Trial registration: The study was registered in the UMIN Clinical Trial Registry before subject enrollment (UMIN-CTR,https://www.umin.ac.jp/ctr/index.htm, UMIN000038218, 2019/11/1 by Yoshifumi Kotake). (277 words)

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Cite This Study

Toyoda et al. (2026) conducted an observational in Intraoperative blood pressure fluctuations during non-cardiac surgery under general anesthesia (n=61). Pulse wave transit time (PWTT), heart rate, and perfusion index algorithm vs. Invasive arterial mean arterial pressure (aMAP) was evaluated on Sensitivity and specificity for predicting ≥ 20% fluctuations of invasive arterial mean arterial pressure (aMAP) during 5-minute noninvasive blood pressure intervals. A noninvasive algorithm using pulse wave transit time, heart rate, and perfusion index predicted ≥20% mean arterial pressure fluctuations within 5 minutes with 78% sensitivity and 77% specificity.

synapsesocial.com/papers/6ae682a3e432e7a5c4ef362e https://doi.org/10.21203/rs.3.rs-11323749/v1

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Predicting Blood Pressure Decreases During Intervals of Non-invasive Blood Pressure Measurement Using Pulse Wave Transit Time

Study Design

Structured PICO

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Predicting Blood Pressure Decreases During Intervals of Non-invasive Blood Pressure Measurement Using Pulse Wave Transit Time

Study Design

Structured PICO

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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