Clinical agreement of a novel algorithm to estimate radial artery blood pressure from the non-invasive finger blood pressure

The utility of wearable devices in the perioperative period

PURPOSE OF REVIEW

Improved perioperative patient monitoring is a crucial step toward better predicting postoperative outcomes. Wearable devices capable of measuring various health-related metrics represent a novel tool that can assist healthcare providers. However, the literature surrounding wearables is wide-ranging, preventing clinicians from drawing definitive conclusions regarding their utility. This review intends to consolidate the recent literature on perioperative wearables and summarize the most salient information.

RECENT FINDINGS

Wearable devices measuring cardiac output and colonic motility have recently been piloted with mixed results. Novel measurement techniques for established metrics have also been studied, including photoplethysmography devices for heart rate and blood pressure along with resistance thermometers for temperature. Nuanced methods of synthesizing data have been piloted, including machine-learning algorithms for predicting adverse events and trajectory curves for step count progression. Wearable devices are generally well accepted, although adjuvant support systems have improved patient satisfaction.

SUMMARY

Perioperative wearables are valuable tools for tracking postoperative health metrics, predicting adverse events, and improving patient satisfaction. Future research on removing barriers such as technological illiteracy, artifact generation, and false-positive alarms would enable better integration of wearables into the hospital setting.

The hypotension prediction index versus mean arterial pressure in predicting intraoperative hypotension: A clinical perspective

BACKGROUND

The hypotension prediction index (HPI) predicts hypotension, with randomised trials showing a significant reduction in hypotension-related metrics. However, the reliability of previous validation studies is debated, and it's unclear if mean arterial pressure (MAP) can be used interchangeably with HPI.

OBJECTIVES

This study compared the effectiveness of HPI versus MAP thresholds in predicting intraoperative hypotension, focusing on three clinically relevant metrics: time from alert to event, positive predictive value (PPV), and proportion of missed hypotensive events.

DESIGN

Prospective observational study conducted between 2018 and 2020.

SETTING

Single-centre, academic hospital in the Netherlands.

PARTICIPANTS

Adults scheduled for elective non-cardiac surgery lasting over two hours. Of the 105 eligible patients, 91 had sufficient data for analysis.

MAIN OUTCOME MEASURES

The primary outcome was the time-to-hypotensive event intervals predicted by HPI popup alerts (≥85 for ≥40 s) and MAP-alerts (70-75 mmHg). Secondary analyses examined differences between these predictors regarding the PPV and missed event rates, as well as the difference in these metrics between instant HPI-85 alerts and the six MAP-alerts.

RESULTS

The largest time-to-event difference was seen between HPI-85 popup and MAP-70 alerts, with a gain of 0.58 (95% confidence interval (CI), 0.57 to 0.58) min, favouring HPI. Higher MAP thresholds reduced this time difference, but worsened PPV values, with 20.5 (95% CI, 20.3 to 20.6)% at MAP-75 compared to 55.6 (95% CI, 55.4 to 55.8)% for HPI-85 popups. Missed event proportions were similar: between one to three percent. Instant HPI-85 and MAP-72 alerts showed comparable performance, but both had suboptimal PPV values around 30%. However, adding a 40-s time-dependence to MAP's alert definition levelled the differences across the three evaluated metrics, aligning more closely with HPI-85 popup alerts.

CONCLUSIONS

Using HPI-85 popup alerts does not provide additional prediction time over MAP-alerts in the 70 to 75 mmHg range, but they may be preferred due to higher PPV values. Instant HPI-85 and MAP-alerts perform similarly, with MAP-72 being closest, though these alerts more frequently occur regardless of subsequent hypotension with the potential to introduce unnecessary treatment. Adding a 40-s time-dependence to MAP-alerts to match the HPI popup characteristic eliminates distinctions between prediction time and missed events, while maintaining the higher PPV. However, whether 40sec-MAP-alerts are clinically equivalent remains to be determined in prospective clinical trials.

TRIAL REGISTRATION

Clinicaltrials.gov (NCT03795831) on 10 January 2019. https://clinicaltrials.gov/study/NCT03795831.

Defining Postinduction Hemodynamic Instability With an Automated Classification Model

BACKGROUND

Postinduction hypotension (PIH) may be associated with increased morbidity and mortality. In earlier studies, the definition of PIH is solely based on different absolute or relative thresholds. However, the time-course (eg, how fast blood pressure drops during induction) is rarely incorporated, whereas it might represent the hemodynamic instability of a patient. We propose a comprehensive model to distinguish hemodynamically unstable from stable patients by combining blood pressure thresholds with the magnitude and speed of decline.

METHODS

This prospective study included 375 adult elective noncardiac surgery patients. Noninvasive blood pressure was continuously measured between 5 minutes before up to 15 minutes after the first induction agent had been administered. An expert panel rated whether the patient experienced clinically relevant hemodynamic instability or not. Interrater correlation coefficient and intraclass correlation were computed to check for consistency between experts. Next, an automated classification model for clinically relevant hemodynamic instability was developed using mean, maximum, minimum systolic, mean, diastolic arterial blood pressure (SAP, MAP, and DAP, respectively) and their corresponding time course of decline. The model was trained and tested based on the hemodynamic instability labels provided by the experts.

RESULTS

In total 78 patients were classified as having experienced hemodynamic instability and 279 as not. The hemodynamically unstable patients were significantly older (7 years, 95% confidence interval (CI), 4-11, P < .001), with a higher prevalence of chronic obstructive pulmonary disease (COPD) (3% higher, 95% CI, 1-8, P = .036). Before induction, hemodynamically unstable patients had a higher SAP (median (first-third quartile): 161 (145-175) mm Hg vs 150 (134-166) mm Hg, P < .001) compared to hemodynamic stable patients. Interrater agreement between experts was 0.92 (95% CI, 0.89-0.94). The random forest classifier model showed excellent performance with an area under the receiver operating curve (AUROC) of 0.96, a sensitivity of 0.84, and specificity of 0.94.

CONCLUSIONS

Based on the high sensitivity and specificity, the developed model is able to differentiate between clinically relevant hemodynamic instability and hemodynamic stable patients. This classification model will pave the way for future research concerning hemodynamic instability and its prevention.

A comparison between invasive and noninvasive measurement of the Hypotension Prediction Index: A post hoc analysis of a prospective cohort study

BACKGROUND

Clinical trials and validation studies demonstrate promising hypotension prediction capability by the Hypotension Prediction Index (HPI). Most studies that evaluate HPI derive it from invasive blood pressure readings, but a direct comparison with the noninvasive alternative remains undetermined. Such a comparison could provide valuable insights for clinicians in deciding between invasive and noninvasive monitoring strategies.

OBJECTIVES

Evaluating predictive differences between HPI when obtained through noninvasive versus invasive blood pressure monitoring.

DESIGN

Post hoc analysis of a prospective observational study conducted between 2018 and 2020.

SETTING

Single-centre study conducted in an academic hospital in the Netherlands.

PATIENTS

Adult noncardiac surgery patients scheduled for over 2 h long elective procedures. After obtaining informed consent, 91 out of the 105 patients had sufficient data for analysis.

MAIN OUTCOME MEASURES

The primary outcome was the difference in area under the receiver-operating characteristics (ROC) curve (AUC) obtained for HPI predictions between the two datasets. Additionally, difference in time-to-event estimations were calculated.

RESULTS

AUC (95% confidence interval (CI)) results revealed a nonsignificant difference between invasive and noninvasive HPI, with areas of 94.2% (90.5 to 96.8) and 95.3% (90.4 to 98.2), respectively with an estimated difference of 1.1 (-3.9 to 6.1)%; P  = 0.673. However, noninvasive HPI demonstrated significantly longer time-to-event estimations for higher HPI values.

CONCLUSION

Noninvasive HPI is reliably accessible to clinicians during noncardiac surgery, showing comparable accuracy in HPI probabilities and the potential for additional response time.

TRIAL REGISTRATION

Clinicaltrials.gov (NCT03795831) https://clinicaltrials.gov/study/NCT03795831.

Continuous non-invasive vs. invasive arterial blood pressure monitoring during neuroradiological procedure: a comparative, prospective, monocentric, observational study

BACKGROUND

In the perioperative setting, the most accurate way to continuously measure arterial blood pressure (ABP) is using an arterial catheter. Surrogate methods such as finger cuff have been developed to allow non-invasive measurements and are increasingly used, but need further evaluation. The aim of this study is to evaluate the accuracy and clinical concordance between two devices for the measurement of ABP during neuroradiological procedure.

METHODS

This is a prospective, monocentric, observational study. All consecutive patients undergoing a neuroradiological procedure were eligible. Patients who needed arterial catheter for blood pressure measurement were included. During neuroradiological procedure, ABP (systolic, mean and diatolic blood pressure) was measured with two different technologies: radial artery catheter and Nexfin. Bland-Altman and error grid analyses were performed to evaluate the accuracy and clinical concordance between devices.

RESULTS

From March 2022 to November 2022, we included 50 patients, mostly ASA 3 (60%) and required a cerebral embolization (94%) under general anaesthesia (96%). Error grid analysis showed that 99% of non-invasive ABP measures obtained with the Nexfin were located in the risk zone A or B. However, 65.7% of hypertension events and 41% of hypotensive events were respectively not detected by Nexfin. Compared to the artery catheter, a significant relationship was found for SAP (r2 = 0.78) and MAP (r2 = 0.80) with the Nexfin (p < 0.001). Bias and limits of agreement (LOA) were respectively 9.6 mmHg (- 15.6 to 34.8 mmHg) and - 0.8 mmHg (- 17.2 to 15.6 mmHg), for SAP and MAP.

CONCLUSIONS

Nexfin is not strictly interchangeable with artery catheter for ABP measuring. Further studies are needed to define its clinical use during neuroradiological procedure.

TRIAL REGISTRATION

Clinicaltrials.gov, registration number: NCT05283824.

Smartphone-Based versus Non-Invasive Automatic Oscillometric Brachial Cuff Blood Pressure Measurements: A Prospective Method Comparison Volunteer Study

Introduction: Mobile health diagnostics have demonstrated effectiveness in detecting and managing chronic diseases. This method comparison study aims to assess the accuracy and precision of the previously evaluated OptiBP™ technology over a four-week study period. This device uses optical signals recorded by placing a patient's fingertip on a smartphone's camera to estimate blood pressure (BP). Methods: In adult participants without cardiac arrhythmias and minimal interarm blood pressure difference (systolic arterial pressure (SAP) < 15 mmHg or diastolic arterial pressure (DAP) < 10 mmHg), three pairs of 30 s BP measurements with the OptiBP™ (test method) were simultaneously compared using three pairs of measurements with the non-invasive oscillometric brachial cuff (reference method) on the opposite arm over a period of four consecutive weeks at a rate of two measurements per week (one in the morning and one in the afternoon). The agreement of BP values between the two technologies was analyzed using Bland-Altman and error grid analyses. The performance of the smartphone application was investigated using the International Organization for Standardization (ISO) definitions, which require the bias ± standard deviation (SD) between two technologies to be lower than 5 ± 8 mmHg. Results: Among the 65 eligible volunteers, 53 participants had adequate OptiBP™ BP values. In 12 patients, no OptiBP™ BP could be measured due to inadequate signals. Only nine participants had known chronic arterial hypertension and 76% of those patients were treated. The mean bias ± SD between both technologies was -1.4 mmHg ± 10.1 mmHg for systolic arterial pressure (SAP), 0.2 mmHg ± 6.5 mmHg for diastolic arterial pressure (DAP) and -0.5 mmHg ± 6.9 mmHg for mean arterial pressure (MAP). Error grid analyses indicated that 100% of the pairs of BP measurements were located in zones A (no risk) and B (low risk). Conclusions: In a cohort of volunteers, we observed an acceptable agreement between BP values obtained with the OptiBPTM and those obtained with the reference method over a four-week period. The OptiBPTM fulfills the ISO standards for MAP and DAP (but not SAP). The error grid analyses showed that 100% measurements were located in risk zones A and B. Despite the need for some technological improvements, this application may become an important tool to measure BP in the future.