Ability of a New Smartphone Pulse Pressure Variation and Cardiac Output Application to Predict Fluid Responsiveness in Patients Undergoing Cardiac Surgery

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.

Reliability of pulse pressure and stroke volume variation in assessing fluid responsiveness in the operating room: a metanalysis and a metaregression

BACKGROUND

Pulse pressure and stroke volume variation (PPV and SVV) have been widely used in surgical patients as predictors of fluid challenge (FC) response. Several factors may affect the reliability of these indices in predicting fluid responsiveness, such as the position of the patient, the use of laparoscopy and the opening of the abdomen or the chest, combined FC characteristics, the tidal volume (Vt) and the type of anesthesia.

METHODS

Systematic review and metanalysis of PPV and SVV use in surgical adult patients. The QUADAS-2 scale was used to assess the risk of bias of included studies. We adopted a metanalysis pooling of aggregate data from 5 subgroups of studies with random effects models using the common-effect inverse variance model. The area under the curve (AUC) of pooled receiving operating characteristics (ROC) curves was reported. A metaregression was performed using FC type, volume, and rate as independent variables.

RESULTS

We selected 59 studies enrolling 2,947 patients, with a median of fluid responders of 55% (46-63). The pooled AUC for the PPV was 0.77 (0.73-0.80), with a mean threshold of 10.8 (10.6-11.0). The pooled AUC for the SVV was 0.76 (0.72-0.80), with a mean threshold of 12.1 (11.6-12.7); 19 studies (32.2%) reported the grey zone of PPV or SVV, with a median of 56% (40-62) and 57% (46-83) of patients included, respectively. In the different subgroups, the AUC and the best thresholds ranged from 0.69 and 0.81 and from 6.9 to 11.5% for the PPV, and from 0.73 to 0.79 and 9.9 to 10.8% for the SVV. A high Vt and the choice of colloids positively impacted on PPV performance, especially among patients with closed chest and abdomen, or in prone position.

CONCLUSION

The overall performance of PPV and SVV in operating room in predicting fluid responsiveness is moderate, ranging close to an AUC of 0.80 only some subgroups of surgical patients. The grey zone of these dynamic indices is wide and should be carefully considered during the assessment of fluid responsiveness. A high Vt and the choice of colloids for the FC are factors potentially influencing PPV reliability.

TRIAL REGISTRATION

PROSPERO (CRD42022379120), December 2022. https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=379120.

Evaluation of a novel optical smartphone blood pressure application: a method comparison study against invasive arterial blood pressure monitoring in intensive care unit patients

Background

Arterial hypertension is a worldwide public health problem. While it is currently diagnosed and monitored non-invasively using the oscillometric method, having the ability to measure blood pressure (BP) using a smartphone application could provide more widespread access to hypertension screening and monitoring. In this observational study in intensive care unit patients, we compared blood pressure values obtained using a new optical smartphone application (OptiBP™; test method) with arterial BP values obtained using a radial artery catheter (reference method) in order to help validate the technology.

Methods

We simultaneously measured three BP values every hour for five consecutive hours on two consecutive days using both the smartphone and arterial methods. Bland–Altman and error grid analyses were used for agreement analysis between both approaches. The performance of the smartphone application was investigated using the Association for the Advancement of Medical Instrumentation (AAMI) and the International Organization for Standardization (ISO) definitions, which require the bias ± SD between two technologies to be below 5 ± 8 mmHg.

Results

Among the 30 recruited patients, 22 patients had adequate OptiBP™ values and were thus analyzed. In the other 8 patients, no BP could be measured due to inadequate signals. The Bland–Altman analysis revealed a mean of the differences ± SD between both methods of 0.9 ± 7 mmHg for mean arterial pressure (MAP), 0.2 ± 14 mmHg for systolic arterial pressure (SAP), and 1.1 ± 6 mmHg for diastolic arterial pressure (DAP). Error grid analysis demonstrated that the proportions of measurement pairs in risk zones A to E were 88.8% (no risk), 10% (low risk), 1% (moderate risk), 0% (significant risk), and 0% (dangerous risk) for MAP and 88.4%, 8.6%, 3%, 0%, 0%, respectively, for SAP.

Conclusions

This method comparison study revealed good agreement between BP values obtained using the OptiBP™ and those done invasively. The OptiBP™ fulfills the AAMI/ISO universal standards for MAP and DAP (but not SAP). Error grid showed that the most measurements (≥ 97%) were in risk zones A and B.

Trial registration

ClinicalTrials.gov registration: NCT04728477

Hemodynamic Monitoring by Smartphone—Preliminary Report from a Comparative Prospective Observational Study

Background: Advanced hemodynamic monitoring supports making therapeutic decisions in critically ill patients. New technologies, including mobile health, have been introduced into the hemodynamic monitoring armamentarium. However, each monitoring method has potential limitations—content, technical and organizational. The aim of this study was to assess the comparability between measurements obtained with two arterial pressure cardiac output methods: Capstesia™ smartphone hemodynamic software (CS) and LiDCO Rapid™ uncalibrated hemodynamic monitor (LR). Methods: The initial analysis included 16 patients in the period 06–09 2020 without limitations that could make the results obtained unreliable. Eighty pairs of cardiac output measurements were obtained. The comparability of cardiac output results obtained with both methods was assessed using the Spearman’s rank correlation coefficient (R), the intra-class correlation (CCC) and the Bland–Altman curves analysis (B-A). Results: The median (IQR) cardiac output measured with CS and LR were 4.6 (3.9–5.7) and 5.5 (4.6–7.4) L min⁻¹, respectively. In the B-A analysis, CS cardiac output values were on average 1.2 (95% CI −2.1–4.4) L min^-1 lower than LR values. The correlation between cardiac output with CS and LR was moderate (r = 0.5; p = 0.04). After adjusting for the presence of the dicrotic notch on the pulse waveform, in the group of eight patients with a visible dicrotic notch, the CS and LR results differed by only 0.1 (95% CI −0.8–1.1) L min⁻¹, the correlation between CS and LR was close to complete (r = 0.96; p < 0.001), and the percentage error was 40%, with a CCC-CS of 0.98 (95% CI 0.95–0.99). Conclusions: The Capstesia^TM smartphone software can provide an alternative method of cardiac output assessment in patients meeting arterial pressure cardiac output evaluation criteria with a clearly discernible dicrotic notch on the arterial pulse pressure waveform. It is necessary to confirm the obtained observations on a larger group of patients; however, it may potentially make objective hemodynamic measurements ubiquitous in patients with invasive arterial pressure monitoring with a clearly discernible dicrotic notch.

Evaluation of a new smartphone optical blood pressure application (OptiBP™) in the post-anesthesia care unit: a method comparison study against the non-invasive automatic oscillometric brachial cuff as the reference method

We compared blood pressure (BP) values obtained with a new optical smartphone application (OptiBP™) with BP values obtained using a non-invasive automatic oscillometric brachial cuff (reference method) during the first 2 h of surveillance in a post-anesthesia care unit in patients after non-cardiac surgery. Three simultaneous BP measurements of both methods were recorded every 30 min over a 2-h period. The agreement between measurements was investigated using Bland-Altman and error grid analyses. We also evaluated the performance of the OptiBP™ using ISO81060-2:2018 standards which requires the mean of the differences ± standard deviation (SD) between both methods to be less than 5 mmHg ± 8 mmHg. Of 120 patients enrolled, 101 patients were included in the statistical analysis. The Bland-Altman analysis demonstrated a mean of the differences ± SD between the test and reference methods of + 1 mmHg ± 7 mmHg for mean arterial pressure (MAP), + 2 mmHg ± 11 mmHg for systolic arterial pressure (SAP), and + 1 mmHg ± 8 mmHg for diastolic arterial pressure (DAP). Error grid analysis showed that the proportions of measurement pairs in risk zones A to E were 90.3% (no risk), 9.7% (low risk), 0% (moderate risk), 0% (significant risk), 0% (dangerous risk) for MAP and 89.9%, 9.1%, 1%, 0%, 0% for SAP. We observed a good agreement between BP values obtained by the OptiBP™ system and BP values obtained with the reference method. The OptiBP™ system fulfilled the AAMI validation requirements for MAP and DAP and error grid analysis indicated that the vast majority of measurement pairs (≥ 99%) were in risk zones A and B.Trial Registration ClinicalTrials.gov Identifier: NCT04262323.

Monitoring of Cardiac Output Using a New Smartphone Application (Capstesia) vs. Vigileo FloTrac System

(1) Background: We tested Capstesia against a reference system, Vigileo FloTrac, in patients undergoing major vascular surgery procedures. (2) Methods: Twenty-two adult patients (236 data pairs) were enrolled. Cardiac output (CO), stroke volume (SV), systemic vascular resistance (SVR), and related indexed parameters from the two monitoring systems were collected and compared at eleven time points during surgery. Intraclass correlation coefficients with 95% confidence intervals (CIs) and Bland–Altman plots with percentages of error were used. (3) Results: The interclass correlation coefficients for CO, SV, and SVR were 0.527 (95%CI 0.387 to 0.634), 0.580 (95%CI 0.454 to 0.676), and 0.609 (95%CI 0.495 to 0.698), respectively. In the Bland–Altman analysis, bias (and limits of agreement) of CO was 0.33 L min−1 (−2.44; 3.10), resulting in a percentage error of 61.91% for CO. For SV, it was 5.02 mL (−36.42; 46.45), with 57.19% of error. Finally, the bias (and limit of agreement) of SVR was −75.99 dyne sec cm−5 (−870.04; 718.06), resulting in an error of 69.94%. (4) Conclusions: Although promising, cost-effective, and easy to use, the moderate level of agreement with Vigileo and the high level of error make Capstesia unsuitable for use in the intraoperative setting of vascular surgery. Critical errors in acquisition or digitalization of the snap might have a strong impact on the accuracy and performance. Further standardization of the acquisition technique and improvements in the processing algorithm are needed.

Mobile Applications in Clinical and Perioperative Care for Anesthesia: Narrative Review

BACKGROUND

The increasing use of smartphones by providers and patients alike demonstrates that digital health utilizing mobile applications has the potential to transform perioperative care and education in anesthesia.

OBJECTIVE

This literature review describes the current scope of the use of mobile applications in anesthesiology.

METHODS

Literature was searched using PubMed, Scopus, and clinicaltrials.gov for articles published from January 1, 2010, through April 1, 2020. Only English language studies were included. Articles were included if they examined the use of a mobile health application in the setting of anesthesia or the perioperative (immediate preoperative, intraoperative, and postoperative) period. Studies were excluded if they explored video interventions or did not examine the feasibility or efficacy of the mobile app.

RESULTS

We included 29 articles, and three areas of clinical functionality were identified: patient-centered care (preoperative, intraoperative, and postoperative), systems-based improvement, and medical education. Several studies demonstrate the feasibility and reliability of mobile apps in these areas, but many are only tested for efficacy in simulated environments or with small patient samples.

CONCLUSIONS

Mobile health applications show promise in improving communication between anesthesiologists, improving workflow efficiency, enhancing medical education, and reducing hospital costs. However, there is a need for validation and improvement before full implementation by the provider, patients, and hospital systems. Future studies are needed to demonstrate meaningful health outcomes to create guidelines and recommendations specific to the application of mobile technology to health care.

Dynamic arterial elastance measured with pressure recording analytical method, and mean arterial pressure responsiveness in hypotensive preload dependent patients undergoing cardiac surgery: A prospective cohort study

BACKGROUND

Organ perfusion is a factor of cardiac output and perfusion pressure. Recent evidence shows that dynamic arterial elastance is a reliable index of the interaction between the left ventricle and the arterial system and, in turn, of left ventricular mechanical efficiency. A practical approach to the assessment of dynamic arterial elastance at the bedside is the ratio between pulse pressure variation and stroke volume variation, which might predict the effect of a fluid challenge on the arterial pressure in patients undergoing cardiac surgery.

OBJECTIVE

To evaluate the ability of dynamic arterial elastance, measured by the pressure recording analytical method (PRAM), to predict the response of mean arterial pressure (MAP) to a fluid challenge.

DESIGN

Prospective observational study.

SETTING

Cardiac surgery patients in a university hospital.

PATIENTS

Preload-dependent (pulse pressure variation ≥13%), hypotensive (MAP ≤65 mmHg) patients, without right ventricular dysfunction, at the end of cardiac surgery.

INTERVENTIONS

A 250 ml fluid challenge infused over 3 min.

MAIN OUTCOME MEASURES

A receiver-operating characteristic curve was generated to test the ability of the baseline (before fluid challenge) dynamic arterial elastance (primary endpoint) and all other haemodynamic variables (secondary endpoint) to predict MAP responsiveness (≥10% increase in MAP) after a fluid challenge.

RESULTS

Of 270 patients undergoing cardiac surgery, 97 (35.9%) were preload-dependent, hypotensive and received a fluid challenge. Of these 97 patients, 50 (51%) were MAP responders (≥10% increase in MAP) and 47 (48%) were MAP nonresponders (<10% increase in MAP). Baseline dynamic arterial elastance (mean ± SD) had an area under the curve of 0.64 ± 0.06 [95% confidence interval (CI), 0.53 to 0.73; P = 0.017]. A dynamic arterial elastance at least 1.07 with a grey zone ranging between 0.9 and 1.5 had 86% sensitivity (95% CI, 73 to 94) and 45% specificity (95% CI, 30 to 60) in predicting MAP increase.

CONCLUSION

In a hypotensive preload-dependent cardiac surgery cohort without right ventricular dysfunction, dynamic arterial elastance measured by PRAM can predict pressure response for values greater than 1.5 or less than 0.9.

Estimation of pulse pressure variation and cardiac output in patients having major abdominal surgery: a comparison between a mobile application for snapshot pulse wave analysis and invasive pulse wave analysis

Pulse pressure variation (PPV) and cardiac output (CO) can guide perioperative fluid management. Capstesia (Galenic App, Vitoria-Gasteiz, Spain) is a mobile application for snapshot pulse wave analysis (PWAsnap) and estimates PPV and CO using pulse wave analysis of a snapshot of the arterial blood pressure waveform displayed on any patient monitor. We evaluated the PPV and CO measurement performance of PWAsnap in adults having major abdominal surgery. In a prospective study, we simultaneously measured PPV and CO using PWAsnap installed on a tablet computer (PPV_PWAsnap, CO_PWAsnap) and using invasive internally calibrated pulse wave analysis (ProAQT; Pulsion Medical Systems, Feldkirchen, Germany; PPV_ProAQT, CO_ProAQT). We determined the diagnostic accuracy of PPV_PWAsnap in comparison to PPV_ProAQT according to three predefined PPV categories and by computing Cohen’s kappa coefficient. We compared CO_ProAQT and CO_PWAsnap using Bland-Altman analysis, the percentage error, and four quadrant plot/concordance rate analysis to determine trending ability. We analyzed 190 paired PPV and CO measurements from 38 patients. The overall diagnostic agreement between PPV_PWAsnap and PPV_ProAQT across the three predefined PPV categories was 64.7% with a Cohen’s kappa coefficient of 0.45. The mean (± standard deviation) of the differences between CO_PWAsnap and CO_ProAQT was 0.6 ± 1.3 L min^− 1 (95% limits of agreement 3.1 to − 1.9 L min^− 1) with a percentage error of 48.7% and a concordance rate of 45.1%. In adults having major abdominal surgery, PPV_PWAsnap moderately agrees with PPV_ProAQT. The absolute and trending agreement between CO_PWAsnap with CO_ProAQT is poor. Technical improvements are needed before PWAsnap can be recommended for hemodynamic monitoring.

Monitoring of pulse pressure variation using a new smartphone application (Capstesia) versus stroke volume variation using an uncalibrated pulse wave analysis monitor: a clinical decision making study during major abdominal surgery

Pulse pressure variation (PPV) and stroke volume variation (SVV) can be used to assess fluid status in the operating room but usually require dedicated advanced hemodynamic monitors. Recently, a smartphone application (Capstesia™), which automatically calculates PPV from a picture of the invasive arterial pressure waveform from any monitor screen (PPV_CAP), has been developed. The purpose of this study was to compare PPV_CAP with SVV from an uncalibrated pulse wave analysis monitor (SVV_PC). In 40 patients undergoing major abdominal surgery, we compared PPV_CAP with SVV_PC at post-induction, pre-incision, post-incision, end of surgery, and during every hypotensive episode (mean arterial pressure < 65 mmHg). We classified PPV_CAP and SVV_PC into three categories reflecting the thresholds used for the decision to administer fluids: no fluid administration (PPV and SVV < 9%), gray zone (PPV and SVV 9-13%), and fluid administration (PPV and SVV > 13%). The agreement between SVV_PC and PPV_CAP for these three categories was measured by the number of concordant paired measurements divided by the total number of paired measurements and Cohen's kappa coefficient. In the 549 pairs of PPV-SVV data obtained, the overall agreement of PPV_CAP with SVV_PC was 79%, and the kappa coefficient was moderate (0.55). The highest agreement and kappa coefficient value were observed after the induction of anesthesia before surgical incision. PPV_CAP and SVV_PC would have resulted in completely opposite clinical decisions regarding fluid administration in 1% of the cases. In this clinical decision making study in patients undergoing major abdominal surgery, we observed a moderate agreement between PPV_CAP and SVV_PC with regard to categories used to guide fluid administration. Trial Registration: Clinical Trials.gov (NCT03137901).