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

Joosten, Alexandre; Boudart, Céline; Vincent, Jean‐Louis; Eynden, Frédéric Vanden; Barvais, Luc; Obbergh, Luc Van; Rinehart, Joseph; Desebbe, Olivier

doi:10.1213/ane.0000000000003652

Cited by 13 publications

(12 citation statements)

References 33 publications

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“…Additional validation studies showed that Capstesia had a percentage of error of 20% for PPV and 13.8% for CO among 20 patients in the intensive care unit [ 7 ]. However, when the app was studied in 57 patients undergoing elective cardiac surgeries, its calculated PPV only weakly predicted fluid responsiveness (sensitivity of 73%, 95% CI 0.54-0.92, and a specificity of 74%, 95% CI 0.48-0.90) [ 11 ]. When comparing Capstesia’s PPV to stroke volume variation from an uncalibrated pulse wave analysis monitor, Joosten et al found that there was 79% overall agreement between the two, with a kappa coefficient of 0.55 [ 10 ].…”

Section: Discussionmentioning

confidence: 99%

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

Pan¹,

Rong²

2021

J Med Internet Res

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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.

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Section: Discussionmentioning

confidence: 99%

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

Pan¹,

Rong²

2021

J Med Internet Res

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“…Furthermore, the percentage error in our analysis exceeded the threshold for clinical interchangeability of 30% [ 20 ]. In another validation study, CO PWAsnap was compared to CO measured with transpulmonary thermodilution, a clinical reference method [ 22 , 23 ], in 57 patients during cardiac surgery [ 11 ]. With a mean of the differences between CO measurements obtained by the two methods of 0.3 L min − 1 , wide 95%-LOA of 3.3 to -2.8 L min − 1 , a high percentage error of 60%, and a poor concordance rate the results of this study are consistent with our findings [11].…”

Section: Discussionmentioning

confidence: 99%

“…Previously, it was reported that close to 10% of the snapshots of the arterial blood pressure waveform cannot be analyzed by PWAsnap [ 11 ]. We did not systematically analyze the quality of the snapshots or the failure rate of PWAsnap to estimate PPV and CO. We thus can only subjectively describe our experiences of using PWAsnap in a clinical setting in the operating room.…”

Section: Discussionmentioning

confidence: 99%

“…We speculate that the poor camera performance of the tablet computer may have contributed to these difficulties in the analysis of the snapshots. Other studies that used PWAsnap installed on smartphones with high resolution cameras did not report these problems [ 11 , 14 ].…”

Section: Discussionmentioning

confidence: 99%

“…In-silico proof of concept studies demonstrated that PWAsnap is basically able to estimate PPV and CO from a waveform snapshot [9,10]. However, the few clinical method comparison studies evaluating PWAsnap used different PPV and CO reference methods and revealed inconsistent results [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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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

Hoppe

Gleibs

Briesenick

et al. 2020

J Clin Monit Comput

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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.

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Mobile Devices for Hemodynamic Monitoring

Briesenick

Michard

Saugel

2020

Annual Update in Intensive Care and Emergency Medicine

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

Abstract: In patients undergoing cardiac surgery, PPVCAP and PPVPC both weakly predict fluid responsiveness. However, COCAP is not a good substitute for COTD and cannot be used to assess fluid responsiveness.

Cited by 13 publications

References 33 publications

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

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

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

Mobile Devices for Hemodynamic Monitoring

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