A Novel Mobile Phone Application for Pulse Pressure Variation Monitoring Based on Feature Extraction Technology

Olivier, D. et al. Anesthesia & Analgesia. July 2016. 123(1). pp. 105–113

Image source: Graeme Paterson // CC BY 2.0

Background: Pulse pressure variation (PPV) can be used to assess fluid status in the operating room. This measurement, however, is time consuming when done manually and unreliable through visual assessment. Moreover, its continuous monitoring requires the use of expensive devices. Capstesia™ is a novel Android™/iOS™ application, which calculates PPV from a digital picture of the arterial pressure waveform obtained from any monitor. The application identifies the peaks and troughs of the arterial curve, determines maximum and minimum pulse pressures, and computes PPV. In this study, we compared the accuracy of PPV generated with the smartphone application Capstesia (PPVapp) against the reference method that is the manual determination of PPV (PPVman).

Methods: The Capstesia application was loaded onto a Samsung Galaxy S4TM phone. A physiologic simulator including PPV was used to display arterial waveforms on a computer screen. Data were obtained with different sweep speeds (6 and 12 mm/s) and randomly generated PPV values (from 2% to 24%), pulse pressure (30, 45, and 60 mm Hg), heart rates (60–80 bpm), and respiratory rates (10–15 breaths/min) on the simulator. Each metric was recorded 5 times at an arterial height scale X1 (PPV5appX1) and 5 times at an arterial height scale X3 (PPV5appX3). Reproducibility of PPVapp and PPVman was determined from the 5 pictures of the same hemodynamic profile. The effect of sweep speed, arterial waveform scale (X1 or X3), and number of images captured was assessed by a Bland-Altman analysis. The measurement error (ME) was calculated for each pair of data. A receiver operating characteristic curve analysis determined the ability of PPVapp to discriminate a PPVman > 13%.

Results: Four hundred eight pairs of PPVapp and PPVman were analyzed. The reproducibility of PPVapp and PPVman was 10% (interquartile range, 7%–14%) and 6% (interquartile range, 3%–10%), respectively, allowing a threshold ME of 12%. The overall mean bias for PPVappX1 was 1.1% within limits of −1.4% (95% confidence interval [CI], −1.7 to −1.1) to +3.5% (95% CI, +3.2 to +3.8). Averaging 5 values of PPVappX1 with a sweep speed of 12 mm/s resulted in the smallest bias (+0.6%) and the best limits of agreement (±1.3%). ME of PPVapp was <12% whenever 3, 4, or 5 pictures were taken to average PPVapp. The best predictive value for PPVapp to detect a PPVman > 13% was obtained for PPVappX1 by averaging 5 pictures showing a PPVapp threshold of 13.5% (95% CI, 12.9–15.2) and a receiver operating characteristic curve area of 0.989 (95% CI, 0.963–0.998) with a sensitivity of 97% and a specificity of 94%.

Conclusions: Our findings show that the Capstesia PPV calculation is a dependable substitute for standard manual PPV determination in a highly controlled environment (simulator study). Further studies are warranted to validate this mobile feature extraction technology to predict fluid responsiveness in real conditions.

Read the abstract here


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