Estimation of Stroke Volume Variance from Arterial Blood Pressure: Using a 1-D Convolutional Neural Network.

Hye-Mee Kwon,Gyu-Sam Hwang,Jae-Man Kim,Woo-Young Seo,Woo-Hyun Shim,Sung-Hoon Kim

doi:10.3390/s21155130

Abstract

Background: We aimed to create a novel model using a deep learning method to estimate stroke volume variation (SVV), a widely used predictor of fluid responsiveness, from arterial blood pressure waveform (ABPW). Methods: In total, 557 patients and 8,512,564 SVV datasets were collected and were divided into three groups: training, validation, and test. Data was composed of 10 s of ABPW and corresponding SVV data recorded every 2 s. We built a convolutional neural network (CNN) model to estimate SVV from the ABPW with pre-existing commercialized model (EV1000) as a reference. We applied pre-processing, multichannel, and dimension reduction to improve the CNN model with diversified inputs. Results: Our CNN model showed an acceptable performance with sample data (r = 0.91, MSE = 6.92). Diversification of inputs, such as normalization, frequency, and slope of ABPW significantly improved the model correlation (r = 0.95), lowered mean squared error (MSE = 2.13), and resulted in a high concordance rate (96.26%) with the SVV from the commercialized model. Conclusions: We developed a new CNN deep-learning model to estimate SVV. Our CNN model seems to be a viable alternative when the necessary medical device is not available, thereby allowing a wider range of application and resulting in optimal patient management.

Highlights

Given the importance of adequate fluid management to a patient’s surgical outcome, stroke volume variation (SVV), an index of fluid responsiveness, is widely used to guide fluid therapy in patients with mechanical ventilation [1]
Evaluation of fluid responsiveness has evolved from classical fluid bolus test to monitoring dynamic hemodynamic parameters such as SVV, which is a quantification of the respiratory variation of stroke volume from theoretical heart-lung interaction principles [3]
We developed a novel model for estimating SVV from arterial blood pressure (ABP) using deep learning method

Summary

Introduction

Given the importance of adequate fluid management to a patient’s surgical outcome, stroke volume variation (SVV), an index of fluid responsiveness, is widely used to guide fluid therapy in patients with mechanical ventilation [1]. SVV estimated by pre-existing commercialized models (e.g., EV1000; Edwards Lifescience, Irvine, CA, USA) is derived from arterial pulse contour analysis and is widely used in current clinical practice [9,10]. This method gained popularity as it is minimally invasive compared to esophageal Doppler or pulmonary artery catheter insertion [11] and provides continuous beat-to-beat data. We aimed to create a novel model using a deep learning method to estimate stroke volume variation (SVV), a widely used predictor of fluid responsiveness, from arterial blood pressure waveform (ABPW). Our CNN model seems to be a viable alternative when the necessary medical device is not available, thereby allowing a wider range of application and resulting in optimal patient management

Objectives

Methods

Discussion

Conclusion