Predicting stroke volume variation using central venous pressure waveform: a deep learning approach.

Objective: This study evaluated the predictive performance of a deep learning approach to predict stroke volume variation (SVV) from central venous pressure (CVP) waveforms.

Approach: Long short-term memory and the feed-forward neural network were sequenced to predict SVV using CVP waveforms obtained from the VitalDB database, an open-source registry. The input for the long short-term memory consisted of 10 sec CVP waveforms sampled at 2 sec intervals throughout the anesthesia duration. Inputs of the feed-forward network were the outputs of long short-term memory and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the SVV. The performance of SVV predicted by the deep learning model was compared to SVV estimated derived from arterial pulse waveform analysis using a commercialized model, EV1000. Main results. The model hyperparameters consisted of 12 memory cells in the long short-term memory layer and 32 nodes in the hidden layer of the feed-forward network. A total of 224 cases comprising 1717978 CVP waveforms and EV1000/SVV data were used to construct and test the deep learning models. The concordance correlation coefficient between estimated SVV from the deep learning model were 0.993 (95% confidence interval [CI], 0.992-0.993) for SVV measured by EV1000. Significance. Using a deep learning approach, CVP waveforms can accurately approximate SVV values close to those estimated using commercial arterial pulse waveform analysis. .