The answer at our fingertips: Volume status in cirrhosis determined by machine learning and pulse oximeter waveform.

OBJECTIVE

The objective of our study was to determine if the waveform from a simple pulse oximeter-like device could be used to accurately assess intravascular volume status in cirrhosis.

METHODS

Patients with cirrhosis underwent waveform recording as well as serum brain natriuretic peptide (BNP) on the day of their cardiac catheterization where invasive cardiac pressures were measured. Waveforms were processed to generate features for machine learning models in order to predict the filling pressures (regression) or to classify the patients as volume overloaded or not (defined as an LVEDP>15).

RESULTS

Nine of 26 patients (35%) had intravascular volume overload. Regression analysis using PPG features (R2  = 0.66) was superior to BNP (R2  = 0.22). Linear discriminant analysis correctly classified patients with an accuracy of 78%, sensitivity of 60%, positive predictive value of 90%, and an AUROC of 0.87.

CONCLUSIONS

Machine learning-enhanced analysis of pulse ox waveforms can estimate intravascular volume overload with a higher accuracy than conventionally measured BNP.