Predicting Volume Responsiveness Among Sepsis Patients Using Clinical Data and Continuous Physiological Waveforms

• Authors: Rishikesan Kamaleswaran, Emory University; Jiaying Lian, Carnegie Mellon University; Dong-Lien Lin, Carnegie Mellon University; Himasagar Molakapuri, Carnegie Mellon University; SriManikanth M Nunna, Carnegie Mellon University; Parth Shah, Carnegie Mellon University; Shiv Dua, West Penn Allegheny Health System; Rema Padman, Carnegie Mellon University
• Journal Title: AMIA ... Annual Symposium proceedings. AMIA Symposium
• Volume: Volume 2020
• Publisher: AMIA | 2020-01-01, Pages 619-628
• Type of Work: Article | Final Publisher PDF
• Subjects: Health Sciences, Medicine and Surgery; Health Sciences, Public Health
• Keywords: MIMIC III; Sepsis; fluid responsiveness prediction; machine learning; waveform data; Fluid Therapy; Humans; Logistic Models; Machine Learning; Male; Middle Aged; Sepsis

Abstract

The efficacy of early fluid treatment in patients with sepsis is unclear and may contribute to serious adverse events due to fluid non-responsiveness. The current method of deciding if patients are responsive to fluid administration is often subjective and requires manual intervention. This study utilizes MIMIC III and associated matched waveform datasets across the entire ICU stay duration of each patient to develop prediction models for assessing fluid responsiveness in sepsis patients. We developed a pipeline to extract high frequency continuous waveform data and included waveform features in the prediction models. Comparing across five machine learning models, random forest performed the best when no waveform information is added (AUC = 0.84), with mean arterial blood pressure and age identified as key factors. After incorporation of features from physiologic waveforms, logistic regression with L1 penalty provided consistent performance and high interpretability, achieving an accuracy of 0.89 and F1 score of 0.90.