Supervised Machine Learning Applied to Automate Flash and Prolonged Capillary Refill Detection by Pulse Oximetry - Hunter, Ryan Brandon; Jiang, Shen; Nishisaki, Akira; Nickel, Amanda J.; Napolitano, Natalie; Shinozaki, Koichiro; Li, Timmy; Saeki, Kota; Becker, Lance B.; Nadkarni, Vinay M.; Masino, Aaron J. (Frontiers in Physiology, 2020)
Objective: Develop an automated approach to detect flash (<1.0 seconds) or slow (>2.0 seconds) capillary refill time (CRT) that correlates with clinician judgment by applying several supervised machine learning (ML) techniques to pulse oximeter plethysmography. Materials and Methods: Data was collected in the Pediatric Intensive Care Unit (ICU), Cardiac ICU, Progressive Care Unit, and Operating Suites in a large academic children’s hospital. Ninety-nine children and 30 adults were enrolled in testing and validation cohorts, respectively. Patients had 5 paired CRT measurements by a modified pulse oximeter device and a clinician, generating 485 waveform pairs for model training. Supervised ML models using gradient boosting (XGBoost), logistic regression (LR), and support vector machines (SVMs) were developed to detect flash (<1 second) or prolonged CRT (≥2 seconds) using clinician CRT assessment as the reference standard. Models were compared using Area Under the Receiver Operating Curve (AUC) and precision-recall curve (positive predictive value vs. sensitivity) analysis. The best performing model was externally validated with 90 measurement pairs from adult patients. Feature importance analysis was performed to identify key waveform characteristics. Results: For flash CRT, XGBoost had a greater mean AUC and mean precision (0.79, 95% CI 0.75-0.83; 0.63, 95% CI 0.56-0.69) than logistic regression (0.77, 0.71-0.82; 0.56, 0.49-0.63) and SVM (0.72, 0.67-0.76; 0.53, 0.47-0.58) models. For prolonged CRT, XGBoost had a greater mean AUC and mean precision (0.77, 0.72-0.82; 0.50, 0.46-0.54) than logistic regression (0.73, 0.68-0.78; 0.37, 0.30-0.43) and SVM (0.75, 0.70-0.79; 0.43, 0.32-0.54) models. XGBoost showed good external validation with AUC of 0.88. Feature importance analysis of XGBoost identified distinct key waveform characteristics for flash and prolonged CRT, respectively. Conclusion: Novel application of supervised ML to pulse oximeter waveforms yielded effective models to identify flash and prolonged CRT, using clinician judgment as the reference standard. XGBoost had a higher mean AUC than logistic regression and SVMs.