Sam Rusk, BS1 • Yoav N. Nygate, MS1 • Chris R. Fernandez, MS1 • Jiaxiao M. Shi, PhD2 • Jessica Arguelles, BS2 • Matthew T. Klimper, BS2 • Nathaniel F. Watson, MD, MS3 • Robert Stretch PhD4 • Michelle Zeidler PhD5 • Anupamjeet Sekhon, MD2 • Kendra Becker, MD2 • Joseph Kim, MD2 • Dennis Hwang, MD2
Machine Learning (ML) algorithms to predict Positive Airway Pressure (PAP) adherence may support personalized clinical management. Models were developed to predict adherence at various time-points after PAP initiation and in moving time windows.
Deep neural network (DNN) models were trained utilizing daily PAP data (Kaiser Permanente, Southern California). The DNN was evaluated with 10-fold cross-validation on N=21,397 patients. Algorithms developed included (a) Models 1 and 2 which utilized early usage to predict adherence at 90-days and 1-year respectively, and (b) Model 3 which utilized 14 and 30-day moving windows to predict subsequent usage. Regression analyses compared ML and Naïve (i.e., future use equals previous use) predictions versus Actual adherence.
Model 1 predicted “% days without usage” for first 90-days based on first 7, 14, 21, 30-days of input and at 1-year (90-day window) based on first 30, 60, 90, 180-days of input. ML was superior to Naïve in predicting adherence [R 2 for ML versus Naïve compared to Actuals for different input days— 0.495-vs-0.193; 0.660-vs-0.465; 0.748-vs-0.607; 0.828-vs-0.735 at 90-days and 0.362-vs-0.104; 0.463-vs- 0.247; 0.513-vs-0.339; 0.680-vs-0.547 at 1-year; all p< 0.05]. Model 2 predicted “hours/night” of use—ML did not outperform the Naïve prediction with similar R 2 ; however, when ML predicted < 3 hours/night, nearly all patients had “no significant usage” at 1-year (comparatively, the naïve model had no differentiating threshold to predict this outcome.) Model 3 utilized different windows of PAP usage to predict subsequent usage. ML predictive accuracy was similar using 14 or 30-days of input [R 2 for ML vs. Actuals in predicting 7, 14, and 30-day “% days used ≥4 hours” were 0.687, 0.701, 0.699 using 14- days input and 0.582, 0.702, 0.77 using 30-days input; all p< 0.05.]
ML algorithms based on PAP usage can predict future adherence, potentially supporting personalized treatment decisions and pre-emptive interventions when upcoming non-adherence is predicted.