  High Flow Nasal Cannula (HFNC) provides non-invasive respiratory support for
critically ill children who may tolerate it more readily than other
Non-Invasive (NIV) techniques. Timely prediction of HFNC failure can provide an
indication for increasing respiratory support. This work developed and compared
machine learning models to predict HFNC failure. A retrospective study was
conducted using EMR of patients admitted to a tertiary pediatric ICU from
January 2010 to February 2020. A Long Short-Term Memory (LSTM) model was
trained to generate a continuous prediction of HFNC failure. Performance was
assessed using the area under the receiver operating curve (AUROC) at various
times following HFNC initiation. The sensitivity, specificity, positive and
negative predictive values (PPV, NPV) of predictions at two hours after HFNC
initiation were also evaluated. These metrics were also computed in a cohort
with primarily respiratory diagnoses. 834 HFNC trials [455 training, 173
validation, 206 test] met the inclusion criteria, of which 175 [103, 30, 42]
(21.0%) escalated to NIV or intubation. The LSTM models trained with transfer
learning generally performed better than the LR models, with the best LSTM
model achieving an AUROC of 0.78, vs 0.66 for the LR, two hours after
initiation. Machine learning models trained using EMR data were able to
identify children at risk for failing HFNC within 24 hours of initiation. LSTM
models that incorporated transfer learning, input data perseveration and
ensembling showed improved performance than the LR and standard LSTM models.
