Predicting post-discharge cancer surgery complications via telemonitoring of patient-reported outcomes and patient-generated health data.

Post-discharge oncologic surgical complications are costly for patients, families, and healthcare systems. The capacity to predict complications and early intervention can improve postoperative outcomes. In this proof-of-concept study, we used a machine learning approach to explore the potential added value of patient-reported outcomes (PROs) and patient-generated health data (PGHD) in predicting post-discharge complications for gastrointestinal (GI) and lung cancer surgery patients.
We formulated post-discharge complication prediction as a binary classification task. Features were extracted from clinical variables, PROs (MD Anderson Symptom Inventory [MDASI]), and PGHD (VivoFit) from a cohort of 52 patients with 134 temporal observation points pre- and post-discharge that were collected from two pilot studies. We trained and evaluated supervised learning classifiers via nested cross-validation.
A logistic regression model with L regularization trained with clinical data, PROs and PGHD from wearable pedometers achieved an area under the receiver operating characteristic of 0.74.
PROs and PGHDs captured through remote patient telemonitoring approaches have the potential to improve prediction performance for postoperative complications.