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

Lorenzo A. Rossi, Laleh G. Melstrom, Yuman Fong, Virginia Sun

Abstract:

Background and Objectives: 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 proofof‐
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.
Methods: 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.
Results: A logistic regression model with L2 regularization trained with clinical data,
PROs and PGHD from wearable pedometers achieved an area under the receiver
operating characteristic of 0.74.
Conclusions: PROs and PGHDs captured through remote patient telemonitoring
approaches have the potential to improve prediction performance for postoperative
complications.