Predicting post-discharge cancer surgery complications via telemonitoring of patient-reported outcomes and patient-generated health data.
J Surg Oncol
; 123(5): 1345-1352, 2021 Apr.
Article
em En
| MEDLINE
| ID: mdl-33621378
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 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.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.Palavras-chave
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Assunto principal:
Alta do Paciente
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Complicações Pós-Operatórias
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Assistência ao Convalescente
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Tecnologia sem Fio
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Avaliação de Resultados da Assistência ao Paciente
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Medidas de Resultados Relatados pelo Paciente
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Neoplasias
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article