Predicting Hospital Readmission for Surgical Patients Using Deep Learning Models With Smart Watch and Smart Ring Sensors Data

Summary

Hospital readmissions are an important measure of healthcare quality and safety. These events create a substantial burden for patients, families, and health systems because they may increase costs, extend recovery time, and lead to more serious postoperative complications. Predicting which patients are at higher risk of readmission remains difficult, as many complications begin silently and are not easily identified in routine clinical evaluations.

This study aims to evaluate whether artificial intelligence (AI) can help predict hospital readmissions in surgical patients by analyzing physiological and behavioral data collected before and after surgery. To achieve this, participants will use wearable devices-specifically a smartwatch and a smart ring-capable of continuously monitoring health biomarkers such as heart rate, electrocardiogram (ECG), oxygen saturation, sleep patterns, blood pressure trends, body composition through bioimpedance, and stress indicators. These devices are provided through a technology partnership and sponsorship from Samsung, which supports the study with advanced health technologies.

This is a prospective, single-center cohort study conducted at the main tertiary hospital in the state of Amazonas. Approximately 225 to 300 adults undergoing medium- or large-scale elective surgeries will be invited to participate over a 25-month period. All participants will provide informed consent. After enrollment, the study will collect demographic information, preoperative assessments, validated sleep questionnaires, comorbidity indexes such as the Charlson Comorbidity Index, laboratory exams, pulmonary function tests, intraoperative and postoperative data, and hospital discharge information.

Participants will be continuously monitored using wearable devices during their hospital stay-including the first 48 hours in the intensive care unit when applicable-and for 30 days after hospital discharge. These physiological data will be integrated with clinical and laboratory information to create a comprehensive dataset.

The primary objective is to develop and test artificial intelligence models capable of predicting 30-day hospital readmission following elective surgery. Both deep learning approaches and classical machine-learning techniques will be evaluated. By analyzing large volumes of continuous physiologic data, these models may identify early signs of postoperative deterioration that would otherwise go unnoticed.

If successful, this study may improve postoperative care, support earlier clinical intervention, reduce complications, and help healthcare teams provide safer recovery pathways for surgical patients.

Conditions

• Patient Readmission

Sponsors & Collaborators

• Universidade Federal do Amazonas

  collaborator OTHER

• Samsung Eletrônica da Amazônia Ltda

  collaborator UNKNOWN

• Getúlio Vargas University Hospital

  lead OTHER_GOV

Principal Investigators

• Maria Elizete de Almeida Araújo, Doctor of Health Science · Getúlio Vargas University Hospital

• Marly Guimarães Fernandes Costa · Federal University of Amazonas

• Robson Luís Oliveira de Amorim · Getúlio Vargas University Hospital

• Caio Eduardo Rodrigues Falcão · Getúlio Vargas University Hospital

• Cícero Ferreira Fernandes Costa Filho · Federal University of Amazonas

• José Corrêa Lima Netto · Getúlio Vargas University Hospital

• Francisco de Assis Pereira Januário · Federal University of Amazonas

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2026-03-04

Primary Completion

2026-08-31

Completion

2028-06-30

Countries

• Brazil

Study Locations