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

Recruiting now

Conditions studied: Patient Readmission

In brief

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.

Key facts

Study ID: NCT07349901
Run by: Getúlio Vargas University Hospital
People needed: 300
Starts: 2026-03-04
Expected to finish: 2028-06-01
Last updated by the study team: 2026-03-17

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

Adults over 18 years of age;
Hospitalization for medium and/or large elective surgery at HUGV;
Conscious and oriented patients who have sufficient understanding to answer questionnaires and use wearable devices for the study;
Have minimal skills in the use of wearable technologies;
Patients who have agreed to participate voluntarily in the research by signing the Informed Consent Form (ICF).
Non-inclusion Criteria:
Presence of tattoos or any other skin condition (skin pathologies or skin diseases such as vitiligo, lupus, and atopic dermatitis, among others) that affects the area of the wrist or finger where the wearable sensors are located;
Presence of any type of sensitivity or allergic reaction, of any degree, to the materials of the wearables (smartwatch and smartring);
Pregnant and lactating women;
Participants with implantable cardiac devices, such as pacemakers, cardioverter defibrillators, and resynchronization devices;
Participants in drugs abuse;

You may not qualify if…

Severe medical conditions and decompensations prior to surgery;
Patients who die before hospital discharge;
Patients with an expected postoperative hospital stay of more than 10 days.

Where it is running

Getúlio Vargas University Hospital — Manaus, Amazonas, Brazil (enrolling)

Full record on ClinicalTrials.gov

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