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

NCT07349901 | Recruiting

• 2 other identifiers: 91090825.8.0000.5020Convênio No 03/2025
• Study Type: observational
• Target: 300
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

Hospital Readmission; Postoperative Complications; Artificial Intelligence; Machine Learning; Wearable Electronic Devices; Physiological Monitoring; Sleep Disorders; Surgical Procedures, Operative; Perioperative Care; Amazon Region; Predictive Modeling

Outcome Measures

Primary Outcomes (1)

• 30-day hospital readmission

  Occurrence of any unplanned hospital readmission within 30 days after discharge following medium- and large-scale surgical procedures. The outcome is binary (Yes/No).

  Within 30 days post-surgery

Secondary Outcomes (13)

• Length of hospital stay

  From hospital admission through hospital discharge, up to postoperative day 10

• Need for reoperation

  Up to 30 days after surgery

• Number of Participants With Surgical Site Infection

  Up to 30 days post-surgery (or up to hospital discharge if earlier)

• 30-day mortality

  Up to 30 days post-surgery

• Subjective Sleep Quality Assessed by the Pittsburgh Sleep Quality Index (PSQI) Total Score

  Pre-operative baseline (2 days before admission) and post-operative follow-up at 14 days after hospital discharge.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population will consist of adult patients scheduled for medium- and large-scale elective surgeries at the Hospital Universitário Getúlio Vargas, a tertiary referral center in Amazonas. Patients are initially evaluated at the affiliated outpatient clinic, Ambulatório Araújo Lima. Eligible individuals will be invited to participate during preoperative consultations with the attending surgeon, approximately 15 days before hospital admission. Study procedures, information delivery, and informed consent will be conducted by the surgical specialists responsible for outpatient care.

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).
• 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.

Sponsors & Collaborators

• Getúlio Vargas University Hospital: lead
• Universidade Federal do Amazonas: collaborator
• Samsung Eletrônica da Amazônia Ltda: collaborator

Study Sites (1)

Getúlio Vargas University Hospital

Manaus, Amazonas, 69020-170, Brazil

RECRUITING

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MeSH Terms

Conditions

Postoperative Complications; Sleep Wake Disorders

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms; Nervous System Diseases; Neurologic Manifestations; Signs and Symptoms; Mental Disorders

Study Officials

• Maria Elizete de Almeida Araújo, Doctor of Health Science

  Getúlio Vargas University Hospital

  STUDY DIRECTOR

• Marly Guimarães Fernandes Costa

  Federal University of Amazonas

  STUDY DIRECTOR

• Robson Luís Oliveira de Amorim

  Getúlio Vargas University Hospital

  PRINCIPAL INVESTIGATOR

• Caio Eduardo Rodrigues Falcão

  Getúlio Vargas University Hospital

  STUDY CHAIR

• Cícero Ferreira Fernandes Costa Filho

  Federal University of Amazonas

  STUDY CHAIR

• José Corrêa Lima Netto

  Getúlio Vargas University Hospital

  STUDY CHAIR

• Francisco de Assis Pereira Januário

  Federal University of Amazonas

  STUDY CHAIR

Central Study Contacts

Robson Luís Oliveira de Amorim, PhD

CONTACT

+55 92 99403-4101; robsonamorim@ufam.edu.br

Maria Elizete de Almeida Araújo, Doctor of Health Science- DHSc

CONTACT

+55 92 99114-3693; elizetemanaus@ufam.edu.br

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER GOV
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 2, 2025
• First Posted: January 20, 2026
• Study Start: March 4, 2026
• Primary Completion (Estimated): August 1, 2026
• Study Completion (Estimated): June 1, 2028
• Last Updated: March 17, 2026

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will not share

Locations

BR (1)