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.
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;
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.
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.
Cohort
Prospective
Not listed
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).
Length of hospital stay
Total number of days from hospital admission to discharge following the surgical procedure.
Need for reoperation
Any surgical reintervention required during hospitalization or within the 30-day post-operative period.
Number of Participants With Surgical Site Infection
30-day mortality
All-cause death occurring within 30 days after the surgical procedure.
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Getúlio Vargas University Hospital
Lead sponsor
Universidade Federal do Amazonas
Collaborator
Samsung Eletrônica da Amazônia Ltda
Collaborator
