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

March 14, 2026 updated by: Getúlio Vargas University Hospital

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.

Study Overview

Status

Recruiting

Conditions

Patient Readmission

Detailed Description

Hospital readmissions are well established as key indicators of healthcare quality, particularly in the postoperative setting. Unplanned readmissions often reflect complications that were not fully predicted or adequately managed, generating substantial financial and clinical burden for health systems. In the United States, 3.8 million adult readmissions occurred within 30 days in 2018, with an average cost of US$15,200 per event. In Brazil, similarly high readmission rates have been reported, with 13.4% of more than 22,000 hospitalizations resulting in return visits, largely due to hospital infection, obstructive sleep apnea, and cardiac arrhythmias. Sleep disturbances-including obstructive sleep apnea-are among the most significant contributors to postoperative complications and readmissions. They delay recovery, increase pain sensitivity, impair cognition, and elevate cardiovascular risk. Vascular comorbidities such as hypertension, diabetes, and hyperlipidemia further worsen outcomes by increasing the likelihood of ischemic events, impaired wound healing, and respiratory instability. Understanding how physiologic, behavioral, and sleep-related variables interact to influence postoperative recovery is essential for designing early detection and prevention strategies.

Artificial intelligence (AI) has shown increasing promise in predicting clinical deterioration and identifying high-risk patients across multiple surgical fields. A recent systematic review of 26 studies demonstrated that AI-based models-including natural language processing, classical machine-learning algorithms, and deep learning methods-can accurately predict hospital readmissions following major surgery. However, most available datasets do not combine preoperative, intraoperative, postoperative, and continuous physiologic signals collected in real time from wearable devices, representing a major gap in the literature. This prospective cohort study aims to address this gap by integrating high-resolution physiologic and behavioral data captured from wearable devices-specifically smartwatches and smart rings-into a unified perioperative database, with the overarching goal of developing AI models capable of predicting unplanned hospital readmissions within 30 days after surgery.

The study seeks to build a comprehensive database of patients undergoing medium- to large-scale elective surgeries, capturing physiologic and behavioral data from wearables and multiparametric monitors during the pre- and post-operative periods, including continuous monitoring in the first 48 hours of intensive care when indicated. Data collection will include continuous wearable monitoring via a web-based interface developed by CETELI/UFAM, pre- and post-operative clinical assessments, laboratory tests, imaging studies, pulmonary function evaluations, polysomnography, and validated sleep questionnaires. The study will evaluate correlations between physiologic and sleep-related variables derived from smartwatches and smart rings and the risk of hospital readmission.

Additionally, the study will develop applications for smartwatch and smart ring use in perioperative data capture and employ machine learning approaches-including deep architectures such as causal convolutional networks and recurrent neural networks, as well as classical methods such as support vector machines and decision trees-to predict hospital readmissions. Associations between wearable-derived sleep parameters and 30-day readmission risk will be analyzed, pre- and post-operative sleep data will be correlated with polysomnography and questionnaire results, and sleep quality will be compared between preoperative and post-operative periods.

This research is conducted through a multidisciplinary collaboration between the Hospital Universitário Getúlio Vargas (HUGV-UFAM/Ebserh), and the Center for Research and Development in Electronic and Information Technology (CETELI/UFAM). CETELI will be responsible for developing the data-collection software, storage architecture, and predictive algorithms, while clinical oversight, recruitment, and protocol execution will be led by HUGV.

Beyond scientific relevance, this project has significant practical importance. In geographically complex regions such as the Amazon, continuous remote monitoring using wearable devices can enhance follow-up, reduce disparities in access to postoperative care, and enable earlier identification of complications. Integrating AI-based prediction models with hospital workflows may ultimately reduce costs, optimize patient flow, and improve the safety and quality of surgical care.

Study Type

Observational

Enrollment (Estimated)

300

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Name: Robson Luís Oliveira de Amorim, PhD
Phone Number: +55 92 99403-4101
Email: robsonamorim@ufam.edu.br

Study Contact Backup

Name: Maria Elizete de Almeida Araújo, Doctor of Health Science- DHSc
Phone Number: +55 92 99114-3693
Email: elizetemanaus@ufam.edu.br

Study Locations

Brazil
- Amazonas
  - Manaus, Amazonas, Brazil, 69020-170
    - Recruiting
    - Getúlio Vargas University Hospital
    - Contact:
      
      Plinío José Cavalcante Monteiro
      
      Phone Number: +55 (92) 3305-4813
      
      Email: plinio.monteiro@ebserh.gov.br

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

Adult
Older Adult

Accepts Healthy Volunteers

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.

Description

Inclusion Criteria:

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;

Exclusion Criteria:

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
30-day hospital readmission Time Frame: Within 30 days post-surgery	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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Length of hospital stay Time Frame: From hospital admission through hospital discharge, up to postoperative day 10	Total number of days from hospital admission to discharge following the surgical procedure.	From hospital admission through hospital discharge, up to postoperative day 10
Need for reoperation Time Frame: Up to 30 days after surgery	Any surgical reintervention required during hospitalization or within the 30-day post-operative period.	Up to 30 days after surgery
Number of Participants With Surgical Site Infection Time Frame: Up to 30 days post-surgery (or up to hospital discharge if earlier)		Up to 30 days post-surgery (or up to hospital discharge if earlier)
30-day mortality Time Frame: Up to 30 days post-surgery	All-cause death occurring within 30 days after the surgical procedure.	Up to 30 days post-surgery
Subjective Sleep Quality Assessed by the Pittsburgh Sleep Quality Index (PSQI) Total Score Time Frame: Pre-operative baseline (2 days before admission) and post-operative follow-up at 14 days after hospital discharge.	Subjective sleep quality will be assessed using the Pittsburgh Sleep Quality Index (PSQI) total score, which ranges from 0 to 21, with higher scores indicating worse sleep quality.	Pre-operative baseline (2 days before admission) and post-operative follow-up at 14 days after hospital discharge.
Insomnia Severity Assessed by the Insomnia Severity Index (ISI) Total Score Time Frame: Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.	Insomnia severity will be assessed using the Insomnia Severity Index (ISI) total score, which ranges from 0 to 28, with higher scores indicating more severe insomnia	Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.
Risk of Sleep-Disordered Breathing Assessed by the Neck, Obesity, Snoring, Age, Sex (NoSAS) Score Time Frame: Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.	Risk of sleep-disordered breathing will be assessed using the Neck, Obesity, Snoring, Age, Sex (NoSAS) score, which ranges from 0 to 17, with higher scores indicating a higher risk of sleep-disordered breathing.	Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.
Daytime Sleepiness Assessed by the Epworth Sleepiness Scale (ESS) Total Score Time Frame: Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.	Daytime sleepiness will be assessed using the Epworth Sleepiness Scale (ESS) total score, which ranges from 0 to 24, with higher scores indicating greater daytime sleepiness.	Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.
Total Sleep Time Assessed by Polysomnography Time Frame: Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.	Total sleep time measured in minutes using overnight polysomnography.	Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.
Sleep Efficiency Assessed by Polysomnography Time Frame: Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.	Sleep efficiency measured as a percentage (%) using overnight polysomnography.	Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.
Sleep Latency Assessed by Polysomnography Time Frame: Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.	Sleep latency measured in minutes using overnight polysomnography.	Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.
Wake After Sleep Onset Assessed by Polysomnography Time Frame: Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.	Wake after sleep onset measured in minutes using overnight polysomnography.	Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.
Apnea-Hypopnea Index Assessed by Polysomnography Time Frame: Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.	Apnea-Hypopnea Index (AHI), measured as the number of events per hour of sleep using overnight polysomnography. Higher values indicate more severe sleep-disordered breathing	Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Getúlio Vargas University Hospital

Collaborators

Universidade Federal do Amazonas

Samsung Eletrônica da Amazônia Ltda

Investigators

Study Director: Maria Elizete de Almeida Araújo, Doctor of Health Science, Getúlio Vargas University Hospital
Study Director: Marly Guimarães Fernandes Costa, Federal University of Amazonas
Principal Investigator: Robson Luís Oliveira de Amorim, Getúlio Vargas University Hospital
Study Chair: 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

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

March 4, 2026

Primary Completion (Estimated)

August 1, 2026

Study Completion (Estimated)

June 1, 2028

Study Registration Dates

First Submitted

December 2, 2025

First Submitted That Met QC Criteria

January 13, 2026

First Posted (Actual)

January 20, 2026

Study Record Updates

Last Update Posted (Actual)

March 17, 2026

Last Update Submitted That Met QC Criteria

March 14, 2026

Last Verified

December 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

91090825.8.0000.5020
Convênio No 03/2025 (Other Grant/Funding Number: SAMSUNG ELETRÔNICA DA AMAZONIA)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

Studies a U.S. FDA-regulated device product

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