Patient ReadmissionNCT07349901
Predicting Hospital Readmission for Surgical Patients Using Deep Learning Models With Smart Watch and Smart Ring Sensors Data
An observational study of Patient Readmission, run by Getúlio Vargas University Hospital at 1 site in Brazil.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Patient Readmission
- How many people
- 300 participants
- Where
- 1 siteBrazil
- Study dates
- 4 Mar 2026to Jun 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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:
Show all 11Show fewer
- 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;
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Maria Elizete de Almeida Araújo, Doctor of Health Science- DHSc contact
Brazil1 site, 1 recruiting
- Recruiting
Getúlio Vargas University Hospital
Manaus, Amazonas, 69020-170
Plinío José Cavalcante Monteiro, +55 (92) 3305-4813, plinio.monteiro@ebserh.gov.br
About this study
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.
Read more
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.
Read the 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.
- 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
- Medical subject headings
- Postoperative Complications, Sleep Wake Disorders
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: Within 30 days post-surgery
Secondary outcomes (13)
Length of hospital stay
Total number of days from hospital admission to discharge following the surgical procedure.
Measured: From hospital admission through hospital discharge, up to postoperative day 10
Need for reoperation
Any surgical reintervention required during hospitalization or within the 30-day post-operative period.
Measured: Up to 30 days after surgery
Number of Participants With Surgical Site Infection
Measured: Up to 30 days post-surgery (or up to hospital discharge if earlier)
30-day mortality
All-cause death occurring within 30 days after the surgical procedure.
Measured: Up to 30 days post-surgery
Subjective Sleep Quality Assessed by the Pittsburgh Sleep Quality Index (PSQI) Total Score
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.
Measured: 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
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
Measured: 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
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.
Measured: 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
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.
Measured: 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
Total sleep time measured in minutes using overnight polysomnography.
Measured: Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.
Sleep Efficiency Assessed by Polysomnography
Sleep efficiency measured as a percentage (%) using overnight polysomnography.
Measured: Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.
Sleep Latency Assessed by Polysomnography
Sleep latency measured in minutes using overnight polysomnography.
Measured: 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
Wake after sleep onset measured in minutes using overnight polysomnography.
Measured: 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
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
Measured: Pre-operative baseline (2 days before hospital admission) and post-operative follow-up at 14 days after hospital discharge.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Jan 2026
- Study start4 Mar 2026
- Primary completionAug 2026 (estimated)
- Study completionJun 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (21)Papers the sponsor has linked to this study.
- Other trials for Patient ReadmissionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Getúlio Vargas University Hospital
- Sponsor type
- Other government
- Collaborators
- Universidade Federal do Amazonas, Samsung Eletrônica da Amazônia LTDA
- Responsible party
- Sponsor
- Investigators
- Maria Elizete de Almeida Araújo, Doctor of Health Science, Study director, Getúlio Vargas University HospitalMarly Guimarães Fernandes Costa, Study director, Federal University of AmazonasRobson Luís Oliveira de Amorim, Principal investigator, Getúlio Vargas University HospitalCaio Eduardo Rodrigues Falcão, Study chair, Getúlio Vargas University HospitalCícero Ferreira Fernandes Costa Filho, Study chair, Federal University of AmazonasJosé Corrêa Lima Netto, Study chair, Getúlio Vargas University HospitalFrancisco de Assis Pereira Januário, Study chair, Federal University of Amazonas
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07349901
- Sponsor's protocol ID
- 91090825.8.0000.5020
- Other IDs
- Convênio No 03/2025
- Status last verified
- Dec 2025
- First submitted
- 2 Dec 2025
- Last updated
- 17 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Iucif N Jr, Rocha JS. [Study of inequalities in hospital mortality using the Charlson comorbidity index]. Rev Saude Publica. 2004 Dec;38(6):780-6. doi: 10.1590/s0034-89102004000600005. Epub 2004 Dec 10. Portuguese.
PubMed 15608895Duarte RL, Magalhaes-da-Silveira FJ, Oliveira-E-Sa TS, Silva JA, Mello FC, Gozal D. Obstructive Sleep Apnea Screening with a 4-Item Instrument, Named GOAL Questionnaire: Development, Validation and Comparative Study with No-Apnea, STOP-Bang, and NoSAS. Nat Sci Sleep. 2020 Jan 23;12:57-67. doi: 10.2147/NSS.S238255. eCollection 2020.
PubMed 32158294Bertolazi AN, Fagondes SC, Hoff LS, Pedro VD, Menna Barreto SS, Johns MW. Portuguese-language version of the Epworth sleepiness scale: validation for use in Brazil. J Bras Pneumol. 2009 Sep;35(9):877-83. doi: 10.1590/s1806-37132009000900009. English, Portuguese.
PubMed 19820814Yanagimori M, Fernandes MD, Grad GF, Monare AB, Garcia BMSP, Rosanelli I, Mariano PMMS, Drummond SPA, O'Driscoll DM, Edwards BA, Lorenzi-Filho G, Genta PR. Abbreviated versions of the insomnia severity index to screen for comorbid insomnia among obstructive sleep apnea patients. Sleep Med. 2025 Feb;126:19-24. doi: 10.1016/j.sleep.2024.11.035. Epub 2024 Nov 26.
PubMed 39615273Bertolazi AN, Fagondes SC, Hoff LS, Dartora EG, Miozzo IC, de Barba ME, Barreto SS. Validation of the Brazilian Portuguese version of the Pittsburgh Sleep Quality Index. Sleep Med. 2011 Jan;12(1):70-5. doi: 10.1016/j.sleep.2010.04.020. Epub 2010 Dec 9.
PubMed 21145786Hegab Z, Gibbons S, Neyses L, Mamas MA. Role of advanced glycation end products in cardiovascular disease. World J Cardiol. 2012 Apr 26;4(4):90-102. doi: 10.4330/wjc.v4.i4.90.
PubMed 22558488Vistoli G, De Maddis D, Cipak A, Zarkovic N, Carini M, Aldini G. Advanced glycoxidation and lipoxidation end products (AGEs and ALEs): an overview of their mechanisms of formation. Free Radic Res. 2013 Aug;47 Suppl 1:3-27. doi: 10.3109/10715762.2013.815348.
PubMed 23767955Atzeni IM, van de Zande SC, Westra J, Zwerver J, Smit AJ, Mulder DJ. The AGE Reader: A non-invasive method to assess long-term tissue damage. Methods. 2022 Jul;203:533-541. doi: 10.1016/j.ymeth.2021.02.016. Epub 2021 Feb 23.
PubMed 33636313Amorim RL, Oliveira LM, Malbouisson LM, Nagumo MM, Simoes M, Miranda L, Bor-Seng-Shu E, Beer-Furlan A, De Andrade AF, Rubiano AM, Teixeira MJ, Kolias AG, Paiva WS. Prediction of Early TBI Mortality Using a Machine Learning Approach in a LMIC Population. Front Neurol. 2020 Jan 24;10:1366. doi: 10.3389/fneur.2019.01366. eCollection 2019.
PubMed 32038454Hale AT, Stonko DP, Lim J, Guillamondegui OD, Shannon CN, Patel MB. Using an artificial neural network to predict traumatic brain injury. J Neurosurg Pediatr. 2019 Feb 1;23(2):219-226. doi: 10.3171/2018.8.PEDS18370. Epub 2018 Nov 2.
PubMed 30485240Fares MY, Liu HH, da Silva Etges APB, Zhang B, Warner JJP, Olson JJ, Fedorka CJ, Khan AZ, Best MJ, Kirsch JM, Simon JE, Sanders B, Costouros JG, Zhang X, Jones P, Haas DA, Abboud JA; Avant-Garde Health and Codman Society Value-Based Care Group. Utility of Machine Learning, Natural Language Processing, and Artificial Intelligence in Predicting Hospital Readmissions After Orthopaedic Surgery: A Systematic Review and Meta-Analysis. JBJS Rev. 2024 Aug 22;12(8). doi: 10.2106/JBJS.RVW.24.00075. eCollection 2024 Aug 1.
PubMed 39172864Shameer K, Johnson KW, Yahi A, Miotto R, Li LI, Ricks D, Jebakaran J, Kovatch P, Sengupta PP, Gelijns S, Moskovitz A, Darrow B, David DL, Kasarskis A, Tatonetti NP, Pinney S, Dudley JT. PREDICTIVE MODELING OF HOSPITAL READMISSION RATES USING ELECTRONIC MEDICAL RECORD-WIDE MACHINE LEARNING: A CASE-STUDY USING MOUNT SINAI HEART FAILURE COHORT. Pac Symp Biocomput. 2017;22:276-287. doi: 10.1142/9789813207813_0027.
PubMed 27896982Min X, Yu B, Wang F. Predictive Modeling of the Hospital Readmission Risk from Patients' Claims Data Using Machine Learning: A Case Study on COPD. Sci Rep. 2019 Feb 20;9(1):2362. doi: 10.1038/s41598-019-39071-y.
PubMed 30787351Majumder S, Mondal T, Deen MJ. Wearable Sensors for Remote Health Monitoring. Sensors (Basel). 2017 Jan 12;17(1):130. doi: 10.3390/s17010130.
PubMed 28085085Rumalla K, Srinivasan VM, Gaddis M, Kan P, Lawton MT, Burkhardt JK. Readmission following extracranial-intracranial bypass surgery in the United States: nationwide rates, causes, risk factors, and volume-driven outcomes. J Neurosurg. 2020 Nov 6;135(2):431-439. doi: 10.3171/2020.6.JNS202117. Print 2021 Aug 1.
PubMed 33157529
Study at a glance
From the registry record, last updated by the sponsor on 17 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Hospital Readmission for Surgical Patients Using Deep Learning Models With Smart Watch and Smart Ring Sensors Data
- ClinicalTrials.gov ID
- NCT07349901
- Acronym / sponsor ID
- 91090825.8.0000.5020
- Conditions
- Patient Readmission
- Keywords
- Hospital Readmission, Postoperative Complications, Artificial Intelligence, Machine Learning, Wearable Electronic Devices, Physiological Monitoring, Sleep Disorders, Surgical Procedures, Operative
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Getúlio Vargas University HospitalOther government
- Collaborators
- Universidade Federal do Amazonas, Samsung Eletrônica da Amazônia LTDA
- Study start
- 4 Mar 2026 (actual)
- Primary completion
- Aug 2026 (estimated)
- Study completion
- Jun 2028 (estimated)
- First posted
- 20 Jan 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (11 listed)
- 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).
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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).
13 secondary outcomes
Length of hospital stay
Time frame: From hospital admission through hospital discharge, up to postoperative day 10
Need for reoperation
Time frame: 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)
30-day mortality
Time frame: 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.
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.
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.
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.
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.
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 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.
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.
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Mar 2026.
- Background: PubMed 15608895Iucif N Jr, Rocha JS. [Study of inequalities in hospital mortality using the Charlson comorbidity index]. Rev Saude Publica. 2004 Dec;38(6):780-6. doi: 10.1590/s0034-89102004000600005. Epub 2004 Dec 10. Portuguese.
- Background: PubMed 32158294Duarte RL, Magalhaes-da-Silveira FJ, Oliveira-E-Sa TS, Silva JA, Mello FC, Gozal D. Obstructive Sleep Apnea Screening with a 4-Item Instrument, Named GOAL Questionnaire: Development, Validation and Comparative Study with No-Apnea, STOP-Bang, and NoSAS. Nat Sci Sleep. 2020 Jan 23;12:57-67. doi: 10.2147/NSS.S238255. eCollection 2020.
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- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Maria Elizete de Almeida Araújo, Doctor of Health Science- DHSc contact
Maria Elizete de Almeida Araújo, Doctor of Health Science study director
Getúlio Vargas University Hospital
Marly Guimarães Fernandes Costa study director
Federal University of Amazonas
Robson Luís Oliveira de Amorim principal investigator
Getúlio Vargas University Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Patient Readmission, run by Getúlio Vargas University Hospital at 1 site in Brazil.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 11 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 17 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Brazil. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Robson Luís Oliveira de Amorim, PhD (+55 92 99403-4101, robsonamorim@ufam.edu.br), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
30-day hospital readmission (time frame: Within 30 days post-surgery). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07349901, which also lists 21 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Mar 2026; AzTrials last refreshed its copy on 30 Oct 2026.