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Wearable Device-Based Early Warning of Postoperative Complications in Thoracic Surgery (wearable)

12 juni 2026 bijgewerkt door: Zhang Ni, Tongji Hospital

Development and Validation of a Wearable Device-Based Early Warning Model for Postoperative Complications in Thoracic Surgery: A Retrospective and Prospective Cohort Study

After thoracic surgery, some patients may develop complications such as lung infection, abnormal heart rhythm, fluid around the lung, prolonged air leak, wound infection, emergency department visits, or hospital readmission. These problems may not be found early if monitoring is only done during routine vital sign checks or follow-up visits.

This study will evaluate whether data collected by a wearable device can help identify early warning signs of postoperative complications in patients undergoing thoracic surgery. The wearable device will collect information such as heart rate, oxygen level, skin temperature, physical activity, sleep, and wearing status.

The study includes two parts. First, the researchers will review previously collected wearable device and medical record data to develop an early warning model. Second, new patients undergoing thoracic surgery will wear the device from hospital admission until about 30 days after discharge. The model will then be tested to see how well it predicts complications that require medical intervention within 30 days after surgery.

The main goal is to evaluate how accurately the wearable device-based model can identify patients who develop postoperative complications and how early the model can provide a warning before the complication is clinically confirmed.

Studie Overzicht

Gedetailleerde beschrijving

Postoperative complications are an important concern after thoracic surgery. Conventional perioperative monitoring mainly relies on intermittent vital sign measurements, nursing assessments, physician rounds, and routine post-discharge follow-up. This approach may miss short-lasting, nighttime, or activity-related physiological changes that occur before a complication becomes clinically apparent.

Wearable devices can continuously collect physiological and behavioral information, including heart rate, oxygen saturation, skin temperature, activity, sleep, and wearing status. Combining these data with electronic medical record information may help identify early changes associated with postoperative complications.

This study uses a retrospective and prospective cohort design. In the retrospective stage, previously collected wearable device data and electronic medical record data from thoracic surgery patients will be analyzed. The researchers will evaluate the agreement between wearable device measurements and routine clinical vital signs, describe physiological signal patterns before postoperative complications, and develop a preliminary early warning model.

In the prospective validation stage, newly enrolled patients scheduled for thoracic surgery will wear the study wearable device from hospital admission to approximately 30 days after discharge. The early warning model developed from the retrospective data will be applied prospectively using predefined parameters. Model-generated warnings will be recorded. The occurrence of postoperative complications will be verified through inpatient medical records and follow-up after discharge.

The primary reference outcome is postoperative complications requiring medical intervention within 30 days after surgery. These complications may include pulmonary infection, pleural effusion requiring treatment, prolonged air leak, wound infection, postoperative arrhythmia, emergency department visit, or readmission. Model performance will be evaluated using sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and warning lead time. Wearable device adherence, data completeness, and the added predictive value of wearable device-derived features beyond routine clinical variables will also be assessed.

Studietype

Observationeel

Inschrijving (Geschat)

650

Contacten en locaties

In dit gedeelte vindt u de contactgegevens van degenen die het onderzoek uitvoeren en informatie over waar dit onderzoek wordt uitgevoerd.

Studiecontact

Studie Locaties

    • Hubei
      • Wuhan, Hubei, China, 430030
        • Werving
        • Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
        • Contact:

Deelname Criteria

Onderzoekers zoeken naar mensen die aan een bepaalde beschrijving voldoen, de zogenaamde geschiktheidscriteria. Enkele voorbeelden van deze criteria zijn iemands algemene gezondheidstoestand of eerdere behandelingen.

Geschiktheidscriteria

Leeftijden die in aanmerking komen voor studie

  • Volwassen
  • Oudere volwassene

Accepteert gezonde vrijwilligers

Nee

Bemonsteringsmethode

Niet-waarschijnlijkheidssteekproef

Studie Bevolking

The study population includes adult patients aged 18 years or older who are hospitalized in the Department of Thoracic Surgery of Tongji Hospital and are scheduled to undergo thoracic surgery. Eligible patients should be able to wear the study-designated wearable device after hospital admission and are expected to continue wearing the device and/or uploading data within 30 days after discharge.

Beschrijving

Inclusion Criteria:

  1. Age 18 years or older.
  2. Hospitalized in the Department of Thoracic Surgery of Tongji Hospital and scheduled to undergo thoracic surgery.
  3. Able to wear the study-designated wearable device after hospital admission and expected to continue wearing the device and/or uploading data within 30 days after discharge.

Exclusion Criteria:

  1. Patients or family members are unwilling to wear the wearable device or unable to meet the required wearing time.
  2. Severe or unstable psychiatric disease, such as severe depression or schizophrenia.
  3. Pregnancy or lactation.
  4. Allergy to the watch strap material or local skin conditions that prevent wearing the device.
  5. Unable to complete follow-up within 30 days after discharge.

Studie plan

Dit gedeelte bevat details van het studieplan, inclusief hoe de studie is opgezet en wat de studie meet.

Hoe is de studie opgezet?

Ontwerpdetails

Cohorten en interventies

Groep / Cohort
Interventie / Behandeling
Retrospective Cohort
Patients who previously underwent thoracic surgery and had available wearable device data and electronic medical record data during the perioperative period. This cohort will be used to evaluate agreement between wearable device measurements and routine clinical vital signs, analyze physiological signal trajectories before postoperative complications, and develop a preliminary early warning model.
Participants will wear a study wearable device that continuously collects physiological and behavioral data, including heart rate, oxygen saturation, skin temperature, activity, sleep, and wearing status, from hospital admission to approximately 30 days after discharge. The device is used for monitoring and data collection and does not change routine clinical care.
Prospective Validation Cohort
Newly enrolled patients scheduled for thoracic surgery who will wear the study wearable device from hospital admission to approximately 30 days after discharge. This cohort will be used to prospectively validate the wearable device-based early warning model for postoperative complications within 30 days after surgery.
Participants will wear a study wearable device that continuously collects physiological and behavioral data, including heart rate, oxygen saturation, skin temperature, activity, sleep, and wearing status, from hospital admission to approximately 30 days after discharge. The device is used for monitoring and data collection and does not change routine clinical care.

Wat meet het onderzoek?

Primaire uitkomstmaten

Uitkomstmaat
Maatregel Beschrijving
Tijdsspanne
Area under the receiver operating characteristic curve of the wearable device-based early warning model for 30-day postoperative complications
Tijdsspanne: From the day of surgery to 30 days after surgery
The area under the receiver operating characteristic curve will be used to evaluate the ability of the wearable device-based early warning model to distinguish participants who develop postoperative complications requiring medical intervention within 30 days after surgery from those who do not. Postoperative complications may include pulmonary infection, pleural effusion requiring treatment, prolonged air leak, wound infection, postoperative arrhythmia, emergency department visit, or hospital readmission.
From the day of surgery to 30 days after surgery

Secundaire uitkomstmaten

Uitkomstmaat
Maatregel Beschrijving
Tijdsspanne
Warning lead time before clinically confirmed postoperative complications
Tijdsspanne: From the day of surgery to 30 days after surgery
Warning lead time will be defined as the time interval between the first model-generated warning signal and the clinical confirmation of a postoperative complication requiring medical intervention.
From the day of surgery to 30 days after surgery
Sensitivity of the wearable device-based early warning model for 30-day postoperative complications
Tijdsspanne: From the day of surgery to 30 days after surgery
Sensitivity will be defined as the proportion of participants with postoperative complications requiring medical intervention within 30 days after surgery who are correctly identified by the wearable device-based early warning model.
From the day of surgery to 30 days after surgery
Specificity of the wearable device-based early warning model for 30-day postoperative complications
Tijdsspanne: From the day of surgery to 30 days after surgery
Specificity will be defined as the proportion of participants without postoperative complications requiring medical intervention within 30 days after surgery who are correctly classified as not having a warning signal by the wearable device-based early warning model.
From the day of surgery to 30 days after surgery

Medewerkers en onderzoekers

Hier vindt u mensen en organisaties die betrokken zijn bij dit onderzoek.

Sponsor

Studie record data

Deze datums volgen de voortgang van het onderzoeksdossier en de samenvatting van de ingediende resultaten bij ClinicalTrials.gov. Studieverslagen en gerapporteerde resultaten worden beoordeeld door de National Library of Medicine (NLM) om er zeker van te zijn dat ze voldoen aan specifieke kwaliteitscontrolenormen voordat ze op de openbare website worden geplaatst.

Bestudeer belangrijke data

Studie start (Werkelijk)

16 december 2025

Primaire voltooiing (Geschat)

30 november 2026

Studie voltooiing (Geschat)

15 december 2026

Studieregistratiedata

Eerst ingediend

9 juni 2026

Eerst ingediend dat voldeed aan de QC-criteria

12 juni 2026

Eerst geplaatst (Werkelijk)

15 juni 2026

Updates van studierecords

Laatste update geplaatst (Werkelijk)

15 juni 2026

Laatste update ingediend die voldeed aan QC-criteria

12 juni 2026

Laatst geverifieerd

1 juni 2026

Meer informatie

Termen gerelateerd aan deze studie

Andere studie-ID-nummers

  • TJ-IRB202512090 (Andere identificatie: Medical Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology)

Plan Individuele Deelnemersgegevens (IPD)

Bent u van plan om gegevens van individuele deelnemers (IPD) te delen?

NEE

Beschrijving IPD-plan

No individual participant data will be shared because the study data include wearable device monitoring data and clinical information from electronic medical records, which may contain sensitive health information. Only aggregate results will be reported.

Informatie over medicijnen en apparaten, studiedocumenten

Bestudeert een door de Amerikaanse FDA gereguleerd geneesmiddel

Nee

Bestudeert een door de Amerikaanse FDA gereguleerd apparaatproduct

Nee

Deze informatie is zonder wijzigingen rechtstreeks van de website clinicaltrials.gov gehaald. Als u verzoeken heeft om uw onderzoeksgegevens te wijzigen, te verwijderen of bij te werken, neem dan contact op met register@clinicaltrials.gov. Zodra er een wijziging wordt doorgevoerd op clinicaltrials.gov, wordt deze ook automatisch bijgewerkt op onze website .

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