Wearable Devices for Early Detection of Postoperative Infection

NCT07334925 | Completed DMC

• 1 other identifier: AI-Assisted Detect Infection
• Study Type: observational
• Target: 1,284
• Locations: 1 country
• Sites: 1

Brief Summary

This systematic review aims to evaluate the efficacy, accuracy, and clinical applicability of wearable infection detection wristbands in postoperative patients across ophthalmology, orthopaedic surgery, and general surgery. The review focuses on devices capable of monitoring inflammatory biomarkers-particularly white blood cell (WBC) counts and C-reactive protein (CRP)-and examines the added value of artificial intelligence (AI) algorithms for early infection detection. The study synthesizes available evidence on clinical outcomes, predictive accuracy, usability, and feasibility of biosensor-based infection surveillance in postoperative care. It is expected to provide an evidence-based framework for integrating wearable biosensors into perioperative management protocols and to guide future multicenter clinical validation studies.

Conditions

Wearable Devices

Keywords

Wearable biosensors; postoperative infection; artificial intelligence

Outcome Measures

Primary Outcomes (3)

• Diagnostic Accuracy of Wearable Devices for Detection of Postoperative Infection

  Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of wearable devices for detecting postoperative infection, using standard clinical diagnosis as the reference standard.

  1 week

• Time to Postoperative Infection Detection Using Wearable Devices Compared With Standard Care

  Time interval between clinical onset of postoperative infection and detection by wearable devices compared with detection by standard postoperative care protocols.

  1 week

• Predictive Accuracy of AI-Integrated Wearable Monitoring for Early Postoperative Infection

  Improvement in early postoperative infection prediction accuracy achieved by AI-integrated wearable monitoring compared with traditional laboratory-based monitoring methods.

  1 week

Secondary Outcomes (4)

• Secondary outcomes

  1 week

• Time to Wound Healing

  1 week

• Feasibility and Usability of Wearable Devices in Postoperative Monitoring

  1 week

• Methodological Quality and Risk of Bias of Wearable Device Validation

  1 week

Study Arms (1)

Group Monitoring

Wearable infection detection wristbands / biosensors

Diagnostic Test: White Blood Cell (WBC); Diagnostic Test: C-reactive Protein (CRP)

Interventions

White Blood Cell (WBC) DIAGNOSTIC_TEST

Detected by wearable device

Group Monitoring

C-reactive Protein (CRP) DIAGNOSTIC_TEST

detected by wearable device

Group Monitoring

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 (≥18 years) undergoing elective or semi-elective surgical procedures in ophthalmology, orthopedics, and general surgery departments at participating tertiary hospitals. Eligible procedures include surgeries associated with moderate to high postoperative infection risk, such as cataract surgery, corneal transplantation, total joint replacement, and abdominal surgery. Participants must be clinically stable at enrollment and capable of providing informed consent. Both male and female patients will be included without restriction based on race or ethnicity. This population reflects typical postoperative clinical settings in which early infection detection using wearable technology may improve outcomes and reduce morbidity.

You may qualify if:

• Adults aged 18 years or older.
• Patients undergoing ophthalmologic, orthopedic, or general surgical procedures.
• Postoperative patients monitored using a wearable infection detection device or biosensor capable of continuous or intermittent assessment of inflammatory biomarkers, including:
• White blood cell (WBC) count and/or
• C-reactive protein (CRP) levels.
• Wearable devices may incorporate artificial intelligence or machine-learning algorithms for infection prediction.
• Patients receiving standard postoperative care, including conventional laboratory testing and/or clinical monitoring, for comparison.
• Ability to provide written informed consent.

You may not qualify if:

• Patients aged \<18 years.
• Non-human studies (animal or in-vitro).
• Use of wearable devices that monitor only physiological parameters (e.g., temperature, heart rate, oxygen saturation) without inflammatory biomarker assessment (WBC or CRP).
• Patients who are hemodynamically unstable at the time of enrollment.
• Inability or unwillingness to provide informed consent.
• Duplicate enrollment or participation in another interventional study that may interfere with outcomes.

Sponsors & Collaborators

• Benha University: lead

Study Sites (1)

Benha University

Banhā, Benha, 13111, Egypt

Location

MeSH Terms

Interventions

Leukocyte Count

Intervention Hierarchy (Ancestors)

Blood Cell Count; Cell Count; Cytological Techniques; Clinical Laboratory Techniques; Diagnostic Techniques and Procedures; Diagnosis; Hematologic Tests; Investigative Techniques; Cell Physiological Phenomena; Blood Physiological Phenomena; Circulatory and Respiratory Physiological Phenomena

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Target Duration: 48 Months
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Lecturer

Study Record Dates

• First Submitted: December 21, 2025
• First Posted: January 12, 2026
• Study Start: July 1, 2025
• Primary Completion: October 26, 2025
• Study Completion: October 27, 2025
• Last Updated: January 12, 2026

Record last verified: 2025-07

Locations

EG (1)