Early Detection of Infection Using the Fitbit in Pediatric Surgical Patients

NCT06395636 | Recruiting FDA Device

• 1 other identifier: IRB 2024-6701
• Study Type: interventional
• Target: 500
• Locations: 1 country
• Sites: 4

Brief Summary

The purpose of this study is to analyze Fitbit data to predict infection after surgery for complicated appendicitis and the effect this prediction has on clinician decision making.

Conditions

Appendectomy; Appendicitis; Appendicitis Acute

Keywords

consumer wearables; machine learning; ML; Fitbit; infection; detection; algorithm; prediction

Outcome Measures

Primary Outcomes (1)

• Trends in Participant Fitbit Data (Physical Activity, Heart Rate, Sleep) during the Recovery Period post Complicated Appendectomy

  Participant Fitbit data metrics (particularly PA, HR, Sleep) will be extracted from the app and analyzed using Machine Learning methods to eventually develop an algorithm to predict infection during the postoperative recovery period.

  Fitbit data metrics will be collected for 30 days starting at date of enrollment.

Secondary Outcomes (3)

• Number of Reported Symptoms and Complications during Recovery

  Daily Diary/Survey Submissions will be asked to be completed daily for 30 days starting day of enrollment.

• Healthcare Utilizations during Recovery Period

  The diary / survey will require a submission every day for 30 days starting at day of enrollment.

• Change in Clinician Decision Making from Algorithm Results

  For 30 days starting at day of participant enrollment

Study Arms (2)

Aim 1 - Validation

NO INTERVENTION

1a. Development and Internal validation * analyze Fitbit data (PA, HR, sleep) by applying ML methods to create an infection algorithm indicating onset of infection. 1b. External Validation * Once the ML classifier has been internally validated (using Lurie Children's data only) for its ability to detect the presence or absence of postoperative infection using LOSO cross-validation, where each subject is iteratively held out from the training data and used as a test set. External validation will involve applying this classifier to a newer cohort at LCH and cohorts at Loyola University Hospital and CDH and evaluating its performance.

Aim 2 - Implementation of Algorithm

EXPERIMENTAL

2a. Exploratory \& Inductive analysis * one transcript will be coded to generate initial themes, using qualitative analytic software 2b. Time to first contact with the healthcare system \& Healthcare use * Cox regression model will be used to model the time to first contact, adjusted for covariates * All comparisons between the two groups will be tested using a chi-square test. Cost will be modeled as a continuous variable and is expected to be skewed, as is typical of cost data. We will use a general linear model (GLM) to model cost outcomes.

Device: Infection-Prediction Algorithm

Interventions

Infection-Prediction Algorithm DEVICE

This machine learning algorithm will be developed(Aim1a) and validated(Aim 1b) using the participant Fitbit data and survey results collected during Aim 1. In Aim 2 the algorithm will be used in real time to predict postoperative infection.

Aim 2 - Implementation of Algorithm

Eligibility Criteria

• Age: 3 Years - 18 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64)

You may qualify if:

• children aged 3-18 years
• must be post-surgical laparoscopic appendectomy for complicated appendicitis (Appendicitis is categorized as complicated if perforation, phlegmon, or abscess was present at surgery.)

You may not qualify if:

• children who are non-ambulatory or have any pre-existing mobility limitations
• children who have a doctor-ordered physical activity limit \>48 hours post-surgery
• children who have a comorbidity which will impact a patient's recovery
• children and/or parents who do not speak English or Spanish (Translation services beyond Spanish will not be available at this time)

Sponsors & Collaborators

• Ann & Robert H Lurie Children's Hospital of Chicago: lead
• Northwestern University: collaborator
• Central DuPage Hospital: collaborator
• University of Chicago: collaborator
• Loyola University Chicago: collaborator

Study Sites (4)

Ann & Robert H. Lurie Children's Hospital of Chicago

Chicago, Illinois, 60611, United States

RECRUITING

Northwestern University (Feinberg School of Medicine, Shirley Ryan AbilityLab)

Chicago, Illinois, 60611, United States

NOT YET RECRUITING

Loyola University Medical Center

Maywood, Illinois, 60153, United States

NOT YET RECRUITING

Northwestern Medicine Central DuPage Hospital

Winfield, Illinois, 60190, United States

RECRUITING

MeSH Terms

Conditions

Appendicitis; Infections

Condition Hierarchy (Ancestors)

Intraabdominal Infections; Gastroenteritis; Gastrointestinal Diseases; Digestive System Diseases; Cecal Diseases; Intestinal Diseases

Study Officials

• Fizan Abdullah, MD, PhD

  Ann & Robert H Lurie Children's Hospital of Chicago

  PRINCIPAL INVESTIGATOR

• Hassan Ghomrawi, PhD, MPH

  University of Alabama at Birmingham

  PRINCIPAL INVESTIGATOR

• Arun Jayaraman, PT, PhD

  Shirley Ryan AbilityLab

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Fizan Abdullah, MD, PhD

CONTACT

312-227-4210; fabdullah@luriechildrens.org

Arianna Edobor, CRC

CONTACT

312-227-2118; idetect@luriechildrens.org

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SEQUENTIAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Fizan Abdullah M.D., Ph.D

Study Record Dates

• First Submitted: April 29, 2024
• First Posted: May 2, 2024
• Study Start: September 22, 2023
• Primary Completion (Estimated): June 30, 2027
• Study Completion (Estimated): June 30, 2027
• Last Updated: April 30, 2026

Record last verified: 2026-04

Locations

US (4)