Early Detection of Infection Using the Fitbit in Pediatric Surgical Patients

NCT ID: NCT06395636

Last Updated: 2025-01-30

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 500 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-01-07
• Study Completion Date: 2027-07-31

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.

Detailed Description

We propose to investigate the use of objective near-real time data from the Fitbit consumer wearable device (CWD) for early detection of postoperative infection in children after appendectomy for complicated appendicitis, and its influence on clinician decision-making, time to first contact with the healthcare system, and postoperative healthcare use. SSI is usually associated with increased heart rate (HR) and reduced physical activity (PA), and sleep disturbances due to discomfort, pain, and fever.13-15 To help monitor patients post-discharge, CDWs can be used to detect physiologic changes, prompting early management.16,17 CWDs generate continuous, valid HR data comparable to clinical-grade HR monitor data for children, as well as objective PA and sleep data, which are good indicators of recovery.18-21 CWDs, then transmit these data in near-real time to a cloud-based system potentially accessible to a clinician. Although health systems have incorporated CWD data into electronic health records,16,17 use in post-discharge monitoring of pediatric surgery patients has been limited18 since it is difficult to monitor and interpret the large volumes of data generated by CWD in clinically meaningful ways.18 Machine learning (ML) methods, which reduce CWD data into clinically meaningful signals are needed.22 Since these algorithms are based on data from multiple CWD sensors, they are more accurate than threshold-based alerts.

We collected Fitbit data on 160 pediatric appendectomy patients21,23,24 and showed slower normative recovery PA trajectories in children with complicated versus simple appendicitis, and deviations from normative PA trajectory (decreased PA) before parents sought healthcare for complications.20 We then applied ML methods to Fitbit data of 80 post appendectomy patients with complicated appendicitis to predict infection. The preliminary algorithm predicted 90% of infections, 2 days before parental report. In parallel, we developed a proof-of-concept dashboard that delivers Fitbit data daily and on-demand in near real-time to clinicians. Using the dashboard, clinicians evaluated hypothetical post-discharge pediatric appendectomy scenarios with and without Fitbit dashboard data. Availability of Fitbit data (even without ML) substantially changed clinicians' likelihood of recommending ED care. While our early results are promising, a larger study is needed to definitively elucidate the association of changes in Fitbit data with postoperative infection and to assess the effect of Fitbit data on clinician decision-making and healthcare use. We propose to develop a ML algorithm for postoperative infection using Fitbit data of children 3-18 years old undergoing a appendectomy for complicated appendicitis at the Ann and Robert H. Lurie Children's Hospital of Chicago (LCH), a tertiary care children's hospital and two affiliated hospitals Loyola University Medical Center, a university hospital), and Central DuPage Hospital (CDH), a community hospital. Our two aims are:

Aim 1: Develop and externally validate an ML algorithm for postoperative infection. In addition to the 80 patients already recruited in our preliminary study, we will prospectively recruit 170 patients for a total of 250 from LCH for development and internal validation. We will then externally validate our infection ML algorithm using data on 122 appendectomy patients from LCH and its two affiliates.

Aim 2: Conduct a pre-post study to determine the effect of near real-time availability of the infection alert from Fitbit on clinical decision-making, time to first contact with the healthcare system, and healthcare utilization. We will place a Fitbit on 94 children after appendectomy recruited from LCH and its two affiliates, and send their surgeons daily reports of their recovery progress and near real-time, ML-based, clinical alerts of infection. In Aim 2a, we will use critical incident technique to qualitatively assess surgeons' decision-making after receiving Fitbit alerts and daily reports. In Aim 2b, we will compare median time to first contact with the healthcare system, healthcare use patterns (e.g., ED visits) and costs pre and post receiving alerts and daily reports.

Impact: This study is well aligned with NINR's priority to advance symptoms science. Developing CWD alerts to detect infection and evaluating their effect on clinical care have the potential to transform pediatric surgical care and pave the way for wide uptake of CWD. By proactively reaching to patients, this technology also has the potential to reduce existing disparities in seeking care.

Conditions

Appendectomy; Appendicitis; Appendicitis Acute

Study Design

• Allocation Method: NON_RANDOMIZED
• Intervention Model: SEQUENTIAL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

Aim 1 - Validation

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.

Group Type: NO_INTERVENTION

No interventions assigned to this group

Aim 2 - Implementation of Algorithm

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.

Group Type: EXPERIMENTAL

Infection-Prediction Algorithm

Intervention Type: 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.

Interventions

Infection-Prediction Algorithm

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.

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• 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.)

Exclusion Criteria

• 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)

• Minimum Eligible Age: 3 Years
• Maximum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Northwestern University

OTHER

Sponsor Role: collaborator

Central DuPage Hospital

OTHER

Sponsor Role: collaborator

University of Chicago

OTHER

Sponsor Role: collaborator

Loyola University Chicago

OTHER

Sponsor Role: collaborator

Ann & Robert H Lurie Children's Hospital of Chicago

OTHER

Sponsor Role: lead

Responsible Party

Fizan Abdullah, MD

Fizan Abdullah M.D., Ph.D

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Fizan Abdullah, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Ann & Robert H Lurie Children's Hospital of Chicago

Hassan Ghomrawi, PhD, MPH

Role: PRINCIPAL_INVESTIGATOR

University of Alabama at Birmingham

Arun Jayaraman, PT, PhD

Role: PRINCIPAL_INVESTIGATOR

Shirley Ryan AbilityLab

Locations

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

Chicago, Illinois, United States

Site Status: RECRUITING

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

Chicago, Illinois, United States

Site Status: NOT_YET_RECRUITING

Loyola University Medical Center

Maywood, Illinois, United States

Site Status: NOT_YET_RECRUITING

Northwestern Medicine Central DuPage Hospital

Winfield, Illinois, United States

Site Status: RECRUITING

Countries

United States

Central Contacts

Fizan Abdullah, MD, PhD

Role: CONTACT

fabdullah@luriechildrens.org

312-227-4210

Arianna Edobor, CRC

Role: CONTACT

idetect@luriechildrens.org

312-227-2118

Facility Contacts

Fizan Abdullah, MD, PhD

Role: primary

FAbdullah@luriechildrens.org

312-227-4210

Arianna Edobor, BS

Role: backup

aedobor@luriechildrens.org

312-227-2118

Fizan Abdullah, MD, PhD

Role: primary

fabdullah@luriechildrens.org

312-227-4210

Clinical Research Coordinator

Role: backup

aedobor@luriechildrens.org

312-227-2118

Clinical Research Coordinator

Role: primary

aedobor@luriechildrens.org

312-227-2118

Clinical Research Coordinator

Role: primary

aedobor@luriechildrens.org

312-227-2118

Other Identifiers

IRB 2024-6701

Identifier Type: -

Identifier Source: org_study_id