Early Detection of Infection Using the Fitbit in Pediatric Surgical Patients

Conditions

• Appendectomy
• Appendicitis
• Appendicitis Acute

Eligibility Criteria

Sex

ALL

Age

3 Years - 18 Years

Healthy Volunteers

Not accepted

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.

Study Details

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.

Key Dates

Start date

Jan 7, 2025

Status verified

May 2026

Primary completion

Jun 30, 2027

Completion

Jun 30, 2027

Study Design

Enrollment

500 participants (estimated)

Allocation

NON_RANDOMIZED

Intervention model

SEQUENTIAL

Primary purpose

DIAGNOSTIC

Arms

• No Intervention: 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.
• Experimental: 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.

Primary Outcome Measure

Trends in Participant Fitbit Data (Physical Activity, Heart Rate, Sleep) during the Recovery Period post Complicated Appendectomy [ Time Frame: Fitbit data metrics will be collected for 30 days starting at date of enrollment. ]

Central Contacts

• Fizan Abdullah, MD, PhD
  312-227-4210
  [email protected]
• Arianna Edobor, CRC
  312-227-2118
  [email protected]

Locations (4)

Find similar trials in Chicago, IL

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