Fitbit Data for Detecting Infections After Appendicitis Surgery

(i-DETECT Trial)

Recruiting at 3 trial locations

Overseen By Arianna Edobor, CRC

Age: < 65

Sex: Any

Trial Phase: Academic

Sponsor: Ann & Robert H Lurie Children's Hospital of Chicago

Disqualifiers: Non-ambulatory, Mobility limitations, Language barriers, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

500 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial aims to determine if Fitbit data can predict infections after surgery for severe appendicitis. Researchers will use this data to develop an infection-prediction algorithm that alerts doctors to potential infections early, potentially improving patient care. Participants will include children aged 3-18 who have undergone complicated appendicitis surgery and can move without restrictions. As an unphased trial, this study offers participants the chance to contribute to innovative research that could enhance post-surgical care for future patients.

Do I need to stop taking my current medications for this trial?

The trial information does not specify whether you need to stop taking your current medications.

What prior data suggests that this infection-prediction algorithm is safe for use in detecting infections after appendicitis surgery?

Research has shown that computer programs predicting infections can be safe for patients. The infection-prediction program in this study requires no medication or medical procedures, reducing the risk of side effects.

Studies have found that automated systems, like this program, can help detect infections early by analyzing data such as heart rate, physical activity, and sleep patterns. This approach primarily involves data analysis, so it doesn't directly affect a person's body.

Overall, using data to predict health issues is considered safe. There are no known reports of negative effects from similar programs, making the process easy to manage, as it mainly involves monitoring and analysis rather than physical treatment. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about this trial because it explores a novel way to detect infections after appendicitis surgery using data from wearable technology. Unlike traditional methods that rely on physical examinations and lab tests, this approach uses an infection-prediction algorithm that analyzes data from Fitbit devices, such as physical activity, heart rate, and sleep patterns. This method could potentially catch infections earlier and more conveniently, offering a non-invasive and continuous monitoring option. Additionally, by employing machine learning techniques, this algorithm might provide more personalized and accurate predictions, leading to improved post-operative care and outcomes.

What evidence suggests that this infection-prediction algorithm is effective for detecting infections after appendicitis surgery?

Research shows that Fitbit data might help predict infections after surgery for complicated appendicitis. A review of 35 studies found the infection rate after surgery to be 15.4%, indicating that many patients could benefit from early detection. This trial involves participants in two separate arms. The first arm validates an infection-prediction algorithm using Fitbit data, analyzing physical activity, heart rate, and sleep data to indicate the onset of infection. The second arm implements this algorithm to explore its impact on healthcare contact and use. By identifying infections sooner, this method could help doctors make better decisions and improve patient outcomes. ^{5 6 7 8 9}

Who Is on the Research Team?

Arun Jayaraman, PT, PhD

Principal Investigator

Shirley Ryan AbilityLab

Fizan Abdullah, MD, PhD

Principal Investigator

Ann & Robert H Lurie Children's Hospital of Chicago

Hassan Ghomrawi, PhD, MPH

Principal Investigator

University of Alabama at Birmingham

Are You a Good Fit for This Trial?

This trial is for pediatric patients who have undergone surgery for complicated appendicitis. Specific eligibility criteria are not provided, but typically participants would be children with recent appendectomies.

Inclusion Criteria

I had surgery to remove my appendix due to severe infection or rupture.

Exclusion Criteria

My child has a health condition that affects their recovery.

My child cannot walk or has difficulty moving around.

My child has been advised to limit physical activity for more than 48 hours after surgery.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Postoperative Monitoring

Participants' Fitbit data (PA, HR, Sleep) is collected and analyzed using ML methods to predict infection during the recovery period.

4 weeks

Continuous data collection

Follow-up

Participants are monitored for safety and effectiveness after treatment, including daily diary/survey submissions for symptoms and healthcare utilization.

4 weeks

Daily virtual submissions

Implementation of Algorithm

Clinicians receive daily reports and near real-time alerts based on Fitbit data to assess the impact on clinical decision-making and healthcare utilization.

4 weeks

What Are the Treatments Tested in This Trial?

Interventions

• Infection-Prediction Algorithm

Trial Overview The study is testing an infection-prediction algorithm using data from Fitbits to foresee infections after appendectomy in young patients and how these predictions influence doctors' decisions.

How Is the Trial Designed?

Group I: Aim 2 - Implementation of Algorithm Experimental Treatment 1 Intervention

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 II: Aim 1 - Validation Active Control 1 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.

Who Is Running the Clinical Trial?

Ann & Robert H Lurie Children's Hospital of Chicago

Lead Sponsor

Trials

275

Recruited

5,182,000+

Loyola University Chicago

Collaborator

Trials

23

Recruited

13,100+

Northwestern University

Collaborator

Trials

1,674

Recruited

989,000+

Central DuPage Hospital

Collaborator

Trials

15

Recruited

3,900+

University of Chicago

Collaborator

Trials

1,086

Recruited

844,000+

Published Research Related to This Trial

A study involving 162 children aged 3-17 who underwent appendectomy showed that a consumer-grade wearable device, Fitbit, can effectively monitor postoperative recovery by detecting abnormal recovery events with high accuracy (83% for complicated cases and 70% for simple cases).

The use of machine learning algorithms trained on Fitbit data demonstrates potential for early detection of complications, suggesting that wearables could enhance postoperative monitoring and reduce unnecessary emergency visits.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy. Ghomrawi, HMK., O'Brien, MK., Carter, M., et al.[2023]

Children aged 3-11 years recover more quickly to a plateau in physical activity after laparoscopic appendectomy compared to those aged 12-18, indicating that age significantly influences postoperative recovery trajectories.

Using consumer wearable devices like Fitbits provides objective data on recovery patterns, suggesting that tailored discharge instructions based on age and other factors could enhance patient counseling and recovery expectations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Using Wearable Devices to Profile Demographic-Specific Recovery After Pediatric Appendectomy. Zeineddin, S., Figueroa, A., Pitt, JB., et al.[2023]

Machine learning algorithms applied to a large dataset of 223,214 appendectomies showed moderate accuracy in predicting postoperative sepsis, with an overall occurrence rate of 0.96%.

Key risk factors identified for postoperative sepsis included preoperative congestive heart failure, transfusion, and acute renal failure, which could help in early intervention and reducing complications.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Application of machine learning to the prediction of postoperative sepsis after appendectomy. Bunn, C., Kulshrestha, S., Boyda, J., et al.[2022]

In a study of 476 patients undergoing appendectomy, a computer-aided diagnosis system was developed to differentiate between acute appendicitis and normal appendixes, but it showed no significant improvement over traditional clinical diagnosis.

The abdominal pain index derived from the analysis had a sensitivity of 82% but a low specificity of 39%, meaning while it could correctly identify many cases of appendicitis, it also misclassified a significant number of patients, potentially leading to unnecessary surgeries.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A feasibility study of computer aided diagnosis in appendicitis. Van Way, CW., Murphy, JR., Dunn, EL., et al.[2009]

The study involved 26 children aged 3-18 who underwent appendectomy, using the FitBit Inspire HR to track their recovery over 21 days, revealing that those with simple appendicitis showed a faster recovery trajectory compared to those with complicated appendicitis.

Wearable technology like FitBit can effectively characterize postoperative recovery in children and differentiate recovery patterns based on the severity of the condition, potentially aiding in the early detection of complications.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Utility of Wearable Sensors to Assess Postoperative Recovery in Pediatric Patients After Appendectomy. De Boer, C., Ghomrawi, H., Many, B., et al.[2021]

A new method using machine learning and artificial intelligence can diagnose acute appendicitis with 90% accuracy, significantly reducing unnecessary surgeries for patients without appendicitis.

The method also shows potential to differentiate between complicated and uncomplicated appendicitis, although it has lower accuracy (51%) for this specific distinction, indicating room for improvement in identifying the severity of the condition.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach. Reismann, J., Romualdi, A., Kiss, N., et al.[2020]

In a study of 86 pediatric patients aged 3-17 who underwent laparoscopic appendectomy for complicated appendicitis, those who developed postoperative complications showed a slower increase in daily step counts, indicating a deviation from normal recovery patterns.

The use of consumer-grade wearable devices like Fitbits can provide objective data on recovery trajectories, allowing for earlier identification of complications after hospital discharge, which could improve postoperative monitoring in outpatient settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Using Consumer Wearable Devices to Profile Postoperative Complications After Pediatric Appendectomy. Pitt, JB., Zeineddin, S., Carter, M., et al.[2023]

A study involving 66-118 patients who underwent weight loss surgery showed that post-operative weeks were linked to decreased daily steps, less time spent walking, and improved sleep metrics, indicating significant lifestyle changes after surgery.

The research highlights the effectiveness of consumer-grade wearable devices, like Fitbits, in monitoring behavioral and physiological changes during recovery, suggesting they could be valuable tools for tracking patient progress in a convenient and continuous manner.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Continuous Digital Assessment for Weight Loss Surgery Patients. Ramirez, E., Marinsek, N., Bradshaw, B., et al.[2020]

Approximately 250,000 cases of appendicitis occur annually in the U.S., with the highest incidence in males aged 10-19 years, indicating a significant public health concern.

The lifetime risk of developing appendicitis is about 8.6% for males and 6.7% for females, with a notable decline in appendicitis rates of 14.6% from 1970 to 1984, although the reasons for this decrease remain unclear.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The epidemiology of appendicitis and appendectomy in the United States. Addiss, DG., Shaffer, N., Fowler, BS., et al.[2022]

Over a 15-year period, the use of computed tomography (CT) for diagnosing appendicitis increased significantly, leading to more non-operative management and longer time to surgery, but ultimately contributing to improved patient outcomes.

Laparoscopic surgery became the preferred method for treating appendicitis, resulting in shorter hospital stays and a decrease in 30-day mortality rates, which fell to 0.33% overall during the study period.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Temporal Trends in the Investigation, Management and Outcomes of Acute Appendicitis over 15 Years in the North of England: A Retrospective Cohort Study. Bhaskar, J., McLean, RC., Bhaskar, K., et al.[2022]

Citations

1. clinicaltrials.gov clinicaltrials.gov/study/NCT06395636?term=AREA%5BConditionSearch%5D(%22Cecal%20Diseases%22)&rank=4

Early Detection of Infection Using the Fitbit in Pediatric ... 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 ...

2. healthcare-bulletin.co.uk healthcare-bulletin.co.uk/article/postoperative-wound-infection-rates-following-open-vs-laparoscopic-appendectomy-a-comparative-study-3202/

Postoperative Wound Infection Rates Following Open vs. ... Results: The overall postoperative wound infection rate was significantly lower in the laparoscopic group (4%, n=6) compared to the open appendectomy group (12% ...

3. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC12466744/

Postoperative Infections After Appendectomy for Acute ... A comprehensive meta-analysis combining data from 35 studies, including over 5300 patients, reported a postoperative infection rate of 15.4% in ...

4. cureus.com cureus.com/articles/372206-a-retrospective-cohort-study-on-postoperative-surgical-site-infections-following-open-appendectomy-predictive-factors-and-development-and-validation-of-a-risk-prediction-model

A Retrospective Cohort Study on Postoperative Surgical ... This study aimed to evaluate the prevalence of SSIs following open appendectomy, conduct microbiological analyses, identify modifiable risk ...

5. wjes.biomedcentral.com wjes.biomedcentral.com/articles/10.1186/s13017-018-0186-1

Clinical prediction score for superficial surgical site infection ... This study aims to determine a clinical prediction score for SSI after appendectomy in complicated appendicitis.

6. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC12243941/

A Retrospective Cohort Study on Postoperative Surgical ... Surgical site infections (SSIs) are a common complication after open appendectomy, increasing postoperative morbidity and healthcare costs.

7. sciencedirect.com sciencedirect.com/science/article/abs/pii/S0196655325006248

Estimation of Risk-Adjusted Postoperative Infection ... Automated surveillance of postoperative infections can augment manual review. · Parsimonious models can be scaled to cover all operations.

8. thelancet.com thelancet.com/journals/lanepe/article/PIIS2666-7762(24)00332-6/fulltext

Development and validation of artificial intelligence models ... We aimed to develop locally valid postoperative infection predictive models to assist early detection of a postoperative infection, one of the ...

9. academic.oup.com academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocaf145/8244921

Prediction of postoperative infections by strategic data ... This study evaluated whether integrating postoperative laboratory values and their kinetics could improve outcome prediction. Materials and ...