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