Prediction of Rehospitalization Following a Sepsis Admission Using a Wearable Biopatch

Sepsis Clinical Trial

Official title: Prediction of Rehospitalization Following a Sepsis Admission Using a Wearable Biopatch and Deep Learning Model

Status Recruiting

Clinical Trial Summary

The goal of this observational study is to learn about the utility of biopatches predicting 30-day readmissions in patients discharged from the hospital with sepsis. The main question[s] it aims to answer are: • Does the application of a biopatch provide data that can improve prediction of an unplanned 30-day readmission following a hospitalization for sepsis. Participants will be asked to wear a biopatch on their chest for 30-days following hospital discharge or until readmission to the hospital.

Clinical Trial Description

Study Design: Longitudinal cohort study with repeated measure of outcomes and predictors. The BioIntelliSense patch is an FDA approved wearable device that is applied to the chest with a 30-day battery lifespan and allows for real-time monitoring of heart rate, respiratory rate, skin temperature, general activity, severe cough episodes, and sedentary body position, among others. Outcomes of Interest: Hospital readmission within 30 days of discharge following an index admission with a diagnosis of sepsis is the primary outcome of interest for this study. We will calculate the positive predictive value (PPV) of readmission prediction as the the primary outcome of interest from the following approaches: analytic score plus biopatch, analytic score alone, LACE+ score. Secondary outcomes include area under the curve of the receiver operator characteristic (AUCroc) of predictive scores (analytic score and biopatch, analytic score alone, LACE+ score) and number of patients readmitted to the hospital within 30 days of discharge. Protocol for Patient Selection and Application of Biopatch: Patients who meet "Sepsis 3" definition will be identified with institutional review board (IRB)-approved screening protocols. Our previously derived and validated machine-learning algorithm to predict unplanned 30-day readmissions will then generate daily predictions about 30-day readmission probability which will be recorded, as well as LACE+ scores. As a patient approaches discharge, the treatment team and patient (or legally authorized representative) will be approached about potential enrollment. If there is agreement to enroll in this prospective study, then we will apply the patch at the time of discharge. Patients will then be followed with the BioIntellisence patch with augmented and real-time risk predictions based on data obtained from this. For this proposal, we will use data from the BioIntelliSense patch and are not providing clinicians with data on risk of readmission. ;

Related Conditions & MeSH terms

• Sepsis
• Toxemia

• NCT number: NCT05806762
• Study type: Observational [Patient Registry]
• Source: University of California, San Diego
• Contact: Gabriel Wardi
• Phone: 6195436463
• Email: gwardi@health.ucsd.edu
• Status: Recruiting
• Start date: May 1, 2023
• Completion date: May 2, 2027