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Acute lymphoblastic leukemia (ALL) is the most common cancer of childhood and long-term survival has risen to above 90%, but 1-4% of treated patients die from infections. Early detection and treatment of infection can improve these outcomes by preventing increased severity and death. This study aims to determine whether continuous analysis of information from wearable devices (Like a watch and sticky patch) that measure temperature, pulse rate, oxygen level, and other similar information can predict infection before it is apparent to the patient or caregiver. About 65 patients will be enrolled and will wear these devices for 10 days; during that time the information will be recorded, but not available. After completion, information collected immediately before infection will be compared to other times to identify features that predict infections.
Primary Objectives (Feasibility Phase) To determine the feasibility of non-invasive collection of continuous physiological data in children with acute lymphoblastic leukemia during outpatient treatment.
(Completion Phase) To develop competing sepsis prediction algorithms using continuous physiological data during outpatient treatment in children with acute lymphoblastic leukemia during high-risk periods of induction therapy.
Secondary Objectives To estimate the PPV and NPV of competing sepsis prediction algorithms using continuous physiological data for prediction of fever and sepsis in children with acute lymphoblastic leukemia during high-risk periods of induction therapy.
To estimate the frequency of undocumented fever episodes in children with acute lymphoblastic leukemia during high-risk periods of induction therapy.
To estimate the time from detection of fever or sepsis by a wearable device to identification by standard practice in children with acute lymphoblastic leukemia during high-risk periods of induction therapy.
This study aims to determine whether continuous analysis of those biosignals in children treated for ALL can predict early onset of sepsis. To collect those biosignals, we combine two wearable sensors, namely TempTraq, an adhesive temperature sensor, and Empatica E4, a wearable physiological monitoring device.
Data will be collected during two selected high-risk 5-day periods of induction therapy for acute lymphoblastic leukemia. Statistical models will be applied and a series of validation methodologies will be developed to arrive at an optimal predictive model for subsequent external validation .
This study will use a two-stage design. The initial stage (the feasibility phase) will evaluate the feasibility of data collection, and the second stage (the completion phase) will provide sufficient data for the development of predictive models and the estimation of the sensitivity and specificity of these models .
The feasibility phase of the study will comprise the first 10 study participants, including at least 3 in each age group (5 to <10 years, and >/= 10 years). The aim of this stage is to estimate the proportion of time that continuous monitoring data are available, and these results will determine whether the study progresses to the completion phase
The Completion Phase will comprise approximately 55 participants. Enrollment will continue until:
1. A total of 15 participants have experienced a fever or sepsis event for the prediction model, and
2. At least 15 participants have completed the study without experiencing a fever or sepsis event.
Acute Lymphoblastic Leukemia
St. Jude Children's Research Hospital
Not yet recruiting
St. Jude Children's Research Hospital
Published on BioPortfolio: 2020-03-28T03:55:24-0400
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