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Data collection study to establish a predictive model of infection observed during childhood cancer therapy using data captured by wearable technology.
Rationale: Development in treatment for childhood cancers has improved remarkably with the 5-year survival rate now exceeding 80% in developed countries. However, these treatments are not without their adverse effects. The international Childhood Cancer Survivor study revealed that 62.3% of survivors had at least one chronic health condition and 27.5% had a severe or life-threatening condition as a direct result of their cancer treatment (DOI: 10.1056/NEJMsa060185). One of the adverse events experienced by 90% of children treated for cancer is infection. Septic shock, the most severe of infection outcomes, is characterized by life-threatening organ dysfunction, is the most and carriers a mortality rate of 41 to 46% (DOI: 10.1016/j.jped.2023.01.001). Beyond mortality, delayed first antibiotic administration (> 1 hour from fever onset >38 degrees) is associated with intensive care admissions, prolonged hospital stays, and adverse outcomes. Fluctuations in physiology can precede fever onset by 72 hours in patients with infection. This may provide a window for early detection of infection via wearable technology. The WEARABLES study will combine wearable technology with machine learning to develop an infection prediction model to allow earlier detection and reduce the suffering of children with cancer.
Trial Design: This is a non-interventional silent pilot trial to establish a predictive model for infection observed during childhood cancer therapy using data passively captured via wearables.
The study will be conducted in patients (5-18 years) with a new cancer diagnosis, currently receiving treatment at The Royal Children's Hospital, and have access to an iPhone (either themselves as an adolescent and young adult or via their parents/guardian). Once consented the wearable device will be paired to the patients or parent/guardians phone, and the WEARABLES app will be downloaded onto the phone. Once the device has been set up correctly, the wearable device will collect a range of vital signs for the duration of the study (4 weeks), and a weekly survey will be sent to check for symptoms and/or hospital admissions for infection. At the end of the 4 weeks, participants will receive a final survey to evaluate the feasibility of using a wearable device for toxicity detection. No further involvement will be asked of participants for this pilot trial. All data collected will be utilized to develop a machine learning model for sepsis/infection before being prospectively validated in a second trial.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Wearable Device | Device | Wearable device to collect the following health metrics directly from participants for the duration of the study (4 weeks). Health metrics are collected every 15 minutes, except for the ECG which will be collected once per week. Data points:
|
| Measure | Description | Time Frame |
|---|---|---|
| Changes in cardiac electrical activity patterns on Electrocardiogram (ECG) | ECG data will be collected once per week over a 4-week period for each participant to identify changes in cardiac electrical activity that may be associated with early infection. These data will be used as input features for a machine learning model aimed at predicting infection risk in children receiving cancer treatment. | Baseline (Day 1), Day 8, Day 15, Day 22, Day 29 |
| Changes in physical activity | Exercise data will be collected every 15 minutes over a 4-week period for each participant to identify changes that may correlate with early signs of infection. These data points will be used as input features for a machine learning model aimed at predicting infection risk in children receiving cancer treatment. | Baseline (Day 1) and every 15 minutes until Study completion at Day 29 |
| Changes in heart rate | Heart rate data will be collected every 15 minutes over a 4-week period for each participant to identify changes that may be indicative of infection. These data points will be used as input features for a machine learning model aimed at predicting infection risk in children receiving cancer treatment. | Baseline (Day 1) and every 15 minutes until Study completion at Day 29 |
| Changes in heart rhythm | Detection of irregular heart rhythms that may reflect early signs of infection. These data points will be used as input features for a machine learning model aimed at predicting infection risk in children receiving cancer treatment. | Baseline (Day 1) and every 15 minutes until Study completion at Day 29 |
| Changes in blood oxygen saturation | Blood oxygen saturation data will be collected every 15 minutes over a 4-week period for each participant to identify changes that may be indicative of infection. These data points will be used as input features for a machine learning model aimed at predicting infection risk in children receiving cancer treatment. |
| Measure | Description | Time Frame |
|---|---|---|
| The acceptability of using (and not using) wearable devices in children receiving cancer therapies | The acceptability of using wearable devices in children receiving cancer therapies as determined at study completion using the Theoretical Framework of Acceptability (TFA). | Day 29 |
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Inclusion Criteria:
Exclusion Criteria:
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Patients aged 5-18 years currently receiving treatment for cancer at The Royal Children's Hospital
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Lane Collier | Contact | 03 9936 6324 | lane.collier@mcri.edu.au | |
| A/Prof Rachel Conyers | Contact | 03 9345 5522 | rachel.conyers@rch.org.au |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| The Royal Children's Hospital | Recruiting | Parkville | Victoria | Australia |
The de-identified data set collected for this trial will be available 12 months after publication of the primary outcome.
The study protocol, analysis plan and consent forms will also be available. The data may be obtained from the Murdoch Children's Research Institute by emailing MCTC@mcri.edu.au.
The de-identified data set collected for this trial will be available 12 months after publication of the primary outcome. The data may be obtained from the Murdoch Children's Research Institute by emailing MCTC@mcri.edu.au.
Prior to accessing any data, the following would be required: a data access agreement must be signed by all relevant parties, the investigators of the study must see and approve the analysis plan describing how the data will be analyzed. There must also be an agreement around appropriate acknowledgement in any future publications.
The data may be obtained from the Murdoch Children's Research Institute by emailing MCTC@mcri.edu.au.
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| Baseline (Day 1) and every 15 minutes until Study completion at Day 29 |
| Changes in respiratory rate | Respiratory rate data will be collected every 15 minutes over a 4-week period for each participant to identify changes that may be indicative of infection. These data points will be used as input features for a machine learning model aimed at predicting infection risk in children receiving cancer treatment. | Baseline (Day 1) and every 15 minutes until Study completion at Day 29 |
| Infection-Related Hospital Admission | An infection survey will be completed by patients once per week over a 4-week period to identify confirmed episodes of infections requiring admission to hospital. This data will be used to determine when patients are controls (non-infectious) vs cases (infectious), and used as an input feature for a machine learning model. | Baseline (Day 1), Day 8, Day 15, Day 22, Day 29 |
| ID | Term |
|---|---|
| D009369 | Neoplasms |
| D007239 | Infections |
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| ID | Term |
|---|---|
| D000076251 | Wearable Electronic Devices |
| ID | Term |
|---|---|
| D055615 | Electrical Equipment and Supplies |
| D004864 | Equipment and Supplies |
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