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| ID | Type | Description | Link |
|---|---|---|---|
| 2P50HD093074 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) | NIH |
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This is a pivotal, prospective, double-blind, study to evaluate the sensitivity and specificity of the SenseToKnow device for the detection of autism spectrum disorder in children 16-36 months of age.
This is a pivotal, prospective, double-blind, study to evaluate the sensitivity and specificity of the SenseToKnow device for the classification of autism spectrum disorder when administered by parents in a sample of patients 16-36 months of age. The trial design is a non-interventional cross-sectional study comparing the SenseToKnow device classification of autism spectrum disorder ("autism") versus non-autism with the patient's diagnostic status based on expert clinical diagnosis in a population of pediatric patients.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Pediatric patients, 16-36 months of age, recruited through pediatric medical clinics | Consecutive pediatric participants will be recruited and enrolled via >= 6 participating sites comprised of pediatric medical clinics (e.g., primary care and family medicine clinics) that are part of the broader Duke University Health System (DUHS) located in North Carolina. Enrollment will proceed until the targets of N = 150 participants diagnosed with autism spectrum disorder and N = 200 without autism are reached. |
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| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity of the SenseToKnow screening device based on a machine learning algorithm that combines SenseToKnow digital data with data from the SenseToKnow Caregiver survey for autism detection | Sensitivity = #participants positive for autism on both (1) the SenseToKnow screening device based on a machine learning algorithm that combines SenseToKnow digital data with the SenseToKnow Caregiver Survey data and (2) expert clinical diagnosis / #participants positive for autism on both SenseToKnow and expert clinical diagnosis | Will be calculated based on data from Baseline/Timepoint 1 |
| Specificity of the SenseToKnow screening device based on machine earning algorithm that combines SenseToKnow digital data with data from the SenseToKnow Caregiver survey for autism detection | Specificity = #participants negative for autism on both (1) the SenseToKnow screening device based on a machine learning algorithm that combines SenseToKnow digital data with the SenseToKnow Caregiver Survey data, and (2) expert clinical diagnosis / #participants negative for autism on autism by expert clinical diagnosis | Will be calculated based on data from Baseline/Timepoint 1 |
| Measure | Description | Time Frame |
|---|---|---|
| Positive Predictive Value of SenseToKnow screening device (based on a machine learning algorithm using the SenseToKnow digital data, combined with the SenseToKnow Caregiver Survey data) for autism detection in comparison to expert clinical diagnosis | The likelihood that a participant with a positive test result (based on a machine learning algorithm using the SenseToKnow digital data, combined with the SenseToKnow Caregiver Survey data) has a diagnosis of autism (based on expert clinical diagnosis). Positive Predictive Value will be calculated with and without adjustment for population prevalence. |
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Inclusion Criteria:
Exclusion Criteria:
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Participants will be patients 16-36 months of age recruited from > 6 sites comprised of pediatric medical clinics that are part of the broader Duke University Health System in North Carolina.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Geraldine Dawson, PhD | Contact | 9196680070 | geraldine.dawson@duke.edu | |
| Charlotte Stoute, BA | Contact | 919-681-9730 | charlotte.stoute@duke.edu |
| Name | Affiliation | Role |
|---|---|---|
| Geraldine Dawson, PhD | Duke University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Duke University | Recruiting | Durham | North Carolina | 27705 | United States |
All individual-level data that meets PHI and IRB confidentiality requirements will be submitted to the NIH/NIMH Data Repository by the end of the grant period.
We will submit an electronic version of the final, peer-reviewed work, including the statistical analysis code, to the National Library of Medicine PubMed Central, to be made publicly available no later than 12 months after the official date of publication.
Publically available via PubMed Central
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| ID | Term |
|---|---|
| D001321 | Autistic Disorder |
| D000067877 | Autism Spectrum Disorder |
| D004194 | Disease |
| ID | Term |
|---|---|
| D002659 | Child Development Disorders, Pervasive |
| D065886 | Neurodevelopmental Disorders |
| D001523 | Mental Disorders |
| D010335 | Pathologic Processes |
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| Will be calculated based on data from Baseline/Timepoint 1 |
| Negative Predictive Value of SenseToKnow screening device (based on a machine learning algorithm using the SenseToKnow digital data, combined with the SenseToKnow Caregiver Survey data) for autism detection in comparison to expert clinical diagnosis | The likelihood that a participant with a negative test result (based on a machine learning algorithm using the SenseToKnow digital data, combined with the SenseToKnow Caregiver Survey data) does not have a diagnosis of autism (based on expert clinical diagnosis). Negative Predictive Value will be calculated with and without adjustment for population prevalence. | Will be calculated based on data from Baseline/Timepoint 1 |
| Receiver Operating Characteristic Curve and Area Under the Curve with respect to the accuracy of the SenseToKnow screening device (using the SenseToKnow digital data and SenseToKnow Caregiver survey data) for autism versus non-autism classification | Receiver Operating Characteristic Curve (ROC) is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. Area Under the Curve (AUC) measures the area underneath the entire ROC curve. Accuracy of test is based on a machine learning algorithm using the SenseToKnow digital data, combined with the SenseToKnow Caregiver Survey data, in comparison to expert clinical diagnosis. | Will be calculated based on data from Baseline/Timepoint 1 |
| Sensitivity of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection | Sensitivity = #participants positive for autism on both (1) the SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data and (2) expert clinical diagnosis / # participants positive for autism on expert clinical diagnosis | Will be calculated based on data from Baseline/Timepoint 1 |
| Specificity of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection | Specificity = #participants negative for autism on both (1) the SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data and (2) expert clinical diagnosis / #participants negative for autism on expert clinical diagnosis. | Will be calculated based on data from Baseline/Timepoint 1 |
| Positive Predictive Value of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection in comparison to expert clinical diagnosis | The likelihood that a participant with a positive test result has a diagnosis of autism (based on expert clinical diagnosis). Positive Predictive Value will be calculated with and without adjustment for population prevalence. | Will be calculated based on data from Baseline/Timepoint 1 |
| Negative Predictive Value of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection in comparison to expert clinical diagnosis | The likelihood that a participant with a negative test result does not have a diagnosis of autism (based on expert clinical diagnosis). Negative Predictive Value will be calculated with and without adjustment for population prevalence. | Will be calculated based on data from Baseline/Timepoint 1 |
| Receiver Operating Characteristic Curve and Area Under the Curve with respect to the accuracy of the SenseToKnow device using only the SenseToKnow digital data for autism versus non-autism classification | Receiver Operating Characteristic Curve (ROC) is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. Area Under the Curve (AUC) measures the area underneath the entire ROC curve. Accuracy of test is based on a machine learning algorithm using only the SenseToKnow digital data, in comparison to expert clinical diagnosis. | Will be calculated based on data from Baseline/Timepoint 1 |
| D013568 | Pathological Conditions, Signs and Symptoms |