Ignite Creation Date:
2024-05-06 @ 7:09 PM
Last Modification Date:
2024-10-26 @ 3:01 PM
Study NCT ID:
NCT05918003
Status:
RECRUITING
Last Update Posted:
2023-06-26
First Post:
2023-06-13
Brief Title:
Actimetry Protocol in COPD Patients
Sponsor:
Association pour la Complementarite des Connaissances et des Pratiques de la Pneumologie
Organization:
Association pour la Complementarite des Connaissances et des Pratiques de la Pneumologie
Study Overview
Official Title:
Validation by Three-dimensional Actimetry Measurements of a Predictive Algorithm for Excessive Inactivity in Chronic Obstructive Pulmonary Disease COPD Patients
Status:
RECRUITING
Status Verified Date:
2023-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Recently the principal investigator published an EI predictive Machine Learning algorithm based solely on clinical data without any physical activity measures collected from 1 409 patients The GOLD standard of EI was defined on the basis of interrogation criteria Patients considered as EI reported walking less than 10 minutes per day on average and the pulmonologist judged that the patient had mainly domestic activities
Despite the subjective nature of the GOLD standard the algorithm validated on a test sample had an error rate of only 137 AUROC 084 CI95 075-092 In the total study population n1409 34 of patients were ultimately classified as EIs by the algorithm in agreement with the results of studies using actimetry as the GOLD standard
The principal investigator now wish to verify and improve the validity of the MLA on a new smaller population of 104 patients using a physiological GOLD standard such as three-dimensional actimetry
Despite the subjective nature of the GOLD standard the algorithm validated on a test sample had an error rate of only 137 AUROC 084 CI95 075-092 In the total study population n1409 34 of patients were ultimately classified as EIs by the algorithm in agreement with the results of studies using actimetry as the GOLD standard
The principal investigator now wish to verify and improve the validity of the MLA on a new smaller population of 104 patients using a physiological GOLD standard such as three-dimensional actimetry
Detailed Description:
It has been shown that COPD patients have a significantly decreased daily physical activity DPA compared to matched subjects Moreover the severity of inactivity is correlated with several prognostic indices such as the frequency of exacerbations quality of life and mortality These findings lead to the recommendation with a level of evidence A of DPA in the context of medically supervised respiratory rehabilitation programs andor by encouraging patients to participate in programs promoting physical activity
However despite the established benefits it is estimated that this rehabilitative management actually involves only 10 of the patients who should benefit from it Among the various causes of this situation the underestimation of excessive inactivity EI by pulmonologists is one of the causes of this care deficit
Currently only actimetry can accurately assess the patients level of physical activity
To alert pulmonologists to this excessive situation justifying priority care without resorting to actimetry the aCCPP developed a Machine Learning Algorithm MLA based on clinical data from the Colibri-BPCO digital consultation that predicts excessive inactivity
In this study the GOLD standard EI was defined using clinical criteria summarized below EI patients reported walking for an average of less than 10 minutes per day and the pulmonologist judged on questioning that the DPA was indeed essentially domestic The objective of the MLA was to correctly classify EI subjects versus obviously active subjects hereafter referred to as Overtly Active OA
The MLA was validated on a test sample with an error rate of 137 AUROC 084 IC95 075- 092 In the total population studied n1409 34 of patients were finally classified as EIs in line with the results of studies using actimetry as the GOLD standard
Following the publication of this work the principal investigator would like to verify the validity of the algorithm on a new population using the recognized GOLD standard three-dimensional actimetry measurements
However despite the established benefits it is estimated that this rehabilitative management actually involves only 10 of the patients who should benefit from it Among the various causes of this situation the underestimation of excessive inactivity EI by pulmonologists is one of the causes of this care deficit
Currently only actimetry can accurately assess the patients level of physical activity
To alert pulmonologists to this excessive situation justifying priority care without resorting to actimetry the aCCPP developed a Machine Learning Algorithm MLA based on clinical data from the Colibri-BPCO digital consultation that predicts excessive inactivity
In this study the GOLD standard EI was defined using clinical criteria summarized below EI patients reported walking for an average of less than 10 minutes per day and the pulmonologist judged on questioning that the DPA was indeed essentially domestic The objective of the MLA was to correctly classify EI subjects versus obviously active subjects hereafter referred to as Overtly Active OA
The MLA was validated on a test sample with an error rate of 137 AUROC 084 IC95 075- 092 In the total population studied n1409 34 of patients were finally classified as EIs in line with the results of studies using actimetry as the GOLD standard
Following the publication of this work the principal investigator would like to verify the validity of the algorithm on a new population using the recognized GOLD standard three-dimensional actimetry measurements
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None