Actimetry Protocol in COPD Patients
Study Details
Study Description
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 13.7% (AUROC: 0.84, CI95% [0.75-0.92]). In the total study population (n=1409), 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.
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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 and/or 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 patient's 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 13.7% (AUROC: 0.84, IC95% [0.75- 0.92]). In the total population studied (n=1409), 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 Design
Outcome Measures
Primary Outcome Measures
- AUC-ROC as the primary endpoint to judge the performance of the algorithm [December 2023]
A contingency table recording the algorithm's performance metrics will be constructed in parallel from the actimetry data. The AUC-ROC will be used as the primary endpoint to judge the performance of the algorithm.
Eligibility Criteria
Criteria
Inclusion Criteria:
- Any patient with a diagnosis of COPD, all GOLD stages combined, who receives a properly informed Colibri-COPD digital consultation.
Exclusion Criteria:
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Patients with other unstable conditions with treatment,
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Unstable patients:
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Who had an exacerbation in the previous 2 months,
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Patients who have had surgery, a heart attack, a fall, or an accident limiting usual movements in the previous 3 months.
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Protected patients within the meaning of the French Public Health Code
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Private Practice | Grenoble | France |
Sponsors and Collaborators
- Association pour la Complementarite des Connaissances et des Pratiques de la Pneumologie
- Icadom
Investigators
- Study Director: Bernard Aguilaniu, M.D. PhD, Association pour la Complementarite des Connaissances et des Pratiques de la Pneumologie
Study Documents (Full-Text)
More Information
Additional Information:
Publications
- Furlanetto KC, Donaria L, Schneider LP, Lopes JR, Ribeiro M, Fernandes KB, Hernandes NA, Pitta F. Sedentary Behavior Is an Independent Predictor of Mortality in Subjects With COPD. Respir Care. 2017 May;62(5):579-587. doi: 10.4187/respcare.05306. Epub 2017 Mar 7.
- Rabinovich RA, Louvaris Z, Raste Y, Langer D, Van Remoortel H, Giavedoni S, Burtin C, Regueiro EM, Vogiatzis I, Hopkinson NS, Polkey MI, Wilson FJ, Macnee W, Westerterp KR, Troosters T; PROactive Consortium. Validity of physical activity monitors during daily life in patients with COPD. Eur Respir J. 2013 Nov;42(5):1205-15. doi: 10.1183/09031936.00134312. Epub 2013 Feb 8.
- Schneider LP, Furlanetto KC, Rodrigues A, Lopes JR, Hernandes NA, Pitta F. Sedentary Behaviour and Physical Inactivity in Patients with Chronic Obstructive Pulmonary Disease: Two Sides of the Same Coin? COPD. 2018 Oct;15(5):432-438. doi: 10.1080/15412555.2018.1548587.
- van Gestel AJ, Clarenbach CF, Stowhas AC, Rossi VA, Sievi NA, Camen G, Russi EW, Kohler M. Predicting daily physical activity in patients with chronic obstructive pulmonary disease. PLoS One. 2012;7(11):e48081. doi: 10.1371/journal.pone.0048081. Epub 2012 Nov 2.
- Watz H, Pitta F, Rochester CL, Garcia-Aymerich J, ZuWallack R, Troosters T, Vaes AW, Puhan MA, Jehn M, Polkey MI, Vogiatzis I, Clini EM, Toth M, Gimeno-Santos E, Waschki B, Esteban C, Hayot M, Casaburi R, Porszasz J, McAuley E, Singh SJ, Langer D, Wouters EF, Magnussen H, Spruit MA. An official European Respiratory Society statement on physical activity in COPD. Eur Respir J. 2014 Dec;44(6):1521-37. doi: 10.1183/09031936.00046814. Epub 2014 Oct 30.
- Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017 Apr 4;12(4):e0174944. doi: 10.1371/journal.pone.0174944. eCollection 2017.
- ACCPP-PROT2022-02