Abstract
Facilitating the identification of extreme inactivity (EI) has the potential to improve morbidity and mortality in COPD patients. Apart from patients with obvious EI, the identification of a such behavior during a real-life consultation is unreliable. We therefore describe a machine learning algorithm to screen for EI, as actimetry measurements are difficult to implement. Complete datasets for 1409 COPD patients were obtained from COLIBRI-COPD, a database of clinicopathological data submitted by French pulmonologists. Patient- and pulmonologist-reported estimates of PA quantity (daily walking time) and intensity (domestic, recreational, or fitness-directed) were first used to assign patients to one of four PA groups (extremely inactive [EI], overtly active [OA], intermediate [INT], inconclusive [INC]). The algorithm was developed by (i) using data from 80% of patients in the EI and OA groups to identify 'phenotype signatures' of non-PA-related clinical variables most closely associated with EI or OA; (ii) testing its predictive validity using data from the remaining 20% of EI and OA patients; and (iii) applying the algorithm to identify EI patients in the INT and INC groups. The algorithm's overall error for predicting EI status among EI and OA patients was 13.7%, with an area under the receiver operating characteristic curve of 0.84 (95% confidence intervals: 0.75-0.92). Of the 577 patients in the INT/INC groups, 306 (53%) were reclassified as EI by the algorithm. Patient- and physician- reported estimation may underestimate EI in a large proportion of COPD patients. This algorithm may assist physicians in identifying patients in urgent need of interventions to promote PA.
Highlights
Patients with chronic obstructive pulmonary disease (COPD) are known to be substantially less physically active than age- and sex-matched healthy subjects [1]
The seven categories encompassed by inconclusively determined (INC) a–d and INT a–c together account for about 40% of the total cohort, highlighting the need for a tool to more accurately assess daily physical activity (PA)
The main contribution of this study is to demonstrate the predictive validity of an algorithm for predicting the least active COPD patients from information available in routine pulmonologist practice independently of PA-related measures
Summary
Patients with chronic obstructive pulmonary disease (COPD) are known to be substantially less physically active than age- and sex-matched healthy subjects [1]. Accelerometers can be worn over several days to analyze the full range of different activities and their distribution over time Data from such devices have generally correlated well with assessments of daily metabolic expenditure, as measured using the doubly labeled water method, and accelerometers are sufficiently sensitive to detect low levels of PA in COPD patients [4]. The PROactive consortium proposed that a combination of questionnaires and accelerometric measurements be used to assess the behavior of COPD patients [8, 9] This approach does not eliminate the drawbacks of accelerometry, and does not resolve the primary clinical concern, which is to accurately and objectively detect extreme inactivity (referred to hereafter as EI) in patients whose PA status initially presents as unclear or equivocal [10, 11]. Given the proven benefit of pulmonary exercise programs in COPD patients, we sought to develop a predictive algorithm that can reliably detect EI patients, who might most benefit from interventions such as pulmonary rehabilitation programs
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