Multidimensional outcome assessment of pulmonary rehabilitation in traits-based clusters of COPD patients
Notice bibliographique
Résumé
BACKGROUND: Clusters of COPD patients have been reported in order to individualize the treatment program. Neither co-morbidity clusters, nor integrated respiratory physiomics clusters contributed to a better prediction of outcomes. Based on a thoroughly assessed set of pulmonary and extra-pulmonary traits at the start of a pulmonary rehabilitation (PR) program, we recently described seven clusters of COPD patients. The aims of this study are to confirm multidimensional differential response and to assess the potential of pulmonary and extra-pulmonary traits-based clusters to predict this multidimensional response to PR pulmonary in COPD patients. METHODS: Outcomes of a 40-session PR program for COPD patients, referred by a chest physician, were evaluated based on the minimal clinically important difference (MCID) for 6-minute walk distance (6MWD), cycle endurance time, Canadian Occupational Performance Measure performance and satisfaction scores, Hospital Anxiety and Depression Scale anxiety and depression scores, MRC dyspnea grade and St George's Respiratory Questionnaire. The aforementioned response indicators were used to calculate the overall multidimensional response and patients were grouped in very good, good, moderate and poor responders. In the same way, responses to pulmonary rehabilitation were compared based on seven previously identified pulmonary and extra-pulmonary traits-based clusters. RESULTS: Of the whole sample, drop out was 19% and 419 patients (55.4% males, age: 64.3 ± 8.8, FEV1% of predicted: 48.9 ± 20) completed the pulmonary rehabilitation program. Very good responders had significantly worse baseline characteristics with a higher burden of disease, a higher proportion of rollator-users, higher body mass index (BMI), more limitations of activities in daily life, emotional dysfunction, higher symptoms of dyspnea and worse quality of life. Of the seven pre-identified clusters, 'the overall best functioning cluster' and 'the low disease burden cluster' both including the best 6MWD, the lowest dyspnea score and the overall best health status, demonstrated attenuated outcomes, while in 'the cluster of disabled patients', 76% of the patients improved health status with at least 2 times MCID. This 'cluster of disabled patients' as well as 'the multimorbid cluster', 'the emotionally dysfunctioning cluster', 'the overall worst-functioning cluster' and 'the physically dysfunctioning cluster' all demonstrated improvements in performance and satisfaction for occupational activities (more than 65% of patients improved with > 1MCID), emotional functioning (more than 50% of patients improved with > 1 MCID) and overall health status (more than 58%). CONCLUSION: The current study confirms the differential response to pulmonary rehabilitation based on multidimensional response profiling. Cluster analysis of baseline traits illustrates that non-linear, clinically important differences can be achieved in the most functionally and emotionally impaired clusters and that 'the overall best functional cluster' as well as 'the low disease burden cluster' had an attenuated outcome.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».