Key result
A decision tree based on CAT score, age, and FEV1 correctly assigned 71.7% of patients with COPD to their actual clinical profile, though 51.1% of patients migrated across profiles over 6 months.
Why the study?
Existing COPD profiles often do not describe treatable traits, lack validation, and have unknown stability over time.
Observational (n=352)
Yes
Effect estimate: Cohen's Kappa 0.62
p-value: p=<0.001
A simple decision tree using CAT score, age, and FEV1 can accurately allocate COPD patients into distinct clinical profiles to guide personalized interventions, though profile stability fluctuates over time.
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Decision tree using CAT, age and FEV1 classifies COPD into four profiles with 72% agreement; supports validation trials before clinical use.
Marques et al. (2022) conducted an observational in Chronic obstructive pulmonary disease (COPD) (n=352). COPD clinical profiles was evaluated on Agreement between the profile predicted by the decision tree and the profile defined by the clustering procedure (Cohen's Kappa 0.62, p=<0.001). A decision tree based on CAT score, age, and FEV1 correctly assigned 71.7% of patients with COPD to their actual clinical profile, though 51.1% of patients migrated across profiles over 6 months.
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