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Vol. 60. Issue 3.
Pages 153-160 (March 2024)
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Vol. 60. Issue 3.
Pages 153-160 (March 2024)
Original article
Predicting Response to In-Hospital Pulmonary Rehabilitation in Individuals Recovering From Exacerbations of Chronic Obstructive Pulmonary Disease
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Michele Vitaccaa,
Corresponding author
michele.vitacca@icsmaugeri.it

Corresponding author.
, Alberto Malovinib, Mara Paneronia, Antonio Spanevelloc,d, Piero Cerianae, Armando Capellif, Rodolfo Murgiag, Nicolino Ambrosinog
a Respiratory Rehabilitation of the Institute of Lumezzane, Istituti Clinici Scientifici Maugeri IRCCS, Brescia, Italy
b Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy
c Respiratory Rehabilitation of the Institute of Tradate, Istituti Clinici Scientifici Maugeri IRCCS, Varese, Italy
d Department of Medicine and Surgery, University of Insubria, Varese, Italy
e Respiratory Rehabilitation of the Institute of Pavia, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy
f Respiratory Rehabilitation of the Institute of Veruno, Istituti Clinici Scientifici Maugeri IRCCS, Novara, Italy
g Respiratory Rehabilitation of the Institute of Montescano, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy
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Tables (3)
Table 1. Baseline participants’ characteristics.23
Table 2. Quantile regression models predicting 6MWT, CAT, BId, and MRC change values.
Table 3. Performances in identifying patients reaching MCID for single and combined outcome variables on the test set.
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Additional material (1)
Abstract
Background

Predicting the response to pulmonary rehabilitation (PR) could be valuable in defining admission priorities. We aimed to investigate whether the response of individuals recovering from a COPD exacerbation (ECOPD) could be forecasted using machine learning approaches.

Method

This multicenter, retrospective study recorded data on anthropometrics, demographics, physiological characteristics, post-PR changes in six-minute walking distance test (6MWT), Medical Research Council scale for dyspnea (MRC), Barthel Index dyspnea (BId), COPD assessment test (CAT) and proportion of participants reaching the minimal clinically important difference (MCID). The ability of multivariate approaches (linear regression, quantile regression, regression trees, and conditional inference trees) in predicting changes in each outcome measure has been assessed.

Results

Individuals with lower baseline 6MWT, as well as those with less severe airway obstruction or admitted from acute care hospitals, exhibited greater improvements in 6MWT, whereas older as well as more dyspnoeic individuals had a lower forecasted improvement. Individuals with more severe CAT and dyspnea, and lower 6MWT had a greater potential improvement in CAT. More dyspnoeic individuals were also more likely to show improvement in BId and MRC. The Mean Absolute Error estimates of change prediction were 44.70m, 3.22 points, 5.35 points, and 0.32 points for 6MWT, CAT, BId, and MRC respectively. Sensitivity and specificity in discriminating individuals reaching the MCID of outcomes ranged from 61.78% to 98.99% and from 14.00% to 71.20%, respectively.

Conclusion

While the assessed models were not entirely satisfactory, predictive equations derived from clinical practice data might help in forecasting the response to PR in individuals recovering from an ECOPD. Future larger studies will be essential to confirm the methodology, variables, and utility.

Keywords:
COPD exacerbations
Pulmonary rehabilitation
Exercise
Respiratory measurement
Dyspnea
Machine learning
Abbreviations:
6MWT
BI
BId
BMI
BODE
CAT
CIRS
COPD
CRF
ECOPD
FEV1
FVC
GOLD
GPs
HRQL
ICS
LoS
MAE
MCID
ML
MRC
NPV
PPV
PR

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