Brief Summary

Chronic Obstructive Pulmonary Disease (COPD) is a debilitating and chronic lung syndrome that causes accelerated lung function decline and death in the 20% of cases. Mostly, the non-adherence to therapy contributes to symptoms increase, mortality, inability and therapies failure, highly influencing the management costs associated to COPD. The existing procedure of diagnosing COPD is effective and fast. The acute treatment and the subsequent disease management, instead, strictly depend on the currently long and complex process of identification of three factors: COPD phenotype, adherence to chosen therapy and probability of exacerbation events. The knowledge of these factors is needed by clinicians to stratify patients and personalise the therapies and rehabilitation procedures, to initiate an effective disease management. The application of Raman spectroscopy on saliva, representing an easy collectable and highly informative biofluid, has been already proposed for different infective, neurological and cancer diseases, with promising results in the diagnostic and monitoring fields. In this project, we propose the use of Deep Learning analysis of Raman spectra collected from COPD patient's saliva to be combined with other clinical data for the development of a system able to provide fast and sensitive information regarding COPD phenotypes, adherence and exacerbation risks. This will support clinicians to personalise COPD therapies and treatments, and to monitor their effectiveness.

Trial Health

At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date

Enrollment

250

participants targeted

Target at P75+ for all trials

Timeline

Completed

Started Feb 2022

Typical duration for all trials

Geographic Reach

4 countries

5 active sites

Status

unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

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Study Timeline

Key milestones and dates

2.9 years study duration

First Submitted

Initial submission to the registry

November 9, 2020

Completed

7 days until next milestone

First Posted

Study publicly available on registry

November 16, 2020

Completed

1.2 years until next milestone

Study Start

First participant enrolled

February 1, 2022

Completed

1.8 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

November 30, 2023

Completed

1.1 years until next milestone

Study Completion

Last participant's last visit for all outcomes

January 1, 2025

Completed

Last Updated

April 4, 2022

Status Verified

February 1, 2022

Enrollment Period

1.8 years

First QC Date

November 9, 2020

Last Update Submit

March 22, 2022

Conditions

Chronic Obstructive Pulmonary Disease

Keywords

Raman SpectroscopyCOPDMachine LearningBiomarker

Outcome Measures

Primary Outcomes (6)

Identification of the salivary COPD Raman signature
Raman spectroscopy will be used to analyse saliva of COPD patients, leading to the characterization of a specific COPD signature highlighting the differences between the one of asthma patients and healthy subjects. Using multivariate analysis, the possibility to create a classification model will be tested.
Two years
Characterization of the spectral differences of COPD patients
Raman data will be interpreted comparing the signatures of the different experimental groups (COPD vs asthma vs healthy subjects), identifing the molecular classes responsible for the principal differences
Two years
Stratification of the 4 COPD phenotypes through the Raman signature
An intra COPD class analysis will be performed, identifying the specific Raman signature of each phenotype considered in the study. The multivariate analysis will be performed evaluating the possibility to create a classification model able to perform a fast diagnosis based on the analysis of saliva
Two years
Monitoring of therapy adherence and effects
Raman data will be correlated with the clinical parameters, identifying hidden trends and relationships between the two investigated factors. In particular, the effects of a full and missing therapy adherence will be evaluated in terms of changing in salivary Raman signatures
Two years
Determination of the exacerbation index
The Raman signal associated with frequently exacerbator patients will be computed through linear discriminant analysis, obtaing coefficients related to the exacerbation event. In this way, a measurable parameter will be created in order to monitor and potentially forecast the exacerbation events
Two years
Application of a portable Raman spectrometer as Point of Care
All the data, databases and classification models created in the previous outcomes will be integrated in a portable Raman instrument that will be applied directly on new patinets, in order to test the reliability of the methodology. At the same time, the new data will be used to train the model, increasing the discriminatory power in terms of accuracy, precision, sensitivity and specificity
Three years

Study Arms (6)

Asthma-COPD Overlapped (aCOPD)

50 subjects affected by Asthma-COPD Overlapped comparable by age and sex with the other recruited subjects. The diagnosis of the mixed phenotypes will be established by the presence of a combination of the following factors: history of asthma and/or atopy, reversibility in the bronchodilator test, notable eosinophilia in respiratory and/or peripheral secretions, high IgE, positive prick test to pneumoallergens and high concentrations of exhaled NO