Pulmonary Fibrosis Lung Sounds Study

Royal Devon and Exeter NHS Foundation Trust100 enrolled

Overview

The goal of this observational study is to test whether it is possible to detect particular lung sounds that are unique to patients with the lung disease pulmonary fibrosis and whether any such sounds could be analysed using machine learning to make diagnosing disease easier. Participants will have a sound detection device placed in different locations on the chest and audio sounds will be recorded for analysis. Researchers will compare audio recordings from clinically diagnosed patients with recordings from healthy controls of a similar age to see whether the sounds are sufficiently different within that age group.

This is a study of chest audio recordings obtained using a sound enhancer, in this case a Bluetooth device, combined with intelligent computer-processing and analysis. It is being carried out amongst pulmonary fibrosis patients and healthy controls of a similar age, with the aim to improve diagnosis of pulmonary fibrosis and remote monitoring of disease progression. Expert respiratory doctors gain important insights about the health of a patient's lungs by listening to the chest with a stethoscope. Currently, there are insufficient respiratory experts and specialist equipment to meet the patient demand, leading to delays in diagnosis and treatment and a shortage of specialist care following diagnosis. In this study the investigators are aiming to make that specialist practice much more available by recording lung sounds and developing software to do the intelligent analysis. Initial tests with publicly available recordings of expertly diagnosed respiratory sounds have shown that different lung diseases can be detected with a very high degree of accuracy using new software. Here the investigators want to test that software with a cost-effective digital sound device in a clinical setting. The aim is for respiratory diseases to be diagnosed quickly and easily and also, in future, for patients to be offered the option to monitor how well they are after diagnosis in their own home.

Study Type

OBSERVATIONAL

Enrollment

100

Conditions

Pulmonary Fibrosis

Eligibility

Sex: ALLHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria * Able to understand what the study involves * Able and willing to give informed consent For patients: * Age≥60 (PF affects adults, with the majority of age≥60) * A diagnosis of progressive pulmonary fibrosis (to include all diagnosed interstitial lung disease (ILD) patients presenting at the ILD clinic) For healthy controls: * Age≥60 (to ensure that age of controls is similar to that of patients so that younger age does not bias results) * No known lung disease Exclusion Criteria * Unable to understand what the study involves * Unwilling or unable to give consent * Age\<60 (to ensure that age does not confound results and cases and controls are of similar age, since the majority of pulmonary fibrosis patients are of age≥60)

Outcomes

Primary Outcomes

Number of clinical lung sound recordings stored from pulmonary fibrosis cases and controls

A measure of the feasibility of gathering 12 lung sound files from each of 50 PF patients and 50 healthy volunteers in a similar age-group in the available timeframe.

Time frame: 6 months

Measure of ability of this system to classify participants as PF patients or healthy controls

A measure of the capability of the machine learning model combined with the cost-effective bluetooth stethoscope to classify study participants as PF patients or healthy controls from lung sound recordings alone in a clinical setting

Time frame: 8 months

Feedback from patients and study clinicians

Feedback from patients and study clinicians about the acceptability of digital sound monitoring for improving future diagnosis and monitoring of disease progression in pulmonary fibrosis

Time frame: 8 months

Secondary Outcomes

A correlation between clinical measures of pulmonary fibrosis severity and the audio waveform

A demonstrable correlation between objective markers of pulmonary function tests (forced vital capacity percent of predicted (FVC%) or diffusing capacity in the lung for carbon monoxide percentage predicted (DLCO%)) or breathlessness symptoms (mMRC Dyspnoea score) and the waveform of the audio recording.