Ignite Creation Date:
2024-10-26 @ 3:37 PM
Last Modification Date:
2024-10-26 @ 3:37 PM
Study NCT ID:
NCT06555757
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-06-07
Brief Title:
Utilising AI Analysis of Sounds To prEdict heaRt failurE decOmpensation
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Utilising AI Analysis of Sounds To prEdict heaRt failurE decOmpensation
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
STEREO
Brief Summary:
Heart failure impacts more than 2 of people in the UK United Kingdom and leads to about 5 of emergency hospital visits Patients might have slowly worsening symptoms or suddenly face acute decompensated heart failure ADHF marked by intense difficulty in breathing due to fast-developing lung congestion This is a serious emergency requiring in-hospital treatment and monitoring Once stable patients usually have a phase where symptoms remain constant But as time goes on those with heart failure often face more frequent and prolonged episodes of ADHF
Fluid build-up pulmonary congestion in the lungs is a key issue in heart failure and catching it early helps avoid unexpected hospital stays Spotting these early signs outside the hospital can be tough as symptoms arent always clear Study investigators are working on a new non-invasive way to identify these early signs using AI artificial intelligence to analyse subtle changes in a patients voice cough and breathing sounds This tool will act as an early warning for patients and their heart care teams allowing quicker treatment This could make heart failure episodes less severe and reduce the need for hospital visits
This research has two parts First a small pilot trial with up to 50 patients The findings will guide and inform a larger study involving up to 200 patients From this larger study investigators will develop the final version of the AI algorithm The results from the Part A and Part B of this research will guide the investigators in planning a future clinical trial This trial will confirm if the AI algorithm can be effectively used as a medical tool for heart failure care within the NHS National Health Service Study investigators will seek the necessary ethical approval before starting this trial
Fluid build-up pulmonary congestion in the lungs is a key issue in heart failure and catching it early helps avoid unexpected hospital stays Spotting these early signs outside the hospital can be tough as symptoms arent always clear Study investigators are working on a new non-invasive way to identify these early signs using AI artificial intelligence to analyse subtle changes in a patients voice cough and breathing sounds This tool will act as an early warning for patients and their heart care teams allowing quicker treatment This could make heart failure episodes less severe and reduce the need for hospital visits
This research has two parts First a small pilot trial with up to 50 patients The findings will guide and inform a larger study involving up to 200 patients From this larger study investigators will develop the final version of the AI algorithm The results from the Part A and Part B of this research will guide the investigators in planning a future clinical trial This trial will confirm if the AI algorithm can be effectively used as a medical tool for heart failure care within the NHS National Health Service Study investigators will seek the necessary ethical approval before starting this trial
Detailed Description:
Heart failure is a common condition in which the heart is unable to deliver the desired cardiac output either due to a weakened or stiff heart muscle It affects more than 2 of the UK population the incidence is around 200000 cases per annum resulting in 5 of all the emergency hospital admissions and it consumes approximately 2 of the annual NHS budget approximately 2 billion per annum Therefore heart failure is not only a major driver for hospitalisation but provides the leading opportunity to reduce preventable admissions
Acute decompensated heart failure ADHF is a medical emergency requiring urgent attention It usually results in inpatient hospitalisation and is a major driver for associated healthcare costs ADHF is usually characterised by rapid deterioration of breathlessness at rest or exertion because of pulmonary oedema pulmonary venous congestion and fluid retention resulting in swollen legs as well as a myriad of other symptoms including fatigue lack of appetite and so on
The patient normally presents with gradual or sudden onset of typical symptoms breathlessness fatigue and fluid accumulation in the legs After stabilisation and the initial treatment of ADHF patients enter a plateau phase where the heart remains stable However over time most patients experience multiple episodes of ADHF which typically become longer and separated by shorter intervals The congestion is related to underlying increased cardiac pressure usually secondary to volume overload which plays a central role in the pathophysiology presentation and prognosis of heart failure Pulmonary congestion is one of the most important diagnostic and therapeutic targets in heart failure Detecting pulmonary congestion earlier on due to volume overload is key to preventing impending rehospitalisation and presents an ideal opportunity to optimise heart failure treatment in the community
Early community detection of ADHF is ultimately the first step in providing effective patient care Poor recognition of HF due to its multitude of vaguenon-specific symptomatology of presentations often leads to delays in diagnosis and treatment The delay between a patient developing symptoms of HF decompensation and seeking medical attention is often considerable and is influenced by the speed of onset and severity of the symptoms Therefore a reliable and easily accessible means of assessing chronic fluid status in ambulatory outpatients is needed to detect early decompensation when appropriate intervention is possible The sudden development of breathlessness dyspnoea from the accumulation of fluid in the lungs acute pulmonary oedema usually prompts rapid contact with medical services whereas the gradual appearance of swollen legs and ankles peripheral oedema is more likely to be associated with delays in seeking care The average delay between symptom onset and hospital admission ranged from 2 hours to 7 days The symptoms of heart failure often develop gradually and appear non-threatening potentially explaining some of the observed delays in seeking care
In recent years several pilot studies demonstrated a relationship between speech biomarkers and the extent of systemic andor pulmonary congestion in heart failure patients For example in 2017 a study of 10 8 M 2F patients with acute decompensated heart failure undergoing inpatient treatment with intravenous diuretic therapy showed that after treatment patients displayed a higher proportion of automatically identified creaky voice increased fundamental frequency and decreased cepstral peak prominence variation suggesting that speech biomarkers can be early indicators of HF The study also showed that the severity of HF-related oedema required to measurably change the voice is small compared to the severity needed to increase body weight suggesting that speech biomarkers could become a more effective non-invasive tool to monitor HF patients than daily weights In 2021 another study evaluated the feasibility of remote speech analysis in the evaluation of dynamic fluid overload in heart failure patients undergoing hemodynamic treatment They performed serial speechvoice measurements in 5 patients undergoing haemodialysis The analysis was done with an app that does not share its AI algorithm They demonstrated statistically significant differences in select speech biomarkers at different fluid status levels as the patients progressed through the treatment Subsequently in 2022 a comparison of sound recordings for patients admitted with ADHF on the day of admission and the day of discharge with a sample of 40 patients who were admitted with acute decompensated heart failure identified significant differences in all 5 tested speech measures of wet admission vs dry discharge recordings
Separately in 2022 a study evaluated speech and pause alterations in voice recordings of acute N68 and stable N36 patients and found that the pause ratio was a 149 increase in patients of acute HF They also found a positive correlation with NT-Pro-BNP level Another study in 2022 examined both Mel-Frequency cepstral coefficient MFCC features and glottal speech features comparing a sample of 25 healthy speakers 7F 18M and 20 patients with HF of any aetiology regardless of LVEF Following feature selection they developed predictive models using four different classification methods SVM ET Adaboost and FFNN Based on a combination of MFCC and Glottal speech features they were able to predict ADHF with accuracies ranging from 88-94 with a true positive rate of 7947 and true negative rate 8269
By performing an extensive panel of clinical assessments investigations as well as symptom-based questionnaires in a study involving up to 250 heart failure patients the investigators aim to build upon recent work and develop a novel AI-based application deployed on a smart device which can detect an increase in pulmonary congestion from subtle changes in a patients cough voice breathing and chest sounds This will provide key information for patients with heart failure and their clinical teams by correctly detecting progressive fluid accumulation in a patients lungs prior to the patient developing significant symptoms Detecting early-phase pulmonary congestion will enable clinicians to target therapy more effectively It is hoped that this will help minimise and ultimately prevent the need for recurrent emergency hospital admission by alerting the patient to contact their community heart failure team and enable earlier outpatient treatment prior to the need to be re-hospitalised entering the acute phase
Subject to the successful outcome of this research a prospective interventional clinical trial will then be undertaken to test the clinical and operational benefits of the AI tool derived from this research on NHS heart failure care paving the way for the eventual adoption of such solutions in routine clinical practice
Acute decompensated heart failure ADHF is a medical emergency requiring urgent attention It usually results in inpatient hospitalisation and is a major driver for associated healthcare costs ADHF is usually characterised by rapid deterioration of breathlessness at rest or exertion because of pulmonary oedema pulmonary venous congestion and fluid retention resulting in swollen legs as well as a myriad of other symptoms including fatigue lack of appetite and so on
The patient normally presents with gradual or sudden onset of typical symptoms breathlessness fatigue and fluid accumulation in the legs After stabilisation and the initial treatment of ADHF patients enter a plateau phase where the heart remains stable However over time most patients experience multiple episodes of ADHF which typically become longer and separated by shorter intervals The congestion is related to underlying increased cardiac pressure usually secondary to volume overload which plays a central role in the pathophysiology presentation and prognosis of heart failure Pulmonary congestion is one of the most important diagnostic and therapeutic targets in heart failure Detecting pulmonary congestion earlier on due to volume overload is key to preventing impending rehospitalisation and presents an ideal opportunity to optimise heart failure treatment in the community
Early community detection of ADHF is ultimately the first step in providing effective patient care Poor recognition of HF due to its multitude of vaguenon-specific symptomatology of presentations often leads to delays in diagnosis and treatment The delay between a patient developing symptoms of HF decompensation and seeking medical attention is often considerable and is influenced by the speed of onset and severity of the symptoms Therefore a reliable and easily accessible means of assessing chronic fluid status in ambulatory outpatients is needed to detect early decompensation when appropriate intervention is possible The sudden development of breathlessness dyspnoea from the accumulation of fluid in the lungs acute pulmonary oedema usually prompts rapid contact with medical services whereas the gradual appearance of swollen legs and ankles peripheral oedema is more likely to be associated with delays in seeking care The average delay between symptom onset and hospital admission ranged from 2 hours to 7 days The symptoms of heart failure often develop gradually and appear non-threatening potentially explaining some of the observed delays in seeking care
In recent years several pilot studies demonstrated a relationship between speech biomarkers and the extent of systemic andor pulmonary congestion in heart failure patients For example in 2017 a study of 10 8 M 2F patients with acute decompensated heart failure undergoing inpatient treatment with intravenous diuretic therapy showed that after treatment patients displayed a higher proportion of automatically identified creaky voice increased fundamental frequency and decreased cepstral peak prominence variation suggesting that speech biomarkers can be early indicators of HF The study also showed that the severity of HF-related oedema required to measurably change the voice is small compared to the severity needed to increase body weight suggesting that speech biomarkers could become a more effective non-invasive tool to monitor HF patients than daily weights In 2021 another study evaluated the feasibility of remote speech analysis in the evaluation of dynamic fluid overload in heart failure patients undergoing hemodynamic treatment They performed serial speechvoice measurements in 5 patients undergoing haemodialysis The analysis was done with an app that does not share its AI algorithm They demonstrated statistically significant differences in select speech biomarkers at different fluid status levels as the patients progressed through the treatment Subsequently in 2022 a comparison of sound recordings for patients admitted with ADHF on the day of admission and the day of discharge with a sample of 40 patients who were admitted with acute decompensated heart failure identified significant differences in all 5 tested speech measures of wet admission vs dry discharge recordings
Separately in 2022 a study evaluated speech and pause alterations in voice recordings of acute N68 and stable N36 patients and found that the pause ratio was a 149 increase in patients of acute HF They also found a positive correlation with NT-Pro-BNP level Another study in 2022 examined both Mel-Frequency cepstral coefficient MFCC features and glottal speech features comparing a sample of 25 healthy speakers 7F 18M and 20 patients with HF of any aetiology regardless of LVEF Following feature selection they developed predictive models using four different classification methods SVM ET Adaboost and FFNN Based on a combination of MFCC and Glottal speech features they were able to predict ADHF with accuracies ranging from 88-94 with a true positive rate of 7947 and true negative rate 8269
By performing an extensive panel of clinical assessments investigations as well as symptom-based questionnaires in a study involving up to 250 heart failure patients the investigators aim to build upon recent work and develop a novel AI-based application deployed on a smart device which can detect an increase in pulmonary congestion from subtle changes in a patients cough voice breathing and chest sounds This will provide key information for patients with heart failure and their clinical teams by correctly detecting progressive fluid accumulation in a patients lungs prior to the patient developing significant symptoms Detecting early-phase pulmonary congestion will enable clinicians to target therapy more effectively It is hoped that this will help minimise and ultimately prevent the need for recurrent emergency hospital admission by alerting the patient to contact their community heart failure team and enable earlier outpatient treatment prior to the need to be re-hospitalised entering the acute phase
Subject to the successful outcome of this research a prospective interventional clinical trial will then be undertaken to test the clinical and operational benefits of the AI tool derived from this research on NHS heart failure care paving the way for the eventual adoption of such solutions in routine clinical practice
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None