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| Name | Class |
|---|---|
| Imperial College Healthcare NHS Trust | OTHER |
| King's College Hospital NHS Trust | OTHER |
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Development of pacing induced cardiomyopathy (PICM) is correlated to a high morbidity as signified by an increase in heart failure admissions and mortality. At present a lack of data leads to a failure to identify patients who are at risk of PICM and would benefit from pre-selection to physiological pacing. In the light of the foregoing, there is an urgent need for novel non-invasive detection techniques which would aid risk stratification, offer a better understanding of the prevalence and incidence of PICM in individuals with pacing devices and the contribution of additional risk factors.
Retrospective review of patient characteristics including 12 lead resting electrocardiograms and imaging data (CMR, CT, echo, CXR and fluoroscopy of pacing leads) of patients with right sided ventricular pacing lead due to symptomatic bradycardia, who developed pacing induced cardiomyopathy (or need for CRT upgrade) versus patients who did not using supervised machine learning methods. Development of personalised predictive pacing algorithm to improve right ventricular lead placement, such as conduction system pacing or pre-emptive implantation of an additional left ventricular lead to prevent left ventricular dilatation and pacemaker-induced cardiomyopathy (PICM) with heart failure (left ventricular ejection fraction <50% by Simpson method), hospitalisation or death with the use of the retrospective patient data through machine learning.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Pacing induced cardiomyopathy | Patients who received a pacing device and developed pacing induced cardiomyopathy |
| |
| Non-pacing induced cardiomyopathy | Patients who received a pacing device and did not develop pacing induced cardiomyopathy |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Machine learning | Other | Analysis of data with machine learning methods |
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| Measure | Description | Time Frame |
|---|---|---|
| Primary aim | Number of risk factors in participants who developed pacing induced cardiomyopathy | 2.5 years |
| Measure | Description | Time Frame |
|---|---|---|
| Secondary aim | 1. To establish, through the GSTT/RBH/KCH/ICH RV-paced study population the prevalence of pacemaker induced cardiomyopathy (PICM) | 2.5 years |
| Tertiary aim | 2. To establish, through the GSTT/RBH/KCH/ICH RV-paced study population the incidence of PCIM 2. To establish, through the GSTT/RBH/KCH/ICH RV-paced study population the incidence of PCIM |
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Inclusion Criteria:
Exclusion Criteria:
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All patients who received a pacemaker at GSTT and RBH in the last 10 years and all patients who received a pacemaker at KCH and ICH in the last 3 years.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Guys' and St Thomas' Hospital NHS Trust | London | SE1 7EH | United Kingdom |
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| ID | Term |
|---|---|
| D006333 | Heart Failure |
| ID | Term |
|---|---|
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
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| ID | Term |
|---|---|
| D000069550 | Machine Learning |
| ID | Term |
|---|---|
| D001185 | Artificial Intelligence |
| D000465 | Algorithms |
| D055641 | Mathematical Concepts |
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| 2.5 years |
| Quarternary aim | 3.• To establish mortality of PICM | 2.5 years |
| Quinary aim | 4. To establish the morbidity of PICM | 2.5 years |
| Senary aims | 5.• To include predictive value for pacing induced cardiomyopathy risk with combination of imaging data of right ventricular lead position or leadless pacemaker position | 2.5 years |
| Septenary aim | 6.• To include predictive value for pacing induced cardiomyopathy risk with combination of imaging data of myocardial pathology from echocardiogram and cardiac MRI | 2.5 years |
| Kings' College London Healthcare Trust | London | SE5 9RS | United Kingdom |
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| Imperial College London Healthcare Trust | London | W12 0HS | United Kingdom |
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