At a glance
ClinicalIndex Comparison RecordN/ACompleted· 558 enrolled
Drug / intervention
Machine learning algorithmother
Likely dose
Not stated in record
Structured eligibility isn't available for this trial yet — see the full criteria in the Eligibility tab below.
Standardized by ClinicalIndex from the ClinicalTrials.gov record · verify against the source.
A Retrospective Chart Validation Study Evaluating the Performance of Machine Learning Algorithm (ML) to Predict the Clinical Diagnosis of Wild-type Transthyretin Amyloid Cardiomyopathy (ATTRwt-CM) and Non-amyloid Heart Failure Among Patients With Heart Failure (HF)
In Brief
An observational study evaluating Machine learning algorithm for ATTR-CM. Completed, enrolled 558 participants across 1 site.
Detailed Summary
This is an observational, retrospective non-inferiority study with a study sample from a large national database. A machine learning (ML) model will use a national database to predict the clinical diagnosis of ATTRwt-CM among HF patients. This study will include HF patients ≥50 years old.
Study Details
Study Typeobservational
Allocation--
Masking--
Primary Purpose--
ConditionsATTR-CM
CountriesUnited States
Collaborators--
Timeline
N/ACompletedFinished
202420252026
Enrollment StartSep 2023
First PostedSep 2023
Primary CompletionNov 2023
TodayJul 2026
First PostedSep 8, 2023
Enrollment StartSep 1, 2023
Primary CompletionNov 14, 2023
TodayJul 2, 2026
Enrollment to primary: 2 monthsPosted 2.8 years ago
Interventions
Machine learning algorithmother
Software to calculate the predicted probability of ATTRwt-CM for these heart failure patients based on the presence and absence of certain features