Transthyretin amyloid cardiomyopathy among patients hospitalized for heart failure and performance of an adapted wild-type ATTR-CM machine learning model: Findings from GWTG-HF.
| dc.contributor.author | Peters, Anthony E | |
| dc.contributor.author | Solomon, Nicole | |
| dc.contributor.author | Chiswell, Karen | |
| dc.contributor.author | Fonarow, Gregg C | |
| dc.contributor.author | Khouri, Michel G | |
| dc.contributor.author | Baylor, Lori | |
| dc.contributor.author | Alvir, Jose | |
| dc.contributor.author | Bruno, Marianna | |
| dc.contributor.author | Huda, Ahsan | |
| dc.contributor.author | Allen, Larry A | |
| dc.contributor.author | Sharma, Kavita | |
| dc.contributor.author | DeVore, Adam D | |
| dc.contributor.author | Greene, Stephen J | |
| dc.date.accessioned | 2024-06-06T14:49:33Z | |
| dc.date.available | 2024-06-06T14:49:33Z | |
| dc.date.issued | 2023-11 | |
| dc.description.abstract | BackgroundAn 11-factor random forest model has been developed among ambulatory heart failure (HF) patients for identifying potential wild-type amyloidogenic TTR cardiomyopathy (wtATTR-CM). The model has not been evaluated in a large sample of patients hospitalized for HF.MethodsThis study included Medicare beneficiaries aged ≥65 years hospitalized for HF in the Get With The Guidelines-HF® Registry from 2008-2019. Patients with and without a diagnosis of ATTR-CM were compared, as defined by inpatient and outpatient claims data within 6 months pre- or post-index hospitalization. Within a cohort matched 1:1 by age and sex, univariable logistic regression was used to evaluate relationships between ATTR-CM and each of the 11 factors of the established model. Discrimination and calibration of the 11-factor model were assessed.ResultsAmong 205,545 patients (median age 81 years) hospitalized for HF across 608 hospitals, 627 patients (0.31%) had a diagnosis code for ATTR-CM. Univariable analysis within the 1:1 matched cohort of each of the 11-factors in the ATTR-CM model found pericardial effusion, carpal tunnel syndrome, lumbar spinal stenosis, and elevated serum enzymes (e.g., troponin elevation) to be strongly associated with ATTR-CM. The 11-factor model showed modest discrimination (c-statistic 0.65) and good calibration within the matched cohort.ConclusionsAmong US patients hospitalized for HF, the number of patients with ATTR-CM defined by diagnosis codes on an inpatient/outpatient claim within 6 months of admission was low. Most factors within the prior 11-factor model were associated with greater odds of ATTR-CM diagnosis. In this population, the ATTR-CM model demonstrated modest discrimination. | |
| dc.identifier.issn | 0002-8703 | |
| dc.identifier.issn | 1097-6744 | |
| dc.identifier.uri | ||
| dc.publisher | Elsevier BV | |
| dc.relation.ispartof | American heart journal | |
| dc.relation.isversionof | 10.1016/j.ahj.2023.06.013 | |
| dc.rights.uri | ||
| dc.title | Transthyretin amyloid cardiomyopathy among patients hospitalized for heart failure and performance of an adapted wild-type ATTR-CM machine learning model: Findings from GWTG-HF. | |
| dc.type | Conference | |
| duke.contributor.orcid | Solomon, Nicole|0000-0002-5643-9958 | |
| duke.contributor.orcid | Chiswell, Karen|0000-0002-0279-9093 | |
| duke.contributor.orcid | DeVore, Adam D|0000-0002-4679-2221 | |
| duke.contributor.orcid | Greene, Stephen J|0000-0001-6912-7374 | |
| pubs.begin-page | 22 | |
| pubs.end-page | 30 | |
| pubs.organisational-group | Duke | |
| pubs.organisational-group | School of Medicine | |
| pubs.organisational-group | Staff | |
| pubs.organisational-group | Basic Science Departments | |
| pubs.organisational-group | Clinical Science Departments | |
| pubs.organisational-group | Institutes and Centers | |
| pubs.organisational-group | Biostatistics & Bioinformatics | |
| pubs.organisational-group | Medicine | |
| pubs.organisational-group | Medicine, Cardiology | |
| pubs.organisational-group | Duke Clinical Research Institute | |
| pubs.publication-status | Published | |
| pubs.volume | 265 |
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