Abstract
Background: Wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM), an increasingly recognized cause of heart failure (HF), often remains undiagnosed until later stages of the disease. Methods and Results: A previously developed machine learning algorithm was simplified to create a random forest model based on 11 selected phenotypes predictive of ATTRwt-CM to estimate ATTRwt-CM risk in hypothetical patient scenarios. Using U.S. medical claims datasets (IQVIA), International Classification of Diseases codes were extracted to identify a training cohort of patients with ATTRwt-CM (cases) or nonamyloid HF (controls). After assessment in a 20% test sample of the training cohort, model performance was validated in cohorts of patients with International Classification of Diseases codes for ATTRwt-CM or cardiac amyloidosis vs nonamyloid HF derived from medical claims (IQVIA) or electronic health records (Optum). The simplified model performed well in identifying patients with ATTRwt-CM vs nonamyloid HF in the test sample, with an accuracy of 74%, sensitivity of 77%, specificity of 72%, and area under the curve of 0.82; robust performance was also observed in the validation cohorts. Conclusions: This simplified machine learning model accurately estimated the empirical probability of ATTRwt-CM in administrative datasets, suggesting it may serve as an easily implementable tool for clinical assessment of patient risk for ATTRwt-CM in the clinical setting. Brief lay summary: Wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM for short) is a frequently overlooked cause of heart failure. Finding ATTRwt-CM early is important because the disease can worsen rapidly without treatment. Researchers developed a computer program that predicts the risk of ATTRwt-CM in patients with heart failure. In this study, the program was used to check for 11 medical conditions linked to ATTRwt-CM in the medical claims records of patients with heart failure. The program was 74% accurate in identifying ATTRwt-CM in patients with heart failure and was then used to develop an educational online tool for doctors (the wtATTR-CM estimATTR).
| Original language | English (US) |
|---|---|
| Pages (from-to) | 778-787 |
| Number of pages | 10 |
| Journal | Journal of Cardiac Failure |
| Volume | 30 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 2024 |
Funding
This study was sponsored by Pfizer. Medical writing support was provided by Donna McGuire of Engage Scientific Solutions and funded by Pfizer . All authors attest that they meet the current ICMJE criteria for authorship, i.e. they made substantial contributions to the conception and design of the study, or acquisition of data, or analysis and interpretation of data; drafted the article or revised it critically for important intellectual content; provided final approval of the version to be submitted; and agreed to be accountable for the work. The IQVIA training and validation datasets are available for download (<<url to be inserted>>). The Optum datasets used for this study are not available publicly owing to data use agreements but will be made available to qualified investigators upon request with evidence of Institutional Review Board approval. The code used for training and validation of the models (including instructions for use of the code and notations for the software needed to run the code) are also provided with this paper (https://www.nature.com/articles/s41467-021-22876-9#Sec16). This study was sponsored by Pfizer. Medical writing support was provided by Donna McGuire of Engage Scientific Solutions and funded by Pfizer. Use of IQVIA and Optum datasets was approved by the Institutional Review Board at Northwestern University and was conducted in accordance with the Declaration of Helsinki. Informed consent was waived under Institutional Review Board regulations because the datasets were de-identified. The authors thank Rahul C. Deo, MD, of Brigham and Women's Hospital, for his valuable contributions to this research.
Keywords
- Wild-type transthyretin amyloidosis
- cardiomyopathy
- heart failure
- machine learning
ASJC Scopus subject areas
- Cardiology and Cardiovascular Medicine