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Abstract AimsNeural network classifiers can detect aortic stenosis (AS) using limited cardiac ultrasound images. While networks perform very well using cart-based imaging, they have never been tested or fine-tuned for use with focused cardiac ultrasound (FoCUS) acquisitions obtained on handheld ultrasound devices. Methods and resultsProspective study performed at Tufts Medical Center. All patients ≥65 years of age referred for clinically indicated transthoracic echocardigraphy (TTE) were eligible for inclusion. Parasternal long axis and parasternal short axis imaging was acquired using a commercially available handheld ultrasound device. Our cart-based AS classifier (trained on ∼10 000 images) was tested on FoCUS imaging from 160 patients. The median age was 74 (inter-quartile range 69–80) years, 50% of patients were women. Thirty patients (18.8%) had some degree of AS. The area under the received operator curve (AUROC) of the cart-based model for detecting AS was 0.87 (95% CI 0.75–0.99) on the FoCUS test set. Last-layer fine-tuning on handheld data established a classifier with AUROC of 0.94 (0.91–0.97). AUROC during temporal external validation was 0.97 (95% CI 0.89–1.0). When performance of the fine-tuned AS classifier was modelled on potential screening environments (2 and 10% AS prevalence), the positive predictive value ranged from 0.72 (0.69–0.76) to 0.88 (0.81–0.97) and negative predictive value ranged from 0.94 (0.94–0.94) to 0.99 (0.99–0.99) respectively. ConclusionOur cart-based machine-learning model for AS showed a drop in performance when tested on handheld ultrasound imaging collected by sonographers. Fine-tuning the AS classifier improved performance and demonstrates potential as a novel approach to detecting AS through automated interpretation of handheld imaging.
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Use of blood pressure measurements extracted from the electronic health record in predicting Alzheimer's disease: A retrospective cohort study at two medical centers
Abstract IntroductionStudies investigating the relationship between blood pressure (BP) measurements from electronic health records (EHRs) and Alzheimer's disease (AD) rely on summary statistics, like BP variability, and have only been validated at a single institution. We hypothesize that leveraging BP trajectories can accurately estimate AD risk across different populations. MethodsIn a retrospective cohort study, EHR data from Veterans Affairs (VA) patients were used to train and internally validate a machine learning model to predict AD onset within 5 years. External validation was conducted on patients from Michigan Medicine (MM). ResultsThe VA and MM cohorts included 6860 and 1201 patients, respectively. Model performance using BP trajectories was modest but comparable (area under the receiver operating characteristic curve [AUROC] = 0.64 [95% confidence interval (CI) = 0.54–0.73] for VA vs. AUROC = 0.66 [95% CI = 0.55–0.76] for MM). ConclusionApproaches that directly leverage BP trajectories from EHR data could aid in AD risk stratification across institutions.
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- Award ID(s):
- 2124127
- PAR ID:
- 10380451
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Alzheimer's & Dementia
- Volume:
- 18
- Issue:
- 11
- ISSN:
- 1552-5260
- Format(s):
- Medium: X Size: p. 2368-2372
- Size(s):
- p. 2368-2372
- Sponsoring Org:
- National Science Foundation
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