Artificial intelligence-enabled tools in cardiovascular medicine: A survey of current use, perceptions, and challenges.

Background: Numerous artificial intelligence (AI)-enabled tools for cardiovascular diseases have been published, with a high impact on public health. However, few have been adopted into, or have meaningfully affected, routine clinical care. Objective: To evaluate current awareness, perceptions, and clinical use of AI-enabled digital health tools for patients with cardiovascular disease, and challenges to adoption. Methods: This mixed-methods study included interviews with 12 cardiologists and 8 health information technology (IT) administrators, and a follow-on survey of 90 cardiologists and 30 IT administrators. Results: We identified 5 major challenges: (1) limited knowledge, (2) insufficient usability, (3) cost constraints, (4) poor electronic health record interoperability, and (5) lack of trust. A minority of cardiologists were using AI tools; more were prepared to implement AI tools, but their sophistication level varied greatly. Conclusion: Most respondents believe in the potential of AI-enabled tools to improve care quality and efficiency, but they identified several fundamental barriers to wide-scale adoption.

Clinical characteristics and health care resource use of patients at risk for wild-type transthyretin amyloid cardiomyopathy identified by machine learning model.

BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a progressive, life-threatening systemic disorder that is an underrecognized cause of heart failure (HF). When the diagnosis of wild-type ATTR-CM (ATTRwt-CM) is delayed, patients often undergo additional assessments, deferring appropriate management as symptoms potentially worsen. Prompt recognition of patients at risk for ATTRwt-CM is essential to facilitate earlier diagnosis and disease-modifying treatment. A previously developed machine learning model performed well in identifying ATTRwt-CM in patients with HF vs controls with nonamyloid HF using medical claims/electronic health records, providing a systematic framework to raise disease suspicion. OBJECTIVE: To further evaluate this model's performance in identifying ATTRwt-CM using a large claims database of older adults with HF and confirmed ATTRwt-CM or nonamyloid HF; and to explore the characteristics and health care resource utilization (HCRU) of patients with confirmed and suspected ATTRwt-CM. METHODS: In this retrospective study, the prior model was applied using Humana administrative claims for patients diagnosed with ATTRwt-CM (cases) and nonamyloid HF (controls [1:1]). Patients were aged 65-89 years, had at least 2 claims for HF diagnosis (2015-2020), and were continuously enrolled in a Medicare Advantage prescription drug plan for at least 12 months before and at least 6 months after HF diagnosis. For the assessment of characteristics and HCRU, the suspected risk level was categorized based on the predicted probability (PP) from model output (high, moderate, and low risk: PP≥0.70; ≥0.50 and < 0.70; and < 0.50, respectively). RESULTS: Of 267,025 eligible patients, 119 (0.04%) had confirmed ATTRwt-CM; of 266,906 patients with nonamyloid HF, 10,997 (4.1%), 68,174 (25.5%), and 187,735 (70.3%) were categorized as high, moderate, and low risk for ATTRwt-CM, respectively. The model demonstrated sensitivity/specificity/accuracy/receiver operating characteristic area under the concentration-time curve of 88%/65%/77%/0.89, respectively, in differentiating ATTRwt-CM from nonamyloid HF. In patients with confirmed ATTRwt-CM, the mean (SD) time between HF and ATTRwt-CM diagnoses was 751 (528) days; 65% and 48% were hospitalized before and after ATTRwt-CM diagnosis, respectively. Atrial fibrillation was more common in patients with confirmed ATTRwt-CM and high risk (39% and 55%) vs low risk (27%). Hospitalization and emergency department visits after HF diagnosis were reported in 57% and 46% of patients with high ATTRwt-CM risk, respectively. CONCLUSIONS: The ATTRwt-CM predictive model performed well in identifying disease risk in the Humana Research Database. Patients at high risk for ATTRwt-CM had high HCRU and may benefit from the earlier suspicion of ATTRwt-CM. The model may be used as a tool to identify patients with a suspected high risk for the disease to facilitate earlier detection and treatment. DISCLOSURES: This study was sponsored by Pfizer. Medical writing support was provided by Donna McGuire of Engage Scientific Solutions and funded by Pfizer. Drs Bruno and Schepart and Mr Casey are currently employees of Pfizer and equity holders in this publicly traded company. Dr Reed was an employee of Pfizer at the time that this analysis was planned and conducted. Mr Sheer and Dr Simmons are currently employees of Humana, which received research funding from Pfizer. Dr Nair was an employee of Humana at the time that this analysis was planned and conducted.

Temporal Trends in Diagnostic Testing Patterns for Wild-Type Transthyretin Amyloid Cardiomyopathy in the Medicare Fee-for-Service Population.

Wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM) is frequently misdiagnosed or diagnosed late in the disease course. ATTRwt-CM can be diagnosed invasively through tissue biopsy, but current diagnostic recommendations indicate technetium-99m pyrophosphate (99mTc-PYP) bone scintigraphy is an acceptable noninvasive alternative. The relative use of these confirmatory diagnostic tests in routine clinical practice is unknown. A retrospective observational study assessed temporal trends in biopsy and 99mTc-PYP scintigraphy and differences in patient characteristics using in/outpatient claims data from the US Medicare fee-for-service database. Claims prevalence for biopsy alone (≥1 claim for cardiac/extracardiac biopsy), imaging alone (≥1 claim for 99mTc-PYP scintigraphy), and both tests and patient demographic, geographic, and clinical characteristics were examined. Of patients (n = 1226) receiving an ATTRwt-CM diagnostic code, 29%, 47%, and 24% were diagnosed by biopsy alone, 99mTc-PYP scintigraphy alone, and both tests, respectively. Patients with claims for 99mTc-PYP scintigraphy alone were older than those with claims for biopsy alone (79.9 vs 76.5; p <0.001). Fewer patients in the southern United States and more patients in the northeastern United States had claims for 99mTc-PYP scintigraphy alone than biopsy alone (p <0.001). There was a temporal trend toward more claims for 99mTc-PYP scintigraphy alone (odds ratio 1.21; p <0.001) and both tests (odds ratio 1.10; p = 0.008) versus biopsy alone. From 2017 to 2019, claims increased for 99mTc-PYP scintigraphy alone. In conclusion, these data suggest a growing preference for the noninvasive imaging technique, which has high sensitivity/specificity, usability, and accessibility and may help facilitate earlier disease diagnosis. United States regional differences in the use of 99mTc-PYP scintigraphy highlight the need for education initiatives.

Applying diagnosis support systems in electronic health records to identify wild-type transthyretin amyloid cardiomyopathy risk.

Aim: Wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM) is frequently misdiagnosed, and delayed diagnosis is associated with substantial morbidity and mortality. At three large academic medical centers, combinations of phenotypic features were implemented in electronic health record (EHR) systems to identify patients with heart failure at risk for ATTRwt-CM. Methods: Phenotypes/phenotype combinations were selected based on strength of correlation with ATTRwt-CM versus non-amyloid heart failure; different clinical decision support and reporting approaches and data sources were evaluated on Cerner and Epic EHR platforms. Results: Multiple approaches/sources showed potential usefulness for incorporating predictive analytics into the EHR to identify at-risk patients. Conclusion: These preliminary findings may guide other medical centers in building and implementing similar systems to improve recognition of ATTRwt-CM in patients with heart failure.