Rodríguez, Fátima

Cardiovascular disease remains the world's leading cause of death, with a disproportionate burden falling on low- and middle-income countries. Emerging precision medicine approaches, such as artificial intelligence, multiomics, and digital diagnostics, offer transformational potential for prevention and management. Yet, without equity-centered policies, these innovations risk amplifying disparities. The World Heart Federation (WHF) proposes a framework premised on innovation as a public good and equity as a universal right. Through the World Heart Observatory, Digital Health Roadmap, and Emerging Leaders Program, the WHF operationalizes equity across governance, workforce, and care delivery. Drawing on exemplars from Africa, Latin America, and South/Southeast Asia, we outline implementation strategies and policy levers to translate science into population-level impact. Precision cardiovascular medicine can become the standard for all—but only when designed with equity from inception.

Importance Transthyretin amyloid cardiomyopathy (ATTR-CM) is underdiagnosed despite expanding treatment options. Objective To develop and evaluate an artificial intelligence (AI)–augmented clinical program for ATTR-CM detection. Design, Setting, and Participants This nonrandomized clinical trial involved constructing an AI model, ATTRACTnet, using electrocardiogram waveforms, echocardiographic measurements, demographics, and diagnosis codes for orthopedic manifestations of amyloidosis. A single-system, multisite, single-arm, open-label trial was conducted to evaluate its real-world performance. The model was trained and validated at a large academic referral site for ATTR-CM with external validation at an academic site. The trial was conducted as a single-system, multisite trial. Patients with left ventricular (LV) wall thickness 12 mm or more and an ATTRACTnet score 0.5 or higher were eligible. Exclusions included prior ATTR-CM testing, hypertrophic cardiomyopathy, expected life span less than 1 year, nursing home residence, advanced dementia, or LV wall thickness less than 14 mm explained by uncontrolled hypertension or moderate/severe aortic stenosis. Intervention Eligible patients were notified and offered nuclear scintigraphy testing, monoclonal protein testing, and follow-up care on agreement from the treating physician. Main Outcomes and Measure The primary outcome was a diagnosis of ATTR-CM by consensus criteria. ATTR-CM testing positivity was compared with historical and contemporary controls. Results ATTRACTnet was developed in an internal test set of 799 patients (mean [SD] age, 75.1 [11.1] years; 516 [64.7%] male and 283 [35.3%] female) using 5-fold cross-validation with an additional external test set of 422 patients. It had good discrimination for ATTR-CM detection with an area under the receiver operator characteristic curve of 0.85 (5-fold cross-validation, 0.77-0.85) in the internal set and 0.82 (95% CI, .81-0.83) in the external test set with similar performance in Hispanic, non-Hispanic Black, and non-Hispanic White patients. A total of 1471 patients were identified with positive AI model scores 0.5 or more during the study period, with 256 eligible patients who met study criteria. Of these patients, 50 underwent amyloidosis testing after physician and patient approval; 24 (48%) were diagnosed with ATTR-CM, and 21 (88%) initiated treatment within 3 months. The positivity rate was more than 2.8 times higher than historical controls (15.3%; 95% CI, 13.1%-17.9%; P < .001), with an 18% relative increase in new diagnoses vs the prior year. Conclusions and Relevance AI-augmented screening may improve ATTR-CM detection and identify patients who are missed by usual care. Prospective randomized trials are needed to determine if outcomes are improved. Trial Registration ClinicalTrials.gov Identifier: NCT06469372

Background Statin Choice is a shared decision-making encounter tool embedded in the electronic health record. Objective To describe the association between the use of Statin Choice, statin prescriptions by clinicians, prescription fills (primary adherence), and statin adherence at 12 mo (secondary adherence). Design Observational cohort study at the Cleveland Clinic Health System. Subjects Statin-naïve adults aged 40 to 75 y with a 10-y atherosclerotic cardiovascular disease (ASCVD) risk of ≥5% and a primary care appointment between January 2020 and July 2021. Main Measures The primary exposure was the use of Statin Choice during a clinical encounter. We measured whether the use of Statin Choice was associated with statin prescriptions. We measured statin adherence based on pharmacy fill data at 60 d (primary adherence) and 12 mo (secondary adherence). We used mixed-effects logistic regression to estimate the adjusted odds of statin prescriptions and adherence at the 3 time points by the use of Statin Choice. Key Results Among 17,001 statin-naïve patients, 13% viewed Statin Choice and 7% were prescribed a statin. The median ASCVD risk was 10%. Patients who were shown Statin Choice had 9.04 higher odds of being prescribed a statin compared with patients not shown Statin Choice (95% confidence interval [CI]: 7.86–10.4). Among patients prescribed a statin, the use of Statin Choice was associated with 5.75 higher odds of primary adherence compared with usual care (95% CI: 4.22–7.83). At 12 mo, Statin Choice use was significantly associated with adherence in the unadjusted analysis (OR: 1.58; 95% CI: 1.05–2.08) but was not significant after adjustment for patient factors. Patients shown Statin Choice had an average of 12 mg/dL reduction in low-density lipoprotein cholesterol at 12 mo (95% CI: −16 mg/dL, −10) compared with those not shown Statin Choice. Conclusion In this observational study, Statin Choice use was strongly associated with statin prescription and fills and weakly associated with adherence to statins for up to 1 y. A randomized trial is needed to confirm causality. Highlights Statin Choice is an electronic health record–embedded shared decision-making encounter tool available for free in many health care systems. Small randomized controlled trials have found modest associations between the use of Statin Choice and statin adherence using patient-reported data. In our large study using pharmacy fill data, clinician use of Statin Choice during a medical encounter was associated with significantly greater patient adherence with statins up to 1 y later. Exposure to Statin Choice was associated with a significant reduction in low-density lipoprotein cholesterol over 1 y.

Background: VICTORION-INITIATE (NCT04929249) showed significantly greater LDL-C lowering in patients (pts) with ASCVD with an inclisiran first (IF) strategy (adding inclisiran immediately on failure to achieve LDL-C <70 mg/dL on maximally tolerated statin therapy) vs usual care (UC). Tokenization can be leveraged to link clinical trial data to pt-level real-world data (RWD) to provide a comprehensive overview of the pt journey pre- and post-trial and enhance understanding of clinical trial and long-term pt outcomes. Aim: To utilize tokenization and linked RWD to characterize the pt journey pre-VICTORION-INITIATE enrollment. Methods: Eligible pts from VICTORION-INITIATE were deidentified and tokenized with unique IDs, allowing linking to RWD (EMR, claims, hospital, and lab records) to follow the pt journey in the 3-year pre-trial and 2-year post-trial periods. Here we present demographics, clinical characteristics, and lipid-lowering therapy (LLT) use of pts with linked pre-trial RWD. Results: Of 450 pts randomized, 383 (85.1%) were linked with pre-trial RWD (IF arm, n=193; UC arm, n=190); 26.9% were female, first available recorded mean (SD) age was 64 (9.36) years. Among the 74.2% (n=284) of pts with linked, pre-trial, lipid test RWD, mean (SD) earliest and latest LDL-C values were 100.1 mg/dL (35.70) and 99.3 mg/dL (30.06), respectively, for pts in the IF arm vs 97.3 mg/dL (40.43) and 97.8 mg/dL (27.60) for pts in the UC arm ( Table ). Overall, in the pre-trial period, LLT was dispensed/prescribed to 66.1% of pts, statins to 64.5%, ezetimibe to 4.4%, and PCSK9i to 4.4%. Of pts with ≥1 recorded ASCVD events in the pre-trial period (n=141), 53.2% and 68.8% had available LLT dispensing/prescription records pre- and post-event, respectively. Conclusions: In the 3 years pre-VICTORION-INITIATE, mean LDL-C levels were above recommended goals, non-statin LLT prescription was infrequent, and ~31% of pts with ≥1 recorded ASCVD event had no evidence of post-event LLT use, highlighting a need for more aggressive LDL-C management. Full data visibility for each pt is a limitation of this approach. Tokenization post-trial will allow for long-term evaluation of LLT practices.

Background: Coronary artery calcium (CAC) can be identified on nongated chest computed tomography (CT) scans, but this finding is not consistently incorporated into care. A deep learning algorithm enables opportunistic CAC screening of nongated chest CT scans. Our objective was to evaluate the effect of notifying clinicians and patients of incidental CAC on statin initiation. Methods: NOTIFY-1 (Incidental Coronary Calcification Quality Improvement Project) was a randomized quality improvement project in the Stanford Health Care System. Patients without known atherosclerotic cardiovascular disease or a previous statin prescription were screened for CAC on a previous nongated chest CT scan from 2014 to 2019 using a validated deep learning algorithm with radiologist confirmation. Patients with incidental CAC were randomly assigned to notification of the primary care clinician and patient versus usual care. Notification included a patient-specific image of CAC and guideline recommendations regarding statin use. The primary outcome was statin prescription within 6 months. Results: Among 2113 patients who met initial clinical inclusion criteria, CAC was identified by the algorithm in 424 patients. After chart review and additional exclusions were made, a radiologist confirmed CAC among 173 of 194 patients (89.2%) who were randomly assigned to notification or usual care. At 6 months, the statin prescription rate was 51.2% (44/86) in the notification arm versus 6.9% (6/87) with usual care ( P <0.001). There was also more coronary artery disease testing in the notification arm (15.1% [13/86] versus 2.3% [2/87]; P =0.008). Conclusions: Opportunistic CAC screening of previous nongated chest CT scans followed by clinician and patient notification led to a significant increase in statin prescriptions. Further research is needed to determine whether this approach can reduce atherosclerotic cardiovascular disease events. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT04789278.