Randomized controlled trials (RCTs) remain the cornerstone of causal inference on the safety and efficacy of medicinal products. But their limited follow-up, controlled settings, and narrowly defined data collection to balance the burden to patients and maintain study feasibility often results in unaddressed important questions for health authorities and other clinical decision makers. Linkage of RCT data to routinely collected health data [real-world data (RWD)] offers a mechanism for addressing these gaps by extending observations and outcomes assessment into routine practice. This paper synthesizes methodological and operational considerations for RCTs with RWD linkage, drawing on deterministic, probabilistic, referential, and privacy-preserving record linkage methodologies and on four case studies that span different indications and regional settings: a long-term follow-up of a human papillomavirus vaccine trial using deterministic linkage via universal Personal Identity Numbers; a U.S. linkage of a respiratory syncytial virus vaccine trial to administrative health claims using privacy-preserving tokenization; and two transcatheter aortic valve replacement trials linked to Medicare claims to reproduce randomized treatment-effect estimates and to assess transportability to the broader Medicare population. We offer actionable methodological considerations and recommendations, drawing on the case studies and the broader linkage literature. Progress beyond the current state will require global health authority guidance specific to linking clinical trials with routinely collected data, shared benchmarks for evaluating linkage approaches, routine reporting and assessments of impact of use cases on regulatory and reimbursement decisions. Linkage of clinical trial data with routinely collected data sources can meaningfully accelerate clinical development and inform health authority and other clinical decision making.
Publications by Year: 2026
2026
BACKGROUND: The utility of routine troponin testing to identify recurrent myocardial infarction (MI) after an incident MI is unclear. We assessed the incidence and prognosis of recurrent MIs identified from centralized troponin review in patients from the Myocardial Ischemia and Transfusion (MINT) trial.
METHODS: The MINT trial randomized patients with acute MI and anemia to a liberal versus restrictive red blood cell transfusion strategy. Suspected recurrent MIs were identified through both site-report and centralized review of troponin levels collected for 3 days following randomization. Differences in cardiac, non-cardiac, and all-cause death at 30 and 180 days were compared across patients with any site-reported MI, only centrally identified MI, and no recurrent MI.
RESULTS: Among 3,504 patients, 275 (7.8%) had a recurrent MI within 30 days; 119 (43.3%) by site-report, and 156 (56.7%) by central troponin review only. Rates of cardiac and all-cause death at 30 and 180 days were highest for patients with site-reported MI, intermediate for centrally identified MI, and lowest for no recurrent MI; rates of non-cardiac death did not vary. Patients with only centrally identified recurrent MI had an increased risk of cardiac death at 30 days (RR 1.9, 95% CI 1.0-3.4) and 180 days (RR 1.7, 95% CI 1.1-2.7) compared to those without recurrent MI.
CONCLUSIONS: In patients with acute MI and anemia, centralized troponin review identified more than half of all recurrent MI events. Patients with centrally identified MI had a higher risk of cardiac death than those with no recurrent MI.
TRIAL REGISTRATION: ClinicalTrials.gov NCT02981407 https://clinicaltrials.gov/study/NCT02619136.
BACKGROUND: Reported adverse events (AEs) to the Food and Drug Administration Adverse Event Reporting System (FAERS) have suggested an increased rate of serious AEs (SAEs) during the COVID-19 pandemic, but the extent to which this may be related to overall changes in reporting during this time period is uncertain. Accordingly, we aimed to evaluate trends in SAE reporting across commercially available ultrasound enhancing agent (UEA) brands as a function of overall trends in AE reporting.
METHODS: We retrospectively analyzed the FAERS public database, 2014-2024, to evaluate risks of UEAs overall and by brand, compared to similar contrast media.
RESULTS: Between 2014 and 2024, 21,960,760 AEs were reported to FAERS, of which 11,450,891 (52.1%) were categorized as SAEs. Overall SAE reports to FAERS increased from 678,953 in 2014 to 1,368,393 in 2021 before subsequently declining to 1,065,845 in 2024 (-7.9% change from 2021 to 2024). During the same period, overall death reports to FAERS increased from 124,055 in 2014 to 195,207 in 2018 before declining to 147,046 in 2024. During this period of decline, there was a 23.9% relative increase in SAEs to UEAs which peaked in 2023 at 350 before declining to 326 in 2024. Deaths attributed to SAEs increased from 1 in 2014 to 19 in 2023 before declining to 9 in 2024. Overall, these data suggest that 11.2% of the observed increase in SAEs to UEAs can be attributed to reporting changes. Despite changes in relative risks for SAEs, absolute SAE rates remained small and lower than other types of contrast media.
CONCLUSIONS: In this analysis of the FAERS dataset, 2014-2024, 11.2% of SAEs to UEAs were attributable to temporal changes in AE reporting. Absolute risks are small and declining, suggesting broad safety of UEAs as a class. Collectively, these results support continued use of UEAs, but motivate improved safety screening and preparedness to mitigate small but existing risks.
BACKGROUND: Artificial intelligence-enabled quantitative coronary computed tomography angiography (AI-QCCTA) offers automated assessment of coronary plaque burden and morphology. Although AI-QCCTA has improved diagnostic consistency and downstream testing efficiency, its prognostic value for major adverse cardiovascular events (MACE) has not been comprehensively quantified.
METHODS: We systematically searched PubMed, Embase, and Cochrane through October 2025 for studies evaluating AI-based plaque analysis in patients without prior MACE undergoing CCTA. Outcomes of interest were pooled using random-effects GLMM models, and prognostic associations were synthesized using inverse-variance random-effects meta-analysis of hazard ratios (HRs). The primary endpoint was MACE; secondary outcomes included myocardial infarction (MI), revascularization, angina, stroke, and mortality. Subgroup analysis was done to identify the association of different plaque characteristics in predicting MACE/MI/Death.
RESULTS: Ten studies (n = 20,195) were included. Across six cohorts (n = 18,804), pooled rates were: all-cause mortality 1.20% (95% CI 0.38-3.77%), cardiovascular mortality 0.32% (0.21-0.48%), MACE 5.07% (1.25-18.46%), MI 1.30% (0.41-3.99%), and revascularization 13.09% (6.57-24.40%). AI-enabled plaque burden predicted MACE (HR 1.95, 95% CI 1.29-2.94; I2 = 99%), consistent in sensitivity analysis as per same AI platform use (HR 1.88, 95% CI 1.15-3.07). Low-attenuation plaque showed the strongest association (HR 2.95, 95% CI 1.95-4.45).
CONCLUSIONS: AI-QCCTA provides prognostic value beyond stenosis severity, with vulnerable plaque characteristics-particularly low-attenuation and non-calcified plaque most strongly predicting adverse cardiovascular outcomes. These findings support the integration of AI-enabled plaque analysis into contemporary risk stratification.
BACKGROUND: Whether the 2025 American Society of Echocardiography (ASE) diastolic dysfunction (DD) algorithm (DD25) improves mortality prognostication compared to the 2016 algorithm (DD16) in real-world practice is uncertain.
METHODS: We applied the DD25 algorithm to adult transthoracic echocardiography reports across a large academic multisite echocardiography laboratory from 2018 2024, linked to state mortality data, and determined reclassification of DD and mortality risk.
RESULTS: Of 12,948 included (age 62.8 ± 18.1, 51.4% women, 55.8% outpatient), 10,205 (78.8%) had diastology quantified by the 2016 and 2025 guidelines. Of these, 2,601 (25.5%) were reclassified by DD25 with increases in DD grade in 1,428 (54.9%) and decreases in 1,173 (45.1%). Among those reclassified, 2,391 (91.9%) had a single grade change in DD severity. A larger proportion of female patients (52% vs 48.7%) were classified as having DD by DD25 versus DD16. The rate of indeterminate DD was lower by DD25 (1,358 [10.4%]) vs DD16 (1,983 [15.3%]). The DD25 algorithm improved discrimination of mortality risk compared to DD16 (difference in areas under the curve = 0.02; 95% CI, 0.001-0.04; P = .03), although the magnitude of the associated risk across the follow-up period was similar after multivariable adjustment (P value for comparison of adjusted hazard ratios = .67).
CONCLUSIONS: In a large academic health system, one-quarter of patients had reclassification of diastolic function by the 2025 ASE diastology guidelines. As compared to the 2016 algorithm, the 2025 algorithm resulted in a larger proportion with DD, less indeterminate diastolic function, and improved discrimination of mortality.
BACKGROUND: Sepsis remains a leading cause of mortality, and optimizing treatment is challenging due to patient heterogeneity. Identification of cardiac phenotypes may inform precision medicine approaches and guide resuscitation. We performed a clustering analysis of patients with sepsis using echocardiographic data without using any a priori definitions of cardiac dysfunction or outcomes to establish the subgroups.
METHODS: This was a retrospective cohort study of patients admitted to the hospital with sepsis at a single academic center. Patients were identified using sepsis-related ICD codes, and those who had echocardiogram performed within 14 days of admission underwent chart review to ensure sepsis-3 criteria were met. Those with preexisting heart disease were excluded. Clustering by echocardiographic variables was performed using latent profile analysis. Clinical features such as patient characteristics, laboratory studies, sepsis source, and outcomes were compared across the clusters.
RESULTS: There were 2,071 patients included in the analysis. Our cluster analysis yielded five phenotypes: cluster 1, elevated mean E/e' 24.5 (SD 9.6); cluster 2, reduced ejection fraction, mean 33.1% (SD 10.6), and cardiac index 2.6 L/min/m2 (SD 0.9); cluster 3, right ventricular dilation with right ventricular basal diameter 4.5 cm (SD 0.9) and elevated tricuspid regurgitation gradient 60.0 mmHg (SD 13.5); cluster 4, hyperdynamic with mean left ventricular ejection fraction 75% (SD10.9) and mean cardiac index 6.6 L/min/m2 (SD 2.6); and lastly cluster 5, normal echocardiographic parameters. Group 3 had the highest mortality compared to the normal group (36.9% vs. 19.6%, p = 0.002), with an odds ratio of 2.3 (95%CI 1.4-3.9).
CONCLUSIONS: Using an unsupervised clustering analysis, we identified five phenotypes of cardiac function in sepsis based on commonly recorded echocardiographic data: diastolic dysfunction, left ventricular systolic dysfunction with low cardiac index, right ventricular dilation with elevated tricuspid regurgitation gradient, hyperdynamic cardiac function, and normal. The right ventricular dilation group had the highest mortality. Future research should explore mechanisms and potential treatment implications for these groups.
BACKGROUND: Low left ventricular ejection fraction (LEF) can progress undiagnosed. Artificial intelligence-based electrocardiogram (ECG-AI) screening may provide a scalable means to detect LEF.
OBJECTIVES: The purpose of this study was to validate a complete ECG-AI software as a medical device for LEF detection.
METHODS: Four geographically diverse sites in the United States identified patients with both ECGs and transthoracic echocardiograms performed within 30 days of each other in clinical practice. Data were electronically extracted to specific guidelines and transmitted to the coordinating center for analysis.
RESULTS: Records of 16,000 subjects were extracted, resulting in an evaluable set of 13,960 subjects (mean age 66 years; 52% male). The device demonstrated excellent discrimination (AUROC: 0.92 [95% CI: 0.91-0.93]) and was 84.5% (95% CI: 82.2%-86.6%) sensitive and 83.6% (95% CI: 82.9%-84.2%) specific for LEF. The overall prevalence of LEF in the study data set was 7.9%, with LEF among 1.6% of the ECG-AI negative and 30.5% of ECG-AI positive subjects, contributing to positive and negative predictive values of 30.5% (95% CI: 28.8%-32.1%) and 98.4% (95% CI: 98.2%-98.7%), respectively.
CONCLUSIONS: External validation studies such as this one provide a rigorous framework to validate an algorithm's performance. This study demonstrated the algorithm's strong diagnostic accuracy over a geographically diverse, independent set of patients. In this generally unselected population, the algorithm produced a test negative result in 78% of the cases, suggesting potential utility as a rule-out strategy to defer echocardiography when other clinical findings are absent.