Randomized controlled trials investigating colchicine for secondary prevention of cardiovascular events following acute myocardial infarction (AMI) have yielded conflicting results, and the real-world use and effectiveness of colchicine in this context remains unknown. As such, we sought to evaluate the use of colchicine following AMI in clinical practice and the associated outcomes. We performed a retrospective analysis of patients diagnosed with AMI and longitudinally followed in a large academic health system between 2018 and 2024 to describe the clinical use of colchicine for secondary prevention following AMI, as well as patient-level demographic and clinical characteristics associated with colchicine use. Next, using both multivariable logistic regression models with and without propensity matching, we examined the association between colchicine prescription following AMI and composite cardiovascular outcomes (comprised of recurrent AMI, any revascularization, stroke, and death). Kaplan-Meier Event-free Survival Analysis and Cox Proportional Hazards Models were performed. Of 1,796, 126 (7.0%) were prescribed colchicine after AMI. There was no association between use of colchicine and the composite cardiovascular events in either standard multivariable adjusted (Odds Ratio 1.00, 95% CI 0.66-1.50, p = 0.99) or propensity matched models (0.98, 0.57-1.66, p = 0.93). There was no difference in event-free survival between patients who were prescribed colchicine and those who were not. In summary, we report the first real-world data on the use and effectiveness of colchicine for prevention of cardiovascular events after AMI. Colchicine was infrequently prescribed for this indication and was not associated with lower rates of subsequent cardiovascular events.

Background: Despite advances in preventive cardiology, there remain differences in ST Elevation Myocardial Infarction (STEMI) rates among certain populations and across neighborhoods. While gentrification is an important neighborhood-level factor that may contribute to disparities, no studies have examined the relationship between gentrification and STEMI. We sought to analyze the change in STEMI rates over time for gentrifying neighborhoods compared to non-gentrifying, stably low-SES neighborhoods. Methods: This retrospective cohort study included all emergency medical service STEMI activations occurring within low-SES census tracts (i.e. tracts susceptible to gentrification) in Los Angeles County from 2011-2019. Low-SES tracts that did or did not gentrify in 2015-2019 were defined using American Community Survey data following methods from the Urban Displacement Project. The outcome was rate of STEMI activations per 1000 tract residents. Multivariable Poisson regression was performed to assess the association of gentrification with change in STEMI rates between baseline (2011-2014) and follow-up (2015-2019), adjusted for demographics. As displacement is an inherent characteristic of gentrification, we performed subgroup analyses comparing tracts with the lowest and highest quartiles of geographic mobility, a characteristic sourced from the American Community Survey. Results: A total of 868 low-SES census tracts in Los Angeles County were included, of which 128 gentrified in the 2015-2019 time period while 740 remained low-SES. Multivariable analysis found no significant change in STEMI rates in gentrifying versus stably low-SES tracts. Subgroup analysis found that among tracts in the highest quartile of mobility (213 tracts), STEMI rates were 35% higher in gentrifying versus stably low-SES tracts (95% CI 19%-54%), with no differences in low-mobility tracts. Conclusion: There was no significant association between gentrification and neighborhood STEMI rates. However, among neighborhoods with high levels of displacement, STEMI rates were higher among gentrifying tracts. Changing group composition due to displacement may result in declining social cohesion among remaining residents, which is a known protective factor against myocardial infarction. Future work is needed to clarify this relationship, understand subgroups that differentially experience gentrification, and determine mechanisms through which gentrification affects cardiovascular health.

BACKGROUND:A widely representative health system cohort with longitudinal specimen collection can serve as an efficient clinical biobank resource for multiple studies. Because the full scope of a health system cohort can include both health care workers and patients, enrollment and biobanking efforts may be designed to engage these specific participant populations. METHODS:For a multisite health system cohort that initially enrolled health care workers and then expanded to enroll patients, we evaluated the relative success of initiatives that specifically targeted enrollment of various health care worker and patient populations. We also compared enrollment rate success based on engagement type (active vs. passive), modality (in-person vs. virtual), and venue (clinical-based or community-based). Across each method of engagement, we compared the conversion rate from study consent to collected biospecimen. RESULTS:For recruitment activities involving health care workers, enrollment rates varied based on active versus passive (62% vs. 0.8%) and in-person versus virtual (9.6% vs. 0.8%) engagement as well as clinical-based versus community-based (65% vs. 3.9%) venues (p < 0.001 for all). For health care workers, the overall conversion rate from consent to biospecimen collection was 87%. For recruitment activities involving patients, enrollment rates also varied based on active versus passive (53% vs. 0.8%) and in-person versus virtual (62% vs. 0.8%) engagement, as well as clinical-based versus community-based (70% vs. 41%) venues (p < 0.001 for all). For patients, the overall conversion rate from consent to biospecimen collection was 75%. CONCLUSIONS:For studies aiming to build a biorepository resource involving both health care worker and patient participants, the active rather than passive engagement methods are likely to achieve not only a higher rate of contact to consented enrollment but also a higher rate of conversion from consent to biospecimen collection. Further studies are needed to guide resource planning around biorepository building capacity for specific study designs.

Background Atrial fibrillation (AF) can cause a reduction in left ventricular ejection fraction (LVEF) that resolves rapidly upon restoration of sinus rhythm. We used artificial intelligence to understand (1) how often transient LVEF reduction during AF is from mismeasurement due to AF's beat‐to‐beat variability and (2) whether true transient AF‐LVEF reduction has prognostic significance. Methods In this observational study, we analyzed all patients at a large academic center with a transthoracic echocardiogram in AF and subsequent transthoracic echocardiogram in sinus rhythm within 90 days. We classified patients by their clinically reported LVEFs: no AF‐LVEF reduction, transient AF‐LVEF reduction that recovered after conversion to sinus rhythm, or persistent AF‐LVEF reduction that did not recover. We evaluated how automated multicycle AF‐LVEF measurement using a validated artificial intelligence algorithm affected AF‐LVEF and reclassified patients. We used Fine–Gray hazard modeling to analyze 1‐year heart failure hospitalization risk. Results In 810 patients (mean age 74.1 years, 34.3% female), 459 (56.7%) had no reduced AF‐LVEF, 71 (8.8%) had transient AF‐LVEF reduction, and 280 (34.6%) had persistent AF‐LVEF reduction. In the group with transient AF‐LVEF reduction, LVEF increased by 19.5% (95% CI, 12.0%–22.1%) upon conversion to sinus rhythm. AI reassessment increased AF‐LVEF by 8.2% (95% CI, 6.0%–10.4%), reclassifying 20 (28.2%) patients as no longer having reduced AF‐LVEF. The group with transient AF‐LVEF reduction, as determined by AI, had significantly higher 1‐year heart failure hospitalization risk (hazard ratio, 2.28 [95% CI, 1.23–4.21], P=0.003). Conclusion Artificial intelligence may decrease misdiagnosis of reduced LVEF during AF and more accurately identify true transient AF‐LVEF reduction, a potentially high‐risk phenotype.

Introduction: Uncontrolled hypertension is a potent cardiovascular risk factor, with a higher prevalence in adults with disadvantaged social determinants of health (SDoH). While prior work has shown an association with uncontrolled blood pressure (BP) and adverse SDoH in general, less is known about specific SDoH domain drivers of this association, knowledge of which may inform interventions to improve health and reduce disparities. Methods: We examined health records in all adults with ≥2 outpatient visits/year in at least 2 consecutive years from 2017-2023. BP level was determined by the first 2 visits in the first year and last 2 readings in the second year; BP control was based on contemporary thresholds (<130/80 mmHg). Loss of BP control (controlled in first year and uncontrolled in second year) was determined year over year. Neighborhood SDoH was assessed by linking patients’ zip codes to the Healthy Places Index (HPI), including 8 domains (economic, education, transportation, social, housing, health insurance, clean environment, and neighborhood). Multivariable regression analyses were used to examine associations of demographics, clinical, and SDoH characteristics. Results: A total of 145,318 patients were identified, of whom 94,276 (64.9%) lost BP control at some point during the study period. In analyses adjusting for demographic, clinical, and SDoH factors (Figure 1A), risk of losing BP control was associated with living in a neighborhood with a HPI score in the worst quartile (OR 1.12, 95% CI 1.08-1.15). Evaluation of SDoH domain identified the economic (1.15 [1.12-1.18], P = 0.012) and education (1.15 [1.12-1.18], P < 0.001) as the only SDoH domains associated with loss of BP control, though positive association trends were also appreciated for transportation (1.09 [1.06-1.12], P = 0.079) and health insurance (1.1 [1.07-1.13], P = 0.097) domains (Figure 1B). Conclusions: In over 140,000 adults with controlled BP, almost two-thirds lost BP control in the subsequent year. This loss of BP control was associated with many neighborhood level SDoH factors, specifically economic and educational domains. These results inform potential targets to help reduce disparities and improve BP related health outcomes.

Blood pressure variability (BPV) and heart rate variability (HRV) have been associated with Alzheimer’s Disease and Related Dementias (ADRD) in rigorously controlled studies. However, the extent to which BPV and HRV may offer predictive information in real-world, routine clinical care is unclear. In a retrospective cohort study of 48,204 adults (age 54.9 ± 17.5 years, 60% female) receiving continuous care at a single center, we derived BPV and HRV from routinely collected clinical data. We use multivariable Cox models to evaluate the association of BPV and HRV, separately and in combination, with incident ADRD. Over a median 3 [2.4, 3.0] years, there were 443 cases of new-onset ADRD. We found that clinically derived measures of BPV, but not HRV, were consistently associated with incident ADRD. In combined analyses, only patients in both the highest quartile of BPV and lowest quartile of HRV had increased ADRD risk (HR 2.34, 95% CI 1.44–3.81). These results indicate that clinically derived BPV, rather than HRV, offers a consistent and readily available metric for ADRD risk assessment in a real-world patient care setting. Thus, implementation of BPV as a widely accessible tool could allow clinical providers to efficiently identify patients most likely to benefit from comprehensive ADRD screening.

Introduction: The burden of hypertension is not felt uniformly across the population, with non-Hispanic Black (NHB) adults experiencing the highest rates of hypertension-attributable mortality. The 2017 AHA guidelines recommend calcium channel blockers (CCBs) and thiazide-like diuretics as initial medications for NHB patients, with ACE/ARBs a first line recommendation for other populations. It remains unclear to what degree providers adhere to these race-specific recommendations. Methods: We examined electronic health record data of all hypertensive adults (first blood pressure >140/90 mmHg) starting antihypertensive therapy between 1/2013-12/2022. We excluded patients with a diagnosis of CAD/MI, diabetes, stroke, heart failure, or CKD. We specifically identified the order in which each antihypertensive medication class was prescribed. Results: We identified a total of n=26,862 patients, of whom n=4015 (14.95%) were NHB. CCBs were the most prescribed first line antihypertensive agent for NHBs, followed by ACE/ARBs, then diuretics. By comparison, for all other patients, ACE/ARBs were the most prescribed first agent, followed by CCBs, then beta-blockers (Figure 1A-B). Over the study period, initial use of CCBs increased and diuretic use decreased for both groups (Figure 1C-D). Conclusions: Following guidelines, the most frequently prescribed first line antihypertensive agents were CCBs and ACE/ARBs for NHB and non-NHB populations, respectively, with decreasing diuretic use. The identified patterns increased over the study period, indicating an overuse of ACE/ARB and underuse of diuretics among NHB patients.

Background: The Vasoactive-Inotropic Score (VIS) quantifies vasopressor and inotropic use in cardiogenic shock (CS), with higher VIS indicating greater severity and mortality. This study examines VIS changes within 48 hours post-Mechanical Circulatory Support (MCS) implementation and their correlation with mortality. Methods: We retrospectively analyzed 2,261 CS patients admitted from Jan 1, 2013, to Dec 31, 2023. Primary outcome is the mean change in VIS score at designated time intervals in the 48 hours after receiving MCS or medical treatment alone. A multivariable Cox proportional hazard regression model was used to evaluate the independent relationship between VIS change and in-hospital mortality. Results: Patients were categorized into the strategy they received in the first 72 hours of shock onset: medical treatment (n=2044) IABP (n=146), Impella (n=33), or ECMO (n=39). A total of 534 patient died within the index hospitalization. A decrease in the change of the mean VIS score was observed at various time intervals within the first 48 hours (Figure 1). The mean change in VIS score incorporates patients who had a decrease, no change or increase in score in the first 48 hours. Therefore, a hazard ratio <1 in a Cox model would indicate that a more negative mean VIS values correlates with lower mortality. A multivariable Cox model assessing for in-patient mortality incorporated the strategies that patients received (medical treatment, IABP, etc.), age, gender, race medical history, left ventricular ejection fraction, serum creatinine, lactate > 4.5 or pH < 7.2. Our model suggests that a decreased need in the vasoactive medication requiring in the first 48hrs is associated with a reduction in mortality (Figure 2). Conclusion: All device strategies effectively reduce VIS within 48 hours. The change in mean VIS score within the first 48 hours is independently associated with lower mortality, regardless of the strategy patients received, their past medical history or other clinical covariates.

Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiograms contain hidden risk markers that can help prognosticate post-operative mortality. Methods. In a derivation cohort of 45,969 pre-operative patients (age 59+- 19 years, 55 percent women), a deep learning algorithm was developed to leverage waveform signals from pre-operative ECGs to discriminate post-operative mortality. Model performance was assessed in a holdout internal test dataset and in two external hospital cohorts and compared with the Revised Cardiac Risk Index (RCRI) score. Results. In the derivation cohort, there were 1,452 deaths. The algorithm discriminates mortality with an AUC of 0.83 (95% CI 0.79-0.87) surpassing the discrimination of the RCRI score with an AUC of 0.67 (CI 0.61-0.72) in the held out test cohort. Patients determined to be high risk by the deep learning model's risk prediction had an unadjusted odds ratio (OR) of 8.83 (5.57-13.20) for post-operative mortality as compared to an unadjusted OR of 2.08 (CI 0.77-3.50) for post-operative mortality for RCRI greater than 2. The deep learning algorithm performed similarly for patients undergoing cardiac surgery with an AUC of 0.85 (CI 0.77-0.92), non-cardiac surgery with an AUC of 0.83 (0.79-0.88), and catherization or endoscopy suite procedures with an AUC of 0.76 (0.72-0.81). The algorithm similarly discriminated risk for mortality in two separate external validation cohorts from independent healthcare systems with AUCs of 0.79 (0.75-0.83) and 0.75 (0.74-0.76) respectively. Conclusion. The findings demonstrate how a novel deep learning algorithm, applied to pre-operative ECGs, can improve discrimination of post-operative mortality.

Background and objectivesRecognized as a potential risk factor for Alzheimer's disease and related dementias (ADRD), blood pressure variability (BPV) could be leveraged to facilitate identification of at-risk individuals at a population level. Granular BPV data are available during acute care hospitalization periods for potentially high-risk patients, but the incident ADRD risk association with BPV measured in this setting is unknown. Our objective was to evaluate the relation of BPV, measured during acute care hospitalization, and incidence of ADRD.MethodsWe retrospectively studied adults, without a prior ADRD diagnosis, who were admitted to a large quaternary care medical center in Southern California between January 1, 2013 and December 31, 2019. For all patients, determined BPV, calculated as variability independent of the mean (VIM), using blood pressure readings obtained as part of routine clinical care. We used multivariable Cox proportional hazards regression to examine the association between BP VIM during hospitalization and the development of incident dementia, determined by new ICD-9/10 coding or the new prescription of dementia medication, occurring at least 2 years after the index hospitalization.ResultsOf 81,892 adults hospitalized without a prior ADRD diagnosis, 2,442 (2.98%) went on to develop ADRD (2.6 to 5.2 years after hospitalization). In multivariable-adjusted Cox models, both systolic (HR 1.05, 95% CI 1.00–1.09) and diastolic (1.06, 1.02–1.10) VIM were associated with incident ADRD. In pre-specified stratified analyses, the VIM associations with incident ADRD were most pronounced in individuals over age 60 years and among those with renal disease or hypertension. Results were similar when repeated to include incident ADRD diagnoses made at least 1 or 3 years after index hospitalization.DiscussionWe found that measurements of BPV from acute care hospitalizations can be used to identify individuals at risk for developing a diagnosis of ADRD within approximately 5 years. Use of the readily accessible BPV measure may allow healthcare systems to risk stratify patients during periods of intense patient-provider interaction and, in turn, facilitate engagement in ADRD screening programs.

Postural orthostatic tachycardia syndrome (POTS) has been previously described after SARS-CoV-2 infection; however, limited data is available on the relation of POTS with COVID-19 vaccination. Here we show in a cohort of 284,592 COVID-19 vaccinated individuals using a sequence-symmetry analysis, that the odds of POTS are higher 90 days after vaccine exposure than 90 days prior to exposure, and that the odds for POTS are higher than referent conventional primary care diagnoses, but lower than the odds of new POTS diagnosis after SARS-CoV-2 infection. Our results identify a possible association between COVID-19 vaccination and incidence of POTS. Notwithstanding the probable low incidence of POTS after COVID-19 vaccination, particularly when compared to SARS-Cov-2 post-infection odds which were five times higher, our results suggest that further studies, are needed to investigate the incidence and etiology of POTS occurring after COVID-19 vaccination.

This cohort study compares the risk of new-onset hypertension, hyperlipidemia, and diabetes before and after COVID-19 infection among patients who were vaccinated vs unvaccinated before infection.

BackgroundIndividuals with post-acute sequelae of COVID (PASC) may have a persistence in immune activation that differentiates them from individuals who have recovered from COVID without clinical sequelae. To investigate how humoral immune activation may vary in this regard, we compared patterns of vaccine-provoked serological response in patients with PASC compared to individuals recovered from prior COVID without PASC.MethodsWe prospectively studied 245 adults clinically diagnosed with PASC and 86 adults successfully recovered from prior COVID. All participants had measures of humoral immunity to SARS-CoV-2 assayed before or after receiving their first-ever administration of COVID vaccination (either single-dose or two-dose regimen), including anti-spike (IgG-S and IgM-S) and anti-nucleocapsid (IgG-N) antibodies as well as IgG-S angiotensin-converting enzyme 2 (ACE2) binding levels. We used unadjusted and multivariable-adjusted regression analyses to examine the association of PASC compared to COVID-recovered status with post-vaccination measures of humoral immunity.ResultsIndividuals with PASC mounted consistently higher post-vaccination IgG-S antibody levels when compared to COVID-recovered (median log IgG-S 3.98 versus 3.74, P < 0.001), with similar results seen for ACE2 binding levels (median 99.1 versus 98.2, P = 0.044). The post-vaccination IgM-S response in PASC was attenuated but persistently unchanged over time (P = 0.33), compared to in COVID recovery wherein the IgM-S response expectedly decreased over time (P = 0.002). Findings remained consistent when accounting for demographic and clinical variables including indices of index infection severity and comorbidity burden.ConclusionWe found evidence of aberrant immune response distinguishing PASC from recovered COVID. This aberrancy is marked by excess IgG-S activation and ACE2 binding along with findings consistent with a delayed or dysfunctional immunoglobulin class switching, all of which is unmasked by vaccine provocation. These results suggest that measures of aberrant immune response may offer promise as tools for diagnosing and distinguishing PASC from non-PASC phenotypes, in addition to serving as potential targets for intervention.