Association of Lipoprotein (a) and Standard Modifiable Cardiovascular Risk Factors With Incident Myocardial Infarction: The Mass General Brigham Lp(a) Registry.

BACKGROUND: Lipoprotein (a) [Lp(a)] is a robust predictor of coronary heart disease outcomes, with targeted therapies currently under investigation. We aimed to evaluate the association of high Lp(a) with standard modifiable risk factors (SMuRFs) for incident first acute myocardial infarction (AMI). METHODS AND RESULTS: This retrospective study used the Mass General Brigham Lp(a) Registry, which included patients aged ≥18 years with an Lp(a) measurement between 2000 and 2019. Exclusion criteria were severe kidney dysfunction, malignant neoplasm, and prior known atherosclerotic cardiovascular disease. Diabetes, dyslipidemia, hypertension, and smoking were considered SMuRFs. High Lp(a) was defined as >90th percentile, and low Lp(a) was defined as <50th percentile. The primary outcome was fatal or nonfatal AMI. A combination of natural language processing algorithms, International Classification of Diseases (ICD) codes, and laboratory data was used to identify the outcome and covariates. A total of 6238 patients met the eligibility criteria. The median age was 54 (interquartile range, 43-65) years, and 45% were women. Overall, 23.7% had no SMuRFs, and 17.8% had ≥3 SMuRFs. Over a median follow-up of 8.8 (interquartile range, 4.2-12.8) years, the incidence of AMI increased gradually, with higher number of SMuRFs among patients with high (log-rank P=0.031) and low Lp(a) (log-rank P<0.001). Across all SMuRF subgroups, the incidence of AMI was significantly higher for patients with high Lp(a) versus low Lp(a). The risk of high Lp(a) was similar to having 2 SMuRFs. Following adjustment for confounders and number of SMuRFs, high Lp(a) remained significantly associated with the primary outcome (hazard ratio, 2.9 [95% CI, 2.0-4.3]; P<0.001). CONCLUSIONS: Among patients with no prior atherosclerotic cardiovascular disease, high Lp(a) is associated with significantly higher risk for first AMI regardless of the number of SMuRFs.

Lipoprotein(a): Emerging insights and therapeutics.

The strong association between lipoprotein (a) [Lp(a)] and atherosclerotic cardiovascular disease has led to considerations of Lp(a) being a potential target for mitigating residual cardiovascular risk. While approximately 20 % of the population has an Lp(a) level greater than 50 mg/dL, there are no currently available pharmacological lipid-lowering therapies that have demonstrated substantial reduction in Lp(a). Novel therapies to lower Lp(a) include antisense oligonucleotides and small-interfering ribonucleic acid molecules and have shown promising results in phase 2 trials. Phase 3 trials are currently underway and will test the causal relationship between Lp(a) and ASCVD and whether lowering Lp(a) reduces cardiovascular outcomes. In this review, we summarize emerging insights related to Lp(a)'s role as a risk-enhancing factor for ASCVD, association with calcific aortic stenosis, effects of existing therapies on Lp(a) levels, and variations amongst patient populations. The evolving therapeutic landscape of emerging therapeutics is further discussed.

Statin Twitter: Human and Automated Bot Contributions, 2010 to 2022.

BACKGROUND: Many individuals eligible for statin therapy decline treatment, often due to fear of adverse effects. Misinformation about statins is common and drives statin reluctance, but its prevalence on social media platforms, such as Twitter (now X) remains unclear. Social media bots are known to proliferate medical misinformation, but their involvement in statin-related discourse is unknown. This study examined temporal trends in volume, author type (bot or human), and sentiment of statin-related Twitter posts (tweets). METHODS AND RESULTS: We analyzed original tweets with statin-related terms from 2010 to 2022 using a machine learning-derived classifier to determine the author's bot probability, natural language processing to assign each tweet a negative or positive sentiment, and manual qualitative analysis to identify statin skepticism in a random sample of all tweets and in highly influential tweets. We identified 1 155 735 original statin-related tweets. Bots produced 333 689 (28.9%), humans produced 699 876 (60.6%), and intermediate probability accounts produced 104 966 (9.1%). Over time, the proportion of bot tweets decreased from 47.8% to 11.3%, and human tweets increased from 43.6% to 79.8%. The proportion of negative-sentiment tweets increased from 27.8% to 43.4% for bots and 30.9% to 38.4% for humans. Manually coded statin skepticism increased from 8.0% to 19.0% for bots and from 26.0% to 40.0% for humans. CONCLUSIONS: Over the past decade, humans have overtaken bots as generators of statin-related content on Twitter. Negative sentiment and statin skepticism have increased across all user types. Twitter may be an important forum to combat statin-related misinformation.

Lipoprotein(a) and Major Adverse Cardiovascular Events in Patients With or Without Baseline Atherosclerotic Cardiovascular Disease.

BACKGROUND: Lipoprotein(a) [Lp(a)] is associated with an increased risk of atherosclerotic cardiovascular disease (ASCVD). However, whether the optimal Lp(a) threshold for risk assessment should differ based on baseline ASCVD status is unknown. OBJECTIVES: The purpose of this study was to assess the association between Lp(a) and major adverse cardiovascular events (MACE) among patients with and without baseline ASCVD. METHODS: We studied a retrospective cohort of patients with Lp(a) measured at 2 medical centers in Boston, Massachusetts, from 2000 to 2019. To assess the association of Lp(a) with incident MACE (nonfatal myocardial infarction [MI], nonfatal stroke, coronary revascularization, or cardiovascular mortality), Lp(a) percentile groups were generated with the reference group set at the first to 50th Lp(a) percentiles. Cox proportional hazards modeling was used to assess the association of Lp(a) percentile group with MACE. RESULTS: Overall, 16,419 individuals were analyzed with a median follow-up of 11.9 years. Among the 10,181 (62%) patients with baseline ASCVD, individuals in the 71st to 90th percentile group had a 21% increased hazard of MACE (adjusted HR: 1.21; P < 0.001), which was similar to that of individuals in the 91st to 100th group (adjusted HR: 1.26; P < 0.001). Among the 6,238 individuals without established ASCVD, there was a continuously higher hazard of MACE with increasing Lp(a), and individuals in the 91st to 100th Lp(a) percentile group had the highest relative risk with an adjusted HR of 1.93 (P < 0.001). CONCLUSIONS: In a large, contemporary U.S. cohort, elevated Lp(a) is independently associated with long-term MACE among individuals with and without baseline ASCVD. Our results suggest that the threshold for risk assessment may be different in primary vs secondary prevention cohorts.

Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification.

Coronary artery calcium (CAC) is a marker of coronary atherosclerosis, and the presence and severity of CAC have been shown to be powerful predictors of future cardiovascular events. Due to its value in risk discrimination and reclassification beyond traditional risk factors, CAC has been supported by recent guidelines, particularly for the purposes of informing shared decision-making regarding the use of preventive therapies. In addition to dedicated ECG-gated CAC scans, the presence and severity of CAC can also be accurately estimated on non-contrast chest computed tomography scans performed for other clinical indications. However, the presence of such "incidental" CAC is rarely reported. Advances in artificial intelligence have now enabled automatic CAC scoring for both cardiac and non-cardiac CT scans. Various AI approaches, from rule-based models to machine learning algorithms and deep learning, have been applied to automate CAC scoring. Convolutional neural networks, a deep learning technique, have had the most successful approach, with high agreement with manual scoring demonstrated in multiple studies. Such automated CAC measurements may enable wider and more accurate detection of CAC from non-gated CT studies, thus improving the efficiency of healthcare systems to identify and treat previously undiagnosed coronary artery disease.

Influenza vaccination and use of lipid lowering therapies in adults with atherosclerotic cardiovascular disease: An analysis of the Behavioral Risk Factor Surveillance System (BRFSS).

BACKGROUND: Influenza vaccination and lipid lowering therapy (LLT) are evidence-based interventions with substantial benefit for individuals with established atherosclerotic cardiovascular disease (ASCVD). However, levels of influenza immunization and LLT use are low, possibly due to pervasive fear-based misinformation uniquely targeting vaccines and LLT. Whether being unvaccinated for influenza predicts lower utilization of LLT is unknown. OBJECTIVES: We tested the hypothesis that American adults with ASCVD who are unvaccinated for influenza have lower use of LLT even after accounting for traditional factors associated with underuse of preventive therapies. METHODS: We pooled 2017, 2019, and 2021 survey data from the Behavioral Risk Factor Surveillance System (BRFSS), and selected respondents aged 40 to 75 years with self-reported ASCVD. We used logistic regression models adjusted for potential confounders to examine the association between influenza vaccination and self-reported LLT use. We performed a sensitivity analysis with multiple imputation to account for missing data. All analyses accounted for complex survey weighting. RESULTS: Of 66,923 participants with ASCVD, 55% reported influenza vaccination in the last year and 76% reported using LLT. Being unvaccinated for influenza was associated with lower odds of LLT use (OR 0.54; 95% CI 0.50, 0.58; P< .001). In a multivariable regression model adjusting for demographics and comorbidities, this association remained statistically significant (aOR 0.58, 95% CI 0.52, 0.64, P < .001). After additional adjustment for preventive care engagement, health care access, and use patterns of other cardiovascular medications this association persisted (aOR 0.66; 95% CI 0.60, 0.74; P < .001). There were no significant differences across subgroups, including those with and without hyperlipidemia. CONCLUSIONS: Unvaccinated status for influenza was independently associated with 34% lower odds of LLT use among American adults with ASCVD after adjustment for traditional factors linked to underuse of preventive therapies. This finding identifies a population with excess modifiable ASCVD risk, and supports investigation into nontraditional mechanisms driving underuse of preventive therapies, including fear-based misinformation.

The association between COVID-19 infection and incident atrial fibrillation: results from a retrospective cohort study using a large US commercial insurance database.

BACKGROUND: We sought to examine a 1-year incidence of atrial fibrillation (AF) among patients with SARS-CoV-2 virus (COVID-19) in comparison to those with non-COVID-19 acute upper respiratory infection (AURI). METHODS: Patients with a diagnosis of COVID-19 (in any setting) between April 2020 and June 2021 were identified in Optum Clinformatics. Two comparator cohorts were constructed: an 'AURI pandemic' cohort (AURI diagnosis between April 2020 and June 2021) and an 'AURI prepandemic' cohort (AURI diagnosis between January 2018 and December 2018). One-year incidence of AF was compared among: COVID-19 versus AURI pandemic cohort; COVID-19 versus AURI prepandemic cohort; and AURI pandemic versus AURI prepandemic cohort. For each comparison, we applied a matching weights technique to balance covariates. Logistic regression was used to compare the odds of incident AF among the matched cohorts. RESULTS: When comparing the matched COVID-19 (n=102 227) cohort with the AURI pandemic (n=102 101) cohort, higher incidence of AF was observed among the COVID-19 cohort (2.2% vs 1.2%; p<0.001; OR 1.83; 95% CI 1.72 to 1.95). Similar findings were observed for the COVID-19 (n=169 687) versus AURI prepandemic (n=169 486) comparison (2.7% vs 1.6%; p<0.001; OR 1.70; 95% CI 1.63 to 1.78). When comparing the AURI pandemic (n=1 26 392) versus AURI prepandemic (n=1 26 394) cohort, no significant differences in incident AF were observed (1.1% vs 1.2%; p=0.133; OR 0.95, 95% CI 0.90 to 1.01). CONCLUSION: Patients diagnosed with COVID-19 were found to be at a higher risk of incident AF as compared with those with AURI. Timely diagnosis and appropriate treatment of AF may potentially mitigate the burden of AF conferred by COVID-19.

Natural Language Processing for the Ascertainment and Phenotyping of Left Ventricular Hypertrophy and Hypertrophic Cardiomyopathy on Echocardiogram Reports.

Extracting and accurately phenotyping electronic health documentation is critical for medical research and clinical care. We sought to develop a highly accurate and open-source natural language processing (NLP) module to ascertain and phenotype left ventricular hypertrophy (LVH) and hypertrophic cardiomyopathy (HCM) diagnoses from echocardiogram reports within a diverse hospital network. After the initial development on 17,250 echocardiogram reports, 700 unique reports from 6 hospitals were randomly selected from data repositories within the Mass General Brigham healthcare system and manually adjudicated by physicians for 10 subtypes of LVH and diagnoses of HCM. Using an open-source NLP system, the module was formally tested on 300 training set reports and validated on 400 reports. The sensitivity, specificity, positive predictive value, and negative predictive value were calculated to assess the discriminative accuracy of the NLP module. The NLP demonstrated robust performance across the 10 LVH subtypes, with the overall sensitivity and specificity exceeding 96%. In addition, the NLP module demonstrated excellent performance in detecting HCM diagnoses, with sensitivity and specificity exceeding 93%. In conclusion, we designed a highly accurate NLP module to determine the presence of LVH and HCM on echocardiogram reports. Our work demonstrates the feasibility and accuracy of NLP to detect diagnoses on imaging reports, even when described in free text. This module has been placed in the public domain to advance research, trial recruitment, and population health management for patients with LVH-associated conditions.

Association between implanted cardioverter-defibrillators and mortality for patients with left ventricular ejection fraction between 30% and 35.

BACKGROUND: Consensus guidelines support the use of implanted cardioverter-defibrillators (ICD) for primary prevention of sudden cardiac death in patients with either non-ischaemic or ischaemic cardiomyopathy with left ventricular ejection fraction (LVEF) ≤35%. However, evidence from trials for efficacy specifically for patients with LVEF near 35% is weak. Past trials are underpowered for this population and future trials are unlikely to be performed. METHODS: Patients with lowest LVEF between 30% and 35% without an ICD prior to the lowest-LVEF echo (defined as 'time zero') were identified by querying echocardiography data from 28 November 2001 to 9 July 2020 at the Massachusetts General Hospital linked to ICD treatment status. To assess the association between ICD and mortality, propensity score matching followed by Cox proportional hazards models considering treatment status as a time-dependent covariate was used. A secondary analysis was performed for LVEF 36%-40%. RESULTS: Initially, 526 440 echocardiograms representing 266 601 unique patients were identified. After inclusion and exclusion criteria were applied, 6109 patients remained for the analytical cohort. In bivariate unadjusted comparisons, patients who received ICDs were substantially more often male (79.8% vs 65.4%, p<0.0001), more often white (87.5% vs 83.7%, p<0.046) and more often had a history of ventricular tachycardia (74.5% vs 19.1%, p<0.0001) and myocardial infarction (56.1% vs 38.2%, p<0.0001). In the propensity matched sample, after accounting for time-dependence, there was no association between ICD and mortality (HR 0.93, 95% CI 0.75 to 1.15, p=0.482). CONCLUSIONS: ICD therapy was not associated with reduced mortality near the conventional LVEF threshold of 35%. Although this treatment design cannot definitively demonstrate lack of efficacy, our results are concordant with available prior trial data. A definitive, well-powered trial is needed to answer the important clinical question of primary prevention ICD efficacy between LVEF 30% and 35%.

SliDL: A toolbox for processing whole-slide images in deep learning.

The inspection of stained tissue slides by pathologists is essential for the early detection, diagnosis and monitoring of disease. Recently, deep learning methods for the analysis of whole-slide images (WSIs) have shown excellent performance on these tasks, and have the potential to substantially reduce the workload of pathologists. However, WSIs present a number of unique challenges for analysis, requiring special consideration of image annotations, slide and image artefacts, and evaluation of WSI-trained model performance. Here we introduce SliDL, a Python library for performing pre- and post-processing of WSIs. SliDL makes WSI data handling easy, allowing users to perform essential processing tasks in a few simple lines of code, bridging the gap between standard image analysis and WSI analysis. We introduce each of the main functionalities within SliDL: from annotation and tile extraction to tissue detection and model evaluation. We also provide 'code snippets' to guide the user in running SliDL. SliDL has been designed to interact with PyTorch, one of the most widely used deep learning libraries, allowing seamless integration into deep learning workflows. By providing a framework in which deep learning methods for WSI analysis can be developed and applied, SliDL aims to increase the accessibility of an important application of deep learning.

Cardiovascular outcomes in patients with coronary artery disease and elevated lipoprotein(a): implications for the OCEAN(a)-outcomes trial population.

Aims: The ongoing Olpasiran Trials of Cardiovascular Events and Lipoprotein(a) Reduction [OCEAN(a)]-Outcomes trial is evaluating whether Lp(a) lowering can reduce the incidence of cardiovascular events among patients with prior myocardial infarction (MI) or percutaneous coronary intervention (PCI) and elevated Lp(a) (≥200 nmol/L). The purpose of this study is to evaluate the association of elevated Lp(a) with cardiovascular outcomes in an observational cohort resembling the OCEAN(a)-Outcomes trial main enrolment criteria. Methods and results: This study included patients aged 18-85 years with Lp(a) measured as part of their clinical care between 2000 and 2019. While patients were required to have a history of MI, or PCI, those with severe kidney dysfunction or a malignant neoplasm were excluded. Elevated Lp(a) was defined as ≥200 nmol/L consistent with the OCEAN(a)-Outcomes trial. The primary outcome was a composite of coronary heart disease death, MI, or coronary revascularization. Natural language processing algorithms, billing and ICD codes, and laboratory data were employed to identify outcomes and covariates. A total of 3142 patients met the eligibility criteria, the median age was 61 (IQR: 52-73) years, 28.6% were women, and 12.3% had elevated Lp(a). Over a median follow-up of 12.2 years (IQR: 6.2-14.3), the primary composite outcome occurred more frequently in patients with versus without elevated Lp(a) [46.0 vs. 38.0%, unadjHR = 1.30 (95% CI: 1.09-1.53), P = 0.003]. Following adjustment for measured confounders, elevated Lp(a) remained independently associated with the primary outcome [adjHR = 1.33 (95% CI: 1.12-1.58), P = 0.001]. Conclusion: In an observational cohort resembling the main OCEAN(a)-Outcomes Trial enrolment criteria, patients with an Lp(a) ≥200 nmol/L had a higher risk of cardiovascular outcomes.

Creation and Validation of an Algorithm for Predicting the Recurrence of Atrial Fibrillation Following Pulmonary Vein Isolation by Utilizing Real-World Data and Ensemble Modeling Techniques.

Introduction Catheter ablation (CA) of atrial fibrillation (AF) represents a mainstay in the treatment of this increasingly prevalent arrhythmia. Prospective clinical trials investigating the efficacy of CA may poorly represent real-world patient populations. However, many real-world clinical datasets possess missing data, which may impede their applicability in research. Thus, we sought to use ensemble modeling to address missing data and develop a model to estimate the probability of AF recurrence following CA. Methods We retrospectively analyzed clinical variables in 476 patients who underwent an initial CA of AF. Univariate and multivariate logistic regression was performed to determine those variables predictive of AF recurrence. A multivariate logistic model was created to estimate the probability of AF recurrence after CA. Missing data were addressed using ensemble modeling, and variable selection was performed using the aggregate of multiple models. Results After analysis, six variables remained in the model: AF during the post-procedural blanking period, coexistence of atrial flutter, end-stage renal disease, reduced left ventricular ejection fraction, prior failure of anti-arrhythmic drugs, and valvular heart disease. Predictive modeling was performed using these variables for 1000 randomly partitioned datasets (80% training, 20% testing) and 1000 random imputations for each partitioned dataset. The model predicted AF recurrence with an accuracy of 74.34 ± 3.99% (recall: 54.03 ± 8.15%; precision: 89.30 ± 4.21%; F1 score: 81.08 ± 3.65%).  Conclusion We successfully identified six clinical variables that, when modeled, predicted AF recurrence following CA with a high degree of classification accuracy. Application of this model to patients undergoing CA of AF may help identify those at risk of post-procedural AF recurrence.

A pragmatic clinical trial assessing the effect of a targeted notification and clinical support pathway on the diagnostic evaluation and treatment of individuals with left ventricular hypertrophy (NOTIFY-LVH).

BACKGROUND: Electronic health records contain vast amounts of cardiovascular data, including potential clues suggesting unrecognized conditions. One important example is the identification of left ventricular hypertrophy (LVH) on echocardiography. If the underlying causes are untreated, individuals are at increased risk of developing clinically significant pathology. As the most common cause of LVH, hypertension accounts for more cardiovascular deaths than any other modifiable risk factor. Contemporary healthcare systems have suboptimal mechanisms for detecting and effectively implementing hypertension treatment before downstream consequences develop. Thus, there is an urgent need to validate alternative intervention strategies for individuals with preexisting-but potentially unrecognized-LVH. METHODS: Through a randomized pragmatic trial within a large integrated healthcare system, we will study the impact of a centralized clinical support pathway on the diagnosis and treatment of hypertension and other LVH-associated diseases in individuals with echocardiographic evidence of concentric LVH. Approximately 600 individuals who are not treated for hypertension and who do not have a known cardiomyopathy will be randomized. The intervention will be directed by population health coordinators who will notify longitudinal clinicians and offer to assist with the diagnostic evaluation of LVH. Our hypothesis is that an intervention that alerts clinicians to the presence of LVH will increase the detection and treatment of hypertension and the diagnosis of alternative causes of thickened myocardium. The primary outcome is the initiation of an antihypertensive medication. Secondary outcomes include new hypertension diagnoses and new cardiomyopathy diagnoses. The trial began in March 2023 and outcomes will be assessed 12 months from the start of follow-up. CONCLUSION: The NOTIFY-LVH trial will assess the efficacy of a centralized intervention to improve the detection and treatment of hypertension and LVH-associated diseases. Additionally, it will serve as a proof-of-concept for how to effectively utilize previously collected electronic health data to improve the recognition and management of a broad range of chronic cardiovascular conditions. TRIAL REGISTRATION: NCT05713916.

Characteristics of Suspected COVID-19 Discharged Emergency Department Patients Who Returned During the First Wave.

INTRODUCTION: Limited information exists on patients with suspected coronavirus disease 2019 (COVID-19) who return to the emergency department (ED) during the first wave. In this study we aimed to identify predictors of ED return within 72 hours for patients with suspected COVID-19. METHODS: Incorporating data from 14 EDs within an integrated healthcare network in the New York metropolitan region from March 2-April 27, 2020, we analyzed this data on predictors for a return ED visit-including demographics, comorbidities, vital signs, and laboratory results. RESULTS: In total, 18,599 patients were included in the study. The median age was 46 years old [interquartile range 34-58]), 50.74% were female, and 49.26% were male. Overall, 532 (2.86%) returned to the ED within 72 hours, and 95.49% were admitted at the return visit. Of those tested for COVID-19, 59.24% (4704/7941) tested positive. Patients with chief complaints of "fever" or "flu" or a history of diabetes or renal disease were more likely to return at 72 hours. Risk of return increased with persistently abnormal temperature (odds ratio [OR] 2.43, 95% CI 1.8-3.2), respiratory rate (2.17, 95% CI 1.6-3.0), and chest radiograph (OR 2.54, 95% CI 2.0-3.2). Abnormally high neutrophil counts, low platelet counts, high bicarbonate values, and high aspartate aminotransferase levels were associated with a higher rate of return. Risk of return decreased when discharged on antibiotics (OR 0.12, 95% CI 0.0-0.3) or corticosteroids (OR 0.12, 95% CI 0.0-0.9). CONCLUSION: The low overall return rate of patients during the first COVID-19 wave indicates that physicians' clinical decision-making successfully identified those acceptable for discharge.

Race, ethnicity, sex, and socioeconomic disparities in anticoagulation for atrial fibrillation: A narrative review of contemporary literature.

Atrial fibrillation (AF) is the most prevalent arrhythmia in the United States and is responsible for 1 in 7 ischemic strokes. While anticoagulation is effective at preventing strokes, prior work has highlighted significant disparities in anticoagulation prescribing. Furthermore, racial, ethnic, sex, and socioeconomic disparities in AF outcomes have been described. As such, we aimed to review recent data on disparities with respect to anticoagulation for AF published between January 2018 and February 2021. The search string consisted of 7 phrases that combined AF, anticoagulation, and disparities involving sex, race, ethnicity, income, socioeconomic status (SES), and access to care and identified 13 relevant articles. The aggregate data demonstrated that Black patients were less likely to be prescribed anticoagulation than patients of other racial/ethnic groups. Additionally, Black patients were more likely to be prescribed warfarin instead of direct oral anticoagulants (DOACs) despite evidence that DOACs are safer and better tolerated. Lower-income patients and patients with less education were also less likely to receive DOACs. Some studies found that women were less likely to be anticoagulated than men even when their estimated stroke risk was higher, although other studies did not show sex-based differences. Building upon prior work, our study demonstrates that racial and ethnic disparities have persisted in the management of AF. Additionally, we our work highlights that there are significant disparities in anticoagulation management for AF associated with sex, income, and education. More work is needed to identify mechanisms for these disparities and identify potential solutions to achieve pharmacoequity.

Novel Discharge Center for Transition of Care in Vulnerable Emergency Department Treat and Release Patients.

Introduction The majority of emergency department (ED) patients are discharged following evaluation and treatment. Most patients are recommended to follow up with a primary care provider (PCP) or specialist. However, there is considerable variation between providers and EDs in discharge process practices that might facilitate such follow-up (e.g., simply discharging patients with follow-up physician names/contact information vs. making appointments for patients). Patients who do not follow up with their PCPs or specialists are more likely to be readmitted within 30 days than those who do. Furthermore, vulnerable patients have difficulty arranging transitional care appointments due to poor health literacy, inadequate insurance, appointment availability, and self-efficacy. Our innovative ED discharge process utilizes an Emergency Department Discharge Center (EDDC) staffed by ED Care Coordinators and assists patients with scheduling post-discharge appointments to improve rates of follow-up with outpatient providers. This study describes the structure and activities of the EDDC, characterizes the EDDC patient population, and demonstrates the volume and specialties of appointments scheduled by EDDC Care Coordinators. The impact of the EDDC on operational metrics (72-hour returns, 30-day admissions, and length-of-stay [LOS]) and the impact of the EDDC on patient satisfaction are evaluated. Methods The Long Island Jewish Medical Center (LIJMC) EDDC is an intervention developed in July 2020 within a 583-bed urban hospital serving a racially, ethnically, and socio-economically diverse population, with many patients having limited access to healthcare. Data from the Emergency Medicine Service Line (EMSL), an ED Care Coordinator database, and manual chart review were collected from July 2020 to July 2021 to examine the impact of the EDDC on 72-hour returns, 30-day admissions, and Press Ganey's® "likelihood to recommend ED" score (a widely used patient satisfaction survey question). The EDDC pilot cohort was compared to non-EDDC discharged patients during the same period. Results In unadjusted analysis, EDDC patients were moderately less likely to return to the ED within 72 hours (5.3% vs. 6.5%; p = 0.0044) or be admitted within 30 days (3.4% vs. 4.2%). The program was particularly beneficial for uninsured and elderly patients. For both EDDC and non-EDDC patients, most revisits and 30-day admissions were for the same chief complaint as the index visit. The length-of-stay increased by ~10 minutes with no impact on satisfaction with ED visits. Musculoskeletal conditions (~20%) and specialties (~15%) were the most commonly represented. Approximately 10% of referrals were to obtain a PCP. Nearly 90% were to new providers or specialties. Most scheduled appointments occurred within a week.  Conclusion This novel EDDC program, developed to facilitate outpatient follow-up for discharged ED patients, produced a modest but statistically significant difference in 72-hour returns and 30-day admissions for patients with EDDC-scheduled appointments vs. those referred to outpatient providers using the standard discharge process. ED LOS increased by ~10 minutes for EDDC vs. non-EDDC patients, with no difference in satisfaction. Future analyses will investigate impacts on 72-hour returns, 30-day admissions, LOS, and satisfaction after adjusting for characteristics such as age, insurance, having a PCP, and whether the scheduled appointment was attended.

Electrical Stimulation Increases the Secretion of Cardioprotective Extracellular Vesicles from Cardiac Mesenchymal Stem Cells.

Clinical trials have shown that electric stimulation (ELSM) using either cardiac resynchronization therapy (CRT) or cardiac contractility modulation (CCM) approaches is an effective treatment for patients with moderate to severe heart failure, but the mechanisms are incompletely understood. Extracellular vesicles (EV) produced by cardiac mesenchymal stem cells (C-MSC) have been reported to be cardioprotective through cell-to-cell communication. In this study, we investigated the effects of ELSM stimulation on EV secretion from C-MSCs (C-MSCELSM). We observed enhanced EV-dependent cardioprotection conferred by conditioned medium (CM) from C-MSCELSM compared to that from non-stimulated control C-MSC (C-MSCCtrl). To investigate the mechanisms of ELSM-stimulated EV secretion, we examined the protein levels of neutral sphingomyelinase 2 (nSMase2), a key enzyme of the endosomal sorting complex required for EV biosynthesis. We detected a time-dependent increase in nSMase2 protein levels in C-MSCELSM compared to C-MSCCtrl. Knockdown of nSMase2 in C-MSC by siRNA significantly reduced EV secretion in C-MSCELSM and attenuated the cardioprotective effect of CM from C-MSCELSM in HL-1 cells. Taken together, our results suggest that ELSM-mediated increases in EV secretion from C-MSC enhance the cardioprotective effects of C-MSC through an EV-dependent mechanism involving nSMase2.