Early Serial Assessment of Aggregate Vasoactive Support and Mortality in Cardiogenic Shock: Insights from the Critical Care Cardiology Trials Network Registry

Background: Associations of early changes in vasoactive support with cardiogenic shock (CS) mortality remain incompletely defined. Methods: The Critical Care Cardiology Trials Network is a multicenter registry of cardiac intensive care units (CICUs). Patients admitted with CS (2018-2023) had vasoactive dosing assessed at 4 and 24 hours (h) from CICU admission and quantified by the vasoactive-inotropic score (VIS). Prognostic associations of VIS at both timepoints, as well as change in VIS from 4h to 24h, were examined. Interaction testing was performed by mechanical circulatory support (MCS) status. Results: Among 3,665 patients, 82% had a change in VIS <10, with 7% and 11% having a ≥10point increase and decrease from 4h to 24h, respectively. The 4h and 24h VIS were each associated with CICU mortality (13%- 45% and 11%-73% for VIS <10 to ≥40, respectively; ptrend <0.0001 for each). Stratifying by the 4h VIS, changes in VIS from 4h to 24h had a graded association with mortality, ranging from a 2-to->4-fold difference in mortality comparing those with a ≥10-point increase to a ≥10-point decrease in VIS (p-trend <0.0001). The change in VIS alone provided good discrimination of CICU mortality (C-statistic 0.72 [95% CI 0.70-0.75]), and improved discrimination of the 24h SOFA score (0.76 [95% CI 0.74-0.78] from 0.72 [95% CI 0.69-0.74]) and the clinician-assessed SCAI stage (0.77 [95% CI 0.75-0.79] from 0.72 [95% CI 0.70-0.74]). Although present in both groups, the mortality risk associated with VIS was attenuated in patients managed with vs. without MCS (OR per 10-point higher 24h VIS: 1.36 [1.23-1.49] vs. 1.84 [1.69-2.01]; p-interaction<0.0001). Conclusions: Early changes in the magnitude of vasoactive support in CS are associated with a gradient of risk for mortality. These data suggest that early VIS trajectory may improve CS prognostication, with potential to be leveraged for clinical decision-making and research applications in CS.

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.

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.

Predicting Malignant Ventricular Arrhythmias Using Real-Time Remote Monitoring

BACKGROUND

Although implantable cardioverter-defibrillator (ICD) therapies are associated with increased morbidity and mortality, the prediction of malignant ventricular arrhythmias has remained elusive.

OBJECTIVES

The purpose of this study was to evaluate whether daily remote-monitoring data may predict appropriate ICD therapies for ventricular tachycardia or ventricular fibrillation.

METHODS

This was a post hoc analysis of IMPACT (Randomized trial of atrial arrhythmia monitoring to guide anticoagulation in patients with implanted defibrillator and cardiac resynchronization devices), a multicenter, randomized, controlled trial of 2,718 patients evaluating atrial tachyarrhythmias and anticoagulation for patients with heart failure and ICD or cardiac resynchronization therapy with defibrillator devices. All device therapies were adjudicated as either appropriate (to treat ventricular tachycardia or ventricular fibrillation) or inappropriate (all others). Remote monitoring data in the 30 days before device therapy were utilized to develop separate multivariable logistic regression and neural network models to predict appropriate device therapies.

RESULTS

A total of 59,807 device transmissions were available for 2,413 patients (age 64 ± 11 years, 26% women, 64% ICD). Appropriate device therapies (141 shocks, 10 antitachycardia pacing) were delivered to 151 patients. Logistic regression identified shock lead impedance and ventricular ectopy as significantly associated with increased risk of appropriate device therapy (sensitivity 39%, specificity 91%, AUC: 0.72). Neural network modeling yielded significantly better (P < 0.01 for comparison) predictive performance (sensitivity 54%, specificity 96%, AUC: 0.90), and also identified patterns of change in atrial lead impedance, mean heart rate, and patient activity as predictors of appropriate therapies.

CONCLUSIONS

Daily remote monitoring data may be utilized to predict malignant ventricular arrhythmias in the 30 days before device therapies. Neural networks complement and enhance conventional approaches to risk stratification.

Epidemiology and Management of ST-Segment-Elevation Myocardial Infarction in Patients With COVID-19: A Report From the American Heart Association COVID-19 Cardiovascular Disease Registry

Background Early reports from the COVID-19 pandemic identified coronary thrombosis leading to ST-segment-elevation myocardial infarction (STEMI) as a complication of COVID-19 infection. However, the epidemiology of STEMI in patients with COVID-19 is not well characterized. We sought to determine the incidence, diagnostic and therapeutic approaches, and outcomes in STEMI patients hospitalized for COVID-19. Methods and Results Patients with data on presentation ECG and in-hospital myocardial infarction were identified from January 14, 2020 to November 30, 2020, from 105 sites participating in the American Heart Association COVID-19 Cardiovascular Disease Registry. Patient characteristics, resource use, and clinical outcomes were summarized and compared based on the presence or absence of STEMI. Among 15 621 COVID-19 hospitalizations, 54 (0.35%) patients experienced in-hospital STEMI. Among patients with STEMI, the majority (n=40, 74%) underwent transthoracic echocardiography, but only half (n=27, 50%) underwent coronary angiography. Half of all patients with COVID-19 and STEMI (n=27, 50%) did not undergo any form of primary reperfusion therapy. Rates of all-cause shock (47% versus 14%), cardiac arrest (22% versus 4.8%), new heart failure (17% versus 1.4%), and need for new renal replacement therapy (11% versus 4.3%) were multifold higher in patients with STEMI compared with those without STEMI (P<0.050 for all). Rates of in-hospital death were 41% in patients with STEMI, compared with 16% in those without STEMI (P<0.001). Conclusions STEMI in hospitalized patients with COVID-19 is rare but associated with poor in-hospital outcomes. Rates of coronary angiography and primary reperfusion were low in this population of patients with STEMI and COVID-19. Adaptations of systems of care to ensure timely contemporary treatment for this population are needed.

Natural language processing for the assessment of cardiovascular disease comorbidities: The cardio-Canary comorbidity project

Objective: Accurate ascertainment of comorbidities is paramount in clinical research. While manual adjudication is labor‐intensive and expensive, the adoption of electronic health records enables computational analysis of free‐text documentation using natural language processing (NLP) tools.

Hypothesis: We sought to develop highly accurate NLP modules to assess for the presence of five key cardiovascular comorbidities in a large electronic health record system.

Methods: One‐thousand clinical notes were randomly selected from a cardiovascular registry at Mass General Brigham. Trained physicians manually adjudicated these notes for the following five diagnostic comorbidities: hypertension, dyslipidemia, diabetes, coronary artery disease, and stroke/transient ischemic attack. Using the open‐source Canary NLP system, five separate NLP modules were designed based on 800 “training‐set” notes and validated on 200 “test‐set” notes.

Results: Across the five NLP modules, the sentence‐level and note‐level sensitivity, specificity, and positive predictive value was always greater than 85% and was most often greater than 90%. Accuracy tended to be highest for conditions with greater diagnostic clarity (e.g. diabetes and hypertension) and slightly lower for conditions whose greater diagnostic challenges (e.g. myocardial infarction and embolic stroke) may lead to less definitive documentation.

Conclusion: We designed five open‐source and highly accurate NLP modules that can be used to assess for the presence of important cardiovascular comorbidities in free‐text health records. These modules have been placed in the public domain and can be used for clinical research, trial recruitment and population management at any institution as well as serve as the basis for further development of cardiovascular NLP tools.

Association of Socioeconomic Disadvantage With Long-term Mortality After Myocardial Infarction: The Mass General Brigham YOUNG-MI Registry

Importance

Socioeconomic disadvantage is associated with poor health outcomes. However, whether socioeconomic factors are associated with post-myocardial infarction (MI) outcomes in younger patient populations is unknown.

Objective

To evaluate the association of neighborhood-level socioeconomic disadvantage with long-term outcomes among patients who experienced an MI at a young age.

Design, Setting, and Participants

This cohort study analyzed patients in the Mass General Brigham YOUNG-MI Registry (at Brigham and Women's Hospital and Massachusetts General Hospital in Boston, Massachusetts) who experienced an MI at or before 50 years of age between January 1, 2000, and April 30, 2016. Each patient's home address was mapped to the Area Deprivation Index (ADI) to capture higher rates of socioeconomic disadvantage. The median follow-up duration was 11.3 years. The dates of analysis were May 1, 2020, to June 30, 2020.

Exposures

Patients were assigned an ADI ranking according to their home address and then stratified into 3 groups (least disadvantaged group, middle group, and most disadvantaged group).

Main Outcomes and Measures

The outcomes of interest were all-cause and cardiovascular mortality. Cause of death was adjudicated from national registries and electronic medical records. Cox proportional hazards regression modeling was used to evaluate the association of ADI with all-cause and cardiovascular mortality.

Results

The cohort consisted of 2097 patients, of whom 2002 (95.5%) with an ADI ranking were included (median [interquartile range] age, 45 [42-48] years; 1607 male individuals [80.3%]). Patients in the most disadvantaged neighborhoods were more likely to be Black or Hispanic, have public insurance or no insurance, and have higher rates of traditional cardiovascular risk factors such as hypertension and diabetes. Among the 1964 patients who survived to hospital discharge, 74 (13.6%) in the most disadvantaged group compared with 88 (12.6%) in the middle group and 41 (5.7%) in the least disadvantaged group died. Even after adjusting for a comprehensive set of clinical covariates, higher neighborhood disadvantage was associated with a 32% higher all-cause mortality (hazard ratio, 1.32; 95% CI, 1.10-1.60; P = .004) and a 57% higher cardiovascular mortality (hazard ratio, 1.57; 95% CI, 1.17-2.10; P = .003).

Conclusions and Relevance

This study found that, among patients who experienced an MI at or before age 50 years, socioeconomic disadvantage was associated with higher all-cause and cardiovascular mortality even after adjusting for clinical comorbidities. These findings suggest that neighborhood and socioeconomic factors have an important role in long-term post-MI survival.

Study of lipoprotein(a) and its impact on atherosclerotic cardiovascular disease: Design and rationale of the Mass General Brigham Lp(a) Registry

Lipoprotein(a) [Lp(a)] is independently associated with atherosclerotic cardiovascular disease and calcific aortic valve stenosis. Elevated Lp(a) affects approximately one in five individuals and meaningfully contributes to the residual cardiovascular risk in individuals with otherwise well-controlled risk factors. With targeted therapies in the therapeutic pipeline, there is a need to further characterize the clinical phenotypes and outcomes of individuals with elevated levels of this unique biomarker. The Mass General Brigham Lp(a) Registry will be built from the longitudinal electronic health record of two large academic medical centers in Boston, Massachusetts, to develop a detailed cohort of patients who have had their Lp(a) measured. In combination with structured data sources, clinical documentation will be analyzed using natural language processing techniques to accurately characterize baseline characteristics. Important outcome measures including all-cause mortality, cardiovascular mortality, and cardiovascular events will be available for analysis. Approximately 30 000 patients who have had their Lp(a) tested within the Mass General Brigham system from January 2000 to July 2019 will be included in the registry. This large Lp(a) cohort will provide meaningful observational data regarding the differential risk associated with Lp(a) values and cardiovascular disease. With a new frontier of targeted Lp(a) therapies on the horizon, the Mass General Brigham Lp(a) Registry will help provide a deeper understanding of Lp(a)'s role in long term cardiovascular outcomes.