Using electronic health records for population health sciences: a case study to evaluate the associations between changes in left ventricular ejection fraction and the built environment.

OBJECTIVE: Electronic health record (EHR) data linked with address-based metrics using geographic information systems (GIS) are emerging data sources in population health studies. This study examined this approach through a case study on the associations between changes in ejection fraction (EF) and the built environment among heart failure (HF) patients. MATERIALS AND METHODS: We identified 1287 HF patients with at least 2 left ventricular EF measurements that are minimally 1 year apart. EHR data were obtained at an academic medical center in New York for patients who visited between 2012 and 2017. Longitudinal clinical information was linked with address-based built environment metrics related to transportation, air quality, land use, and accessibility by GIS. The primary outcome is the increase in the severity of EF categories. Statistical analyses were performed using mixed-effects models, including a subgroup analysis of patients who initially had normal EF measurements. RESULTS: Previously reported effects from the built environment among HF patients were identified. Increased daily nitrogen dioxide concentration was associated with the outcome while controlling for known HF risk factors including sex, comorbidities, and medication usage. In the subgroup analysis, the outcome was significantly associated with decreased distance to subway stops and increased distance to parks. CONCLUSIONS: Population health studies using EHR data may drive efficient hypothesis generation and enable novel information technology-based interventions. The availability of more precise outcome measurements and home locations, and frequent collection of individual-level social determinants of health may further drive the use of EHR data in population health studies.

Development of novel machine learning model for right ventricular quantification on echocardiography-A multimodality validation study.

PURPOSE: Echocardiography (echo) is widely used for right ventricular (RV) assessment. Current techniques for RV evaluation require additional imaging and manual analysis; machine learning (ML) approaches have the potential to provide efficient, fully automated quantification of RV function. METHODS: An automated ML model was developed to track the tricuspid annulus on echo using a convolutional neural network approach. The model was trained using 7791 image frames, and automated linear and circumferential indices quantifying annular displacement were generated. Automated indices were compared to an independent reference of cardiac magnetic resonance (CMR) defined RV dysfunction (RVEF < 50%). RESULTS: A total of 101 patients prospectively underwent echo and CMR: Fully automated annular tracking was uniformly successful; analyses entailed minimal processing time (<1 second for all) and no user editing. Findings demonstrate all automated annular shortening indices to be lower among patients with CMR-quantified RV dysfunction (all P < .001). Magnitude of ML annular displacement decreased stepwise in relation to population-based tertiles of TAPSE, with similar results when ML analyses were localized to the septal or lateral annulus (all P ≤ .001). Automated segmentation techniques provided good diagnostic performance (AUC 0.69-0.73) in relation to CMR reference and compared to conventional RV indices (TAPSE and S') with high negative predictive value (NPV 84%-87% vs 83%-88%). Reproducibility was higher for ML algorithm as compared to manual segmentation with zero inter- and intra-observer variability and ICC 1.0 (manual ICC: 0.87-0.91). CONCLUSIONS: This study provides an initial validation of a deep learning system for RV assessment using automated tracking of the tricuspid annulus.

Utilizing electronic health data and machine learning for the prediction of 30-day unplanned readmission or all-cause mortality in heart failure.

Background: Existing risk assessment tools for heart failure (HF) outcomes use structured databases with static, single-timepoint clinical data and have limited accuracy. Objective: The purpose of this study was to develop a comprehensive approach for accurate prediction of 30-day unplanned readmission and all-cause mortality (ACM) that integrates clinical and physiological data available in the electronic health record system. Methods: Three predictive models for 30-day unplanned readmissions or ACM were created using an extreme gradient boosting approach: (1) index admission model; (2) index discharge model; and (3) feature-aggregated model. Performance was assessed by the area under the curve (AUC) metric and compared with that of the HOSPITAL score, a widely used predictive model for hospital readmission. Results: A total of 3774 patients with a primary billing diagnosis of HF were included (614 experienced the primary outcome), with 796 variables used in the admission and discharge models, and 2032 in the feature-aggregated model. The index admission model had AUC = 0.723, the index discharge model had AUC = 0.754, and the feature-aggregated model had AUC = 0.756 for prediction of 30-day unplanned readmission or ACM. For comparison, the HOSPITAL score had AUC = 0.666 (admission model: P = .093; discharge model: P = .022; feature aggregated: P = .012). Conclusion: These models predict risk of HF hospitalizations and ACM in patients admitted with HF and emphasize the importance of incorporating large numbers of variables in machine learning models to identify predictors for future investigation.

Clinical and Socioeconomic Predictors of Heart Failure Readmissions: A Review of Contemporary Literature.

Heart failure represents a clinical syndrome that results from a constellation of disease processes affecting myocardial function. Although recent studies have suggested a declining or stable incidence of heart failure, patients with heart failure continue to have high hospitalization and readmission rates, resulting in a substantial economic and public health burden. We searched PubMed and Google Scholar to identify published literature from 1998 through 2018 using the following keywords: heart failure, readmissions, predictors, prediction models, and interventions. Cited references were also used to identify relevant literature. Developments in the diagnosis and management of patients with heart failure have improved hospitalization and readmission rates in the past few decades. However, heart failure remains the most common cause of hospitalization in persons older than 65 years. As a result, given the enormous clinical and financial burden associated with heart failure readmissions on health care, there has been growing interest in the investigation of mechanisms aimed at improving outcomes and curtailing associated costs of care. Herein, we review the current literature on clinical and socioeconomic predictors of heart failure readmissions, briefly discussing limitations of existing strategies and providing an overview of current technology aimed at reducing hospitalizations.

Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification.

BACKGROUND: Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow. METHODS: A machine learning model was designed to track aortic valve borders based on neural network approaches. The model was trained in a derivation cohort encompassing 150 patients who underwent clinical PC-CMR then compared to manual and commercially-available automated segmentation in a prospective validation cohort. Further validation testing was performed in an external cohort acquired from a different site/CMR vendor. RESULTS: Among 190 coronary artery disease patients prospectively undergoing CMR on commercial scanners (84% 1.5T, 16% 3T), machine learning segmentation was uniformly successful, requiring no human intervention: Segmentation time was < 0.01 min/case (1.2 min for entire dataset); manual segmentation required 3.96 ± 0.36 min/case (12.5 h for entire dataset). Correlations between machine learning and manual segmentation-derived flow approached unity (r = 0.99, p < 0.001). Machine learning yielded smaller absolute differences with manual segmentation than did commercial automation (1.85 ± 1.80 vs. 3.33 ± 3.18 mL, p < 0.01): Nearly all (98%) of cases differed by ≤5 mL between machine learning and manual methods. Among patients without advanced mitral regurgitation, machine learning correlated well (r = 0.63, p < 0.001) and yielded small differences with cine-CMR stroke volume (∆ 1.3 ± 17.7 mL, p = 0.36). Among advanced mitral regurgitation patients, machine learning yielded lower stroke volume than did volumetric cine-CMR (∆ 12.6 ± 20.9 mL, p = 0.005), further supporting validity of this method. Among the external validation cohort (n = 80) acquired using a different CMR vendor, the algorithm yielded equivalently small differences (∆ 1.39 ± 1.77 mL, p = 0.4) and high correlations (r = 0.99, p < 0.001) with manual segmentation, including similar results in 20 patients with bicuspid or stenotic aortic valve pathology (∆ 1.71 ± 2.25 mL, p = 0.25). CONCLUSION: Fully automated machine learning PC-CMR segmentation performs robustly for aortic flow quantification - yielding rapid segmentation, small differences with manual segmentation, and identification of differential forward/left ventricular volumetric stroke volume in context of concomitant mitral regurgitation. Findings support use of machine learning for analysis of large scale CMR datasets.

Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging.

Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field.

Discharge Processes and 30-Day Readmission Rates of Patients Hospitalized for Heart Failure on General Medicine and Cardiology Services.

Given high rates of heart failure (HF) hospitalizations and widespread adoption of the hospitalist model, patients with HF are often cared for on General Medicine (GM) services. Differences in discharge processes and 30-day readmission rates between patients on GM and those on Cardiology during the contemporary hospitalist era are unknown. The present study compared discharge processes and 30-day readmission rates of patients with HF admitted on GM services and those on Cardiology services. We retrospectively studied 926 patients discharged home after HF hospitalization. The primary outcome was 30-day all-cause readmission after discharge from index hospitalization. Although 60% of patients with HF were admitted to Cardiology services, 40% were admitted to GM services. Prevalence of cardiovascular and noncardiovascular co-morbidities were similar between patients admitted to GM services and Cardiology services. Discharge summaries for patients on GM services were less likely to have reassessments of ejection fraction, new study results, weights, discharge vital signs, discharge physical examinations, and scheduled follow-up cardiologist appointments. In a multivariable regression analysis, patients on GM services were more likely to experience 30-day readmissions compared with those on Cardiology services (odds ratio 1.43 95% confidence interval [1.05 to 1.96], p = 0.02). In conclusion, outcomes are better among those admitted to Cardiology services, signaling the need for studies and interventions focusing on noncardiology hospital providers that care for patients with HF.

Patterns of scheduled follow-up appointments following hospitalization for heart failure: insights from an urban medical center in the United States.

OBJECTIVES: Although postdischarge outpatient follow-up appointments after a hospitalization for heart failure represent a potentially effective strategy to prevent heart failure readmissions, patterns of scheduled follow-up appointments upon discharge are poorly described. We aimed to characterize real-world patterns of scheduled follow-up appointments among adult patients with heart failure upon hospital discharge. PATIENTS AND METHODS: This was a retrospective cohort study performed at a large urban academic center in the United States among adults hospitalized with a principal diagnosis of congestive heart failure between January 1, 2013, and December 31, 2014. Patient demographics, administrative data, clinical parameters, echocardiographic indices, and scheduled postdischarge outpatient follow-up appointments were collected. RESULTS: Of the 796 patients hospitalized for heart failure, just over half of the cohort had a scheduled follow-up appointment upon discharge. Follow-up appointments were less likely among patients who were white and had heart failure with preserved ejection fraction and more likely among patients with Medicaid and chronic obstructive pulmonary disease. In an adjusted multivariable regression model, age ≥65 years was inversely associated with a scheduled follow-up appointment upon hospital discharge, despite higher rates of several cardiovascular and noncardiovascular comorbidities. CONCLUSION: Just half of the patients discharged home following a hospitalization for heart failure had a follow-up appointment scheduled, representing a missed opportunity to provide a recommended care transition intervention. Despite a greater burden of both cardiovascular and noncardiovascular comorbidities, older adults (age ≥65 years) were less likely to have a follow-up appointment scheduled upon discharge compared with younger adults, revealing a disparity that warrants further investigation.