scholarly journals Predictive Accuracy of Heart Failure-Specific Risk Equations in an Electronic Health Record-Based Cohort

2020 ◽  
Vol 13 (11) ◽  
Author(s):  
Aakash Bavishi ◽  
Matthew Bruce ◽  
Hongyan Ning ◽  
Priya M. Freaney ◽  
Peter Glynn ◽  
...  
Keyword(s):  
Heart Failure ◽  
Electronic Health Record ◽  
Real World ◽  
Predictive Accuracy ◽  
Risk Scores ◽  
Health Record ◽  
Public Health Burden ◽  
Specific Risk ◽  
Electronic Health

Background: Guidelines recommend identification of individuals at risk for heart failure (HF). However, implementation of risk-based prevention strategies requires validation of HF-specific risk scores in diverse, real-world cohorts. Therefore, our objective was to assess the predictive accuracy of the Pooled Cohort Equations to Prevent HF within a primary prevention cohort derived from the electronic health record. Methods: We retrospectively identified patients between the ages of 30 to 79 years in a multi-center integrated healthcare system, free of cardiovascular disease, with available data on HF risk factors, and at least 5 years of follow-up. We applied the Pooled Cohort Equations to Prevent HF tool to calculate sex and race-specific 5-year HF risk estimates. Incident HF was defined by the International Classification of Diseases codes. We assessed model discrimination and calibration, comparing predicted and observed rates for incident HF. Results: Among 31 256 eligible adults, mean age was 51.4 years, 57% were women and 11% Black. Incident HF occurred in 568 patients (1.8%) over 5-year follow-up. The modified Pooled Cohort Equations to Prevent HF model for 5-year risk prediction of HF had excellent discrimination in White men (C-statistic 0.82 [95% CI, 0.79–0.86]) and women (0.82 [0.78–0.87]) and adequate discrimination in Black men (0.69 [0.60–0.78]) and women (0.69 [0.52–0.76]). Calibration was fair in all race-sex subgroups (χ 2 <20). Conclusions: A novel sex- and race-specific risk score predicts incident HF in a real-world, electronic health record-based cohort. Integration of HF risk into the electronic health record may allow for risk-based discussion, enhanced surveillance, and targeted preventive interventions to reduce the public health burden of HF.

Hysterectomy utilization among newly diagnosed endometrial cancer patients by body mass index category: A real-world analysis using a claims and electronic health record database.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e17112-e17112
Author(s):  
Debra E. Irwin ◽  
Ellen Thiel
Keyword(s):  
Endometrial Cancer ◽  
Electronic Health Record ◽  
Real World ◽  
Patient Population ◽  
Morbidly Obese ◽  
Health Record ◽  
Newly Diagnosed ◽  
Data Set ◽  
Electronic Health

e17112 Background: For endometrial cancer (EC), laparoscopic hysterectomy (LH) is an effective, minimally invasive surgical treatment; however, this approach may not be recommended for obese patients due to increased risk for complications. Methods: This retrospective study utilized insurance claims linked to electronic health record (EHR) data contained in the IBM MarketScan Explorys Claims-EHR Data Set. Newly diagnosed EC patients (1/1/2007 - 6/30/2017) with continuous enrollment during a 12-month baseline and 6-month follow-up period were selected. Patients were stratified into four BMI subgroups based on baseline BMI on the EHR: normal or underweight (BMI < 25), overweight (BMI 25- < 30), obese (BMI 30- < 40), morbidly obese (BMI > 40), and were required to have had a hysterectomy within the follow-up period. Emergency room visits and rehospitalization within 30 days of hysterectomy were measured. Results: A total of 1,090 newly-diagnosed EC patients met the selection criteria, of whom, 16% were normal/underweight, 19% were overweight, 39% were obese, and 26% were morbidly obese. The proportion of patients receiving LH increased as BMI category increased (Table 1). Among those with LH between 6% and 15% had an ER visit or rehospitalization in 30 days, and rates were higher among other hysterectomy modalities. Conclusions: This real-world analysis shows that LH is utilized in a high proportion of morbidly obese EC patients, despite that it is frequently deemed infeasible in this patient population. Although the rate of ER visits and rehospitalization is lower among LH patients than those undergoing traditional hysterectomy across all BMI strata, further research is needed to determine the optimal patient population to receive LH.[Table: see text]

Abstract 255: Electronic Health Record Alerts Decreased Non-Steroidal Anti-Inflammatory Drug Prescriptions in Patients With Congestive Heart Failure: A Quality Improvement Initiative

2020 ◽  
Vol 13 (Suppl_1) ◽  
Author(s):  
Louis T Vincent ◽  
Mark Jacobs ◽  
neal olarte ◽  
Fahim Pyarali ◽  
Jonathan Salter ◽  
...  
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Quality Improvement ◽  
Relative Risk ◽  
Electronic Health Record ◽  
Health Record ◽  
Secondary Outcomes ◽  
Anti Inflammatory ◽  
Electronic Health

Background: Non-steroidal anti-inflammatory drugs (NSAIDs) are associated with increased morbidity and mortality in patients with congestive heart failure (CHF). Current guidelines recommend discontinuation of NSAIDs in all patients with CHF, but in clinical practice, many patients remain with active prescriptions. We sought to reduce prevalence of active NSAID prescriptions in a veteran patient population with CHF by implementing an electronic health record (EHR) alert advising against NSAID prescriptions. Methods: This single-center quality improvement project was initiated at the Miami Veterans Affairs Medical Center. In patients with any diagnosis of heart failure, when a provider attempted to initiate or renew an NSAID prescription, an EHR alert was activated warning of the potential harms. Providers were required to acknowledge the alert prior to electronic signature. NSAIDs activating the alert included celecoxib, ibuprofen, diclofenac, and naproxen. The primary outcome of interest was the number of patients with CHF and active NSAID prescriptions, assessed 6 months before and after alert implementation. Analysis of the combined long-term secondary outcomes of hospitalization for acute decompensated heart failure and all-cause mortality is ongoing. Relative risk reductions with statistical significance determined by p<0.05 were calculated for both primary and secondary outcomes. Results: A total of 144 patients were included in this study. In the 6 months preceding alert implementation, NSAIDs were discontinued in 30.9% (17/55) of patients. At 6 months follow-up after EHR alert initiation, NSAIDs were discontinued or left to expire in 65.2% (58/89) of patients in which the EHR alert was activated. The relative risk of patients with CHF being prescribed NSAIDs was significantly reduced by 49.6% (relative risk=0.504; 95% confidence interval [0.361-0.704], p=0.0001). After intervention, death was reported in 3.2% of patients persisting on NSAID therapy, compared to 1.7% of patients that had NSAIDs discontinued (p=0.65). Conclusions: Implementation of an EHR alert advising of the harm of NSAIDs in patients with CHF in a veteran population has resulted in a statistically significant decrease in the number of active NSAID prescriptions. Further study with larger patient populations and extended follow-up will help determine whether these findings are sustainable and lead to a clinically significant reduction in mortality and hospitalizations.

Abstract 13797: Enabling Advanced Real-world Evidence in Heart Failure: A Pilot Study Defining Preferred Approaches to Electronic Health Record Data Use

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Dan Riskin ◽  
Keri L Monda ◽  
Ricardo Dent ◽  
A. Reshad Garan
Keyword(s):  
Heart Failure ◽  
Electronic Health Record ◽  
Real World ◽  
Patient Characteristics ◽  
Absolute Difference ◽  
Health Record ◽  
Reference Standard ◽  
Electronic Health Record Data ◽  
Real World Evidence ◽  
Electronic Health

Introduction: Real world evidence (RWE) is increasingly used for regulatory and market access decision-making. In heart failure (HF), typical structured datasets have limitations in data accuracy and identifying relevant patient characteristics. Understanding which characteristics require enhancement from unstructured data and how to validly apply extraction methods will improve the definition of complex patient cohorts. Hypothesis: Augmenting structured with unstructured electronic health record (EHR) data may overcome challenges in accurately identifying relevant HF patient characteristics. Methods: Using EHR data from 4,288 primary care encounters, 20 clinical concepts were defined a priori by 3 HF experts. A reference standard was generated through chart abstraction, with each record reviewed by at least two annotators. Inter-rater reliability (IRR) was measured by Cohen’s kappa. EHR structured data (EHR-S) extracted with traditional query techniques and EHR unstructured (EHR-U) data extracted with artificial intelligence (AI) technologies were tested for accuracy against the reference standard. Results: In EHR-S, recall ranged from 0-95.1% and precision from 52.9-100%. In EHR-U data processed using AI, recall ranged from 80.4-99.7% and precision from 82.3-100%. Results demonstrated a 45.1% absolute difference and 98.1% relative increase in F1-score (Table). Reference standard IRR was 95.3%. Conclusions: RWE credibility and applicability relies on accurate identification of a patient cohort. This study suggests that readily available data sources may not accurately identify patient phenotypes in HF. Novel means of using AI with EHR-U may improve such efforts, particularly for conditions and symptoms. This approach offers a pathway for defining highly accurate HF cohorts that may be useful in studies with narrowly defined or complex phenotypes, such as those where inclusion and exclusion criteria are specific and outcomes require validity.

Real-world evaluation of weight loss in patients with T2DM treated with canagliflozin (CANA) – An electronic health-record (EHR)-based study

2016 ◽  
Vol 120 ◽  
pp. S118-S119
Author(s):  
Patrick Lefebvre ◽  
Wing Chow ◽  
Dominic Pilon ◽  
Bruno Emond ◽  
Marie-Hélène Lafeuille ◽  
...  
Keyword(s):  
Weight Loss ◽  
Electronic Health Record ◽  
Real World ◽  
Health Record ◽  
Electronic Health

scholarly journals Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data

2019 ◽  
Author(s):  
Daniel M. Bean ◽  
James Teo ◽  
Honghan Wu ◽  
Ricardo Oliveira ◽  
Raj Patel ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Natural Language Processing ◽  
Natural Language ◽  
Electronic Health Record ◽  
Open Source ◽  
Language Processing ◽  
Risk Scores ◽  
Free Text ◽  
Health Record ◽  
Electronic Health

AbstractAtrial fibrillation (AF) is the most common arrhythmia and significantly increases stroke risk. This risk is effectively managed by oral anticoagulation. Recent studies using national registry data indicate increased use of anticoagulation resulting from changes in guidelines and the availability of newer drugs.The aim of this study is to develop and validate an open source risk scoring pipeline for free-text electronic health record data using natural language processing.AF patients discharged from 1st January 2011 to 1st October 2017 were identified from discharge summaries (N=10,030, 64.6% male, average age 75.3 ± 12.3 years). A natural language processing pipeline was developed to identify risk factors in clinical text and calculate risk for ischaemic stroke (CHA2DS2-VASc) and bleeding (HAS-BLED). Scores were validated vs two independent experts for 40 patients.Automatic risk scores were in strong agreement with the two independent experts for CHA2DS2-VASc (average kappa 0.78 vs experts, compared to 0.85 between experts). Agreement was lower for HAS-BLED (average kappa 0.54 vs experts, compared to 0.74 between experts).In high-risk patients (CHA2DS2-VASc ≥2) OAC use has increased significantly over the last 7 years, driven by the availability of DOACs and the transitioning of patients from AP medication alone to OAC. Factors independently associated with OAC use included components of the CHA2DS2-VASc and HAS-BLED scores as well as discharging specialty and frailty. OAC use was highest in patients discharged under cardiology (69%).Electronic health record text can be used for automatic calculation of clinical risk scores at scale. Open source tools are available today for this task but require further validation. Analysis of routinely-collected EHR data can replicate findings from large-scale curated registries.

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