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

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.

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Abstract 303: Inpatient Sleep Interruptions Among Patients With Congestive Heart Failure

Circulation Cardiovascular Quality and Outcomes ◽

10.1161/hcq.13.suppl_1.303 ◽

2020 ◽

Vol 13 (Suppl_1) ◽

Author(s):

Evan Claggett ◽

Rachel H Krallman ◽

Delaney Feldeisen ◽

Daniel G Montgomery ◽

Kim Eagle ◽

...

Keyword(s):

Heart Failure ◽

Congestive Heart Failure ◽

Electronic Health Record ◽

Sample Size ◽

Health Record ◽

Post Discharge ◽

Electronic Health Record Data ◽

Care Clinic ◽

Electronic Health ◽

Poor Outcomes

Background: The effects of sleep deprivation are vast, ranging from increased stress responses, to lowered immunity and delayed wound healing. However, sleep disruptions are common in the inpatient setting. This study sought to quantify the number and frequency of inpatient sleep disturbances and analyze post-discharge outcomes (emergency department visit, readmission, death) among congestive heart failure (CHF) patients. Methods: Data were collected retrospectively from 30 randomly selected patients admitted for CHF and referred to a cardiac transitional care clinic from 2014 to 2017. Each night over the course of the hospitalization was broken into 12 one-hour intervals (1900-0659 hours), and the electronic health record was examined for 20 variables indicative of sleep disruption (e.g. vitals taken, medications dispensed, wound care) (Figure 1). Demographics and outcomes were compared between high (above median) and low (below median) groups for average number of nightly interval interruptions and average longest uninterrupted sleep interval (LUSI). Results: On average, patients had a length of admission of 5.4 nights, a LUSI of 2.9 hours (range: 1-4), and 6.3 disruptions between 1900-0659 hours (range: 3-8). The readmission rates for the total population were 23% at 30 days and 63% at 180 days. No significant differences were seen in demographics or outcomes up to 180 days post-discharge when comparing high and low patient groups in either average nightly interval interruptions or average LUSI. Conclusion: Although no differences were seen between groups, the majority of patients had poor outcomes (23% were readmitted at 30 days; 63% at 180 days) as well as poor sleep during their admission. The lack of sleep across the entire patient population may be contributing to the poor outcomes observed. Many of the variables reviewed (e.g. vitals taken, medications dispensed, etc.) had potentially elective timing, which suggests actionable changes to the inpatient process may be possible to improve sleep quantity and quality. This was an exploratory pilot study to determine the ability to use electronic health record data for this purpose. As such, the sample size was too small to detect differences. A larger sample size is needed to better understand the extent to which sleep disruptions impact patient outcomes.

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

Circulation Heart Failure ◽

10.1161/circheartfailure.120.007462 ◽

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.

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