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The cumulative incidence of systolic heart failure is similar in men and women. However, major prognostic differences exist between genders. We sought to measure gender differences in furosemide prescribing patterns for patients with preexisting heart failure with reduced ejection fraction (HFrEF) admitted with Stage C acute decompensation, regardless of the underlying cause. We conducted a single-center retrospective analysis of patients admitted between 2015 and 2018 for acute on chronic decompensated HFrEF. Primary outcomes were differences in initial furosemide dose, total dose over the first 24 hours of hospitalization, and total dose during the entire hospitalization between women and men. Secondary outcomes included acute kidney injury (AKI), intubation, noninvasive ventilation (NIV), and in-hospital 30-day and 1-year mortality. We studied 434 patients (31% female) with similar baseline characteristics. Females received significantly less furosemide compared to men for the initial dose, over the first 24 hours, and throughout their hospitalization. However, AKI was more prevalent in women versus men (p=0.008). Females admitted for acute on chronic decompensated HFrEF receive significantly less furosemide when compared to men, but developed more AKI prior to discharge.

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Background: Despite its high prevalence and clinical impact, research on peripheral artery disease (PAD) remains limited due to poor accuracy of billing codes. Ankle-brachial index (ABI) and toe-brachial index can be used to identify PAD patients with high accuracy within electronic health records. Methods: We developed a novel natural language processing (NLP) algorithm for extracting ABI and toe-brachial index values and laterality (right or left) from ABI reports. A random sample of 800 reports from 94 Veterans Affairs facilities during 2015 to 2017 was selected and annotated by clinical experts. We trained the NLP system using random forest models and optimized it through sequential iterations of 10-fold cross-validation and error analysis on 600 test reports and evaluated its final performance on a separate set of 200 reports. We also assessed the accuracy of NLP-extracted ABI and toe-brachial index values for identifying patients with PAD in a separate cohort undergoing ABI testing. Results: The NLP system had an overall precision (positive predictive value) of 0.85, recall (sensitivity) of 0.93, and F1 measure (accuracy) of 0.89 to correctly identify ABI/toe-brachial index values and laterality. Among 261 patients with ABI testing (49% PAD), the NLP system achieved a positive predictive value of 92.3%, sensitivity of 83.1%, and specificity of 93.1% to identify PAD when compared with a structured chart review. The above findings were consistent in a range of sensitivity analysis. Conclusions: We successfully developed and validated an NLP system for identifying patients with PAD within the Veterans Affairs electronic health record. Our findings have broad implications for PAD research and quality improvement.

Objectives: Develop a natural language processing (NLP) system for identification of patients with peripheral artery disease (PAD). Background: Despite its high prevalence and clinical impact, research on PAD remains limited due to poor accuracy of billing codes. Ankle and toe-brachial index (ABI, TBI) can be used to identify PAD patients with high accuracy using electronic health record (EHR) data. Methods: A random sample of 800 ABI test reports from 94 Veterans Affairs (VA) facilities during 2015-2017 were selected and annotated by clinical experts. We trained the NLP system using random forest models and optimized it through sequential iterations of 10-fold cross validation and error-analysis on 600 test reports and evaluated its final performance on a separate set of 200 reports. We also assessed the accuracy of NLP-extracted ABI and TBI values for identifying patients with PAD in a separate cohort undergoing ABI testing. Results: The NLP system had an overall precision (positive predictive value) of 0.85, recall (sensitivity) of 0.93 and F1-measure (overall accuracy) of 0.89 to correctly identify ABI/TBI values and laterality. Among 261 patients with ABI testing (49% PAD), the NLP system achieved a positive predictive value of 92.3%, sensitivity of 83.1% and specificity of 93.1% to identify PAD when compared to a structured chart review (Table). The above findings were consistent in a range of sensitivity analysis (Table). Conclusion: We successfully developed and validated an NLP system for identifying patients with PAD within the VA’s EHR. Our findings have broad implications for PAD research and quality improvement efforts.