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.

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

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.

scholarly journals Patient and clinical characteristics of heart failure patients concomitantly prescribed SGLT2 inhibitors and sacubitril/valsartan, a database cohort study using the Optum electronic health record data

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
E Loefroth ◽  
M Hughes ◽  
Y Shi ◽  
Y Wang ◽  
C Proudfoot ◽  
...  
Keyword(s):  
Heart Failure ◽  
Electronic Health Record ◽  
Clinical Characteristics ◽  
Funding Source ◽  
Health Record ◽  
Private Company ◽  
Electronic Health Record Data ◽  
Neprilysin Inhibitor ◽  
Dipeptidyl Peptidase 4 Inhibitors ◽  
Electronic Health

Abstract Background and purpose Sacubitril/valsartan (sac/val), an angiotensin receptor neprilysin inhibitor, reduces the risk for cardiovascular (CV) death or hospitalization for heart failure (HF) in HF with reduced ejection fraction (HFrEF). Sodium-glucose cotransporter-2 inhibitors (SGLT2i) are approved in patients with type 2 diabetes (T2D) and have shown to reduce the CV risk in T2D patients with established CV or at risk of CV disease. The SGLT2i dapagliflozin has shown to improve outcomes in patients with chronic HFrEF, with or without T2D, when used in addition to standard of care including sac/val. As the use of SGLT2i in HF evolves, and given the large overlap of HF and T2D populations, it is of interest to understand the population with concomitant use of sac/val and SGLT2i. This study describes the clinical characteristics of patients treated concomitantly with sac/val and SGLT2i or concomitantly with sac/val and dipeptidyl peptidase-4 inhibitors (DPP4i) or glucagon-like peptide-1 receptor agonists (GLP1), two comparable second line anti-diabetic drug classes. Methods This retrospective non-interventional study describes two mutually exclusive adult patient cohorts diagnosed with HF and T2D concomitantly prescribed sac/val and SGLT2i (cohort 1), or concomitantly prescribed sac/val and DDP4i/GLP1 (cohort 2). The index date was defined as the first date of concomitant use with prescriptions overlapping a minimum of 21 days. Patients were identified any time between 1/1/2015 and 30/6/2019 in the Optum® de-identified electronic health record (EHR) data from providers across the continuum of care. Results 2.3 million HF patients were identified, and 41.6% had a T2D diagnosis. 560 patients were concomitantly prescribed sac/val and SGLT2i (cohort 1) and 1,566 concomitantly sac/val and DDP4/GLP1 (cohort 2). There was a higher proportion of females in cohort 2 (35.0% vs 27.9%). Mean age was higher in cohort 2 (66.4 vs 61.4 years). The mean estimated glomerular filtration rate was 85.93 (SD 23.43) ml/min/1.73m2 (cohort 1) and 72.10 (Std. 27.11) ml/min/1.73m2 (cohort 2). The proportion of stage 3 CKD (<60 to >30 ml/min/1.73m2) was 11.8% (cohort 1) and 24.4% (cohort 2). Mean systolic blood pressure was similar, 120 mmHg (cohort 1) and 122 mmHg (cohort 2). Mean hemoglobin was 13.60 g/dl (cohort 1) and 12.43 g/dl (cohort 2). Median (IQR) NT-proBNP differed between the two cohorts, 914 (2154) pg/ml (cohort 1) and 2,290 (5,301) pg/ml (cohort 2) but with complete values available in only 17.7 and 19.0% of each cohort. Conclusions This descriptive analysis of concomitant prescription of sac/val and SGLT2i or DPP4/GLP1 highlights differences in the clinical characteristics between the two cohorts. The patients treated with sac/val and SGLT2i start with a more favorable clinical profile compared to the patients treated with sac/val and DPP4/GLP1. Further analyses are needed to determine if these differences are driven by age, gender or other factors. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Novartis Pharma AG

scholarly journals Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review

2020 ◽  
Vol 27 (7) ◽  
pp. 1173-1185 ◽  
Author(s):  
Seyedeh Neelufar Payrovnaziri ◽  
Zhaoyi Chen ◽  
Pablo Rengifo-Moreno ◽  
Tim Miller ◽  
Jiang Bian ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Record ◽  
Scoping Review ◽  
Real World ◽  
Research Trend ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Explainable Artificial Intelligence ◽  
Record Data ◽  
Electronic Health

Abstract Objective To conduct a systematic scoping review of explainable artificial intelligence (XAI) models that use real-world electronic health record data, categorize these techniques according to different biomedical applications, identify gaps of current studies, and suggest future research directions. Materials and Methods We searched MEDLINE, IEEE Xplore, and the Association for Computing Machinery (ACM) Digital Library to identify relevant papers published between January 1, 2009 and May 1, 2019. We summarized these studies based on the year of publication, prediction tasks, machine learning algorithm, dataset(s) used to build the models, the scope, category, and evaluation of the XAI methods. We further assessed the reproducibility of the studies in terms of the availability of data and code and discussed open issues and challenges. Results Forty-two articles were included in this review. We reported the research trend and most-studied diseases. We grouped XAI methods into 5 categories: knowledge distillation and rule extraction (N = 13), intrinsically interpretable models (N = 9), data dimensionality reduction (N = 8), attention mechanism (N = 7), and feature interaction and importance (N = 5). Discussion XAI evaluation is an open issue that requires a deeper focus in the case of medical applications. We also discuss the importance of reproducibility of research work in this field, as well as the challenges and opportunities of XAI from 2 medical professionals’ point of view. Conclusion Based on our review, we found that XAI evaluation in medicine has not been adequately and formally practiced. Reproducibility remains a critical concern. Ample opportunities exist to advance XAI research in medicine.

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