Faculty Bibliography

BACKGROUND:Guideline-directed medical therapy (GDMT) dramatically improves outcomes in heart failure with reduced ejection fraction (HFrEF). Our goal was to examine GDMT use in community patients with newly diagnosed HFrEF. METHODS AND RESULTS/RESULTS:We performed a population-based, retrospective cohort study of all Olmsted County, Minnesota residents with newly diagnosed HFrEF (EF≤40%) 2007-2017. We excluded patients with contraindications to medication initiation. We examined use of beta blockers, HF beta blockers (metoprolol succinate, carvedilol, bisoprolol), ACEi/ARB/ARNI, and MRA in the first year after HFrEF diagnosis. We used Cox models to evaluate the association of being seen in a HF clinic with initiation of GDMT. From 2007-2017, 1160 patients were diagnosed with HFrEF (mean age 69.7 years, 65.6% men). Most eligible patients received beta blockers (92.6%) and ACEi/ARB/ARNI (87.0%) in the first year. However, only 63.8% of patients were treated with a HF beta blocker, and few received MRAs (17.6%). In models accounting for the role of HF clinic in initiation of these medications, being seen in a HF clinic was independently associated with initiation of new GDMT across all medication classes, with HR (95% CI) of 1.54 (1.15-2.06)for any beta blocker, 2.49 (1.95-3.20) for HF beta blockers, 1.97 (1.46-2.65) for ACEi/ARB/ARNI, and 2.14 (1.49-3.08) for MRAs. CONCLUSIONS:In this population-based study, most patients with newly diagnosed HFrEF received beta blockers and ACEi/ARB/ARNIs. GDMT use was higher in patients seen in a HF clinic, suggesting potential benefit of referral to a HF clinic for patients with newly diagnosed HFrEF.

BACKGROUND:National registries reveal significant gaps in medical therapy for patients with heart failure and reduced ejection fraction (HFrEF), but may not accurately (or fully) characterize the population eligible for therapy. OBJECTIVE:We developed an automated, electronic health record-based algorithm to identify HFrEF patients eligible for evidence-based therapy, and extracted treatment data to assess gaps in therapy in a large, diverse health system. METHODS:In this cross-sectional study of all NYU Langone Health outpatients with EF ≤ 40% on echocardiogram and an outpatient visit from 3/1/2019 to 2/29/2020, we assessed prescription of the following therapies: beta-blocker (BB), angiotensin converting enzyme inhibitor (ACE-I)/angiotensin receptor blocker (ARB)/angiotensin receptor neprilysin inhibitor (ARNI), and mineralocorticoid receptor antagonist (MRA). Our algorithm accounted for contraindications such as medication allergy, bradycardia, hypotension, renal dysfunction, and hyperkalemia. RESULTS:We electronically identified 2732 patients meeting inclusion criteria. Among those eligible for each medication class, 84.8% and 79.7% were appropriately prescribed BB and ACE-I/ARB/ARNI, respectively, while only 23.9% and 22.7% were appropriately prescribed MRA and ARNI, respectively. In adjusted models, younger age, cardiology visit and lower EF were associated with increased prescribing of medications. Private insurance and Medicaid were associated with increased prescribing of ARNI (OR = 1.40, 95% CI = 1.02-2.00; and OR = 1.70, 95% CI = 1.07-2.67). CONCLUSIONS:We observed substantial shortfalls in prescribing of MRA and ARNI therapy to ambulatory HFrEF patients. Subspecialty care setting, and Medicaid insurance were associated with higher rates of ARNI prescribing. Further studies are warranted to prospectively evaluate provider- and policy-level interventions to improve prescribing of these evidence-based therapies.

Background Acute myocardial infarctions are increasingly common among young adults. We investigated sex and racial differences in the evaluation of chest pain (CP) among young adults presenting to the emergency department. Methods and Results Emergency department visits for adults aged 18 to 55 years presenting with CP were identified in the National Hospital Ambulatory Medical Care Survey 2014 to 2018, which uses stratified sampling to produce national estimates. We evaluated associations between sex, race, and CP management before and after multivariable adjustment. We identified 4152 records representing 29 730 145 visits for CP among young adults. Women were less likely than men to be triaged as emergent (19.1% versus 23.3%, respectively, P<0.001), to undergo electrocardiography (74.2% versus 78.8%, respectively, P=0.024), or to be admitted to the hospital or observation unit (12.4% versus 17.9%, respectively, P<0.001), but ordering of cardiac biomarkers was similar. After multivariable adjustment, men were seen more quickly (hazard ratio [HR], 1.15 [95% CI, 1.05-1.26]) and were more likely to be admitted (adjusted odds ratio, 1.40 [95% CI, 1.08-1.81]; P=0.011). People of color waited longer for physician evaluation (HR, 0.82 [95% CI, 0.73-0.93]; P<0.001) than White adults after multivariable adjustment, but there were no racial differences in hospital admission, triage level, electrocardiography, or cardiac biomarker testing. Acute myocardial infarction was diagnosed in 1.4% of adults in the emergency department and 6.5% of admitted adults. Conclusions Women and people of color with CP waited longer to be seen by physicians, independent of clinical features. Women were independently less likely to be admitted when presenting with CP. These differences could impact downstream treatment and outcomes.

BACKGROUND:Accurately identifying patients with hypoglycemia is key to preventing adverse events and mortality. Natural language processing (NLP), a form of artificial intelligence, uses computational algorithms to extract information from text data. NLP is a scalable, efficient, and quick method to extract hypoglycemia-related information when using electronic health record data sources from a large population. OBJECTIVE:The objective of this systematic review was to synthesize the literature on the application of NLP to extract hypoglycemia from electronic health record clinical notes. METHODS:Literature searches were conducted electronically in PubMed, Web of Science Core Collection, CINAHL (EBSCO), PsycINFO (Ovid), IEEE Xplore, Google Scholar, and ACL Anthology. Keywords included hypoglycemia, low blood glucose, NLP, and machine learning. Inclusion criteria included studies that applied NLP to identify hypoglycemia, reported the outcomes related to hypoglycemia, and were published in English as full papers. RESULTS:This review (n=8 studies) revealed heterogeneity of the reported results related to hypoglycemia. Of the 8 included studies, 4 (50%) reported that the prevalence rate of any level of hypoglycemia was 3.4% to 46.2%. The use of NLP to analyze clinical notes improved the capture of undocumented or missed hypoglycemic events using International Classification of Diseases, Ninth Revision (ICD-9), and International Classification of Diseases, Tenth Revision (ICD-10), and laboratory testing. The combination of NLP and ICD-9 or ICD-10 codes significantly increased the identification of hypoglycemic events compared with individual methods; for example, the prevalence rates of hypoglycemia were 12.4% for International Classification of Diseases codes, 25.1% for an NLP algorithm, and 32.2% for combined algorithms. All the reviewed studies applied rule-based NLP algorithms to identify hypoglycemia. CONCLUSIONS:The findings provided evidence that the application of NLP to analyze clinical notes improved the capture of hypoglycemic events, particularly when combined with the ICD-9 or ICD-10 codes and laboratory testing.

BACKGROUND:Both community health workers and the Project ECHO model of specialist telementoring are innovative approaches to support primary care providers in the care of complex patients with diabetes.We studied the effect of an intervention that combined these two approaches on glycemic control. METHODS:Patients with diabetes were recruited from 10 federally qualified health centers in New Mexico. We used electronic health record (EHR) data to compare HbA1c levels prior to intervention enrollment with HbA1c levels after 3 months (early follow-up) and 12 months (late follow-up) following enrollment. We propensity matched intervention patients to comparison patients from other sites within the same EHR databases to estimate the average treatment effect. RESULTS:Among 557 intervention patients with HbA1c data, mean HbA1c decreased from 10.5% to 9.3% in the pre- versus post-intervention periods (p<0.001). As compared to the comparison group, the intervention was associated with a change in HbA1c of -0.2% (95% CI -0.4%-0.5%) and -0.3 (95% CI -0.5-0.0) in the early and late follow-up cohorts, respectively. The intervention was associated with a significant increase in percent of patients with HbA1c<8% in the late follow-up cohort (8.1%, 95%CI 2.2%-13.9%) but not the early follow-up cohort (3.6%, 95% CI -1.5%-8.7%) DISCUSSION: : The intervention was associated with a substantial decrease in HbA1c in intervention patients, although this improvement was not different from matched comparison patients in early follow-up. While combining community health workers with Project ECHO may hold promise for improving glycemic control, particularly in the longer term, further evaluations are needed.

Objective/UNASSIGNED:To develop algorithms to identify patients with advanced heart failure (HF) that can be applied to administrative data. Patients and Methods/UNASSIGNED:In a population-based cohort of all residents of Olmsted County, Minnesota, with greater than or equal to 1 HF billing code 2007-2017 (n=8657), we identified all patients with advanced HF (n=847) by applying the gold standard European Society of Cardiology advanced HF criteria via manual medical review by an HF cardiologist. The advanced HF index date was the date the patient first met all European Society of Cardiology criteria. We subsequently developed candidate algorithms to identify advanced HF using administrative data (billing codes and prescriptions relevant to HF or comorbidities that affect HF outcomes), applied them to the HF cohort, and assessed their ability to identify patients with advanced HF on or after their advanced HF index date. Results/UNASSIGNED:A single hospitalization for HF or ventricular arrhythmias identified all patients with advanced HF (sensitivity, 100%); however, the positive predictive value (PPV) was low (36.4%). More stringent definitions, including additional hospitalizations and/or other signs of advanced HF (hyponatremia, acute kidney injury, hypotension, or high-dose diuretic use), decreased the sensitivity but improved the specificity and PPV. For example, 2 hospitalizations plus 1 sign of advanced HF had a sensitivity of 72.7%, specificity of 89.8%, and PPV of 60.5%. Negative predictive values were high for all algorithms evaluated. Conclusion/UNASSIGNED:Algorithms using administrative data can identify patients with advanced HF with reasonable performance.

BACKGROUND:Some patients with heart failure (HF) will go on to develop advanced HF, characterized by severe HF symptoms despite attempts to optimize medical therapy. The goals of this study were to examine the risk of developing advanced HF in patients with newly diagnosed HF, identify risk factors for developing advanced HF, and evaluate the impact of advanced HF on outcomes. METHODS:This was a population-based, retrospective cohort study of Olmsted County, Minnesota, residents with a new clinical diagnosis of HF between 2007 and 2017. Risk factors for the development of advanced HF (2018 European Society of Cardiology criteria) were examined using cause-specific Cox proportional hazard regression models. The associations of development of advanced HF with risks of hospitalization and mortality were examined using the Andersen-Gill and Cox models, respectively. RESULTS:<0.001), and cardiovascular mortality (hazard ratio, 7.8 [95% CI, 6.7-9.1]). CONCLUSIONS:In this population-based study, development of advanced HF was common and was associated with markedly increased morbidity and mortality.

Structures (context of care delivery) and processes (actions aimed at delivery care) are posited to drive patient outcomes. Despite decades of primary care research, there remains a lack of evidence connecting specific structures/processes to patient outcomes to determine which of the numerous recommended structures/processes to prioritize for implementation. The objective of this study was to identify structures/processes most commonly present in high-performing primary care practices for chronic care management and prevention. We conducted key informant interviews with a national sample of 22 high-performing primary care practices. We identified the 10 most commonly present structures/processes in these practices, which largely enable 2 core functions: mobilizing staff to conduct patient outreach and helping practices avoid gaps in care. Given the costs of implementing and maintaining numerous structures/processes, our study provides a starting list for providers to prioritize and for researchers to investigate further for specific effects on patient outcomes.

OBJECTIVE:To evaluate the impact of structured recommendations on follow-up completion for incidental lung nodules (ILNs). METHODS:Patients with ILNs before and after implementation of structured Fleischner recommendations and electronic tracking were sampled randomly. The cohorts were compared for imaging follow-up. Multivariable logistic regression was used to assess appropriate follow-up and loss to follow-up, with independent variables including use of structured recommendations or tracking, age, gender, race, ethnicity, setting of the index test (inpatient, outpatient, emergency department), smoking history, and nodule features. RESULTS:In all, 1,301 patients met final inclusion criteria, including 255 patients before and 1,046 patients after structured recommendations or tracking. Baseline differences were found in the pre- and postintervention groups, with smaller ILNs and younger age after implementing structured recommendations. Comparing pre- versus postintervention outcomes, 40.0% (100 of 250) versus 29.5% (309 of 1,046) of patients had no follow-up despite Fleischner indications for imaging (P = .002), and among the remaining patients, 56.6% (82 of 145) versus 75.0% (553 of 737) followed up on time (P < .001). Delayed follow-up was more frequent before intervention. Differences postintervention were mostly accounted for by nodules ≤ 8 mm in the outpatient setting (P < .001). In multivariable analysis, younger age, White race, outpatient setting, and larger nodule size showed significant association with appropriate follow-up completion (P < .015), but structured recommendations did not. Similar results applied for loss to follow-up. DISCUSSION/CONCLUSIONS:Consistent use of structured reporting is likely key to mitigate selection bias when benchmarking rates of appropriate follow-up of ILN. Emergency department patients and inpatients are at high risk of missed or delayed follow-up despite structured recommendations.