Obesity and metabolic syndrome in patients with heart failure with preserved ejection fraction: a cross-sectional analysis of the Veradigm Cardiology Registry.

BACKGROUND: The proportion of heart failure patients with preserved ejection fraction has been rising over the past decades and has coincided with increases in the prevalence of obesity and metabolic syndrome. The relationship between these interconnected comorbidities and heart failure with preserved ejection fraction (HFpEF) is still poorly understood. This study characterized obesity and metabolic syndrome among real-world patients with HFpEF. METHODS: We identified adults with heart failure in the Veradigm Cardiology Registry, previously the PINNACLE Registry, with a left ventricular ejection fraction measurement ≥ 50% between 01/01/2016 and 12/31/2019. Patients were stratified by obesity diagnosis and presence of metabolic syndrome (≥ 3 of the following: diabetes, hypertension, hyperlipidemia, and obesity). We captured baseline demographic and clinical characteristics and used multivariable logistic regression to examine the odds of having cardiac (atrial fibrillation, coronary artery disease, coronary artery bypass surgery, myocardial infarction, and stroke/transient ischemic attack) and non-cardiac (chronic kidney disease, chronic liver disease, and peripheral artery disease) comorbidities of interest. The models adjusted for age and sex, and the main covariates of interest were obesity and metabolic burden score (0-3 based on the presence of diabetes, hypertension, and hyperlipidemia). The models were run with and without an obesity*metabolic burden score interaction term. RESULTS: This study included 264,571 patients with HFpEF, of whom 55.7% had obesity, 52.5% had metabolic syndrome, 42.5% had both, and 34.3% had neither. After adjusting for age, sex, and burden of other metabolic syndrome-associated diagnoses, patients with HFpEF with obesity had lower odds of a diagnosis of other evaluated comorbidities relative to patients without obesity. The presence of metabolic syndrome in HFpEF appears to increase comorbidity burden as each additional metabolic syndrome-associated diagnosis was associated with higher odds of assessed comorbidities except atrial fibrillation. CONCLUSION: Obesity was common among patients with HFpEF and not always co-occurring with metabolic syndrome. Multivariable analysis suggested that patients with obesity may develop HFpEF in the absence of other driving factors such as cardiovascular disease or metabolic syndrome.

Glycemic Control Among Patients Newly Prescribed IDegLira Across Prior Therapy Group in US Real-World Practice.

INTRODUCTION: IDegLira is a fixed-ratio combination of insulin degludec and liraglutide indicated for the treatment of type 2 diabetes (T2D). We report the first real-world study describing change in glycated hemoglobin (HbA1c) among US patients who initiated IDegLira. The aim of the study was to observe and describe changes in glycemic control and weight in patients initiating IDegLira in real-world clinical practice. METHODS: Patients in the Practice Fusion electronic medical record database who initiated treatment with IDegLira between March 2017 and June 2018 were identified (n = 1384). To be included in the analyses, the study population needed to meet age, time in database pre- and post-initiation, and availability of HbA1c data at baseline and follow-up requirements. Data were analyzed according to baseline therapy subgroups and whether patients were intensifying (primary analysis group) or simplifying (secondary analysis group) their diabetes treatment. Changes in clinical outcomes from baseline were evaluated by paired t tests and linear regression. RESULTS: The overall study population comprised 296 patients, of whom 206 were included in the primary analysis group and 90 were included in the secondary analysis group. In the adjusted analyses, there was a reduction in HbA1c of - 1.1% in the primary analysis group, with the HbA1c reduction in all prior therapy groups ranging from - 0.8% for those previously on basal insulin to - 1.0% for those previously on non-injectable therapy (p < 0.0001 for all). In a similar adjusted analysis, there was a statistically significant but small (1.0 lb/0.45 kg) change in weight in the primary analysis group. In the secondary analysis, patients previously on more than one injection daily switched to a more simplified therapy without compromising on glycemic control (HbA1c change of - 0.16%). CONCLUSION: Consistent with previous real-world studies, IDegLira lowered HbA1c across different background prior glucose-lowering therapies, with minimal impact on weight.

Patient Characteristics and Healthcare Resource Utilization Among Patients with COPD New to LAMA/LABA Fixed-Dose Combination Treatment in US-Based Real-World Practice.

Introduction: This retrospective, observational cohort study utilized an integrated dataset from an electronic health records system and a claims database to describe demographic and clinical characteristics, healthcare resource utilization (HCRU), and treatment patterns in COPD patients initiating long-acting muscarinic antagonist (LAMA)/long-acting ß2-agonist (LABA) fixed-dose combination (FDC) treatment in the USA. Methods: Patients were aged ≥40 years and had a COPD diagnosis (Practice Fusion system) and ≥1 prescription of LAMA/LABA FDC therapy, with an index date (first prescription) 1 May 2014-31 December 2017. For the HCRU analysis, patients had ≥2 claims from the Symphony Health database within 12 months before index. All analyses of outcomes relating to demographic and clinical characteristics, HCRU, and treatment patterns were descriptive. Results: Patients initiating LAMA/LABA FDCs (n=8224) had a mean age of 67.9 years, 52.8% were female, and mean BMI was 29.2 kg/m2. The most common comorbidities were cardiovascular disease (74.3%), hypertension (64.0%), and hyperlipidemia (45.6%). In the 12 months prior to index, 53.1% of patients had used inhaled therapy: 23.4% short-acting therapy only, 16.7% short-acting and maintenance therapy, and 13.1% maintenance therapy only. Amongst users of inhaled therapies, the pMDI was the most frequently used device (64.3%, n=2812/4370). Of 7050 patients included in the HCRU analysis, 79.8% had COPD-related costs; mean cost/patient was $4174. Mean COPD-related costs per patient for moderate and severe exacerbations were $910 and $23,208, respectively. Per-patient costs included $23,032 for inpatient visits, $2358 for emergency visits, $4432 for outpatient visits, and $1989 for pharmacy claims. Conclusion: This observational study is the first to describe the real-world demographic and clinical characteristics and HCRU of patients initiating LAMA/LABA FDC treatment in the USA. Patients were generally elderly and overweight, with comorbidities of CVD, hypertension, and hyperlipidemia. Inpatient visits were the largest contributor to COPD-related costs per patient in the year prior to initiation of LAMA/LABA FDCs.

Clinical And Economic Burden Of Eosinophilic COPD In A Large Retrospective US Cohort.

Purpose: We sought to describe clinical and economic outcomes for COPD patients by blood eosinophil (EOS) count. Methods: This retrospective cohort study of COPD patients used data from the Practice Fusion electronic medical records (EMR) database linked to Symphony Health Solutions transactional pharmacy, medical, outpatient, and inpatient claims data to evaluate COPD-related and all-cause health care resource utilization and cost in the 12-month period following the date of each patient's greatest recorded blood eosinophil count during the 27-month period from January 2014 to March 2016. A post-index moderate exacerbation was defined as an outpatient or emergency care visit for COPD and a prescription for oral corticosteroid and/or antibiotics within 10 days of the visit. Severe exacerbation was defined as an inpatient hospitalization with COPD as primary diagnosis. Results: Of 48,090 EMR patients, 39,939 (83.1%) had a charge in the claims data both pre- and post-index (mean age 67.2 years, 58.3% female), 17,397 (43.6%) had EOS ≥220 cells/µL. Moderate and severe exacerbations were more frequent for patients with EOS≥220 cells/µL compared with those with EOS <220 cells/µL (moderate: 6.8% vs 6.1%, p<0.05; severe: 3.1% vs 2.5%, p<0.001). After adjustment for baseline clinical characteristics, each 100-unit increase in EOS count was associated with a significant 2.24% increase in total all-cause costs and 4.54% increase in total COPD-related costs (p<0.001 for both). COPD-related costs were significantly greater for patients with an EOS count of ≥220 cells/µL compared with those with EOS <220 cells/µL (p<0.001). These costs appear to have been driven by a greater percentage of patients in the ≥220 cells/µL cohort with COPD-related resource use including hospitalization, office visits, ambulatory procedures and pharmacy prescriptions. Conclusion: COPD patients with EOS counts ≥220 cells/µL were more likely to have had moderate or severe exacerbations and greater cost of care than those with EOS <220 cells/µL.

How well can familial hypercholesterolemia be identified in an electronic health record database?

BACKGROUND: Familial hypercholesterolemia (FH) is a condition characterized by high cholesterol levels and increased risk for coronary heart disease (CHD) that often goes undiagnosed. The Dutch Lipid Network Criteria (DLNC) are used to identify FH in clinical settings via physical examination, personal and family history of CHD, in addition to the presence of deleterious mutations of the LDLR, ApoB, and PCSK9 genes. Agreement between clinical and genetic diagnosis of FH varies. While an ICD diagnosis code was not available for coding FH until 2016, Systematized Nomenclature of Medicine (SNOMED) clinical concept codes, including genetic diagnoses, for FH have been utilized in electronic health records (EHRs). OBJECTIVE: To evaluate the concordance of identifying FH via SNOMED and ICD-10 CM codes vs the DLNC in an EHR database. METHODS: Using the Practice Fusion EHR database, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value were calculated comparing an FH cohort identified via SNOMED and ICD-10 CM codes to one identified via the DLNC. RESULTS: Among 907,616 patients with hypercholesterolemia, 2,180 were identified as FH via SNOMED code (zero were identified via ICD-10 CM), 259 had a DLNC score 6-8 (probable FH), and 45 had a DLNC score >8 (definite FH). Compared to DLNC score >8, the sensitivity, specificity, and PPV of the FH SNOMED code were 84.4%, 99.4%, and 6.4%, respectively. Compared to DLNC score ≥6, the sensitivity was 36.8% and the specificity was 99.5% with a PPV of 18.7%. CONCLUSION: Compared to the clinical criteria for FH, identification of FH patients via SNOMED diagnosis codes had high sensitivity and specificity, but low PPV. The discordance of these two techniques in identifying FH patients speaks to the challenges in identifying FH patients in large electronic databases such as administrative claims and EHR.

Predictors and Clinical Outcomes of Treatment Intensification in Patients With Type 2 Diabetes Uncontrolled on Basal Insulin in a Real-World Setting.

OBJECTIVE: To understand factors associated with intensification of basal insulin therapy and treatment impact on clinical outcomes in patients with type 2 diabetes (T2D). METHODS: In this retrospective, observational study of the Practice Fusion electronic health record database, eligible patients were adults with T2D, ≥1 basal insulin prescription and office visit in the 6 months before a glycated hemoglobin A1c (A1C) test >7.0% (index date), and no other injectable prescriptions in the 12 months before the index date. Patients were categorized to intensifiers with injectables (rapid-acting insulin [RAI], glucagon-like peptide-1 receptor agonist [GLP-1 RA], or other injectables) or nonintensifiers with injectables (including no change, adding an oral antidiabetes drug, or changing basal insulin dose). Principal outcomes were A1C change, hypoglycemia incidence, and change in body weight. RESULTS: Among 14,653 patients, 2,121 (14.5%) and 12,532 (85.5%) were categorized as intensifiers and nonintensifiers with injectables, respectively. Compared with nonintensifiers, intensifiers were more likely to have an endocrinologist as the prescribing physician (odds ratio [OR], 2.52 [95% confidence interval (CI), 2.16 to 2.94]), hypertension (OR, 1.26 [95% CI, 1.08 to 1.47]), higher baseline A1C (OR, 1.22 [95% CI, 1.17 to 1.26]), obesity (OR, 1.17 [95% CI, 1.01 to 1.36]), and higher body mass index (OR, 1.02 [95% CI, 1.01 to 1.03]). In GLP-1 RA intensifiers, the baseline use of dipeptidyl peptidase-4 inhibitors increased the likelihood of intensification. GLP-1 RA intensifiers had equivalent glycemic control to RAI or other injectables, with a nonsignificantly lower risk of hypoglycemia and reduction in body weight. CONCLUSION: Addition of GLP-1 RA to basal insulin may be an effective strategy for overcoming clinical inertia with injectable therapy in patients with T2D. ABBREVIATIONS: A1C = glycated hemoglobin A1c; BMI = body mass index; CI = confidence interval; DCSI = Diabetes Complications Severity Index; DPP-4 = dipeptidyl peptidase-4; EHR = electronic health record; GLP-1 RA = glucagon-like peptide-1 receptor agonist; ICD-9-CM = International Classification of Diseases-Ninth Revision-Clinical Modification; ICD-10-CM = International Classification of Diseases-Tenth Revision-Clinical Modification; OAD = oral antidiabetes drug; OR = odds ratio; RAI = rapid-acting insulin; SGLT-2 = sodium-glucose cotransporter-2; T2D = type 2 diabetes.

Characteristics Associated with the Choice of First Injectable Therapy Among US Patients With Type 2 Diabetes.

PURPOSE: The objective of this retrospective observational study was to describe and identify clinical and demographic characteristics associated with the choice of first injectable therapy (glucagon-like peptide-1 receptor agonist [GLP-1-RA] or basal insulin) among patients with type 2 diabetes mellitus (T2DM). METHODS: This analysis included adults naive to injectable therapy with T2DM who initiated a GLP-1-RA or basal insulin (index date) between November 2014 and February 2016 using data from the Practice Fusion Electronic Health Record database. Patients with T2DM, ≥1 office visit between 6 and 18 months before the index date, and with ≥1 glycosylated hemoglobin (HbA1c) result in the 6-month preindex (baseline) period were included. A generalized boosted regression model was used to determine the patient characteristics most influential in the selection of a GLP-1-RA or basal insulin as first injectable therapy. Sensitivity analysis was performed by using bootstrapped logistic regression. FINDINGS: The study included 3546 and 7507 GLP-1-RA and basal insulin initiators, respectively. At baseline, GLP-1-RA initiators were significantly younger (mean, 58 vs 63 years), had lower HbA1c values (mean, 8.2% vs 9.1%), lower Diabetes Complications Severity Index (DCSI) scores (mean, 1.0 vs 1.7), and a higher body mass index (BMI) (mean, 36 vs 33 kg/m2) compared with basal insulin initiators. Variables selected by using the generalized boosted regression model with the highest relative importance (≥5%) in the selection of GLP-1-RA or basal insulin were HbA1c level (20.43%), BMI (17.73%), age (12.21%), prior prescription of a sodium-glucose cotransporter-2 inhibitor (9.17%), and DCSI score (8.39%). The same variables, as well as race, were selected by using stepwise logistic regression in all the bootstrapped samples. Patients who were older (adjusted odds ratio [OR], 0.975 [95% CI, 0.971-0.979]) and had higher HbA1c values (OR, 0.741 [95% CI, 0.721-0.761]) and DCSI scores (OR, 0.870 [95% CI, 0.848-0.892]) were significantly less likely to be prescribed a GLP-1-RA compared with basal insulin. Patients with higher BMI (OR, 1.046 [95% CI, 1.040-1.053]) and previous prescription of sodium-glucose cotransporter-2 inhibitors (OR, 2.633 [95% CI, 2.224-2.982]) were significantly more likely to be prescribed a GLP-1-RA. IMPLICATIONS: The clinically relevant differences observed between the 2 patient populations suggest that GLP-1-RAs and basal insulin are prescribed to different types of patients with T2DM. Examining patients' demographic and clinical characteristics may be important in assisting physicians in the choice of patient-centered injectable treatment regimens.

Effectiveness of a Cloud-Based EHR Clinical Decision Support Program for Body Mass Index (BMI) Screening and Follow-up.

The effectiveness of a clinical decision support (CDS) program encouraging clinicians to record patient's Body Mass Index (BMI) and document appropriate follow-up plans is evaluated. Test (4,987 practices, 33,445 clinicians) and control groups (881 practices, 6,316 clinicians) were selected using stratified random sampling. Three CDS alerts for BMI screening and follow-up based on evidence based clinical quality guidelines were displayed at the point of care in a cloud-based EHR. The effectiveness of the CDS program was measured over 4 months by tracking recorded BMI and documented follow-up plans. Over the program, BMI recording increased minimally and documentation of follow-up plans increased 5-fold (p=0.05) compared to the control group. The overweight test group patients (18- 64yo) gained less weight (p=0.06) than the control group and underweight patients gained more weight (p<0.01) during the program period. Outcome studies with longer follow-up periods are needed to further confirm positive outcomes.

Visiting multiple sites for immunization and vaccine coverage levels of preschool children in 3 urban clinics: potential indicator of record scatter?

Record scatter is a clinic-level impediment to accurately reporting immunization coverage levels but may be attributed to certain patient characteristics. We determine the association between visiting multiple sites for immunization and underimmunization among urban clinic patients. After collecting immunization histories for patients aged 3-35 months, caregivers were surveyed by telephone. 159/483 caregivers (32.9%) were interviewed. Visiting > 2 sites for immunizations was associated with underimmunization, adjusted Odds Ratio (95% Confidence Interval)=2.7 (1.2-6.5). An interruption in health insurance showed a trend toward association with visiting > or = 2 sites. Our results support the need for working registries, clinic outreach, and continued evaluation of both.

Race is a major determinant of secondary hyperparathyroidism in uremic patients.

In the general population, blacks have higher parathyroid gland mass and circulating parathyroid hormone (PTH) levels than whites. This may predispose black patients to more severe parathyroid disease when renal failure develops. Therefore, racial differences in the severity of uremic hyperparathyroidism were examined in a population of patients with end-stage renal disease (ESRD). Among ESRD patients receiving hemodialysis or peritoneal dialysis, two or more values of intact PTH (immunoradiometric assay, pg/ml) obtained at least 90 d apart were available in 1270 prevalent cases (61.1% blacks, 51% males, and 31.1% diabetic), including 466 incident cases with onset of ESRD after 1993. Maximum PTH levels were analyzed as a function of race, gender, age, diabetic status, and levels of serum calcium, phosphorus, alkaline phosphatase, and aluminum. Using a stepwise multiple regression model, the determinants of maximum PTH in the order of their importance were black race, serum phosphorus, absence of diabetes, younger age, serum calcium, and female gender. The maximum PTH levels averaged 641.7 in blacks and 346.0 in whites after adjusting for age, gender, diabetic status, serum calcium, and phosphorus (P < 0.0001). In blacks compared with whites, the odds ratio (95% confidence interval) for adynamic bone disease (maximum PTH <150 pg/ml) was 0.26 (0.17 to 0.41), whereas the odds ratio for hyperparathyroid bone disease (mean PTH >500 pg/ml) was 4.4 (2.10 to 9.25). Race is a major independent determinant of uremic secondary hyperparathyroidism. Among ESRD patients, blacks may be at an increased risk for hyperparathyroid bone disease and whites for adynamic bone disease.