Objectives
This study was conducted to assess the utility of neutrophil‐to‐lymphocyte ratio (NLR) and platelet‐to‐lymphocyte ratio (PLR) in predicting radiographic sacroiliitis and active disease in axial spondyloarthritis (SpA) and to explore the association between use of a tumor necrosis factor inhibitor (TNFi) and these laboratory values compared with traditional inflammatory markers.
Methods
Observational data from the Program to Understand the Longterm Outcomes in Spondyloarthritis (PULSAR) registry were analyzed. We generated receiver operating characteristic curves to calculate laboratory cutoff values; we used these values in multivariable logistic regression models to identify associations with radiographically confirmed sacroiliitis and active disease. We also used logistic regression to determine the likelihood of elevated laboratory values after initiation of TNFi.
Results
Most study participants (n = 354) were White, male, and HLA–B27 positive. NLR (odds ratio [OR] 1.459, P = 0.034), PLR (OR 4.842, P < 0.001), erythrocyte sedimentation rate (OR 4.397, P < 0.001), and C‐reactive protein (CRP) level (OR 2.911, P = 0.001) were independent predictors of radiographic sacroiliitis. Models that included PLR with traditional biomarkers performed better than those with traditional biomarkers alone. NLR (OR 6.931, P = 0.002) and CRP (OR 2.678, P = 0.004) were predictors of active disease, but the model that included both NLR and CRP performed better than CRP alone. TNFi use reduced the odds of elevated NLR (OR 0.172, P < 0.001), PLR (OR 0.073, P < 0.001), erythrocyte sedimentation rate (OR 0.319, P < 0.001), and CRP (OR 0.407, P < 0.001), but models that included NLR or PLR and traditional biomarkers performed best.
Conclusions
These findings demonstrate an association between NLR and PLR and sacroiliitis and disease activity, with NLR and PLR showing response after TNFi treatment and adding useful clinical information to established biomarkers, thus perhaps assisting in management of axial SpA.
BackgroundAtrial fibrillation (AF) is the most common sustained arrhythmia in adults and increases stroke risk. Treatment with oral anticoagulants (OACs) may reduce this risk however many patients do not receive OAC therapy. This study aimed to use electronic health record data to identify newly diagnosed AF patients at high risk for stroke and not anticoagulated as well as factors associated with OAC prescription.HypothesisTimely prescription of OACs among patients with newly diagnosed AF is poor.MethodsWe performed a retrospective study of patients with a new diagnosis of AF. We assessed stroke risk with the CHA2DS2‐VASc score. The primary outcome was prescription of an OAC within 6 months following diagnosis. We used logistic regression to see how the odds of being prescribed an OAC differs for 17 independent variables.ResultsWe identified 18 404 patients with a new diagnosis of AF. Among patients at high risk for stroke, 41.3% received an OAC prescription within 6 months. Male sex, Caucasian compared to African American race, stroke, obesity, congestive heart failure, vascular disorder, current antiplatelet, beta blocker, or calcium channel blocker prescription, and increasing CHA2DS2‐VASc score were positively associated with receiving an OAC. Whereas anemia, renal dysfunction, liver dysfunction, antiarrhythmic drug use and increasing HAS‐BLED score were negatively associated.ConclusionsMost newly diagnosed AF patients at high stroke risk do not receive an OAC prescription in the first 6 months following diagnosis. Our analysis suggests that patient sex, race, comorbidities, and additional prescriptions are associated with rates of OAC prescribing.
Retrospective StudyCore tip: Clinicians should be cognizant of the potential for rare hypoglycemic effects of the conventional diseasemodifying anti-rheumatic drugs hydroxychloroquine and sulfasalazine, in addition to the well-known hyperglycemic effects of glucocorticoids. Although case reports describe dramatic sporadic hypoglycemic events with the initiation of tumor necrosis factor inhibitors, these effects were not confirmed in our large retrospective study.
Background:While previous studies have demonstrated testosterone's beneficial effects on glycemic control in men with hypogonadism and Type 2 Diabetes, the extent to which these improvements are observed based on the degree of treatment adherence has been unclear.Objectives: To evaluate the effects of long-term testosterone therapy in A1C levels in men with Type 2 Diabetes Mellitus and hypogonadism, controlling for BMI, pretreatment A1C, and age among different testosterone therapy adherence groups.
Materials and methods:We performed a retrospective analysis of 1737 men with diabetes and hypogonadism on testosterone therapy for 5 years of data from 2008-2018, isolating A1C, lipid panels, and BMI results for analysis. Subjects were categorized into adherence groups based on quartiles of the proportion of days covered (> 75% of days, 51-75% of days, 26-50% of days and 0-25% of days), with >75% of days covered considered adherent to therapy.Results: Pre-treatment median A1C was 6.8%. Post-treatment median A1C was 7.1%.The adherent group, >75%, was the only group notable for a decrease in A1C, with a median decrease of −0.2 (p = 0.0022). BMI improvement was associated with improved post-treatment A1C (p = 0.007). When controlling for BMI, age, and pre-treatment A1C, the >75% adherence group was associated with improved post-treatment A1C (p < 0.001).
Discussion:When controlling for all studied variables, testosterone adherence was associated with improved post-treatment A1C. The higher the initial A1C at the initiation of therapy, the higher the potential for lowering the patient's A1C with >75% adherence. Further, all groups showed some reduction in BMI, which may indicate that testosterone therapy may affect A1C independent of weight loss.
Conclusion:Even when controlling for improved BMI, pre-treatment A1C, and age, testosterone positively impacted glycemic control in diabetes patients with hypogonadism, with the most benefit noted in those most adherent to therapy (>75%).
Acute coronary syndrome is a rare complication of vasculitis. We present a case of fulminant medium-vessel vasculitis, most likely PAN, complicated by STEMI and stroke, that was successfully treated with percutaneous revascularization, high-quality stroke care, and immunosuppression. This case highlights the importance of prompt diagnosis and treatment of vasculitis and the recognition of coronary and cerebral ischemia as potentially serious complications.
This study was approved by the Colorado Multiple Institution Review Board under COMIRB protocol number 18-2328. Subjects used in this research provided informed consent before the start of study activities.
Introduction:
A study of 42,000 cardiology consults within the Veterans Health Administration (VHA) in 2016 found that patients who received electronic consultation (e-consults) had similar healthcare costs at 6 months compared to those who received face-to-face (F2F) consults. However, results may have been confounded if patients with less costly conditions received e-consults. Our aim was to compare costs between those receiving F2F vs. e-consults for a similar indication.
Hypothesis:
Electronic rather than F2F consultation for atrial fibrillation (AF) management will be associated with lower total healthcare costs.
Methods:
We conducted a retrospective cohort study of a national sample of VHA patients who received cardiology consultation in 2016. We used a natural language processing script to identify consults for AF management. Primary outcomes were total healthcare costs at 3 and 6 months. Secondary outcomes included inpatient and outpatient costs. We compared costs between groups using a generalized linear model with a gamma distribution and log link. We adjusted for community wage and Charlson comorbidity indices, distance to nearest facility, age, and gender. Standard errors were clustered at the facility level.
Results:
We sampled 176 F2F and 136 e-consults from 43 facilities. Mean total 6-month costs were $12,928 (95% confidence interval [CI]: 1,377; 40,644) and $8,286 (95% CI: 959; 31,320) among e-consult and F2F groups, respectively. The e-consult group had 12.3% higher 3-month (p<0.001) and 41.5% higher 6-month total healthcare costs (p<0.001) in comparison to the F2F group. At 3 months, the e-consult group had 25.1% lower inpatient costs (p<0.001) and 32.5% higher outpatient costs (p<0.001). At 6 months, the e-consult group had 6.3% higher inpatient costs (p<0.001) and 48.4% higher outpatient costs (p<0.001).
Conclusions:
Use of e-consults for AF management is associated with reduced inpatient costs at 3 months, but higher total costs, which were largely driven by outpatient costs. Improving our understanding of healthcare utilization after initial consultation, or in differences in reasons for consultation within AF management may help explain these differences.
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