scholarly journals TREATMENT PATTERNS IN ATHEROSCLEROTIC CARDIOVASCULAR DISEASE PATIENTS WITH HYPERCHOLESTEROLEMIA (ASCVD-H) AND PATIENTS WITH FAMILIAL HYPERCHOLESTEROLEMIA (FH), A RETROSPECTIVE DATABASE COHORT STUDY IN THE US

2021 ◽  
Vol 77 (18) ◽  
pp. 1582
Author(s):  
Raquel Lahoz ◽  
Divyagiri Seshagiri ◽  
Assya Achouba ◽  
Batul Electriwala ◽  
Yingjie Ding ◽  
...  
Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Isaac Acquah ◽  
Javier Valero-Elizondo ◽  
Miguel Cainzos Achirica ◽  
Rahul Singh ◽  
Karan Shah ◽  
...  

Introduction: Barriers to healthcare - financial and nonfinancial - may result in unmet health needs and adverse outcomes. Despite this, the nonfinancial barriers to care among adults with atherosclerotic cardiovascular disease (ASCVD) is poorly defined in the US. We aimed to explore the scope and determinants of nonfinancial barriers to care among individuals with ASCVD. Methods: We analyzed data from the 2013-17 National Health Interview Survey (NHIS). We included adults with self-reported ASCVD (heart attack, angina, and/or stroke). Nine key variables in the NHIS that represent nonfinancial barriers to healthcare were assessed as absent/present, and participants were classified as having 0-1, 2, or ≥3 barriers. Multinomial logistic regression (using 0-1 nonfinancial barriers as reference) was used to evaluate the relationship between various sociodemographic factors, and an increasing number of nonfinancial barriers. Results: Of all the 15,758 adults with ASCVD (8.1% annually in the US; representing 19.6 million), 23.4% reported having at least one nonfinancial barrier to care while 4.9% reported 3 nonfinancial barriers. In a multivariable multinomial logistic regression, after stratifying by age, individuals from low-income families had an almost 2-fold relative prevalence of 3 nonfinancial barriers ( Figure) . In the elderly, however, lack of insurance was the strongest predictor (relative prevalence ratio of 6.51 [95% confidence interval; 2.25, 18.87]) of having ≥3 barriers. Conclusion: Among adults with ASCVD, the relative prevalence of ≥3 nonfinancial barriers was low (4.9%) with low-income being the only modifiable predictor of reporting ≥3 nonfinancial barriers and lack of insurance being the strongest predictor in the elderly. Addressing financial barriers to healthcare may help alleviate these nonfinancial barriers.


Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Emil M deGoma ◽  
Zahid S Ahmad ◽  
Emily O'Brien ◽  
Iris Kindt ◽  
Peter Shrader ◽  
...  

Introduction: In the US, LDL-C levels and treatment patterns of patients with familial hypercholesterolemia (FH) – a group prioritized for statin therapy in the 2013 ACC/AHA cholesterol guidelines – remain poorly described. In 2013 the FH Foundation launched the CAscade SCreening for Awareness and DEtection (CASCADE) of FH Registry, the only active US FH patient registry addressing this knowledge gap. Methods: We conducted a 2-year (2013-2015) cross-sectional analysis among 1,295 adults with heterozygous FH enrolled in the CASCADE FH Registry from 10 US lipid clinics. Results: Mean (SD) age at enrollment was 54 (16) years; mean age at FH diagnosis was 45 (19) years; 59% were female; and 80% were white. Mean pretreatment and post-treatment LDL-C levels were 256 (66) and 156 (71) mg/dl, respectively. At enrollment, 43% of patients were taking high-intensity statin therapy; 25% were not taking a statin; and 45% received >1 LDL-lowering therapy. Among FH patients on LDL-lowering therapy, 25% achieved an LDL-C <100 mg/dl, and 41% achieved a ≥50% LDL-C reduction (Table). Factors associated with a ≥50% LDL-C reduction included high-intensity statin use (adjusted OR 2.24, 95% CI 1.47-3.42) and use of >1 LDL-lowering therapy (1.94, 1.29-2.93) (Figure). Atherosclerotic cardiovascular disease (ASCVD) was present in 37%, of whom 44% achieved a ≥50% LDL-C reduction and 9% achieved an LDL-C <70 mg/dl. Conclusions: Despite the high prevalence of ASCVD, several care gaps exist for FH patients enrolled in the CASCADE US registry: lack of early diagnosis, insufficient use of high-intensity statin therapy, and failure to achieve adequate LDL-C reductions.


2021 ◽  
pp. ASN.2020060856
Author(s):  
Yu Xu ◽  
Mian Li ◽  
Guijun Qin ◽  
Jieli Lu ◽  
Li Yan ◽  
...  

BackgroundThe Kidney Disease Improving Global Outcomes (KDIGO) clinical practice guideline used eGFR and urinary albumin-creatinine ratio (ACR) to categorize risks for CKD prognosis. The utility of KDIGO’s stratification of major CVD risks and predictive ability beyond traditional CVD risk prediction scores are unknown.MethodsTo evaluate CVD risks on the basis of ACR and eGFR (individually, together, and in combination using the KDIGO risk categories) and with the atherosclerotic cardiovascular disease (ASCVD) score, we studied 115,366 participants in the China Cardiometabolic Disease and Cancer Cohort study. Participants (aged ≥40 years and without a history of cardiovascular disease) were examined prospectively for major CVD events, including nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death.ResultsDuring 415,111 person-years of follow-up, 2866 major CVD events occurred. Incidence rates and multivariable-adjusted hazard ratios of CVD events increased significantly across the KDIGO risk categories in ASCVD risk strata (all P values for log-rank test and most P values for trend in Cox regression analysis <0.01). Increases in c statistic for CVD risk prediction were 0.01 (0.01 to 0.02) in the overall study population and 0.03 (0.01 to 0.04) in participants with diabetes, after adding eGFR and log(ACR) to a model including the ASCVD risk score. In addition, adding eGFR and log(ACR) to a model with the ASCVD score resulted in significantly improved reclassification of CVD risks (net reclassification improvements, 4.78%; 95% confidence interval, 3.03% to 6.41%).ConclusionsUrinary ACR and eGFR (individually, together, and in combination using KDIGO risk categories) may be important nontraditional risk factors in stratifying and predicting major CVD events in the Chinese population.


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