Cohen, Sarah S.

Background: Among patients who experience a myocardial infarction (MI), elevated low-density lipoprotein cholesterol (LDL-C) is a major risk factor for experiencing a subsequent cardiovascular (CV) event; however, real-world evidence is lacking in regard to how changes in LDL-C levels after a MI hospitalization are related to subsequent CV events. Methods: Adults with a hospitalized MI and an available baseline LDL-C (closest LDL-C within 2 years prior to hospital discharge) between 2012 and 2018 within a 27-county region across MN and WI were included for study. For each LDL-C measurement during follow-up (starting with the first LDL-C >30 days after baseline and ≤1-year post-discharge), percent change from baseline LDL-C was calculated. Patients were followed for subsequent CV events (composite endpoint 1: MI, ischemic stroke [IS], coronary artery bypass grafting [CABG], percutaneous coronary intervention [PCI]; composite endpoint 2: MI, IS, CABG, PCI, CV death). Cox models were used to evaluate the association between time-dependent LDL-C percent change and CV events, stratified by baseline LDL-C (<70, 70-99, ≥100 mg/dL). Results: Among 3,034 MI patients identified, 740 (24%), 940 (31%) and 1354 (45%) had a baseline LDL-C <70, 70-99 and ≥100 mg/dL, respectively. After a mean follow-up of 3.4 years, 471 and 667 patients experienced the composite endpoint 1 and 2, respectively. After covariate adjustment, a trend towards a lower risk of CV events with decreasing LDL-C from baseline, compared to those with no change or an increase in LDL-C, was observed among those with a LDL-C <70 and > 100 mg/dl but not in those with a LDL-C 70-99 mg/dL (Table). Conclusions: In a population-based cohort of MI patients, there is evidence that patients with a decrease in LDL-C after MI may have a reduced risk of subsequent CV events. These results underscore the importance of lowering LDL-C after a MI and may help to identify subgroups at highest risk for additional CV events and those who may benefit from targeted LDL-C treatment recommendations.

Background: Many patients with atherosclerotic cardiovascular disease (ASCVD) or diabetes mellitus (DM) do not receive guideline-recommended lipid lowering therapy (LLT). We characterized LLT treatment patterns and identified predictors of high intensity statin prescriptions (Rx) in patients with ASCVD and DM. Methods: Olmsted County, MN residents with DM or ASCVD (MI, unstable angina, revascularization, ischemic stroke/TIA) from 2005-2012 were followed for 2 years capturing all LDL-C and LLT Rx. Cox regression examined predictors of high intensity statin Rx, modeling LDL-C as a time-dependent variable. Results: 8408 patients with DM (n=4881) or ASCVD (n=3527) were identified (mean age 63.1, 55.1% male). Over 2 years, mean (SD) number of LDL-C measurements was 2.7 (1.9); 12.1% had none. Among those with ≥1 LDL-C, the first was ≥100 mg/dL in 46.2%. LLT Rx increased within 30 days after the first LDL-C. (Table). However, among those with LDL-C ≥100, 40.1% were not prescribed LLT; these patients were younger (mean age 56.9 vs 60.8), more likely female (52.7% vs 45.6%) and more likely to have DM vs ASCVD (84.5% vs 54.6%) than those prescribed LLT. High intensity statin Rx increased more for those with LDL-C ≥100 than those with LDL-C <100. Men [HR 1.29 (CI 1.13-1.48)] and patients with ASCVD vs DM [6.12 (5.25-7.12)] were more likely to be prescribed high intensity statins. Higher LDL-C was associated with greater high intensity statin Rx, although associations differed between ASCVD and DM (p-interaction=0.004). Compared to DM, ASCVD patients were more likely prescribed high intensity statins at every level of LDL-C [2.73 (1.70-4.37), 6.47 (4.94-8.47), 5.68 (4.45-7.25), and 7.88 (5.89-10.53) for LDL-C <70, 70-99, 100-130, and ≥130]. Conclusions: Although high intensity statin Rx increased in response to increasing LDL-C, a large proportion of patients do not receive guideline-concordant care. Efforts should be made to encourage more aggressive treatment of elevated LDL-C and to understand why treatment is neglected or delayed.

AbstractThe purpose of this analysis was to develop and validate computable phenotypes for heart failure (HF) with preserved ejection fraction (HFpEF) using claims‐type measures using the Rochester Epidemiology Project. This retrospective study utilized an existing cohort of Olmsted County, Minnesota residents aged ≥ 20 years diagnosed with HF between 2007 and 2015. The gold standard definition of HFpEF included meeting the validated Framingham criteria for HF and having an LVEF ≥ 50%. Computable phenotypes of claims‐type data elements (including ICD‐9/ICD‐10 diagnostic codes and lab test codes) both individually and in combinations were assessed via sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with respect to the gold standard. In the Framingham‐validated cohort, 2,035 patients had HF; 1,172 (58%) had HFpEF. One in‐patient or two out‐patient diagnosis codes of ICD‐9 428.3X or ICD‐10 I50.3X had 46% sensitivity, 88% specificity, 84% PPV, and 54% NPV. The addition of a BNP/NT‐proBNP test code reduced sensitivity to 35% while increasing specificity to 91% (PPV = 84%, NPV = 51%). Broadening the diagnostic codes to ICD‐9 428.0, 428.3X, and 428.9/ICD‐10 I50.3X and I50.9 increased sensitivity at the expense of decreasing specificity (diagnostic code‐only model: 87% sensitivity, 8% specificity, 56% PPV, 30% NPV; diagnostic code and BNP lab code model: 61% sensitivity, 43% specificity, 60% PPV, 45% NPV). In an analysis conducted to mimic real‐world use of the computable phenotypes, any one in‐patient or out‐patient code of ICD‐9 428/ICD‐10 150 among the broader population (N = 3,755) resulted in lower PPV values compared with the Framingham cohort. However, one in‐patient or two out‐patient instances of ICD‐9 428.0, 428.9, or 428.3X/ICD‐10 150.3X or 150.9 brought the PPV values from the two cohorts closer together. While some misclassification remains, the computable phenotypes defined here may be used in claims databases to identify HFpEF patients and to gain a greater understanding of the characteristics of patients with HFpEF.