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LDL-cholesterol trajectories and statin treatment in Finnish type 2 diabetes patients: a growth mixture model

ABSTRACT: We aimed to identify distinct longitudinal trends of LDL-cholesterol (LDL-C) levels and investigate these trajectories’ association with statin treatment. This retrospective cohort study used electronic health records from 8592 type 2 diabetes patients in North Karelia, Finland, comprising all primary and specialised care visits 2011‒2017. We compared LDL-C trajectory groups assessing LDL-C treatment target achievement and changes in statin treatment intensity. Using a growth mixture model, we identified four LDL-C trajectory groups. The majority (85.9%) had “moderate-stable” LDL-C levels around 2.3 mmol/L. The second-largest group (7.7%) consisted of predominantly untreated patients with alarmingly “high-stable” LDL-C levels around 3.9 mmol/L. The “decreasing” group (3.8%) was characterised by large improvements in initially very high LDL-C levels, along with the highest statin treatment intensification rates, while among patients with “increasing” LDL-C (2.5%), statin treatment declined drastically. In all the trajectory groups, women had significantly higher average LDL-C levels and received less frequent any statin treatment and high-intensity treatment than men. Overall, 41.9% of patients had no statin prescribed at the end of follow-up. Efforts to control LDL-C should be increased—especially in patients with continuously elevated levels—by initiating and intensifying statin treatment earlier and re-initiating the treatment after discontinuation if possible.

SUBMITTER: Inglin L

PROVIDER: S-EPMC8604948 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThis cross-sectional study aimed to identify actionable factors to improve LDL-cholesterol target achievement and overcome underuse of lipid-lowering treatments in high- or very-high-cardiovascular risk patients.MethodsWe evaluated healthcare records of 934,332 subjects from North-Italy, including subjects with available lipid profile and being on statin treatments up to December 2018. A 6-month-period defined adherence with proportion-of-days-covered ≥ 80%. Treatment was classified as high-intensity-statin (HIS) + ezetimibe, HIS-alone, non-HIS (NHIS) + ezetimibe or NHIS alone.ResultsWe included 27,374 subjects without and 10,459 with diabetes. Among these, 30% and 36% were on secondary prevention, respectively. Adherence was high (78-100%) and increased with treatment intensity and in secondary prevention. Treatment intensity increased in secondary prevention, but only 42% were on HIS. 2019-guidelines LDL-cholesterol targets were achieved in few patients and more often among those with diabetes (7.4% vs. 10.7%, p < 0.001). Patients in secondary prevention had mean LDL-cholesterol levels aligned slightly above 70 mg/dl (range between 68 and 73 mg/dl and between 73 and 85 mg/dl in patients with and without diabetes, respectively). Moreover, the differences in mean LDL-cholesterol levels observed across patients using treatments with well-stablished different LDL-lowering effect were null or much smaller than expected (HIS vs. NHIS from - 3 to - 11%, p < 0.001, HIS + ezetimibe vs. HIS-from - 4 to + 5% n.s.). These findings, given the observational design of the study, might suggest that a "treat to absolute LDL-cholesterol levels" approach (e.g., targeting LDLc of 70 mg/dl) was mainly used by physicians rather than an approach to also achieve the recommended 50% reduction in LDL-cholesterol levels. Our analyses suggested that female sex, younger age, higher HDL-c, and elevated triglycerides are those factors delaying prescription of statin treatments, both in patients with and without diabetes and in those on secondary prevention.ConclusionsAmong patients on statin treatment and high adherence, only a small proportion of patients achieved LDL-cholesterol targets. Late initiation of high-intensity treatments, particularly among those with misperceived low-risk (e.g., female subjects or those with high HDL-cholesterol), appears as pivotal factors needing to be modified to improve CVD prevention.

Project description:Objective: To assess adherence to statin therapy and its association with sociodemographic data, medical characteristics, LDLc levels, and LDLc target attainment in real-world T2D patients treated in secondary care. Research Design and Methods: Cross-sectional analyses were performed on baseline data of 393 patients in the DIAbetes and LifEstyle Cohort Twente (DIALECT). The medication possession ratio (MPR), calculated with pharmacy dispensing data, was used to determine adherence to statins for an intended period of 24 months. Statins were included in the analyses if they were used for at least six consecutive months with at least three dispenses. Adherence was defined as an MPR ≥80%. Associations with adherence were assessed using descriptive statistics and binary logistic regression. Results: Overall, 80% of the patients had a statin prescription and of those, 89% were adherent. The proportion of patients who reached LDLc targets of ≤2.5 mmol/L and <1.8 mmol/L differed significantly between the adherent, nonadherent and non-statin group (90% vs. 74% vs. 46%; p < 0.01 and 56% vs. 26% vs. 6%; p < 0.01, respectively). Serum LDLc levels were lower in the adherent versus the nonadherent and non-statin group (1.76 ± 0.60 vs. 2.23 ± 0.90 vs. 2.71 ± 0.67 mmol/L; p < 0.01). Higher HbA1c levels were independently associated with nonadherence (OR: 1.05, 95% CI 1.01-1.08; p < 0.01). Mediation adherence (OR: 2.88, 95% CI 1.04-7.97; p = 0.041) and lower BMI (OR: 0.88, 95% CI 0.81-0.96; p < 0.01) were independently associated with attaining the LDLc target of ≤2.5 mmol/L. Conclusion: In patients with T2D treated in secondary care, statin adherence was relatively high and was associated with significantly lower LDLc levels. It is important to identify nonadherence as it appeared an important determinant of failure to reach LDLc targets. The finding that many patients who failed to attain LDLc targets did not receive statin treatment offers an opportunity to improve diabetes care.

Project description:HMG-CoA reductase inhibitors (or "statins") are important and commonly used medications to lower cholesterol and prevent cardiovascular disease. Nearly half of patients stop taking statin medications one year after they are prescribed leading to higher cholesterol, increased cardiovascular risk, and costs due to excess hospitalizations. Identifying which patients are at highest risk for not adhering to long-term statin therapy is an important step towards individualizing interventions to improve adherence. Electronic health records (EHR) are an increasingly common source of data that are challenging to analyze but have potential for generating more accurate predictions of disease risk. The aim of this study was to build an EHR based model for statin adherence and link this model to biologic and clinical outcomes in patients receiving statin therapy. We gathered EHR data from the Military Health System which maintains administrative data for active duty, retirees, and dependents of the United States armed forces military that receive health care benefits. Data were gathered from patients prescribed their first statin prescription in 2005 and 2006. Baseline billing, laboratory, and pharmacy claims data were collected from the two years leading up to the first statin prescription and summarized using non-negative matrix factorization. Follow up statin prescription refill data was used to define the adherence outcome (> 80 percent days covered). The subsequent factors to emerge from this model were then used to build cross-validated, predictive models of 1) overall disease risk using coalescent regression and 2) statin adherence (using random forest regression). The predicted statin adherence for each patient was subsequently used to correlate with cholesterol lowering and hospitalizations for cardiovascular disease during the 5 year follow up period using Cox regression. The analytical dataset included 138 731 individuals and 1840 potential baseline predictors that were reduced to 30 independent EHR "factors". A random forest predictive model taking patient, statin prescription, predicted disease risk, and the EHR factors as potential inputs produced a cross-validated c-statistic of 0.736 for classifying statin non-adherence. The addition of the first refill to the model increased the c-statistic to 0.81. The predicted statin adherence was independently associated with greater cholesterol lowering (correlation = 0.14, p < 1e-20) and lower hospitalization for myocardial infarction, coronary artery disease, and stroke (hazard ratio = 0.84, p = 1.87E-06). Electronic health records data can be used to build a predictive model of statin adherence that also correlates with statins' cardiovascular benefits.

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LDL-cholesterol trajectories and statin treatment in Finnish type 2 diabetes patients: a growth mixture model

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