Previously undiagnosed risk factors and medication nonadherence are prevalent in young adults with first‐ever stroke

Sung, Sheng Feng; Lai, Edward Chia Cheng; Wu, Darren Philbert; Hsieh, Cheng‐Yang

doi:10.1002/pds.4250

Cited by 10 publications

(6 citation statements)

References 41 publications

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“…As older age has previously been identified as a major determinant for nonadherence, 23,24 we looked for explanations for these findings. We found that a younger age at the time of a stroke or acute coronary syndrome could possibly be associated with reduced medication adherence 50 . Although this was an inconclusive finding, it suggests that younger patients may be more likely to be nonadherent to preventive medications because of lower perceived risk of another CVD, misconceptions about the duration of treatment, or concerns about potential harm from statins 51 .…”

Section: Discussionmentioning

confidence: 74%

“…We found that a younger age at the time of a stroke or acute coronary syndrome could possibly be associated with reduced medication adherence. 50 Although this was an inconclusive finding, it suggests that younger patients may be more likely to be nonadherent to preventive medications because of lower perceived risk of another CVD, misconceptions about the duration of treatment, or concerns about potential harm from statins. 51 By analyzing the single variable age in relation to adherence in this population, there was no significant difference in adherence among the 3 age groups observed.…”

Section: Discussionmentioning

confidence: 96%

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Identification of Cardiovascular Patient Groups at Risk for Poor Medication Adherence

et al. 2021

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Background Poor medication adherence limits the secondary prevention of cardiovascular diseases (CVDs) and leads to increased morbidity, mortality, and costs. Identifying groups of patients at risk of poor adherence behavior could enable an intervention to be developed and target patients appropriately. Objective The first aim of this study was to identify homogeneous subgroups of cardiovascular outpatients based on their cardiovascular risk factors. Subsequently, differences in medication adherence between these groups were examined. Methods In this retrospective, observational study, patients with an established CVD were included. Well-known cardiovascular risk factors such as smoking, diet, exercise, blood lipid levels, blood pressure, and body mass index were collected. To identify patient subgroups, a 2-step cluster analytic procedure was performed. Differences between the groups on medication adherence were determined on the outcome of the Modified Morisky Scale. Data collection took place between October 2011 and January 2013. Results Cardiovascular risk factors of 530 patients were included in the cluster analysis. Three groups were identified. Compared with other clusters (clusters 1 and 2), cluster 3 contained significantly fewer patients who could be classified as highly adherent and more patients classified as medium adherent (23% and 57%, respectively; P = .024). This group was characterized by a younger age (53% were <55 years old) and using a relatively low number of different medications (41% used <4 different medications). Besides, in this subgroup the most smokers (37%), unhealthy alcohol users (27%), and patients with unhealthy eating habits (14%) were present. Conclusion This study showed that cardiovascular patients who are relatively young and have an unhealthy lifestyle are at risk for nonadherent behavior.

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Section: Discussionmentioning

confidence: 74%

Section: Discussionmentioning

confidence: 96%

Identification of Cardiovascular Patient Groups at Risk for Poor Medication Adherence

et al. 2021

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“…In Taiwan, hyperlipidemia may go undiagnosed until the onset of the first stroke episode in 55% of patients. 22 Furthermore, Taiwan’s NHI Administration has strict reimbursement criteria regarding lipid-lowering drugs for primary prevention of cardiovascular events. 23 Therefore, healthcare providers might find less need in coding hyperlipidemia.…”

Section: Discussionmentioning

confidence: 99%

Validation of Stroke Risk Factors in Patients with Acute Ischemic Stroke, Transient Ischemic Attack, or Intracerebral Hemorrhage on Taiwan’s National Health Insurance Claims Data

Hsieh¹,

Hsieh²,

Tsai³

et al. 2022

CLEP

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Purpose Taiwan has changed the coding system to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding since 2016. This study aimed to determine the optimal algorithms for identifying stroke risk factors in Taiwan’s National Health Insurance (NHI) claims data. Patients and Methods We retrospectively enrolled 4538 patients hospitalized for acute ischemic stroke (AIS), transient ischemic attack (TIA), or intracerebral hemorrhage (ICH) from two hospitals’ stroke registries, which were linked to NHI claims data. We developed several algorithms based on ICD-10-CM diagnosis codes and prescription claims data to identify hypertension, diabetes, hyperlipidemia, atrial fibrillation (AF), and ischemic heart disease (IHD) using registry data as the reference standard. The agreement of risk factor status between claims and registry data was quantified by calculating the kappa statistic. Results According to the registry data, the prevalence of hypertension, diabetes, hyperlipidemia, AF, and IHD among all patients was 77.5%, 41.5%, 47.9%, 12.1%, and 7.1%, respectively. In general, including diagnosis codes from prior inpatient or outpatient claims to those from the stroke hospitalization claims improved the agreement. Incorporating prescription data could improve the agreement for hypertension, diabetes, hyperlipidemia, and AF, but not for IHD. The kappa values of the optimal algorithms were 0.552 (95% confidence interval 0.524–0.580) for hypertension, 0.802 (0.784–0.820) for diabetes, 0.514 (0.490–0.539) for hyperlipidemia, 0.765 (0.734–0.795) for AF, and 0.518 (0.473–0.564) for IHD. Conclusion Algorithms using diagnosis codes alone are sufficient to identify hypertension, AF, and IHD whereas algorithms combining both diagnosis codes and prescription data are more suitable for identifying diabetes and hyperlipidemia. The study results may provide a reference for future studies using Taiwan’s NHI claims data.

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“…Pre-stroke glycemic control, as represented by glycated hemoglobin (HbA1c), and stress hyperglycemia, as defined by the plasma glucose-to-HbA1c ratio (GAR), have been shown to predict stroke outcomes after EVT ( 8–16 ). Furthermore, approximately 30% of patients with AIS have prediabetes ( 17 ), while approximately one-fifth of diabetic stroke patients are newly diagnosed with diabetes after stroke ( 18 ). Although prediabetes and newly diagnosed diabetes may predict poor outcomes in AIS patients treated with or without intravenous thrombolysis (IVT) ( 19–21 ), it remains unclear whether prediabetes or newly diagnosed diabetes after stroke are associated with adverse stroke outcomes after EVT.…”

Section: Introductionmentioning

confidence: 99%

Associations of diabetes status and glucose measures with outcomes after endovascular therapy in patients with acute ischemic stroke: an analysis of the nationwide TREAT-AIS registry

Hsieh,

Yang

et al. 2024

Front. Neurol.

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BackgroundHyperglycemia affects the outcomes of endovascular therapy (EVT) for acute ischemic stroke (AIS). This study compares the predictive ability of diabetes status and glucose measures on EVT outcomes using nationwide registry data.MethodsThe study included 1,097 AIS patients who underwent EVT from the Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke. The variables analyzed included diabetes status, admission glucose, glycated hemoglobin (HbA1c), admission glucose-to-HbA1c ratio (GAR), and outcomes such as 90-day poor functional outcome (modified Rankin Scale score ≥ 2) and symptomatic intracranial hemorrhage (SICH). Multivariable analyses investigated the independent effects of diabetes status and glucose measures on outcomes. A receiver operating characteristic (ROC) analysis was performed to compare their predictive abilities.ResultsThe multivariable analysis showed that individuals with known diabetes had a higher likelihood of poor functional outcomes (odds ratios [ORs] 2.10 to 2.58) and SICH (ORs 3.28 to 4.30) compared to those without diabetes. Higher quartiles of admission glucose and GAR were associated with poor functional outcomes and SICH. Higher quartiles of HbA1c were significantly associated with poor functional outcomes. However, patients in the second HbA1c quartile (5.6–5.8%) showed a non-significant tendency toward good functional outcomes compared to those in the lowest quartile (<5.6%). The ROC analysis indicated that diabetes status and admission glucose had higher predictive abilities for poor functional outcomes, while admission glucose and GAR were better predictors for SICH.ConclusionIn AIS patients undergoing EVT, diabetes status, admission glucose, and GAR were associated with 90-day poor functional outcomes and SICH. Admission glucose was likely the most suitable glucose measure for predicting outcomes after EVT.

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Previously undiagnosed risk factors and medication nonadherence are prevalent in young adults with first‐ever stroke

Abstract: Previously undiagnosed hypertension and nonadherence to treatment of hypertension and diabetes were more prevalent in young adult patients with first-ever stroke in Taiwan. Interventions targeting young people to promote early detection and adequate control of VRFs should be encouraged.

Cited by 10 publications

References 41 publications

Identification of Cardiovascular Patient Groups at Risk for Poor Medication Adherence

Identification of Cardiovascular Patient Groups at Risk for Poor Medication Adherence

Validation of Stroke Risk Factors in Patients with Acute Ischemic Stroke, Transient Ischemic Attack, or Intracerebral Hemorrhage on Taiwan’s National Health Insurance Claims Data

Associations of diabetes status and glucose measures with outcomes after endovascular therapy in patients with acute ischemic stroke: an analysis of the nationwide TREAT-AIS registry

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