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Hydroxymethylglutaryl-CoA Reductase Inhibitors

Hydroxymethylglutaryl-CoA Reductase Inhibitors: These compounds inhibit the enzyme hydroxymethylglutaryl-coenzyme A (HMG-CoA) reductase, which catalyzes the conversion of HMG-CoA to mevalonate, a key step in cholesterol biosynthesis.
By reducing cholesterol levels, these inhibitors are effective in the treatmeant of hypercholestrolemia and the prevention of cardiovascular disease.
Thsi class of drugs includes statins and other synthetic and natural compounds.

Most cited protocols related to «Hydroxymethylglutaryl-CoA Reductase Inhibitors»

1
Propensity Score Matching for Statin Exposure
Cited 1021 times
The propensity score was estimated using logistic regression to regress receipt of a statin prescription at discharge on the 24 baseline covariates described in Table I. The estimated propensity score was the predicted probability of statin exposure derived from the fitted logistic regression model. In the propensity-score model we assumed a linear relationship between continuous covariates and the log-odds of receiving a statin prescription. Furthermore, the propensity-score model did not include any interactions.
We created a matched sample by matching treated and untreated subjects on the logit of the propensity score using calipers of width equal to 0.2 of the standard deviation of the logit of the propensity score [3 (link), 17 (link), 18 (link)]. A greedy, nearest-neighbour matching algorithm was employed to form pairs of treated and untreated subjects.
, & Austin P.C. (2009). Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine, 28(25), 3083-3107.
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Publication 2009
Hydroxymethylglutaryl-CoA Reductase Inhibitors Patient Discharge
2
Statin Prescription and Cardiovascular Outcomes
Cited 247 times
We used data on 9104 patients who were discharged alive following hospitalization with a diagnosis of acute myocardial infarction (AMI or heart attack) from 102 hospitals in Ontario, Canada, between April 1, 1999 and March 31, 2001. These data are similar to those reported on elsewhere [13 (link)–15 (link)], and were collected as part of the Enhanced Feedback for Effective Cardiac Treatment (EFFECT) Study, an initiative focused on improving the quality of care for cardiovascular disease patients in Ontario [16 ]. Data on patient demographics, presenting signs and symptoms, classic cardiac risk factors, comorbid conditions and vascular history, vital signs on admission, and results of laboratory tests, were abstracted directly from patients’ medical records. The exposure of interest was whether the patient was prescribed a statin at hospital discharge. Overall, 3049 (33.5 per cent) of patients received a prescription for a statin at discharge, while 6055 (66.5 per cent) did not receive a prescription at discharge. Table I compares the means of continuous baseline covariates and prevalences of dichotomous baseline covariates between treated and untreated subjects in the original unmatched sample. The prevalence of dichotomous variables was compared between treated and untreated subjects using a Chi-squared test, while a standard two-sample t-test was used to compare continuous baseline covariates.
, & Austin P.C. (2009). Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine, 28(25), 3083-3107.
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Publication 2009
Blood Vessel Cardiovascular Diseases Diagnosis Heart Hospitalization Hydroxymethylglutaryl-CoA Reductase Inhibitors Myocardial Infarction Patient Discharge Patients Quality of Health Care Signs, Vital
3
Algorithmic Approach to Propensity Score Adjustment
Cited 74 times
A wide range of databases of health care utilization data (“claims”) is available for use in pharmacoepidemiology.3 (link) Each database is arranged in specific ways using a variety of classifications to code diagnoses (e.g. International Classification of Diseases [ICD]-8 through ICD-10), procedures (e.g. Current Procedural Terminology, Canadian Classification of Proceddures, ICD-9-Clinical Modification), or medications (e.g. National Drug Codes, American Hospital Formulary Services, Anatomical Therapeutic Chemical Classification). Beyond these basic data dimensions and coding systems, many more data dimensions can be found in such databases. Some databases provide additional dimensions such as laboratory results, other electronic medical record information, and accident registries.
We propose an algorithm that is independent of the specific data source as long as the source’s data dimensions can be identified. In Figure 2 we provide a flow diagram using a typical example of data dimensions available in US Medicare claims data linked to medication use data. First, a temporal window must be defined in which baseline covariates will be identified. A frequent choice is 6 or 12 months preceding the initiation of the study or comparison drug.2 (link) The recording of diagnoses and procedures is correlated with the frequency of health care encounters. Therefore, longer baseline periods increase the number of encounters and therefore yield more covariate information.2 (link)The most basic patient information always available to typical databases is age, sex and calendar time. We assume that given their ubiquity, these demographic covariates will always be adjusted for.
Additional covariates can then be identified from the various data dimensions, but it is first necessary to identify variables that should not be part of covariate adjustment. While it is generally recommended to include many covariates in a propensity score regression model, in specific cases researchers may exclude variables from covariate adjustment.17 (link) Surrogates for the exposure (i.e. covariates that are strong correlates of the study exposure but not associated with the outcome) will not only increase standard errors but may also increase bias—and should therefore not be included in propensity score analyses.18 ,19 (link) Bias can also occur through the inclusion of so-called “collider” variables, although this bias is generally thought to be weak.20 (link),21 (link) In our example study comparing statin initiation with glaucoma drug initiation, diagnostic codes for glaucoma should not be included in a propensity score because of their close correlation with treatment choice. 22 (link),23 At this stage of the procedure, such codes can be identified and removed from the dimension data input to the algorithm. We have developed a screening tool for such covariates as part of the algorithm that will help investigators identify and remove such covariates (eAppendix 1, http://links.lww.com).
Schneeweiss S., Rassen J.A., Glynn R.J., Avorn J., Mogun H, & Brookhart M.A. (2009). High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology (Cambridge, Mass.), 20(4), 512-522.
Publication 2009
Accidents Debility Diagnosis Glaucoma Hydroxymethylglutaryl-CoA Reductase Inhibitors Patient Acceptance of Health Care Patients Pharmaceutical Preparations Selection for Treatment Therapeutics
4
Meta-Analysis of LDL Cholesterol Reduction
Cited 63 times

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Publication 2010
Cholesterol, beta-Lipoprotein Genetic Heterogeneity Hydroxymethylglutaryl-CoA Reductase Inhibitors Lipids Patients PER1 protein, human Treatment Protocols
5
Invasive vs. Conservative Strategy for Myocardial Infarction Patients
Cited 50 times
The VIAMI-trial was conducted to investigate the differences in clinical outcome between an invasive and a conservative strategy in patients with demonstrated viability in the infarct-area. The expected event rate in the viability positive group was estimated to be 35 percent. To demonstrate with a power of 80% (α = 0.05, two-sided) that PCI leads to a 50% reduction in event rate in the invasive group compared to the conservative group, 200 patients would be needed in each group.
As a formal stopping rule for the study the following was used: if one of the treatment strategies appeared significantly superior at interim analysis (p ≤ 0.01), the study would be stopped. Interim analysis was performed each time another 100 patients were included.
Baseline descriptive data are presented as a mean ± standard deviations (SD). Differences in clinical and echocardiographic variables are assessed by unpaired Student's t-test. Differences between proportions are assessed by chi-square analysis; a Fisher's exact test is used when appropriate. Event-free survival curves are computed with the Kaplan-Meier method, and the differences between these curves are tested with a log-rank test. The Cox proportional hazards regression analysis was used to estimate the treatment effect as hazard ratio (HR) with 95% confidence intervals. Besides the "crude" effects, adjustments were made for DM, hypertension, hypercholesterolemia, current smoking, family history of CAD (model a), clinical history (angina, myocardial infarction, PCI or CABG) and medication use at baseline (aspirin, beta-blocker, Ca-inhibitor, statins, ACE-I and AT II antagonist) (model b) and for all covariates (model c).
All analyses were performed on an intention-to-treat basis. Outcome per-protocol was also evaluated, since this would reflect the true influence of PCI on clinical outcome. Because after randomization there was a median waiting-time of two days before a revascularization procedure was performed inevitably some events occurred. In the per-protocol analysis these events are excluded from analysis, because they occurred before the by protocol demanded intervention. To make a fair comparison between the two groups in the per-protocol analysis we also excluded the events in the conservative group occurring during the first two days after randomization. All analyses were performed with the use of SPSS software, version 16.0 (SPSS, Inc., Chigago, Illinois).
van Loon R.B., Veen G., Baur L.H., Kamp O., Bronzwaer J.G., Twisk J.W., Verheugt F.W, & van Rossum A.C. (2012). Improved clinical outcome after invasive management of patients with recent myocardial infarction and proven myocardial viability: primary results of a randomized controlled trial (VIAMI-trial). Trials, 13, 1.
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Publication 2012
Adrenergic beta-Antagonists AGTR2 protein, human Angina Pectoris Aspirin Coronary Artery Bypass Surgery Echocardiography High Blood Pressures Hydroxymethylglutaryl-CoA Reductase Inhibitors Hypercholesterolemia Infarction Myocardial Infarction Patients Pharmaceutical Preparations

Most recents protocols related to «Hydroxymethylglutaryl-CoA Reductase Inhibitors»

1
Cardiovascular Prevention Protocol for Iran
The IraPEN's preventive actions are expected to reduce cardiovascular events. The relative risks (RRs) of these preventive actions and the medications that are used in the program were obtained from meta-analyses or randomized clinical trials (RCTs). By multiplying or adding up the RRs of different medications, there is a risk of effect overestimation, and a correction was made by using the formula below wherever multiple interventions were involved:
This equation has been developed based on a study that compared the effect of controlling the risk factors separately vs. controlling all of them simultaneously (15 (link)).
Based on the field interviews, it was clear which medications are used for each index cohort. Almost in all cases, angiotensin-converting enzyme (ACE) inhibitors are the first choice for hypertension treatment. Enalapril is the most prescribed one as monotherapy. Thiazides (diuretics) are the second choice followed by beta-blockers. In case the hypertension is not controlled by monotherapy instead of increasing the dose, the second drug is added. As recommended by guidelines, small doses of various classes of antihypertensive medications are more useful than a high dose of one (16 ). In general, the combination of ACE inhibitors and thiazide is the most common one. This pattern is aligned with Joint National Committee (JNC8) guidelines. Statins are prescribed for hyperlipidemia treatment. Among statins, Atorvastatin is the choice as it is one of the most potent ones. For diabetes, Metformin is started and increased to the maximum dose (2 g) and then the second medication that is Glibenclamide is added. Due to its potential harm and insufficient evidence of its efficacy, Aspirin was not recommended for primary prevention by PEN protocols. Therefore, Aspirin is not used in IraPEN as well. Here are the list of medications and their daily dosages which are used in IraPEN:
The unit price of each of these medications was derived from the Iranian Annual Pharma Statistics file. For the calculation of the intervention's effects, it is assumed that the adherence of individuals to the treatment is 100%. Table 3 lists the RRs of different interventions (medications) for CHD and stroke.
Jamshidi A., Daroudi R., Aas E, & Khalili D. (2023). A cost-effectiveness analysis of risk-based intervention for prevention of cardiovascular diseases in IraPEN program: A modeling study. Frontiers in Public Health, 11, 1075277.
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Publication 2023
Adrenergic beta-Antagonists Angiotensin-Converting Enzyme Inhibitors Antihypertensive Agents Aspirin Atorvastatin Cardiovascular System Cerebrovascular Accident Diabetes Mellitus Diuretics Enalapril Glyburide High Blood Pressures Hydroxymethylglutaryl-CoA Reductase Inhibitors Hyperlipidemia Joints Metformin PEN protocol Pharmaceutical Preparations Primary Prevention Selection for Treatment Thiazides
2
Covariate Adjustment and Propensity Scoring in SGLT2i Research
To mitigate risk of confounding, we assessed and adjusted for > 30 baseline covariates that were assessed in the 12-month period prior to and including the index date. These covariates included patient sociodemographics (e.g., age at medication initiation, biological sex, and race, calendar year), complications of diabetes (e.g., diabetic neuropathy, nephropathy, retinopathy), oral and injectable glucose lowering therapies (e.g., metformin, sulfonylureas, insulin), diagnosis of cardiovascular conditions (e.g., myocardial infarction, stroke, HF), and cardiovascular medication use (e.g., dispensing of β-blockers, loop diuretics, statins). Frailty status was ascertained using the claims based frailty index, and using a threshold of ≥ 0.25 to define frailty [23 (link)].
Propensity scores were estimated using a logistic regression that modelled the probability of initiating SGLT2i (exposure) versus a non-gliflozin medication (control) conditional on the baseline covariates. These propensity scores were then used to estimate stabilized inverse probability of treatment weights (IPTW) to account for imbalances in patient characteristics [24 (link)].
Gonzalez J., Bates B.A., Setoguchi S., Gerhard T, & Dave C.V. (2023). Cardiovascular outcomes with SGLT2 inhibitors versus DPP4 inhibitors and GLP-1 receptor agonists in patients with heart failure with reduced and preserved ejection fraction. Cardiovascular Diabetology, 22, 54.
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Publication 2023
Biopharmaceuticals Cardiovascular Agents Cardiovascular Diseases Cerebrovascular Accident Complications of Diabetes Mellitus Diabetic Neuropathies Diagnosis Glucose Hydroxymethylglutaryl-CoA Reductase Inhibitors Insulin Kidney Diseases Loop Diuretics Metformin Myocardial Infarction Patients Pharmaceutical Preparations Retinal Diseases Sodium-Glucose Transporter 2 Inhibitors Sulfonylurea Compounds
3
Perioperative Antithrombotic Management for PEA
Before PEA, 8 patients were anticoagulated with warfarin, 6 with LMWH, 2 with fondaparinux and 1 with unfractionated heparin (UFH), and due to the thrombotic burden, 15 of 17 patients also received low-dose aspirin (ASA, 100 mg) and statins. ASA was withheld for 5 days prior to PEA. Postoperative anticoagulation was initiated by UFH (1 patient) or LMWH (dalteparin in 8, enoxaparin in 7, and tinzaparin in 1 patient) at 372 ± 177 min after surgery and ASA 2.8 ± 1.8 days after the operation. LMWH dosage was titrated until the targeted through anti-FXa activity of 0.2-0.5 IU/ml was achieved and switched to fondaparinux before discharge in 2 patients. At 3 months, 14 patients were treated with LMWH (9 with dalteparin and 5 with enoxaparin) and 2 patients with fondaparinux, and 16 patients received ASA. The long-term antithrombotic care and the switch to oral anticoagulation with warfarin was later determined on individual basis.
Myllylahti L., Ropponen J., Lax M., Lassila R, & Nykänen A.I. (2023). Upregulation of Coagulation Factor VIII and Fibrinogen After Pulmonary Endarterectomy in Patients with Chronic Thromboembolic Pulmonary Hypertension. Clinical and Applied Thrombosis/Hemostasis, 29, 10760296231158369.
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Publication 2023
Dalteparin Enoxaparin Fondaparinux Heparin Heparin, Low-Molecular-Weight Hydroxymethylglutaryl-CoA Reductase Inhibitors Long-Term Care Operative Surgical Procedures Patient Discharge Patients Surgery, Day Tinzaparin Warfarin
4
Comprehensive Stroke Risk Assessment
In this study, clinical data were collected from the enrolled patients, including demographics (age and sex); vascular risk factors (hypertension, diabetes mellitus, and ischemic heart disease); baseline blood pressure [systolic blood pressure (SBP) and diastolic blood pressure (DBP)]; Trial of Org 10 172 in Acute Stroke Treatment (TOAST) [large-artery atherosclerosis, cardioembolism, small-vessel occlusion, acute stroke of other determined etiology, stroke of undetermined etiology]; stroke severity (SS) [defined as mild stroke according to the National Institutes of Health Stroke Scale (NIHSS) scores of ≤ 8, moderate-to-severe stroke according to NIHSS scores of ≥9; all assessments completed on admission]; magnetic resonance imaging (MRI) features [stroke distribution (SD; anterior circulation, posterior circulation, and anterior/posterior circulation), side of hemisphere (SOH; left, right, and bilateral), number of stroke lesions (NOSs; single and multiple stroke lesions), site of stroke lesions (SOSs; cortical, cortico-subcortical, subcortical, brainstem, and cerebellum)]; laboratory tests [total cholesterol, triglycerides, low-density lipoprotein (LDL), fasting blood glucose (FBG), homocysteine (HCY), uric acid (UA), fibrinogen (FIB), myoglobin (MB), C-reactive protein (CRP), D-dimer brain natriuretic peptide (BNP), HBALC, neuron-specific enolase (NSE), and S-100β levels], treatment regimen [intravenous thrombolysis, arterial thrombolysis, antiplatelet, anticoagulation, statin, and proton pump inhibitor therapy (PPI)]; and stroke comorbidities [dysphagia and stroke-associated pneumonia (SAP)].
Wang K., Gu L., Liu W., Xu C., Yin C., Liu H., Rong L., Li W, & Wei X. (2023). The predictors of death within 1 year in acute ischemic stroke patients based on machine learning. Frontiers in Neurology, 14, 1092534.
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Publication 2023
Acute Cerebrovascular Accidents Arteries Atherosclerosis Blood Glucose Blood Pressure Blood Vessel Brain Stem Cerebellum Cerebrovascular Accident Cholesterol Cortex, Cerebral C Reactive Protein Deglutition Disorders Dental Occlusion Diabetes Mellitus fibrin fragment D Fibrinogen Fibrinolytic Agents gamma-Enolase High Blood Pressures Homocysteine Hydroxymethylglutaryl-CoA Reductase Inhibitors Low-Density Lipoproteins Myocardial Ischemia Myoglobin Nesiritide Patients Pneumonia Pressure, Diastolic Proton Pump Inhibitors Systolic Pressure Therapeutics Treatment Protocols Triglycerides Uric Acid
5
Propensity-Matched Comparison of ACEI and ARB
For continuous variables, data were expressed as mean ± standard deviation or median (interquartile range) and differences between the two groups were evaluated using the unpaired t-test or Mann-Whitney U test. For discrete variables, differences were expressed as counts and percentages and were analyzed with the χ2 test between the two groups. To adjust for any potential confounders, propensity score-matching (PSM) analysis was performed using the logistic regression model with all available variables that could be of potential relevance: age, gender, body mass index (BMI), history of smoking, Killip class on admission, BP, heart rate, LV ejection fraction (LVEF), CV risk factors or co-morbidity (hypertension, diabetes mellitus, hyperlipidemia, prior HF, prior stroke, prior MI, and prior angina), initial estimated glomerular filtration rate (eGFR) by Modification of Diet in Renal Disease (MDRD) equation, co-medications (aspirin, P2Y12 inhibitors, CCB, beta-blockers and statins) at discharge and types of MI (STEMI or NSTEMI). Patients in the ARB group were 1:1 matched to those in the ACEI group according to propensity score with nearest neighbor matching algorithm. Subjects were matched with a caliper width equal to 0.1 of the standard deviation of the propensity score. The efficacy of the propensity score model was assessed by estimating standardized differences for each covariate between groups. Survival curves for clinical endpoints and cumulative event rates with incidence rates per 100 patient-years up to 2-year were generated using Kaplan–Meier estimates. Cox-proportional hazard models were used to assess the adjusted hazard ratio (HR) comparing the two groups and their 95% confidence interval (CI) for each clinical endpoint. Subgroups that were defined post-hoc according to demographic and clinical characteristics included age (<75 & ≥75 years), gender, diabetes mellitus, Killip class, LVEF (<50% & ≥50%), beta-blockers at discharge, type of MI, multi-vessel disease and infarct-related artery.
All data were processed with SPSS version 23 (IBM Co, Armonk, NY, US) and R version 3.1.3 (R Foundation for Statistical Computing, Vienna, Austria). For all analyses, a two-sided p < 0.05 was considered to be statistically significant.
Lee J.G., Joo S.J., Kim S.Y., Choi J.H., Boo K.Y., Hwang J.Y., Hur S.H, & Jeong M.H. (2023). Impact of angiotensin-converting enzyme inhibitors versus angiotensin receptor blockers on clinical outcomes in hypertensive patients with acute myocardial infarction. PLOS ONE, 18(3), e0281460.
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Publication 2023
Adrenergic beta-Antagonists Angina Pectoris Arteries Aspirin Cerebrovascular Accident Diabetes Mellitus Dietary Modification Gender Glomerular Filtration Rate High Blood Pressures Hydroxymethylglutaryl-CoA Reductase Inhibitors Hyperlipidemia Index, Body Mass Infarction inhibitors Kidney Diseases Non-ST Elevated Myocardial Infarction Patient Discharge Patients Pharmaceutical Preparations Rate, Heart ST Segment Elevation Myocardial Infarction Vascular Diseases

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More about "Hydroxymethylglutaryl-CoA Reductase Inhibitors"

Hydroxymethylglutaryl-CoA Reductase Inhibitors, also known as HMG-CoA Reductase Inhibitors or Statins, are a class of medications that play a crucial role in the treatment and prevention of hypercholesterolemia and cardiovascular disease.
These compounds work by inhibiting the enzyme hydroxymethylglutaryl-coenzyme A (HMG-CoA) reductase, which is a key step in the cholesterol biosynthesis pathway.
Statins, such as Simvastatin, Atorvastatin, and Pravastatin, are the most well-known and widely prescribed HMG-CoA Reductase Inhibitors.
By reducing cholesterol levels, these medications have been shown to be effective in the management of high cholesterol and the prevention of cardiovascular events, such as heart attacks and strokes.
The impact of HMG-CoA Reductase Inhibitors extends beyond just their clinical applications.
Researchers have utilized statistical software like SAS version 9.4 and Stata 14 to analyze the efficacy, safety, and real-world outcomes associated with these drugs.
These analyses have provided valuable insights into the optimization of statin therapy and the overall management of hypercholesterolemia.
For those looking to enhance their Hydroxymethylglutaryl-CoA Reductase Inhibitor research, tools like PubCompare.ai can be leveraged to identify the best protocols and products from published literature, preprints, and patents.
This AI-driven platform empowers researchers to make more informed decisions and improve the reproducibility and accuracy of their studies.
Wheter you're a healthcare professional, a researcher, or simply someone interested in the field of cardiovascular health, understanding the role and impact of Hydroxymethylglutaryl-CoA Reductase Inhibitors is crucial.
With the insights and resources available, you can stay at the forefront of this rapidly evolving area of medicine and contribute to the ongoing efforts to improve patient outcomes.