Stephen E. Kimmel

Bio: Stephen E. Kimmel is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Warfarin & Odds ratio. The author has an hindex of 75, co-authored 274 publications receiving 20726 citations. Previous affiliations of Stephen E. Kimmel include Cleveland Clinic & Wilmington University.

Role of computerized physician order entry systems in facilitating medication errors.

Abstract: ContextHospital computerized physician order entry (CPOE) systems are widely regarded as the technical solution to medication ordering errors, the largest identified source of preventable hospital medical error. Published studies report that CPOE reduces medication errors up to 81%. Few researchers, however, have focused on the existence or types of medication errors facilitated by CPOE.ObjectiveTo identify and quantify the role of CPOE in facilitating prescription error risks.Design, Setting, and ParticipantsWe performed a qualitative and quantitative study of house staff interaction with a CPOE system at a tertiary-care teaching hospital (2002-2004). We surveyed house staff (N = 261; 88% of CPOE users); conducted 5 focus groups and 32 intensive one-on-one interviews with house staff, information technology leaders, pharmacy leaders, attending physicians, and nurses; shadowed house staff and nurses; and observed them using CPOE. Participants included house staff, nurses, and hospital leaders.Main Outcome MeasureExamples of medication errors caused or exacerbated by the CPOE system.ResultsWe found that a widely used CPOE system facilitated 22 types of medication error risks. Examples include fragmented CPOE displays that prevent a coherent view of patients’ medications, pharmacy inventory displays mistaken for dosage guidelines, ignored antibiotic renewal notices placed on paper charts rather than in the CPOE system, separation of functions that facilitate double dosing and incompatible orders, and inflexible ordering formats generating wrong orders. Three quarters of the house staff reported observing each of these error risks, indicating that they occur weekly or more often. Use of multiple qualitative and survey methods identified and quantified error risks not previously considered, offering many opportunities for error reduction.ConclusionsIn this study, we found that a leading CPOE system often facilitated medication error risks, with many reported to occur frequently. As CPOE systems are implemented, clinicians and hospitals must attend to errors that these systems cause in addition to errors that they prevent.

Estimation of the warfarin dose with clinical and pharmacogenetic data

Abstract: Warfarin is one of the most widely used anticoagulants in the world. Treatment is complicated by a large inter-individual variation in the dose needed to reach adequate levels of anticoagulation i.e. INR 2.0 – 3.0. The objective of this thesis was to evaluate which factors, mainly genetic but also non-genetic, that affect the response to warfarin in terms of required maintenance dose, efficacy and safety with special focus on warfarin dose prediction.Through candidate gene and genome-wide studies, we have shown that the genes CYP2C9 and VKORC1 are the major determinants of warfarin maintenance dose. By combining the SNPs CYP2C9 *2, CYP2C9 *3 and VKORC1 rs9923231 with the clinical factors age, height, weight, ethnicity, amiodarone and use of inducers (carbamazepine, phenytoin or rifampicin) into a prediction model (the IWPC model) we can explain 43 % to 51 % of the variation in warfarin maintenance dose. Patients requiring doses < 29 mg/week and doses ≥ 49 mg/week benefitted the most from pharmacogenetic dosing. Further, we have shown that the difference across ethnicities in percent variance explained by VKORC1 was largely accounted for by the allele frequency of rs9923231. Other novel genes affecting maintenance dose (NEDD4 and DDHD1), as well as the replicated CYP4F2 gene, have small effects on dose predictions and are not likely to be cost-effective, unless inexpensive genotyping is available.Three types of prediction models for warfarin dosing exist: maintenance dose models, loading dose models and dose revision models. The combination of these three models is currently being used in the warfarin treatment arm of the European Pharmacogenetics of Anticoagulant Therapy (EU-PACT) study. Other clinical trials aiming to prove the clinical validity and utility of pharmacogenetic dosing are also underway.The future of pharmacogenetic warfarin dosing relies on results from these ongoing studies, the availability of inexpensive genotyping and the cost-effectiveness of pharmacogenetic driven warfarin dosing compared with new oral anticoagulant drugs.

A Pharmacogenetic versus a Clinical Algorithm for Warfarin Dosing

Abstract: Background The clinical utility of genotype-guided (pharmacogenetically based) dosing of warfarin has been tested only in small clinical trials or observational studies, with equivocal results. Methods We randomly assigned 1015 patients to receive doses of warfarin during the first 5 days of therapy that were determined according to a dosing algorithm that included both clinical variables and genotype data or to one that included clinical variables only. All patients and clinicians were unaware of the dose of warfarin during the first 4 weeks of therapy. The primary outcome was the percentage of time that the international normalized ratio (INR) was in the therapeutic range from day 4 or 5 through day 28 of therapy. Results At 4 weeks, the mean percentage of time in the therapeutic range was 45.2% in the genotype-guided group and 45.4% in the clinically guided group (adjusted mean difference, [genotype-guided group minus clinically guided group], −0.2; 95% confidence interval, −3.4 to 3.1; P=0.91). There ...

Clinical Pharmacogenetics Implementation Consortium Guidelines for CYP2C9 and VKORC1 Genotypes and Warfarin Dosing

Abstract: Warfarin is a widely used anticoagulant with a narrow therapeutic index and large interpatient variability in the dose required to achieve target anticoagulation. Common genetic variants in the cytochrome P450-2C9 (CYP2C9) and vitamin K–epoxide reductase complex (VKORC1) enzymes, in addition to known nongenetic factors, account for ~50% of warfarin dose variability. The purpose of this article is to assist in the interpretation and use of CYP2C9 and VKORC1 genotype data for estimating therapeutic warfarin dose to achieve an INR of 2–3, should genotype results be available to the clinician. The Clinical Pharmacogenetics Implementation Consortium (CPIC) of the National Institutes of Health Pharmacogenomics Research Network develops peer-reviewed gene–drug guidelines that are published and updated periodically on http://www.pharmgkb.org based on new developments in the field. 1 Focused Literature review The Supplementary Notes online include a systematic literature review of CYP2C9 and VKORC1 genotype and warfarin dosing, which forms the basis for this guideline. drug: w arF arin Warfarin (Coumadin and others) is the most commonly used oral anticoagulant worldwide, with annual prescriptions typically equaling 0.5–1.5% of the population. It is prescribed for treatment and prevention of thrombotic disorders. 2 Although highly efficacious, warfarin’s narrow therapeutic index and wide interindividual variability make its dosing notoriously challenging. 3–5 Complications from inappropriate warfarin dosing are among the adverse events most frequently reported to the US Food and Drug Administration (FDA) and one of the most common reasons for emergency room visits. 6 Warfarin is often dosed empirically: an initial dose is prescribed, typically followed by at least weekly measurement of the INR and subsequent dose adjustment. The initial dose is often based on population averages (e.g., 3–5 mg/day), but stable doses to achieve an INR of 2–3 can range from 1–20 mg/ day. The iterative process to define the appropriate dose can take weeks to months, and during this period patients are at increased risk of over- or under-anticoagulation and thus at risk of thromboembolism or bleeding.

Potential Effects of Aggressive Decongestion During the Treatment of Decompensated Heart Failure on Renal Function and Survival

Abstract: Background— Overly aggressive diuresis leading to intravascular volume depletion has been proposed as a cause for worsening renal function during the treatment of decompensated heart failure. If diuresis occurs at a rate greater than extravascular fluid can refill the intravascular space, the concentration of such intravascular substances as hemoglobin and plasma proteins increases. We hypothesized that hemoconcentration would be associated with worsening renal function and possibly would provide insight into the relationship between aggressive decongestion and outcomes. Methods and Results— Subjects in the Evaluation Study of Congestive Heart Failure and Pulmonary Artery Catheterization Effectiveness trial limited data set with a baseline/discharge pair of hematocrit, albumin, or total protein values were included (336 patients). Baseline-to-discharge increases in these parameters were evaluated, and patients with ≥2 in the top tertile were considered to have evidence of hemoconcentration. The group expe...

An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

Abstract: The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.

European Guidelines on Cardiovascular Disease Prevention in Clinical Practice (Version 2012)

Abstract: ABI : ankle–brachial index ACCORD : Action to Control Cardiovascular Risk in Diabetes ADVANCE : Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation AGREE : Appraisal of Guidelines Research and Evaluation AHA : American Heart Association apoA1 : apolipoprotein A1 apoB : apolipoprotein B CABG : coronary artery bypass graft surgery CARDS : Collaborative AtoRvastatin Diabetes Study CCNAP : Council on Cardiovascular Nursing and Allied Professions CHARISMA : Clopidogrel for High Athero-thrombotic Risk and Ischemic Stabilisation, Management, and Avoidance CHD : coronary heart disease CKD : chronic kidney disease COMMIT : Clopidogrel and Metoprolol in Myocardial Infarction Trial CRP : C-reactive protein CURE : Clopidogrel in Unstable Angina to Prevent Recurrent Events CVD : cardiovascular disease DALYs : disability-adjusted life years DBP : diastolic blood pressure DCCT : Diabetes Control and Complications Trial ED : erectile dysfunction eGFR : estimated glomerular filtration rate EHN : European Heart Network EPIC : European Prospective Investigation into Cancer and Nutrition EUROASPIRE : European Action on Secondary and Primary Prevention through Intervention to Reduce Events GFR : glomerular filtration rate GOSPEL : Global Secondary Prevention Strategies to Limit Event Recurrence After MI GRADE : Grading of Recommendations Assessment, Development and Evaluation HbA1c : glycated haemoglobin HDL : high-density lipoprotein HF-ACTION : Heart Failure and A Controlled Trial Investigating Outcomes of Exercise TraiNing HOT : Hypertension Optimal Treatment Study HPS : Heart Protection Study HR : hazard ratio hsCRP : high-sensitivity C-reactive protein HYVET : Hypertension in the Very Elderly Trial ICD : International Classification of Diseases IMT : intima-media thickness INVEST : International Verapamil SR/Trandolapril JTF : Joint Task Force LDL : low-density lipoprotein Lp(a) : lipoprotein(a) LpPLA2 : lipoprotein-associated phospholipase 2 LVH : left ventricular hypertrophy MATCH : Management of Atherothrombosis with Clopidogrel in High-risk Patients with Recent Transient Ischaemic Attack or Ischaemic Stroke MDRD : Modification of Diet in Renal Disease MET : metabolic equivalent MONICA : Multinational MONItoring of trends and determinants in CArdiovascular disease NICE : National Institute of Health and Clinical Excellence NRT : nicotine replacement therapy NSTEMI : non-ST elevation myocardial infarction ONTARGET : Ongoing Telmisartan Alone and in combination with Ramipril Global Endpoint Trial OSA : obstructive sleep apnoea PAD : peripheral artery disease PCI : percutaneous coronary intervention PROactive : Prospective Pioglitazone Clinical Trial in Macrovascular Events PWV : pulse wave velocity QOF : Quality and Outcomes Framework RCT : randomized clinical trial RR : relative risk SBP : systolic blood pressure SCORE : Systematic Coronary Risk Evaluation Project SEARCH : Study of the Effectiveness of Additional Reductions in Cholesterol and SHEP : Systolic Hypertension in the Elderly Program STEMI : ST-elevation myocardial infarction SU.FOL.OM3 : SUpplementation with FOlate, vitamin B6 and B12 and/or OMega-3 fatty acids Syst-Eur : Systolic Hypertension in Europe TNT : Treating to New Targets UKPDS : United Kingdom Prospective Diabetes Study VADT : Veterans Affairs Diabetes Trial VALUE : Valsartan Antihypertensive Long-term Use VITATOPS : VITAmins TO Prevent Stroke VLDL : very low-density lipoprotein WHO : World Health Organization ### 1.1 Introduction Atherosclerotic cardiovascular disease (CVD) is a chronic disorder developing insidiously throughout life and usually progressing to an advanced stage by the time symptoms occur. It remains the major cause of premature death in Europe, even though CVD mortality has …

Guidelines for the Early Management of Patients With Acute Ischemic Stroke A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association

Abstract: Background and Purpose—The authors present an overview of the current evidence and management recommendations for evaluation and treatment of adults with acute ischemic stroke. The intended audienc...

Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association

Abstract: WRITING GROUP MEMBERS Emelia J. Benjamin, MD, SCM, FAHA Michael J. Blaha, MD, MPH Stephanie E. Chiuve, ScD Mary Cushman, MD, MSc, FAHA Sandeep R. Das, MD, MPH, FAHA Rajat Deo, MD, MTR Sarah D. de Ferranti, MD, MPH James Floyd, MD, MS Myriam Fornage, PhD, FAHA Cathleen Gillespie, MS Carmen R. Isasi, MD, PhD, FAHA Monik C. Jiménez, ScD, SM Lori Chaffin Jordan, MD, PhD Suzanne E. Judd, PhD Daniel Lackland, DrPH, FAHA Judith H. Lichtman, PhD, MPH, FAHA Lynda Lisabeth, PhD, MPH, FAHA Simin Liu, MD, ScD, FAHA Chris T. Longenecker, MD Rachel H. Mackey, PhD, MPH, FAHA Kunihiro Matsushita, MD, PhD, FAHA Dariush Mozaffarian, MD, DrPH, FAHA Michael E. Mussolino, PhD, FAHA Khurram Nasir, MD, MPH, FAHA Robert W. Neumar, MD, PhD, FAHA Latha Palaniappan, MD, MS, FAHA Dilip K. Pandey, MBBS, MS, PhD, FAHA Ravi R. Thiagarajan, MD, MPH Mathew J. Reeves, PhD Matthew Ritchey, PT, DPT, OCS, MPH Carlos J. Rodriguez, MD, MPH, FAHA Gregory A. Roth, MD, MPH Wayne D. Rosamond, PhD, FAHA Comilla Sasson, MD, PhD, FAHA Amytis Towfighi, MD Connie W. Tsao, MD, MPH Melanie B. Turner, MPH Salim S. Virani, MD, PhD, FAHA Jenifer H. Voeks, PhD Joshua Z. Willey, MD, MS John T. Wilkins, MD Jason HY. Wu, MSc, PhD, FAHA Heather M. Alger, PhD Sally S. Wong, PhD, RD, CDN, FAHA Paul Muntner, PhD, MHSc On behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee Heart Disease and Stroke Statistics—2017 Update