Citations

Background and Purpose— NINDS (National Institute of Neurological Disorders and Stroke)-SiGN (Stroke Genetics Network) is an international consortium of ischemic stroke studies that aims to generate high-quality phenotype data to identify the genetic basis of pathogenic stroke subtypes. This analysis characterizes the etiopathogenetic basis of ischemic stroke and reliability of stroke classification in the consortium. Methods— Fifty-two trained and certified adjudicators determined both phenotypic (abnormal test findings categorized in major pathogenic groups without weighting toward the most likely cause) and causative ischemic stroke subtypes in 16 954 subjects with imaging-confirmed ischemic stroke from 12 US studies and 11 studies from 8 European countries using the web-based Causative Classification of Stroke System. Classification reliability was assessed with blinded readjudication of 1509 randomly selected cases. Results— The distribution of pathogenic categories varied by study, age, sex, and race ( P <0.001 for each). Overall, only 40% to 54% of cases with a given major ischemic stroke pathogenesis (phenotypic subtype) were classified into the same final causative category with high confidence. There was good agreement for both causative (κ 0.72; 95% confidence interval, 0.69–0.75) and phenotypic classifications (κ 0.73; 95% confidence interval, 0.70–0.75). Conclusions— This study demonstrates that pathogenic subtypes can be determined with good reliability in studies that include investigators with different expertise and background, institutions with different stroke evaluation protocols and geographic location, and patient populations with different epidemiological characteristics. The discordance between phenotypic and causative stroke subtypes highlights the fact that the presence of an abnormality in a patient with stroke does not necessarily mean that it is the cause of stroke.

Background— In patients with severe aortic stenosis, transcatheter aortic valve replacement (TAVR) improves survival when compared with nonsurgical therapy but with higher in-hospital and lifetime costs. Complications associated with TAVR may decrease with greater experience and improved devices, thereby reducing the overall cost of the procedure. Therefore, we sought to estimate the effect of periprocedural complications on in-hospital costs and length of stay of TAVR. Methods and Results— Using detailed cost data from 406 TAVR patients enrolled in the Placement of Aortic Transcatheter Valve (PARTNER) I trial, we developed multivariable models to estimate the incremental cost and length of stay associated with specific periprocedural complications. Attributable costs and length of stay for each complication were calculated by multiplying the independent cost of each event by its frequency in the treatment group. Mean cost for the initial hospitalization was $79 619±40 570 ($50 891 excluding the valve); 49% of patients had ≥1 complication. Seven complications were independently associated with increased hospital costs, with major bleeding, arrhythmia, and death accounting for the largest attributable cost per patient. Renal failure and the need for repeat TAVR, although less frequent, were also associated with substantial incremental and attributable costs. Overall, complications accounted for $12 475 per patient in initial hospital costs and 2.4 days of hospitalization. Conclusions— In the PARTNER trial, periprocedural complications were frequent, costly, and accounted for ≈25% of non-implant–related hospital costs. Avoidance of complications should improve the cost-effectiveness of TAVR for inoperable and high-risk patients, but reductions in the cost of uncomplicated TAVR will also be necessary for optimal efficiency. Clinical Trial Registration— URL: http://www.clinicaltrials.gov . Unique identifier: NCT00530894.

Background and Purpose— Meta-analyses of extant genome-wide data illustrate the need to focus on subtypes of ischemic stroke for gene discovery. The National Institute of Neurological Disorders and Stroke SiGN (Stroke Genetics Network) contributes substantially to meta-analyses that focus on specific subtypes of stroke. Methods— The National Institute of Neurological Disorders and Stroke SiGN includes ischemic stroke cases from 24 genetic research centers: 13 from the United States and 11 from Europe. Investigators harmonize ischemic stroke phenotyping using the Web-based causative classification of stroke system, with data entered by trained and certified adjudicators at participating genetic research centers. Through the Center for Inherited Diseases Research, the Network plans to genotype 10 296 carefully phenotyped stroke cases using genome-wide single nucleotide polymorphism arrays and adds to these another 4253 previously genotyped cases, for a total of 14 549 cases. To maximize power for subtype analyses, the study allocates genotyping resources almost exclusively to cases. Publicly available studies provide most of the control genotypes. Center for Inherited Diseases Research–generated genotypes and corresponding phenotypes will be shared with the scientific community through the US National Center for Biotechnology Information database of Genotypes and Phenotypes, and brain MRI studies will be centrally archived. Conclusions— The Stroke Genetics Network, with its emphasis on careful and standardized phenotyping of ischemic stroke and stroke subtypes, provides an unprecedented opportunity to uncover genetic determinants of ischemic stroke.

SummaryMyelofibrosis (MF) patients can present with a wide spectrum of disease characteristics. We analysed the consistency of ruxolitinib efficacy across patient subgroups in the COntrolled MyeloFibrosis Study With ORal JAK Inhibitor Treatment (COMFORT‐I,) a double‐blind trial, where patients with intermediate‐2 or high‐risk MF were randomized to twice‐daily oral ruxolitinib (n = 155) or placebo (n = 154). Subgroups analysed included MF subtype (primary, post‐polycythaemia vera, post‐essential thrombocythaemia), age (≤65, > 65 years), International Prognostic Scoring System risk group, baseline Eastern Cooperative Oncology Group performance status (0, 1, ≥2), JAK2 V617F mutation (positive, negative), baseline haemoglobin level (≥100, <100 g/l), baseline platelet count (100–200 × 109/l, >200 × 109/l), baseline palpable spleen size (≤10, >10 cm), and baseline quartile of spleen volume and Total Symptom Score (TSS; Q1 = lowest, Q4 = highest). Mean percentage change from baseline to week 24 in spleen volume and TSS were calculated for ruxolitinib and placebo in each subgroup. Overall survival was estimated by Kaplan–Meier method according to original randomization group. In ruxolitinib‐treated patients, reductions in spleen volume and TSS and evidence of improved survival relative to placebo across subgroups were consistent with those seen in the COMFORT‐I population, confirming that ruxolitinib is an effective therapy for the spectrum of MF patients studied in COMFORT‐I.

Background. Statins are an important part of the treatment plan for patients with type 2 diabetes. However, patients who are prescribed statins often take less than the prescribed amount or stop taking the drug altogether. This suboptimal adherence may decrease the benefit of statin initiation. Objective. To estimate the influence of adherence on the optimal timing of statin initiation for patients with type 2 diabetes. Method. The authors use a Markov decision process (MDP) model to optimize the treatment decision for patients with type 2 diabetes. Their model incorporates a Markov model linking adherence to treatment effectiveness and long-term health outcomes. They determine the optimal time of statin initiation that minimizes expected costs and maximizes expected quality-adjusted life years (QALYs). Results. In the long run, approximately 25% of patients remain highly adherent to statins. Based on the MDP model, generic statins lower costs in men and result in a small increase in costs in women relative to no treatment. Patients are able to noticeably increase their expected QALYs by 0.5 to 2 years depending on the level of adherence. Conclusions. Adherence-improving interventions can increase expected QALYs by as much as 1.5 years. Given suboptimal adherence to statins, it is optimal to delay the start time for statins; however, changing the start time alone does not lead to significant changes in costs or QALYs.

Abstract Rationale Accurate, early identification of patients at risk for developing acute lung injury (ALI) provides the opportunity to test and implement secondary prevention strategies. Objectives To determine the frequency and outcome of ALI development in patients at risk and validate a lung injury prediction score (LIPS). Methods In this prospective multicenter observational cohort study, predisposing conditions and risk modifiers predictive of ALI development were identified from routine clinical data available during initial evaluation. The discrimination of the model was assessed with area under receiver operating curve (AUC). The risk of death from ALI was determined after adjustment for severity of illness and predisposing conditions. Measurements and Main Results Twenty-two hospitals enrolled 5,584 patients at risk. ALI developed a median of 2 (interquartile range 1–4) days after initial evaluation in 377 (6.8%; 148 ALI-only, 229 adult respiratory distress syndrome) patients. The frequency of ALI varied according to predisposing conditions (from 3% in pancreatitis to 26% after smoke inhalation). LIPS discriminated patients who developed ALI from those who did not with an AUC of 0.80 (95% confidence interval, 0.78–0.82). When adjusted for severity of illness and predisposing conditions, development of ALI increased the risk of in-hospital death (odds ratio, 4.1; 95% confidence interval, 2.9–5.7). Conclusions ALI occurrence varies according to predisposing conditions and carries an independently poor prognosis. Using routinely available clinical data, LIPS identifies patients at high risk for ALI early in the course of their illness. This model will alert clinicians about the risk of ALI and facilitate testing and implementation of ALI prevention strategies. Clinical trial registered with www.clinicaltrials.gov (NCT00889772).

Background . Clinicians often use validated risk models to guide treatment decisions for cardiovascular risk reduction. The most common risk models for predicting cardiovascular risk are the UKPDS, Framingham, and Archimedes models. In this article, the authors propose a model to optimize the selection of patients for statin therapy of hypercholesterolemia, for patients with type 2 diabetes, using each of the risk models. For each model, they evaluate the role of age, gender, and metabolic state on the optimal start time for statins. Method . Using clinical data from the Mayo Clinic electronic medical record, the authors construct a Markov decision process model with health states composed of cardiovascular events and metabolic factors such as total cholesterol and high-density lipoproteins. They use it to evaluate the optimal start time of statin treatment for different combinations of cardiovascular risk models and patient attributes. Results . The authors find that treatment decisions depend on the cardiovascular risk model used and the age, gender, and metabolic state of the patient. Using the UKPDS risk model to estimate the probability of coronary heart disease and stroke events, they find that all white male patients should eventually start statin therapy; however, using Framingham and Archimedes models in place of UKPDS, they find that for male patients at lower risk, it is never optimal to initiate statins. For white female patients, the authors also find some patients for whom it is never optimal to initiate statins. Assuming that age 40 is the earliest possible start time, the authors find that the earliest optimal start times for UKPDS, Framingham, and Archimedes are 50, 46, and 40, respectively, for women. For men, the earliest optimal start times are 40, 40, and 40, respectively. Conclusions . In addition to age, gender, and metabolic state, the choice of cardiovascular risk model influences the apparent optimal time for starting statins in patients with diabetes.

Abstract Rationale Acute lung injury (ALI) that develops 6 hours after transfusion (TRALI) is the leading cause of transfusion-related mortality. Several transfusion characteristics have been postulated as risk factors for TRALI, but the evidence is limited to retrospective studies. Objectives To compare patient and transfusion risk factors between patients who do and do not develop ALI. Methods In this prospective cohort study, consecutive transfused critically ill patients were closely observed for development of ALI. Donor samples were collected from the transfusion bags. Risk factors were compared between patients who developed ALI after transfusion and transfused control patients, matched by age, sex, and admission diagnosis. Measurements and Main Results Seventy-four of 901 transfused patients developed ALI within 6 hours of transfusion (8%). Compared with transfused control subjects, patients with ALI were more likely to have sepsis (37 vs. 22%, P = 0.016) and a history of chronic alcohol abuse (37 vs. 18%, P = 0.006). When adjusted for patient characteristics, transfusion of plasma from female donors (odds ratio [OR], 5.09; 95% confidence interval [95% CI], 1.37–18.85) rather than male donors (OR, 1.60; 95% CI, 0.76 to 3.37), number of pregnancies among the donors (OR, 1.19; 95% CI, 1.05 to 1.34), number of donor units positive for anti-granulocyte antibodies (OR, 4.85; 95% CI, 1.32–17.86) and anti–HLA class II antibodies (OR, 3.08; 95% CI, 1.15–8.25), and concentration of lysophosphatidylcholine in the donor product (OR, 1.69; 95% CI, 1.10 to 2.59) were associated with the development of ALI. Conclusions Both patient and transfusion risk factors determine the probability of ALI after transfusion. Transfusion factors represent attractive targets for the prevention of ALI.