Unstable Angina Induced by Fracture of a Recently Implanted Drug-Eluting Stent.

Stent fracture is a rather infrequent complication associated with in-stent restenosis, thrombosis, aneurysm formation, and ischemic events. Several stent-related parameters, such as the use of longer or multiple stents, stent overlapping, and balloon/stent overexpansion are potential predictors of stent fracture. Stents deployed in right coronary artery lesions with exaggerated motion, tortuosity, or severe calcification are also generally considered to be at higher risk for fracture. This case demonstrates that intravascular ultrasound imaging is extremely useful to confirm the diagnosis and identify the possible mechanism of the stent fracture, as well as to assess the final result after subsequent angioplasty.

An integer-valued time series model for multivariate surveillance.

In recent days, different types of surveillance data are becoming available for public health purposes. In most cases, several variables are monitored and events of different types are reported. As the amount of surveillance data increases, statistical methods that can effectively address multivariate surveillance scenarios are demanded. Even though research activity in this field is increasing rapidly in recent years, only a few approaches have simultaneously addressed the integer-valued property of the data and its correlation (both time correlation and cross-correlation) structure. In this article, we suggest a multivariate integer-valued autoregressive model that allows for both serial and cross-correlations between the series and can easily accommodate overdispersion and covariate information. Moreover, its structure implies a natural decomposition into an endemic and an epidemic component, a common distinction in dynamic models for infectious disease counts. Detection of disease outbreaks is achieved through the comparison of surveillance data with one-step-ahead predictions obtained after fitting the suggested model to a set of clean historical data. The performance of the suggested model is illustrated on a trivariate series of syndromic surveillance data collected during Athens 2004 Olympic Games.

Comparative Assessment of Clinical Benefit Using the ESMO-Magnitude of Clinical Benefit Scale Version 1.1 and the ASCO Value Framework Net Health Benefit Score.

PURPOSE: To better understand the European Society for Medical Oncology-Magnitude of Clinical Benefit Scale version 1.1 (ESMO-MCBS v1.1) and the ASCO Value Framework Net Health Benefit score version 2 (ASCO-NHB v2), ESMO and ASCO collaborated to evaluate the concordance between the frameworks when used to assess clinical benefit attributable to new therapies. METHODS: The 102 randomized controlled trials in the noncurative setting already evaluated in the field testing of ESMO-MCBS v1.1 were scored using ASCO-NHB v2 by its developers. Measures of agreement between the frameworks were calculated and receiver operating characteristic curves used to define thresholds for the ASCO-NHB v2 corresponding to ESMO-MCBS v1.1 categories. Studies with discordant scoring were identified and evaluated to understand the reasons for discordance. RESULTS: The correlation of the 102 pairs of scores for studies in the noncurative setting is estimated to be 0.68 (Spearman's rank correlation coefficient; overall survival, 0.71; progression-free survival, 0.67). Receiver operating characteristic curves identified thresholds for ASCO-NHB v2 for facilitating comparisons with ESMO-MCBS v1.1 categories. After applying pragmatic threshold scores of 40 or less (ASCO-NHB v2) and 2 or less (ESMO-MCBS v1.1) for low benefit and 45 or greater (ASCO-NHB v2) and 4 to 5 (ESMO-MCBS v1.1) for substantial benefit, 37 discordant studies were identified. Major factors that contributed to discordance were different approaches to evaluation of relative and absolute gain for overall survival and progression-free survival, crediting tail of the curve gains, and assessing toxicity. CONCLUSION: The agreement between the frameworks was higher than observed in other studies that sought to compare them. The factors that contributed to discordant scores suggest potential approaches to improve convergence between the scales.

Galectin-3 and Arterial Stiffness in Patients with Heart Failure: A Pilot Study.

BACKGROUND: Cardiac performance depends on optimum ventriculoarterial coupling which is impaired in patients with heart failure (HF). Galectin-3 is a mediator of myocardial fibrosis and remodeling, and is associated with clinical status in patients with chronic HF. We examined the association of arterial stiffness with galectin-3 levels in patients with HF of ischemic etiology. METHODS: We consecutively enrolled 40 patients with stable ischemic HF and reduced ejection fraction. Central aortic stiffness was evaluated non-invasively by measuring carotid femoral pulse wave velocity (PWV). Among other factors, serum levels of galectin-3 and b-type natriuretic peptide (BNP) were measured. RESULTS: The median galectin-3 levels in our study population were 12.9 (10.8-18.7) ng/ml and the mean PWV was 9.31±2.79 m/sec. There was significant association of galectin-3 levels with age (r=0.48, p=0.003), creatinine clearance (r=-0.66, p<0.001) and BNP levels (r=0.36, p=0.05). There was a significant association of galectin-3 levels with PWV (r=0.37, p=0.03) and patients with PWV above median also had significantly increased levels of galectin-3 compared with patients with lower values of PWV [16.1(11.8-25.2) vs. 12.1(10.5-14) ng/ml, p=0.03]. CONCLUSION: We found an association of arterial stiffness and PWV with galectin-3 levels in patients with chronic HF of ischemic etiology. These findings suggest a pathway driving arterial stiffening and myocardial remodelling in HF. This may provide insight into the mechanism determining prognosis and clinical status of patients with HF.

The association among biomarkers of renal and heart function in patients with heart failure: the role of NGAL.

AIM: The NGAL is a biomarker of renal injury associated with the progression of heart failure (HF). We examine the association of NGAL with galectin-3 in patients with chronic HF. METHODS: We consecutively enrolled 115 subjects with stable ischemic HF of reduced ejection fraction. Serum levels of galectin-3, b-type natriuretic peptide and NGAL were measured. RESULTS: NGAL levels were positively correlated with galectin-3 (rho = 0.26; p = 0.04) and b-type natriuretic peptide levels (rho = 0.30; p = 0.005) and inversely correlated with ejection fraction (rho = -0.31; p = 0.02) and creatinine clearance levels. The NGAL was independently associated with galectin-3 levels. CONCLUSION: A positive correlation between NGAL and galectin-3 in HF patients was found, revealing a potential association between renal injury and myocardial fibrosis and remodeling in HF.

A discrete mixture regression for modeling the duration of non-hospitalization medical leave of motor accident victims.

Studies analyzing the temporary repercussions of motor vehicle accidents are scarcer than those analyzing permanent injuries or mortality. A regression model to evaluate the risk factors affecting the duration of temporary disability after injury in such an accident is constructed using a motor insurance dataset. The length of non-hospitalization medical leave, measured in days, following a motor accident is used here as a measure of the severity of temporary disability. The probability function of the number of days of sick leave presents spikes in multiples of five (working week), seven (calendar week) and thirty (month), etc. To account for this, a regression model based on finite mixtures of multiple discrete distributions is proposed to fit the data properly. The model provides a very good fit when the multiples for the working week, week, fortnight and month are taken into account. Victim characteristics of gender and age and accident characteristics of the road user type, vehicle class and the severity of permanent injuries were found to be significant when accounting for the duration of temporary disability.

Exploratory Analysis of Single-Gene Predictive Biomarkers in HERA DASL Cohort Reveals That C8A mRNA Expression Is Prognostic of Outcome and Predictive of Benefit of Trastuzumab.

PURPOSE: The Herceptin Adjuvant study is an international multicenter randomized trial that compared 1 or 2 years of trastuzumab given every 3 weeks with observation in women with human epidermal growth factor 2-positive (HER2+) breast cancer after chemotherapy. Identification of biomarkers predictive of a benefit from trastuzumab will minimize overtreatment and lower health care costs. METHODS: To identify possible single-gene biomarkers, an exploratory analysis of 3,669 gene probes not expected to be expressed in normal breast tissue was conducted. Disease-free survival (DFS) was used as the end point in a Cox regression model, with the interaction term between C8A mRNA and treatment as a categorical variable split on the cohort mean. RESULTS: A significant interaction between C8A mRNA and treatment was detected (P < .001), indicating a predictive response to trastuzumab treatment. For the C8A-low subgroup (mRNA expression lower than the cohort mean), no significant treatment benefit was observed (P = .73). In the C8A-high subgroup, patients receiving trastuzumab experienced a lower hazard of a DFS event by approximately 75% compared with those in the observation arm (hazard ratio [HR], 0.25; P < .001). A significant prognostic effect of C8A mRNA also was seen (P < .001) in the observation arm, where the C8A-high group hazard of a DFS event was three times the respective hazard of the C8A-low group (HR, 3.27; P < .001). C8A mRNA is highly prognostic in the Hungarian Academy of Science HER2+ gastric cancer cohort (HR, 1.72; P < .001). CONCLUSION: C8A as a single-gene biomarker prognostic of DFS and predictive of a benefit from trastuzumab has the potential to improve the standard of care in HER2+ breast cancer if validated by additional studies. Understanding the advantage of overexpression of C8A related to the innate immune response can give insight into the mechanisms that drive cancer.

Detailed statistical assessment of the characteristics of the ESMO Magnitude of Clinical Benefit Scale (ESMO-MCBS) threshold rules.

BACKGROUND: The European Society for Medical Oncology (ESMO) has developed the ESMO Magnitude of Clinical Benefit Scale (ESMO-MCBS), a tool to assess the magnitude of clinical benefit from new cancer therapies. Grading is guided by a dual rule comparing the relative benefit (RB) and the absolute benefit (AB) achieved by the therapy to prespecified threshold values. The ESMO-MCBS v1.0 dual rule evaluates the RB of an experimental treatment based on the lower limit of the 95%CI (LL95%CI) for the hazard ratio (HR) along with an AB threshold. This dual rule addresses two goals: inclusiveness: not unfairly penalising experimental treatments from trials designed with adequate power targeting clinically meaningful relative benefit; and discernment: penalising trials designed to detect a small inconsequential benefit. METHODS: Based on 50 000 simulations of plausible trial scenarios, the sensitivity and specificity of the LL95%CI rule and the ESMO-MCBS dual rule, the robustness of their characteristics for reasonable power and range of targeted and true HRs, are examined. The per cent acceptance of maximal preliminary grade is compared with other dual rules based on point estimate (PE) thresholds for RB. RESULTS: For particularly small or particularly large studies, the observed benefit needs to be relatively big for the ESMO-MCBS dual rule to be satisfied and the maximal grade awarded. Compared with approaches that evaluate RB using the PE thresholds, simulations demonstrate that the MCBS approach better exhibits the desired behaviour achieving the goals of both inclusiveness and discernment. CONCLUSIONS: RB assessment using the LL95%CI for HR rather than a PE threshold has two advantages: it diminishes the probability of excluding big benefit positive studies from achieving due credit and, when combined with the AB assessment, it increases the probability of downgrading a trial with a statistically significant but clinically insignificant observed benefit.

Effects of Estrogen Receptor and Human Epidermal Growth Factor Receptor-2 Levels on the Efficacy of Trastuzumab: A Secondary Analysis of the HERA Trial.

IMPORTANCE: A number of studies suggest that response to antihuman epidermal growth factor receptor-2 (currently known as ERBB2, butreferred to asHER2 in this study) agents differs by estrogen receptor (ER) level status. The clinical relevance of this is unknown. OBJECTIVE: To determine the magnitude of trastuzumab benefit according to quantitative levels of ER and HER2 in the HERceptin Adjuvant (HERA) trial. DESIGN, SETTING, AND PARTICIPANTS: The HERA trial was an international, multicenter, randomized trial that included 5099 patients with early-stage HER2-positive breast cancer, randomized between 2001 and 2005 to receive either no trastuzumab or trastuzumab, after adjuvant chemotherapy. This is a secondary analysis of the HERA study. Local ER immunohistochemical (IHC) analyses, HER2 fluorescence in situ hybridization (FISH) ratio, and copy number results were available for 3037 patients (59.6%) randomized to observation and trastuzumab (1 or 2 years) (cohort 1). Transcript levels of ESR1 and HER2 genes were available for 615 patients (12.1%) (cohort 2). INTERVENTIONS: Patients were randomized to receive either no trastuzumab or 1 year vs 2 years of trastuzumab. Endocrine therapy was given to patients with hormone receptor-positive disease as per local guidelines. MAIN OUTCOMES AND MEASURES: Disease-free survival (DFS) and overall survival (OS) were the primary and secondary end points in the intent-to-treat population (ITT). Analyses adjusting for crossover (censored and inverse probability weighted [IPW]) were also performed. Interactions among treatment, ER status, and HER2 amplification using predefined cutoffs were assessed in Cox proportional hazards regression models. RESULTS: Median follow-up time was 8 years. Levels of FISH and HER2 copy numbers were significantly higher in ER-negative patients (P < .001). In cohort 1, for DFS and OS, a significant treatment effect was found for all ER, IHC, and FISH levels, except for the ER-positive/HER2 low FISH ratio (≥2 to <5) group (DFS: 3-way ITT Pvalue for interaction = .07; censored = .02; IPW = .03; OS ITT Pvalue for interaction = .007; censored = .04; IPW = .03). In cohort 2, consistent with cohort 1, a significant predictive effect of the ESR1 gene for both end points was also observed (DFS Pvalue for interaction = .06; OS = .02), indicating that breast cancers with higher ESR1 levels also derive less benefit from trastuzumab. CONCLUSIONS AND RELEVANCE: Patients with HER2-positive breast cancers that are ER-positive by IHC analyses with low FISH ratio (≥2 to <5), or with higher ESR1 levels derive significantly less benefit from adjuvant trastuzumab after chemotherapy. These data may explain heterogeneity in response to anti-HER2 agents in HER2-positive, ER-positive breast cancers as some may be more luminal-like than HER2 driven. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00045032.

Phase II design with sequential testing of hypotheses within each stage.

The main goal of a Phase II clinical trial is to decide, whether a particular therapeutic regimen is effective enough to warrant further study. The hypothesis tested by Fleming's Phase II design (Fleming, 1982) is [Formula: see text] versus [Formula: see text], with level [Formula: see text] and with a power [Formula: see text] at [Formula: see text], where [Formula: see text] is chosen to represent the response probability achievable with standard treatment and [Formula: see text] is chosen such that the difference [Formula: see text] represents a targeted improvement with the new treatment. This hypothesis creates a misinterpretation mainly among clinicians that rejection of the null hypothesis is tantamount to accepting the alternative, and vice versa. As mentioned by Storer (1992), this introduces ambiguity in the evaluation of type I and II errors and the choice of the appropriate decision at the end of the study. Instead of testing this hypothesis, an alternative class of designs is proposed in which two hypotheses are tested sequentially. The hypothesis [Formula: see text] versus [Formula: see text] is tested first. If this null hypothesis is rejected, the hypothesis [Formula: see text] versus [Formula: see text] is tested next, in order to examine whether the therapy is effective enough to consider further testing in a Phase III study. For the derivation of the proposed design the exact binomial distribution is used to calculate the decision cut-points. The optimal design parameters are chosen, so as to minimize the average sample number (ASN) under specific upper bounds for error levels. The optimal values for the design were found using a simulated annealing method.

Multivariate logit copula model with an application to dental data.

Applications of copulas for multivariate continuous data abound but there are only a few that treat multivariate binary data. In the present paper, we model multivariate binary data based on copulas using mixtures of max-infinitely divisible copulas, introduced by Joe and Hu (J. Multivar. Anal. 1996; 57(2): 240-265). When applying copulas to binary data the marginal distributions also contribute to the dependence measures. We propose the use of covariate information in the copula parameters to obtain a direct effect of a covariate on dependence. To deal with model uncertainty due to selecting among several candidate models, we use a model averaging technique. We apply the model to data from the Signal-Tandmobiel dental study and, in particular, to four binary responses that refer to caries experience in the mandibular and maxillary left and right molars. We aim to model Kendall's tau associations between them, and examine how covariate information affects these associations. We found that there are systematically larger associations between the two mandibular and the two maxillary molars. Using covariates to model these associations more closely, we found that the systematic fluoride and age of the children affect the associations. Note that such relationships could not have been revealed by methods that focus on the marginal models.

Studying the effect of weather conditions on daily crash counts using a discrete time-series model.

In previous research, significant effects of weather conditions on car crashes have been found. However, most studies use monthly or yearly data and only few studies are available analyzing the impact of weather conditions on daily car crash counts. Furthermore, the studies that are available on a daily level do not explicitly model the data in a time-series context, hereby ignoring the temporal serial correlation that may be present in the data. In this paper, we introduce an integer autoregressive model for modelling count data with time interdependencies. The model is applied to daily car crash data, metereological data and traffic exposure data from the Netherlands aiming at examining the risk impact of weather conditions on the observed counts. The results show that several assumptions related to the effect of weather conditions on crash counts are found to be significant in the data and that if serial temporal correlation is not accounted for in the model, this may produce biased results.

Ranking and selecting dangerous crash locations: correcting for the number of passengers and Bayesian ranking plots.

INTRODUCTION: In this paper a sensitivity analysis is performed to investigate how big the impact would be on the current ranking of crash locations in Flanders (Belgium) when only taking into account the most serious injury per crash instead of all the injured occupants. RESULTS: Results show that this would lead to a different selection of 23.8% of the 800 sites that are currently considered as dangerous. CONCLUSIONS: Considering this impact quantity, the researchers want to sensitize government that giving weight to the severity of the crash can correct for the bias that occurs when the number of occupants of the vehicles are subject to coincidence. Additionally, probability plots are generated to provide policy makers with a scientific instrument with intuitive appeal to select dangerous road locations on a statistically sound basis. Impact on industry Considering the impact quantity of giving weight to the severity of the crash instead of to all the injured occupants of the vehicle on the ranking of crash sites, the authors want to sensitize government to carefully choose the criteria for ranking and selecting crash locations in order to achieve an enduring and successful traffic safety policy. Indeed, giving weight to the severity of the crash can correct for the bias that occurs when the number of occupants of the vehicles are subject to coincidence. However, it is up to the government to decide which priorities should be stressed in the traffic safety policy. Then, the appropriate weighting value combination can be chosen to rank and select the most dangerous crash locations. Additionally, the probability plots proposed in this paper can provide policy makers with a scientific instrument with intuitive appeal to select dangerous road locations on a statistically sound basis. Note that, in practice, one should not only rank the crash locations based on the benefits that can be achieved from tackling these locations. Future research is also needed to incorporate the costs of infrastructure measures and other actions that these crash sites require in order to enhance the safety on these locations. By balancing these costs and benefits against each other, the crash locations can then be ranked according to the order in which they should be prioritized.