A power series beta Weibull regression model for predicting breast carcinoma

The postmastectomy survival rates are often based on previous outcomes of large numbers of women who had a disease, but they do not accurately predict what will happen in any particular patient's case. Pathologic explanatory variables such as disease multifocality, tumor size, tumor grade, lymphovascular invasion, and enhanced lymph node staining are prognostically significant to predict these survival rates. We propose a new cure rate survival regression model for predicting breast carcinoma survival in women who underwent mastectomy. We assume that the unknown number of competing causes that can influence the survival time is given by a power series distribution and that the time of the tumor cells left active after the mastectomy for metastasizing follows the beta Weibull distribution. The new compounding regression model includes as special cases several well‐known cure rate models discussed in the literature. The model parameters are estimated by maximum likelihood. Further, for different parameter settings, sample sizes, and censoring percentages, some simulations are performed. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess local influences. The potentiality of the new regression model to predict accurately breast carcinoma mortality is illustrated by means of real data. Copyright © 2015 John Wiley & Sons, Ltd.     

Increase in overall mortality risk in patients with type 2 diabetes receiving glipizide, glyburide or glimepiride monotherapy versus metformin: a retrospective analysis

Aims: It remains uncertain if differences in mortality risk exist among the sulfonylureas, especially in patients with documented coronary artery disease (CAD). The purpose of this study was to assess the overall mortality risk of the individual sulfonylureas versus metformin in a large cohort of patients with type 2 diabetes. Methods: A retrospective cohort study was conducted using an academic health centre enterprise‐wide electronic health record (EHR) system to identify 23 915 patients with type 2 diabetes who initiated monotherapy with metformin (N = 12774), glipizide (N = 4325), glyburide (N = 4279) or glimepiride (N = 2537), ≥18 years of age, with and without a history of CAD, and not on insulin or a non‐insulin injectable at baseline. The patients were followed for mortality by documentation in the EHR and Social Security Death Index. Multivariable Cox models with propensity analysis were used to compare cohorts. Results: An increase in overall mortality risk was observed in the entire cohort with glipizide (HR 1.64; 95% CI 1.39–1.94), glyburide (HR 1.59; 95% CI 1.35–1.88), and glimepiride (HR 1.68; 95% CI 1.37–2.06) versus metformin; however, in those patients with documented CAD, a statistically significant increase in overall mortality risk was only found with glipizide (HR 1.41; 95% CI 1.07–1.87) and glyburide (HR 1.38; 95% CI 1.04–1.83) versus metformin. Conclusions: Glipizide, glyburide and glimepiride are associated with an increased risk of overall mortality versus metformin. Our results suggest that if a sulfonylurea is required to obtain glycaemic control, glimepiride may be the preferred sulfonylurea in those with underlying CAD.     

ClearCode34: A Prognostic Risk Predictor for Localized Clear Cell Renal Cell Carcinoma

Background

Gene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting.

Objective

To generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that could be readily applied to clinical samples to develop an integrated model for biologically defined risk stratification.

Design, setting, and participants

A set of 72 ccRCC sample standards was used to develop a 34-gene classifier (ClearCode34) for assigning ccRCC tumors to subtypes. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed clinical samples collected at the University of North Carolina.

Outcome measurements and statistical analysis

Kaplan-Meier analyses were performed on the individual cohorts to calculate recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Training and test sets were randomly selected from the combined cohorts to assemble a risk prediction model for disease recurrence.

Results and limitations

The subtypes were significantly associated with RFS (p < 0.01), CSS (p < 0.01), and OS (p < 0.01). Hazard ratios for subtype classification were similar to those of stage and grade in association with recurrence risk, and remained significant in multivariate analyses. An integrated molecular/clinical model for RFS to assign patients to risk groups was able to accurately predict CSS above established, clinical risk-prediction algorithms.

Conclusions

The ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients.

Patient summary

We developed a 34-gene subtype predictor to classify clear cell renal cell carcinoma tumors according to ccA or ccB subtypes and built a subtype-inclusive model to analyze patient survival outcomes.     

Asthma exacerbation is associated with particulate matter source factors in children in New York City

Exposure to fine particulate matter (PM_2.5) is linked with asthma exacerbation; however, the role played by specific PM sources is not well understood. Our objective was to investigate the associations between daily cough and wheeze symptoms in a panel of asthmatic children and PM source factors determined by receptor modeling using positive matrix factorization (PMF). We studied 36 children with moderate-to-severe asthma in New York City over both a warm and a cold season. Exposure to ambient air pollutants, including PM_2.5 elements and elemental and organic carbon fractions, was characterized. The mean ambient PM_2.5 concentration for the study periods was 12.0?±?6.7 μg/m³. Six factors were resolved using PMF, including oil, road dust, ships, regional, salt, and traffic. When adjusted for ozone, cough and wheeze symptoms were most strongly associated with the regional and salt factors. Results using tracer elements (as determined from PMF analyses) showed some inconsistency, with two tracers for road dust (K and Si) showing associations in opposite directions to each other. Positive associations were also observed for S, which is a tracer of regional PM. Significant negative associations were observed for the oil factor and one of its tracers (Zn). Mostly nonsignificant associations were found for carbon fractions, with the exception of pyrolized carbon and two elemental carbon fractions. Our results indicate that asthma symptoms are associated with regional and salt factors. In this study, the regional factor was comprised of sulfate as well as carbon-containing PM, the latter which is likely derived from both anthropogenic and biogenic sources.     

Are peak flow and symptom measures good predictors of asthma hospitalizations and unscheduled visits?†

Epidemiologic studies of pediatric respiratory health often include objective measures such as peak expiratory flow (PEF), and subjective measures such as symptom reports. These measures, however, are poorly correlated with each other, and there is little evidence that PEF is useful in predicting important health outcomes. Within a cohort of 791 inner-city children with asthma, we examined correlations between a series of five peak flow measures and five symptom scores obtained from 2-week diaries. The strongest correlations were found between "total peak flow lability" defined as: [(diary maximum - diary minimum)/diary mean] and "% of days with chest tightness" (r = 0.31). Logistic models evaluated peak flow and symptoms as predictors of an important health outcome: hospitalization or emergency department or unscheduled clinic visit for asthma within 30 days of starting the diary. Each of the peak flow and symptom measures was significantly related to utilization. However, the predictive power of each measure was low (range of area under ROC curve, 0.54-0.67). Models including only peak flow or symptoms had greater prediction than models with risk factors such as atopy, asthma persistence, and age. The prediction from a model with the risk factors and symptoms was not improved by adding a peak flow measure to the model (increase in area under ROC, 0.67-0.68). Stratified analyses suggest that prediction was similar in the fall vs. winter, spring, and summer months. Greater prediction of health outcomes was found among more persistent asthmatics and children who were nonatopic. These findings suggest that in a research setting, peak flow monitoring in children did not add prediction beyond that obtained from symptom reports. Pediatr Pulmonol. 2001; 31:190-197. Published 2001 Wiley-Liss, Inc.     

The effects of recombinant-DNA-derived interferons on the growth of myeloid progenitor cells

Interferons (IFNs) have been shown to have significant effects on hematopoietic cell growth. Previous studies defining these effects have utilized mouse and human alpha-, beta-, and gamma-IFN isolated from supernatants of stimulated cells. Despite purification, the possible presence of other lymphokines and soluble factors remains a concern. In this study, the effects of gene-cloned alpha- and gamma-IFN on colony- forming units of granulocyte/macrophage (CFU-GM) progenitors cultured from the peripheral blood of normal volunteers were examined. In addition, blast cell colonies from one patient with acute myelogenous leukemia (AML) were studied. The growth of normal CFU-GM and AML blast cell colonies was inhibited in a dose-dependent manner by gamma- and alpha-IFN. gamma-IFN was ten to 100 times more potent than alpha-IFN in that this species of IFN reduced colony formation by greater than 50% at concentrations of less than 15 antiviral U/mL. The effects of gamma- IFN were neutralized by a monoclonal antibody specific for gamma-IFN. These in vitro studies indicate that human gamma-IFN may be an important modulator of myelopoiesis. Although these data indicate a possible efficacy of gamma-IFN in the treatment of AML, the in vitro results should be considered for their in vivo significance.