[Molecular analyses for (early) recognition of hematologic diseases - sense and sensibility for molecular analysis: the art of intelligent decision-making]

During the last 10 years several molecular markers have been established as useful tools among the armamentarium of a hematologist. As a consequence, the number of performed hematologic molecular analyses has immensely increased. Often, such tests replace or complement other laboratory methods. Molecular markers can be useful in many ways: they can serve for diagnostics, describe the prognostic profile, predict which types of drugs are indicated, and can be used for the therapeutic monitoring of the patient to indicate an adequate response or predict resistance or relapse of the disease. Many markers fulfill more than one of these aspects. Most important, however, is the right choice of analyses at the right time-points!

Meta-analyses qualify metzincins and related genes as acute rejection markers in renal transplant patients

Definition of acute renal allograft rejection (AR) markers remains clinically relevant. Features of T-cell-mediated AR are tubulointerstitial and vascular inflammation associated with excessive extracellular matrix (ECM) remodeling, regulated by metzincins, including matrix metalloproteases (MMP). Our study focused on expression of metzincins (METS), and metzincins and related genes (MARGS) in renal allograft biopsies using four independent microarray data sets. Our own cases included normal histology (N, n = 20), borderline changes (BL, n = 4), AR (n = 10) and AR + IF/TA (n = 7). MARGS enriched in all data sets were further examined on mRNA and/or protein level in additional patients. METS and MARGS differentiated AR from BL, AR + IF/TA and N in a principal component analysis. Their expression changes correlated to Banff t- and i-scores. Two AR classifiers, based on METS (including MMP7, TIMP1), or on MARGS were established in our own and validated in the three additional data sets. Thirteen MARGS were significantly enriched in AR patients of all data sets comprising MMP7, -9, TIMP1, -2, thrombospondin2 (THBS2) and fibrillin1. RT-PCR using microdissected glomeruli/tubuli confirmed MMP7, -9 and THBS2 microarray results; immunohistochemistry showed augmentation of MMP2, -9 and TIMP1 in AR. TIMP1 and THBS2 were enriched in AR patient serum. Therefore, differentially expressed METS and MARGS especially TIMP1, MMP7/-9 represent potential molecular AR markers.

Twist and shout: one decade of meta-analyses of erythropoiesis-stimulating agents in cancer patients

Anemia associated with cancer and cancer therapy is a common and important issue in the treatment of patients with malignant disease. Conventionally, blood transfusions are used to treat severe cancer-related anemia. Short- and long-acting preparations of recombinant human erythropoiesis-stimulating agents (ESAs) offer an alternative treatment option. Multiple studies and subsequent meta-analyses have demonstrated that ESA treatment increases hemoglobin levels and reduces the likelihood of transfusion for a proportion of treated patients. However, studies that attempted to evaluate whether ESAs improve tumor response and survival have generated conflicting evidence. Results of smaller trials reporting improved survival outcomes were contradicted by large randomized controlled trials that reported more deaths in patients receiving ESAs. In addition, there is strong evidence that cancer patients receiving ESAs have an increased risk of thromboembolic and cardiovascular events. We herein review the main meta-analyses published in the field, their strengths and weaknesses, their contribution to patient management and future perspectives for systematic reviews.

Mortality from road traffic accidents in Switzerland: longitudinal and spatial analyses

Road traffic accidents (RTA) are an important cause of premature death. We examined socio-demographic and geographical determinants of RTA mortality in Switzerland by linking 2000 census data to RTA mortality records 2000-2005 (ICD-10 codes V00-V99). Data from 5.5 million residents aged 18-94 years, 1744 study areas, and 1620 RTA deaths were analyzed, including 978 deaths (60.4%) in motor vehicle occupants, 254 (15.7%) in motorcyclists, 107 (6.6%) in cyclists, and 259 (16.0%) in pedestrians. Weibull survival models and Bayesian methods were used to calculate hazard ratios (HR), and standardized mortality ratios (SMR) across study areas. Adjusted HR comparing women with men ranged from 0.04 (95% CI 0.02-0.07) in motorcyclists to 0.43 (95% CI 0.32-0.56) in pedestrians. There was a u-shaped relationship with age in motor vehicle occupants and motorcyclists. In cyclists and pedestrians, mortality increased after age 55 years. Mortality was higher in individuals with primary education (HR 1.53; 95% CI 1.29-1.81), and higher in single (HR 1.24; 95% CI 1.05-1.46), widowed (HR 1.31; 95% CI 1.05-1.65) and divorced individuals (HR 1.62; 95% CI 1.33-1.97), compared to persons with tertiary education or married persons. The association with education was particularly strong for pedestrians (HR 1.87; 95% CI 1.20-2.91). RTA mortality increased with decreasing population density of study areas for motor vehicle occupants (test for trend p<0.0001) and motorcyclists (p=0.0021) but not for cyclists (p=0.39) or pedestrians (p=0.29). SMR standardized for socio-demographic and geographical variables ranged from 82 to 190. Prevention efforts should aim to reduce inequities across socio-demographic and educational groups, and across geographical areas, with interventions targeted at high-risk groups and areas, and different traffic users, including pedestrians.

Small study effects in meta-analyses of osteoarthritis trials: meta-epidemiological study

Objective To examine the presence and extent of small study effects in clinical osteoarthritis research. Design Meta-epidemiological study. Data sources 13 meta-analyses including 153 randomised trials (41 605 patients) that compared therapeutic interventions with placebo or non-intervention control in patients with osteoarthritis of the hip or knee and used patients’ reported pain as an outcome. Methods We compared estimated benefits of treatment between large trials (at least 100 patients per arm) and small trials, explored funnel plots supplemented with lines of predicted effects and contours of significance, and used three approaches to estimate treatment effects: meta-analyses including all trials irrespective of sample size, meta-analyses restricted to large trials, and treatment effects predicted for large trials. Results On average, treatment effects were more beneficial in small than in large trials (difference in effect sizes −0.21, 95% confidence interval −0.34 to −0.08, P=0.001). Depending on criteria used, six to eight funnel plots indicated small study effects. In six of 13 meta-analyses, the overall pooled estimate suggested a clinically relevant, significant benefit of treatment, whereas analyses restricted to large trials and predicted effects in large trials yielded smaller non-significant estimates. Conclusions Small study effects can often distort results of meta-analyses. The influence of small trials on estimated treatment effects should be routinely assessed.

Subjective stress in female elite athletes and non-athletes: Evidence from cortisol analyses

Stress response can be considered a consequence of psychological or physiological threats to the human organism. Elevated cortisol secretion represents a biological indicator of subjective stress. The extent of subjectively experienced stress depends on individual coping strategies or self-regulation skills. Because of their experience with competitive pressure, athletes might show less pronounced biological stress responses during stressful events compared to non-athletes. In the present study, the short version of the Berlin Intelligence Structure Test, a paper-pencil intelligence test, was used as an experimental stressor. Cortisol responses of 26 female Swiss elite athletes and 26 female non-athlete controls were compared. Salivary free cortisol responses were measured 15 minutes prior to, as well as immediately before and after psychometric testing. In both groups, a significant effect of time was found: High cortisol levels prior to testing decreased significantly during the testing session. Furthermore, athletes exhibited reliably lower cortisol levels than non-athlete controls. No significant interaction effects could be observed. The overall pattern of results supports the idea that elite athletes show a less pronounced cortisol-related stress response due to more efficient coping strategies.

Outcomes following three-line vision loss during treatment of neovascular age-related macular degeneration: subgroup analyses from MARINA and ANCHOR

This study aims to assess the impact of continued ranibizumab treatment for neovascular age-related macular degeneration on patients from the MARINA and ANCHOR randomised clinical studies who lost ≥ 3 lines of best-corrected visual acuity (BCVA) at any time during the first year of treatment.

Ten years of meta-analyses on erythropoiesis-stimulating agents in cancer patients

Since erythropoiesis-stimulating agents (ESAs) were licensed in 1993, more than 70 randomized controlled trials and more than 20 meta-analyses and systematic reviews on their effectiveness were conducted. Here, we present a systematic review on the meta-analyses of trials evaluating ESAs in cancer patients.

Multiplicity of data in trial reports and the reliability of meta-analyses: empirical study

Objectives To examine the extent of multiplicity of data in trial reports and to assess the impact of multiplicity on meta-analysis results. Design Empirical study on a cohort of Cochrane systematic reviews. Data sources All Cochrane systematic reviews published from issue 3 in 2006 to issue 2 in 2007 that presented a result as a standardised mean difference (SMD). We retrieved trial reports contributing to the first SMD result in each review, and downloaded review protocols. We used these SMDs to identify a specific outcome for each meta-analysis from its protocol. Review methods Reviews were eligible if SMD results were based on two to ten randomised trials and if protocols described the outcome. We excluded reviews if they only presented results of subgroup analyses. Based on review protocols and index outcomes, two observers independently extracted the data necessary to calculate SMDs from the original trial reports for any intervention group, time point, or outcome measure compatible with the protocol. From the extracted data, we used Monte Carlo simulations to calculate all possible SMDs for every meta-analysis. Results We identified 19 eligible meta-analyses (including 83 trials). Published review protocols often lacked information about which data to choose. Twenty-four (29%) trials reported data for multiple intervention groups, 30 (36%) reported data for multiple time points, and 29 (35%) reported the index outcome measured on multiple scales. In 18 meta-analyses, we found multiplicity of data in at least one trial report; the median difference between the smallest and largest SMD results within a meta-analysis was 0.40 standard deviation units (range 0.04 to 0.91). Conclusions Multiplicity of data can affect the findings of systematic reviews and meta-analyses. To reduce the risk of bias, reviews and meta-analyses should comply with prespecified protocols that clearly identify time points, intervention groups, and scales of interest.

An overview of the objectives of and the approaches to propensity score analyses

The assessment of treatment effects from observational studies may be biased with patients not randomly allocated to the experimental or control group. One way to overcome this conceptual shortcoming in the design of such studies is the use of propensity scores to adjust for differences of the characteristics between patients treated with experimental and control interventions. The propensity score is defined as the probability that a patient received the experimental intervention conditional on pre-treatment characteristics at baseline. Here, we review how propensity scores are estimated and how they can help in adjusting the treatment effect for baseline imbalances. We further discuss how to evaluate adequate overlap of baseline characteristics between patient groups, provide guidelines for variable selection and model building in modelling the propensity score, and review different methods of propensity score adjustments. We conclude that propensity analyses may help in evaluating the comparability of patients in observational studies, and may account for more potential confounding factors than conventional covariate adjustment approaches. However, bias due to unmeasured confounding cannot be corrected for.