The 3-60 criteria challenge established predictors of postoperative mortality and enable timely therapeutic intervention after liver resection.

BACKGROUND: To date, definitions of liver dysfunction (LD) after hepatic resection rely on late postoperative time points. Further, the used parameters are markedly influenced by perioperative management. Thus, we aimed to establish a very early postoperative score to predict postoperative mortality. METHODS: Liver related parameters were evaluated after liver resection in a retrospective evaluation cohort of 228 colorectal cancer patients with liver metastasis (mCRC) and subsequent validation in a prospective set of 482 consecutive patients from 4 independent institutions undergoing hepatic resection was performed. RESULTS: C-reactive protein (CRP, AUC =0.739, P<0.001) and antithrombinIII-activity (ATIII, AUC =0.844, P<0.001) on the first postoperative day (POD) were found to be elevated in patients with LD. Cut-off values for CRP at 3 mg/dL and for ATIII at 60% significantly identified high-risk patients for postoperative LD and mortality (P<0.001) and thus defined the 3-60 criteria on POD1. The 3-60 criteria showed superior sensitivity and specificity compared to established criteria for LD [3-60 criteria: total positive patients: 26 patients (70% mortality detected), odds ratio (OR): 48.8; International Study Group for Liver Surgery: total positive patients: 43 (70% mortality detected), OR: 23.3; Peak7: total positive patients: 9 (30% mortality detected), OR: 27.8; 50-50: total positive patients: 9 (30% mortality detected), OR: 27.8]. These results could be validated in a multi-center analysis and ultimately the 3-60 criteria remained an independent predictor of postoperative mortality upon multivariable analysis. CONCLUSIONS: The 3-60 criteria on POD1 predict postoperative LD and mortality early after liver resection with a comparable or better accuracy than established criteria, allowing for immediate identification of high-risk patients.

Evidence for a Role of TGF-ß-Activated Kinase 1 and MAP3K7 Binding Protein 3 in Peanut-Specific T-Cell Responses.

Peanut allergy is considered to be the most common cause for food-induced anaphylaxis. Currently, no approved treatment is available. Avoidance is the only measure to prevent anaphylactic reactions to peanuts. T-helper cells are of special importance for the sensitization process and the maintenance of allergic inflammation. Identifying markers of allergen-specific T-cell responses may help to develop novel treatment approaches. Therefore, we aimed to define new T-cell target genes in Ara h 2-specific T cells and to investigate the possibility of using them as biomarkers of peanut allergy in peripheral blood mononuclear cells (PBMCs). We performed whole mRNA array analysis (whole human genome oligo microarray) of in vitro expanded Ara h 2-specific T cells (CFSElowCD3+CD4+) from 5 peanut-allergic (PA) and 5 non-peanut-sensitized individuals. Expression of selected genes as a result of a two-step bioinformatic approach was confirmed in a second cohort by quantitative PCR. TGF-ß- activated kinase 1 and MAP3K7 binding protein 3 (TAB3), calcium/calmodulin-dependent protein kinase type IV (CAMK4) and HemK methyltransferase family member 1 (HEMK1) were significantly upregulated in Ara h 2-specific T cells of PA patients. In addition, the expression of these genes was also assessed in unstimulated PBMCs from a cohort (n = 43) of PA, atopic non-PA, and nonatopic controls. Interestingly, in unstimulated PBMCs, TAB3 expression was significantly downregulated in PA patients compared to atopic non-PA individuals. Thus, TAB3 may play a significant role at the level of T-cell activation and may also be a candidate biomarker for PA.

Iron deficiency workup reveals high incidence of autoimmune gastritis with parietal cell antibody as reliable screening test.

Iron deficiency (ID) workup is a common challenge for gastrointestinal endoscopy. In premenopausal women current guidelines recommend serologic evaluation of coeliac disease only. Here we systematically tested serologic screening for autoimmune gastritis (AIG) in a large cohort of patients with ID. This is a retrospective analysis of patients who attended an out-patient clinic specialized for ID. Patients with ferritin <50 µg/L or transferrin saturation <15% were included. Laboratory workup included endomysial antibodies and parietal-cell antibodies (PCA). Upper gastrointestinal endoscopy with pH-measurement of gastric juice and histology was performed to confirm positive serologic results. Three hundred seventy-three patients with ID were included, about half of whom were anemic. Patients were predominately female with a median age of 40 (confidence interval 11). Positive endomysial antibodies were found in 4 (1%) patients, elevated levels of PCA (>20 U/mL) were found in 69 (18.5%) patients, PCA >100 U/mL in 23 (6.2%). Twenty-six were followed up by gastroscopy; in 12 of 26 patients the diagnosis of AIG was confirmed by histology with 2 additional patients diagnosed as early and/or questionable AIG. A sensitivity of 93% and a specificity of 98% were estimated for a PCA cut-off of 100 U/mL. In 20 patients gastric pH was measured. Achlorhydria was found in 7 patients all diagnosed with AIG. In this ID cohort AIG is by far more common than coeliac disease. PCA above 100 U/mL are a sensitive and specific cut-off for workup of patients with ID prior to endoscopy. Serologic suspicion of AIG helps preselection of patients for endoscopic workup for ID.

Estimation after blinded sample size reassessment.

Blinded sample size reassessment is a popular means to control the power in clinical trials if no reliable information on nuisance parameters is available in the planning phase. We investigate how sample size reassessment based on blinded interim data affects the properties of point estimates and confidence intervals for parallel group superiority trials comparing the means of a normal endpoint. We evaluate the properties of two standard reassessment rules that are based on the sample size formula of the z-test, derive the worst case reassessment rule that maximizes the absolute mean bias and obtain an upper bound for the mean bias of the treatment effect estimate.

Early prediction of postoperative liver dysfunction and clinical outcome using antithrombin III-activity.

BACKGROUND AND AIMS: Antithrombin III (ATIII) has been reported to be associated with liver pathologies and was shown to predict outcome in patients undergoing liver resection for hepatocellular carcinoma. We now aimed to assess whether perioperative ATIII-activity could predict postoperative outcome in patients without underlying liver disease, as well as in a routine clinical setting of patients undergoing hepatic resection. METHODS: ATIII-activity was evaluated preoperatively and on the first (POD1) and fifth day after liver resection in a retrospective evaluation cohort of 228 colorectal cancer patients with liver metastasis (mCRC). We further aimed to prospectively validate our results in a set of 177 consecutive patients undergoing hepatic resection. RESULTS: Patients developing postoperative liver dysfunction (LD) had a more pronounced postoperative decrease in ATIII-activity (P<0.001). ATIII-activity on POD1 significantly predicted postoperative LD (P<0.001, AUC = 84.4%) and remained independent upon multivariable analysis. A cut-off value of 61.5% ATIII-activity was determined using ROC analysis. This cut-off was vital to identify high-risk patients for postoperative LD, morbidity, severe morbidity and mortality (P<0.001, respectively) with a highly accurate negative predictive value of 97%, which could be confirmed for LD (P<0.001) and mortality (P = 0.014) in our independent validation cohort. Further, mCRC patients below our cut-off suffered from a significantly decreased overall survival (OS) at 1 and 3 years after surgery (P = 0.011, P = 0.025). CONCLUSIONS: The routine laboratory parameter ATIII-activity on POD1 independently predicted postoperative LD and was associated with clinical outcome. Patients with a postoperative ATIII-activity <61.5% might benefit from close monitoring and timely initiation of supportive therapy. TRIAL REGISTRATION: ClinicalTrials.gov NCT01700231.

Adaptive graph-based multiple testing procedures.

Multiple testing procedures defined by directed, weighted graphs have recently been proposed as an intuitive visual tool for constructing multiple testing strategies that reflect the often complex contextual relations between hypotheses in clinical trials. Many well-known sequentially rejective tests, such as (parallel) gatekeeping tests or hierarchical testing procedures are special cases of the graph based tests. We generalize these graph-based multiple testing procedures to adaptive trial designs with an interim analysis. These designs permit mid-trial design modifications based on unblinded interim data as well as external information, while providing strong family wise error rate control. To maintain the familywise error rate, it is not required to prespecify the adaption rule in detail. Because the adaptive test does not require knowledge of the multivariate distribution of test statistics, it is applicable in a wide range of scenarios including trials with multiple treatment comparisons, endpoints or subgroups, or combinations thereof. Examples of adaptations are dropping of treatment arms, selection of subpopulations, and sample size reassessment. If, in the interim analysis, it is decided to continue the trial as planned, the adaptive test reduces to the originally planned multiple testing procedure. Only if adaptations are actually implemented, an adjusted test needs to be applied. The procedure is illustrated with a case study and its operating characteristics are investigated by simulations.

Graphical approaches for multiple comparison procedures using weighted Bonferroni, Simes, or parametric tests.

The confirmatory analysis of pre-specified multiple hypotheses has become common in pivotal clinical trials. In the recent past multiple test procedures have been developed that reflect the relative importance of different study objectives, such as fixed sequence, fallback, and gatekeeping procedures. In addition, graphical approaches have been proposed that facilitate the visualization and communication of Bonferroni-based closed test procedures for common multiple test problems, such as comparing several treatments with a control, assessing the benefit of a new drug for more than one endpoint, combined non-inferiority and superiority testing, or testing a treatment at different dose levels in an overall and a subpopulation. In this paper, we focus on extended graphical approaches by dissociating the underlying weighting strategy from the employed test procedure. This allows one to first derive suitable weighting strategies that reflect the given study objectives and subsequently apply appropriate test procedures, such as weighted Bonferroni tests, weighted parametric tests accounting for the correlation between the test statistics, or weighted Simes tests. We illustrate the extended graphical approaches with several examples. In addition, we describe briefly the gMCP package in R, which implements some of the methods described in this paper.

Cross-platform comparison of microarray data using order restricted inference.

MOTIVATION: Titration experiments measuring the gene expression from two different tissues, along with total RNA mixtures of the pure samples, are frequently used for quality evaluation of microarray technologies. Such a design implies that the true mRNA expression of each gene, is either constant or follows a monotonic trend between the mixtures, applying itself to the use of order restricted inference procedures. Exploiting only the postulated monotonicity of titration designs, we propose three statistical analysis methods for the validation of high-throughput genetic data and corresponding preprocessing techniques. RESULTS: Our methods allow for inference of accuracy, repeatability and cross-platform agreement, with minimal required assumptions regarding the underlying data generating process. Therefore, they are readily applicable to all sorts of genetic high-throughput data independent of the degree of preprocessing. An application to the EMERALD dataset was used to demonstrate how our methods provide a rich spectrum of easily interpretable quality metrics and allow the comparison of different microarray technologies and normalization methods. The results are on par with previous work, but provide additional new insights that cast doubt on the utility of popular preprocessing techniques, specifically concerning the EMERALD projects dataset. AVAILABILITY: All datasets are available on EBI's ArrayExpress web site http://www.ebi.ac.uk/microarray-as/ae/) under accession numbers E-TABM-536, E-TABM-554 and E-TABM-555. Source code implemented in C and R is available at: http://statistics.msi.meduniwien.ac.at/float/cross_platform/. Methods for testing and variance decomposition have been made available in the R-package orQA, which can be downloaded and installed from CRAN http://cran.r-project.org.

Microarray analysis identifies distinct gene expression profiles associated with histological subtype in human osteosarcoma.

Osteosarcoma is the most common primary malignant bone tumour. Currently osteosarcoma classification is based on histological appearance. It was the aim of this study to use a more systematic approach to osteosarcoma classification based on gene expression analysis and to identify subtype specific differentially expressed genes. We analysed the global gene expression profiles of ten osteosarcoma samples using Affymetrix U133A arrays (five osteoblastic and five non-osteoblastic osteosarcoma patients). Differential gene expression analysis yielded 75 genes up-regulated and 97 genes down-regulated in osteoblastic versus non-osteoblastic osteosarcoma samples, respectively. These included genes involved in cell growth, chemotherapy resistance, angiogenesis, steroid- and neuropeptide hormone receptor activity, acute-phase response and serotonin receptor activity and members of the Wnt/ß-catenin pathway and many others. Furthermore, we validated the highly differential expression of six genes including angiopoietin 1, IGFBP3, ferredoxin 1, BMP, decorin, and fibulin 1 in osteoblastic osteosarcoma relative to non-osteoblastic osteosarcoma. Our results show the utility of gene expression analysis to study osteosarcoma subtypes, and we identified several genes that may play a role as potential therapeutic targets in the future.