Does time of surgery influence the rate of false-negative appendectomies?:A retrospective observational study of 274 patients

Background Multiple disciplines have described an “after-hours effect” relating to worsened mortality and morbidity outside regular working hours. This retrospective observational study aimed to evaluate whether diagnostic accuracy of a common surgical condition worsened after regular hours. Methods Electronic operative records for all non-infant patients (age > 4 years) operated on at a single centre for presumed acute appendicitis were retrospectively reviewed over a 56-month period (06/17/2012–02/01/2017). The primary outcome measure of unknown diagnosis was compared between those performed in regular hours (08:00–17:00) or off hours (17:01–07:59). Pre-clinical biochemistry and pre-morbid status were recorded to determine case heterogeneity between the two groups, along with secondary outcomes of length of stay and complication rate. Results Out of 289 procedures, 274 cases were deemed eligible for inclusion. Of the 133 performed in regular hours, 79% were appendicitis, compared to 74% of the 141 procedures performed off hours. The percentage of patients with an unknown diagnosis was 6% in regular hours compared to 15% off hours (RR 2.48; 95% CI 1.14–5.39). This was accompanied by increased numbers of registrars (residents in training) leading procedures off hours (37% compared to 24% in regular hours). Pre-morbid status, biochemistry, length of stay and post-operative complication rate showed no significant difference. Conclusions This retrospective study suggests that the rate of unknown diagnoses for acute appendicitis increases overnight, potentially reflecting increased numbers of unnecessary procedures being performed off hours due to poorer diagnostic accuracy. Reduced levels of staffing, availability of diagnostic modalities and changes to workforce training may explain this, but further prospective work is required. Potential solutions may include protocolizing the management of common acute surgical conditions and making more use of non-resident on call senior colleagues

Non invasive ventilation after extubation in paediatric patients: a preliminary study

<p>Abstract</p> <p>Background</p> <p>Non-invasive ventilation (NIV) may be useful after extubation in children. Our objective was to determine postextubation NIV characteristics and to identify risk factors of postextubation NIV failure.</p> <p>Methods</p> <p>A prospective observational study was conducted in an 8-bed pediatric intensive care unit (PICU). Following PICU protocol, NIV was applied to patients who had been mechanically ventilated for over 12 hours considered at high-risk of extubation failure -elective NIV (eNIV), immediately after extubation- or those who developed respiratory failure within 48 hours after extubation -rescue NIV (rNIV)-. Patients were categorized in subgroups according to their main underlying conditions. NIV was deemed successful when reintubation was avoided. Logistic regression analysis was performed in order to identify predictors of NIV failure.</p> <p>Results</p> <p>There were 41 episodes (rNIV in 20 episodes). Success rate was 50% in rNIV and 81% in eNIV (p = 0.037). We found significant differences in univariate analysis between success and failure groups in respiratory rate (RR) decrease at 6 hours, FiO₂at 1 hour and PO₂/FiO₂ratio at 6 hours. Neurologic condition was found to be associated with NIV failure. Multiple logistic regression analysis identified no variable as independent NIV outcome predictor.</p> <p>Conclusions</p> <p>Our data suggest that postextubation NIV seems to be useful in avoiding reintubation in high-risk children when applied immediately after extubation. NIV was more likely to fail when ARF has already developed (rNIV), when RR at 6 hours did not decrease and if oxygen requirements increased. Neurologic patients seem to be at higher risk of reintubation despite NIV use.</p

Lazy Lasso for local regression

Locally weighted regression is a technique that predicts the response for new data items from their neighbors in the training data set, where closer data items are assigned higher weights in the prediction. However, the original method may suffer from overfitting and fail to select the relevant variables. In this paper we propose combining a regularization approach with locally weighted regression to achieve sparse models. Specifically, the lasso is a shrinkage and selection method for linear regression. We present an algorithm that embeds lasso in an iterative procedure that alternatively computes weights and performs lasso-wise regression. The algorithm is tested on three synthetic scenarios and two real data sets. Results show that the proposed method outperforms linear and local models for several kinds of scenario

High-Density SNP Mapping of the HLA Region Identifies Multiple Independent Susceptibility Loci Associated with Selective IgA Deficiency

Selective IgA deficiency (IgAD; serum IgA<0.07 g/l) is the most common form of human primary immune deficiency, affecting approximately 1∶600 individuals in populations of Northern European ancestry. The polygenic nature of IgAD is underscored by the recent identification of several new risk genes in a genome-wide association study. Among the characterized susceptibility loci, the association with specific HLA haplotypes represents the major genetic risk factor for IgAD. Despite the robust association, the nature and location of the causal variants in the HLA region remains unknown. To better characterize the association signal in this region, we performed a high-density SNP mapping of the HLA locus and imputed the genotypes of common HLA-B, -DRB1, and -DQB1 alleles in a combined sample of 772 IgAD patients and 1,976 matched controls from 3 independent European populations. We confirmed the complex nature of the association with the HLA locus, which is the result of multiple effects spanning the entire HLA region. The primary association signal mapped to the HLA-DQB1*02 allele in the HLA Class II region (combined P = 7.69×10−57; OR = 2.80) resulting from the combined independent effects of the HLA-B*0801-DRB1*0301-DQB1*02 and -DRB1*0701-DQB1*02 haplotypes, while additional secondary signals were associated with the DRB1*0102 (combined P = 5.86×10−17; OR = 4.28) and the DRB1*1501 (combined P = 2.24×10−35; OR = 0.13) alleles. Despite the strong population-specific frequencies of HLA alleles, we found a remarkable conservation of these effects regardless of the ethnic background, which supports the use of large multi-ethnic populations to characterize shared genetic association signals in the HLA region. We also provide evidence for the location of association signals within the specific extended haplotypes, which will guide future sequencing studies aimed at characterizing the precise functional variants contributing to disease pathogenesis

ESR1 gene promoter region methylation in free circulating DNA and its correlation with estrogen receptor protein expression in tumor tissue in breast cancer patients

[Background] Tumor expression of estrogen receptor (ER) is an important marker of prognosis, and is predictive of response to endocrine therapy in breast cancer. Several studies have observed that epigenetic events, such methylation of cytosines and deacetylation of histones, are involved in the complex mechanisms that regulate promoter transcription. However, the exact interplay of these factors in transcription activity is not well understood. In this study, we explored the relationship between ER expression status in tumor tissue samples and the methylation of the 5′ CpG promoter region of the estrogen receptor gene (ESR1) isolated from free circulating DNA (fcDNA) in plasma samples from breast cancer patients. [Methods] Patients (n = 110) with non-metastatic breast cancer had analyses performed of ER expression (luminal phenotype in tumor tissue, by immunohistochemistry method), and the ESR1-DNA methylation status (fcDNA in plasma, by quantitative methylation specific PCR technique). [Results] Our results showed a significant association between presence of methylated ESR1 in patients with breast cancer and ER negative status in the tumor tissue (p = 0.0179). There was a trend towards a higher probability of ESR1-methylation in those phenotypes with poor prognosis i.e. 80% of triple negative patients, 60% of HER2 patients, compared to 28% and 5.9% of patients with better prognosis such as luminal A and luminal B, respectively. [Conclusion] Silencing, by methylation, of the promoter region of the ESR1 affects the expression of the estrogen receptor protein in tumors of breast cancer patients; high methylation of ESR1-DNA is associated with estrogen receptor negative status which, in turn, may be implicated in the patient’s resistance to hormonal treatment in breast cancer. As such, epigenetic markers in plasma may be of interest as new targets for anticancer therapy, especially with respect to endocrine treatment.The study was funded, in part, by a grant from the Ministerio de Educación y Ciencia (CICYT: SAF 2004–00889)