Evidence-Based Decompression in Malignant Biliary Obstruction

As recent advances in chemotherapy and surgical treatment have improved outcomes in patients with biliary cancers, the search for an optimal strategy for relief of their obstructive jaundice has become even more important. Without satisfactory relief of biliary obstruction, many patients would be ineligible for treatment. We review all prospective randomized trials and recent retrospective non-randomized studies for evidence that would support such a strategy. For distal malignant biliary obstruction, an optimal strategy would be insertion of metallic stents either endoscopically or percutaneously. Evidence shows that a metallic stent inserted percutaneously has better outcomes than plastic stents inserted endoscopically. For malignant hilar obstruction, percutaneous biliary drainage with or without metallic stents is preferred

Immune-mediated genetic pathways resulting in pulmonary function impairment increase lung cancer susceptibility

Impaired lung function is often caused by cigarette smoking, making it challenging to disentangle its role in lung cancer susceptibility. Investigation of the shared genetic basis of these phenotypes in the UK Biobank and International Lung Cancer Consortium (29,266 cases, 56,450 controls) shows that lung cancer is genetically correlated with reduced forced expiratory volume in one second (FEV1: r(g) = 0.098, p = 2.3 x 10(-8)) and the ratio of FEV1 to forced vital capacity (FEV1/FVC: r(g) = 0.137, p = 2.0 x 10(-12)). Mendelian randomization analyses demonstrate that reduced FEV1 increases squamous cell carcinoma risk (odds ratio (OR) = 1.51, 95% confidence intervals: 1.21-1.88), while reduced FEV1/FVC increases the risk of adenocarcinoma (OR = 1.17, 1.01-1.35) and lung cancer in never smokers (OR = 1.56, 1.05-2.30). These findings support a causal role of pulmonary impairment in lung cancer etiology. Integrative analyses reveal that pulmonary function instruments, including 73 novel variants, influence lung tissue gene expression and implicate immune-related pathways in mediating the observed effects on lung carcinogenesis

Assessing Lung Cancer Absolute Risk Trajectory Based on a Polygenic Risk Model

Lung cancer is the leading cause of cancer death globally. An improved risk stratification strategy can increase efficiency of low-dose computed tomography (LDCT) screening. Here we assessed whether individual’s genetic background has clinical utility for risk stratification in the context of LDCT screening. Based on 13,119 lung cancer patients and 10,008 controls with European ancestry in the International Lung Cancer Consortium, we constructed a polygenic risk score (PRS) via 10-fold cross-validation with regularized penalized regression. The performance of risk model integrating PRS, including calibration and ability to discriminate, was assessed using UK biobank data (N=335,931). Absolute risk was estimated based on age-specific lung cancer incidence and all-cause mortality as competing risk. To evaluate its potential clinical utility, the PRS distribution was simulated in the National Lung Screening Trial (N=50,772 participants). The lung cancer odds ratio (ORs) for individuals at the top decile of the PRS distribution versus those at bottom 10% was 2.39 (95%CI=1.92–3.00, P=1.80×10(−14)) in the validation set (trend p-value of 5.26 × 10(−20)). The OR per standard deviation of PRS increase was 1.26 (95%CI=1.20–1.32, P=9.69×10(−23)) for overall lung cancer risk in the validation set. When considering absolute risks, individuals at different PRS deciles showed differential trajectories of 5-year and cumulative absolute risk. The age reaching the LDCT screening recommendation threshold can vary by 4 to 8 years, depending on the individual’s genetic background, smoking status and family history. Collectively, these results suggest that individual’s genetic background may inform the optimal lung cancer LDCT screening strategy

Prediction of second neurological attack in patients with clinically isolated syndrome using support vector machines

The aim of this study is to predict the conversion from clinically isolated syndrome to clinically definite multiple sclerosis using support vector machines. The two groups of converters and non-converters are classified using features that were calculated from baseline data of 73 patients. The data consists of standard magnetic resonance images, binary lesion masks, and clinical and demographic information. 15 features were calculated and all combinations of them were iteratively tested for their predictive capacity using polynomial kernels and radial basis functions with leave-one-out cross-validation. The accuracy of this prediction is up to 86.4% with a sensitivity and specificity in the same range indicating that this is a feasible approach for the prediction of a second clinical attack in patients with clinically isolated syndromes, and that the chosen features are appropriate. The two features gender and location of onset lesions have been used in all feature combinations leading to a high accuracy suggesting that they are highly predictive. However, it is necessary to add supporting features to maximise the accuracy. © 2013 IEEE

Why Lie?: Deception of Health Information

Individuals provide deceptive information in many contexts and for many reasons. We explore the phenomenon of medical patients providing deceptive information to their healthcare providers via both face-to-face and online interactions. We will employ the scenario-based factorial survey method to present research respondents with various situations in which they may consider whether or not to provide accurate or deceptive information. This method has been demonstrated to yield valid data for investigations of norm-violating behaviors. Results will be presented at the conference