A prediction model for assessing residential radon concentration in Switzerland

Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th-90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40-111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69-215 Bq/m³) in the medium category, and 219 Bq/m³ (108-427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be robust through validation with an independent dataset. The model is appropriate for predicting radon level exposure of the Swiss population in epidemiological research. Nevertheless, some exposure misclassification and regression to the mean is unavoidable and should be taken into account in future applications of the model.

IDH/MGMT-driven molecular classification of low-grade glioma is a strong predictor for long-term survival

BACKGROUND Low-grade gliomas (LGGs) are rare brain neoplasms, with survival spanning up to a few decades. Thus, accurate evaluations on how biomarkers impact survival among patients with LGG require long-term studies on samples prospectively collected over a long period. METHODS The 210 adult LGGs collected in our databank were screened for IDH1 and IDH2 mutations (IDHmut), MGMT gene promoter methylation (MGMTmet), 1p/19q loss of heterozygosity (1p19qloh), and nuclear TP53 immunopositivity (TP53pos). Multivariate survival analyses with multiple imputation of missing data were performed using either histopathology or molecular markers. Both models were compared using Akaike's information criterion (AIC). The molecular model was reduced by stepwise model selection to filter out the most critical predictors. A third model was generated to assess for various marker combinations. RESULTS Molecular parameters were better survival predictors than histology (ΔAIC = 12.5, P< .001). Forty-five percent of studied patients died. MGMTmet was positively associated with IDHmut (P< .001). In the molecular model with marker combinations, IDHmut/MGMTmet combined status had a favorable impact on overall survival, compared with IDHwt (hazard ratio [HR] = 0.33, P< .01), and even more so the triple combination, IDHmut/MGMTmet/1p19qloh (HR = 0.18, P< .001). Furthermore, IDHmut/MGMTmet/TP53pos triple combination was a significant risk factor for malignant transformation (HR = 2.75, P< .05). CONCLUSION By integrating networks of activated molecular glioma pathways, the model based on genotype better predicts prognosis than histology and, therefore, provides a more reliable tool for standardizing future treatment strategies.

The Application of Comorbidity Indices to Predict Early Postoperative Outcomes After Laparoscopic Roux-en-Y Gastric Bypass: A Nationwide Comparative Analysis of Over 70,000 Cases

BACKGROUND: Patients undergoing laparoscopic Roux-en-Y gastric bypass (LRYGB) often have substantial comorbidities, which must be taken into account to appropriately assess expected postoperative outcomes. The Charlson/Deyo and Elixhauser indices are widely used comorbidity measures, both of which also have revised algorithms based on enhanced ICD-9-CM coding. It is currently unclear which of the existing comorbidity measures best predicts early postoperative outcomes following LRYGB. METHODS: Using the Nationwide Inpatient Sample, patients 18 years or older undergoing LRYGB for obesity between 2001 and 2008 were identified. Comorbidities were assessed according to the original and enhanced Charlson/Deyo and Elixhauser indices. Using multivariate logistic regression, the following early postoperative outcomes were assessed: overall postoperative complications, length of hospital stay, and conversion to open surgery. Model performance for the four comorbidity indices was assessed and compared using C-statistics and the Akaike's information criterion (AIC). RESULTS: A total of 70,287 patients were included. Mean age was 43.1 years (SD, 10.8), 81.6 % were female and 60.3 % were White. Both the original and enhanced Elixhauser indices modestly outperformed the Charlson/Deyo in predicting the surgical outcomes. All four models had similar C-statistics, but the original Elixhauser index was associated with the smallest AIC for all of the surgical outcomes. CONCLUSIONS: The original Elixhauser index is the best predictor of early postoperative outcomes in our cohort of patients undergoing LRYGB. However, differences between the Charlson/Deyo and Elixhauser indices are modest, and each of these indices provides clinically relevant insight for predicting early postoperative outcomes in this high-risk patient population.

Combination of expression levels of miR-21 and miR-126 is associated with cancer-specific survival in clear-cell renal cell carcinoma

BACKGROUND Renal cell carcinoma (RCC) is marked by high mortality rate. To date, no robust risk stratification by clinical or molecular prognosticators of cancer-specific survival (CSS) has been established for early stages. Transcriptional profiling of small non-coding RNA gene products (miRNAs) seems promising for prognostic stratification. The expression of miR-21 and miR-126 was analysed in a large cohort of RCC patients; a combined risk score (CRS)-model was constructed based on expression levels of both miRNAs. METHODS Expression of miR-21 and miR-126 was evaluated by qRT-PCR in tumour and adjacent non-neoplastic tissue in n = 139 clear cell RCC patients. Relation of miR-21 and miR-126 expression with various clinical parameters was assessed. Parameters were analysed by uni- and multivariate COX regression. A factor derived from the z-score resulting from the COX model was determined for both miRs separately and a combined risk score (CRS) was calculated multiplying the relative expression of miR-21 and miR-126 by this factor. The best fitting COX model was selected by relative goodness-of-fit with the Akaike information criterion (AIC). RESULTS RCC with and without miR-21 up- and miR-126 downregulation differed significantly in synchronous metastatic status and CSS. Upregulation of miR-21 and downregulation of miR-126 were independently prognostic. A combined risk score (CRS) based on the expression of both miRs showed high sensitivity and specificity in predicting CSS and prediction was independent from any other clinico-pathological parameter. Association of CRS with CSS was successfully validated in a testing cohort containing patients with high and low risk for progressive disease. CONCLUSIONS A combined expression level of miR-21 and miR-126 accurately predicted CSS in two independent RCC cohorts and seems feasible for clinical application in assessing prognosis.

Macular atrophy in patients with long-term anti-VEGF treatment for neovascular age-related macular degeneration.

PURPOSE To identify the prevalence and progression of macular atrophy (MA) in neovascular age-related macular degeneration (AMD) patients under long-term anti-vascular endothelial growth factor (VEGF) therapy and to determine risk factors. METHOD This retrospective study included patients with neovascular AMD and ≥30 anti-VEGF injections. Macular atrophy (MA) was measured using near infrared and spectral-domain optical coherence tomography (SD-OCT). Yearly growth rate was estimated using square-root transformation to adjust for baseline area and allow for linearization of growth rate. Multiple regression with Akaike information criterion (AIC) as model selection criterion was used to estimate the influence of various parameters on MA area. RESULTS Forty-nine eyes (47 patients, mean age 77 ± 14) were included with a mean of 48 ± 13 intravitreal anti-VEGF injections (ranibizumab:37 ± 11, aflibercept:11 ± 6, mean number of injections/year 8 ± 2.1) over a mean treatment period of 6.2 ± 1.3 years (range 4-8.5). Mean best-corrected visual acuity improved from 57 ± 17 letters at baseline (= treatment start) to 60 ± 16 letters at last follow-up. The MA prevalence within and outside the choroidal neovascularization (CNV) border at initial measurement was 45% and increased to 74%. Mean MA area increased from 1.8 ± 2.7 mm(2) within and 0.5 ± 0.98 mm(2) outside the CNV boundary to 2.7 ± 3.4 mm(2) and 1.7 ± 1.8 mm(2) , respectively. Multivariate regression determined posterior vitreous detachment (PVD) and presence/development of intraretinal cysts (IRCs) as significant factors for total MA size (R(2) = 0.16, p = 0.02). Macular atrophy (MA) area outside the CNV border was best explained by the presence of reticular pseudodrusen (RPD) and IRC (R(2) = 0.24, p = 0.02). CONCLUSION A majority of patients show MA after long-term anti-VEGF treatment. Reticular pseudodrusen (RPD), IRC and PVD but not number of injections or treatment duration seem to be associated with the MA size.