METHOD: This international multi-center prospective study across 137 hospitals in 41 countries included patients who underwent an esophagectomy for esophageal cancer, with 90-day follow-up. The main explanatory variable was country income, defined according to the World Bank Data classification. The primary outcome was 90-day postoperative mortality, and secondary outcomes were composite leaks (anastomotic leak or conduit necrosis) and major complications (Clavien-Dindo Grade III - V). Multivariable generalized estimating equation models were used to produce adjusted odds ratios (ORs) and 95% confidence intervals (CI95%).
RESULTS: Between April 2018 to December 2018, 2247 patients were included. Patients from HIC were more significantly older, with higher ASA grade, and more advanced tumors. Patients from LMIC had almost three-fold increase in 90-day mortality, compared to HIC (9.4% vs 3.7%, p
METHODS: Patients undergoing curative resection for oesophageal cancer were identified from the international Oesophagogastric Anastomosis Audit (OGAA) from April 2018-December 2018. Definitions for AL and CN were those set out by the Oesophageal Complications Consensus Group. Univariate and multivariate analyses were performed to identify risk factors for both AL and CN. A risk score was then produced for both AL and CN using the derivation set, then internally validated using the validation set.
RESULTS: This study included 2247 oesophagectomies across 137 hospitals in 41 countries. The AL rate was 14.2% and CN rate was 2.7%. Preoperative factors that were independent predictors of AL were cardiovascular comorbidity and chronic obstructive pulmonary disease. The risk scoring model showed insufficient predictive ability in internal validation (area under the receiver-operating-characteristic curve [AUROC] = 0.618). Preoperative factors that were independent predictors of CN were: body mass index, Eastern Cooperative Oncology Group performance status, previous myocardial infarction and smoking history. These were converted into a risk-scoring model and internally validated using the validation set with an AUROC of 0.775.
CONCLUSION: Despite a large dataset, AL proves difficult to predict using preoperative factors. The risk-scoring model for CN provides an internally validated tool to estimate a patient's risk preoperatively.
METHODS: Assessment of neovascular age-related macular degeneration patients with or without PCV after 12 months of ranibizumab treatment during the LUMINOUS study. Outcome measures were visual acuity and central retinal thickness changes from baseline and the rate of ocular adverse events.
RESULTS: At baseline, 572 and 5,644 patients were diagnosed with and without PCV, respectively. The mean visual acuity gain from baseline at Month 12 in the PCV and non-PCV groups was +5.0 and +3.0 letters, respectively; these gains were achieved with a mean of 4.4 and 5.1 ranibizumab injections. Eighty percent of PCV patients and 72.2% of non-PCV patients who had baseline visual acuity ≥73 letters maintained this level of vision at Month 12; 20.6% and 17.9% of patients with baseline visual acuity <73 letters achieved visual acuity ≥73 letters in these groups. Greater reductions in central retinal thickness from baseline were also observed for the PCV group versus the non-PCV group. The rate of serious ocular adverse events was 0.7% (PCV group) and 0.9% (non-PCV group).
CONCLUSION: LUMINOUS confirms the effectiveness and safety of ranibizumab in treatment-naive patients with PCV.
METHODS: Men enrolled in the IMPACT study provided serum samples during regular visits. Hormonal levels were calculated using immunoassays. Free testosterone (FT) was calculated from TT and sex hormone binding globulin (SHBG) using the Sodergard mass equation. Age, body mass index (BMI), prostate-specific antigen (PSA) and hormonal concentrations were compared between genetic cohorts. We also explored associations between age and TT, SHBG, FT and PCa, in the whole subset and stratified by BRCA1/2 PVs status.
RESULTS: A total of 777 participants in the IMPACT study had TT and SHBG measurements in serum samples at annual visits, giving 3940 prospective androgen levels, from 266 BRCA1 PVs carriers, 313 BRCA2 PVs carriers and 198 non-carriers. The median number of visits per patient was 5. There was no difference in TT, SHBG and FT between carriers and non-carriers. In a univariate analysis, androgen levels were not associated with PCa. In the analysis stratified by carrier status, no significant association was found between hormonal levels and PCa in non-carriers, BRCA1 or BRCA2 PVs carriers.
CONCLUSIONS: Male BRCA1/2 PVs carriers have a similar androgen profile to non-carriers. Hormonal levels were not associated with PCa in men with and without BRCA1/2 PVs. Mechanisms related to the particularly aggressive phenotype of PCa in BRCA2 PVs carriers may therefore not be linked with circulating hormonal levels.
METHODS: All consecutive patients admitted to any of the 150 participating general surgery (GS), hepatopancreatobiliary surgery (HPB), internal medicine (IM) and gastroenterology (GA) departments with a diagnosis of biliary acute pancreatitis between 01/01/2019 and 31/12/2020 were included in the study. Categorical data were reported as percentages representing the proportion of all study patients or different and well-defined cohorts for each variable. Continuous data were expressed as mean and standard deviation. Differences between the compliance obtained in the four different subgroups were compared using the Mann-Whitney U, Student's t, ANOVA or Kruskal-Wallis tests for continuous data, and the Chi-square test or the Fisher's exact test for categorical data.
RESULTS: Complete data were available for 5275 patients. The most commonly discordant gaps between daily clinical practice and recommendations included the optimal timing for the index CT scan (6.1%, χ2 6.71, P = 0.081), use of prophylactic antibiotics (44.2%, χ2 221.05, P
METHODS: In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.
RESULTS: The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.
CONCLUSIONS: We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.
MATERIALS AND METHOD: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.
RESULTS: 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.
CONCLUSIONS: Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.