Summary Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest—namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial—ENTHUSE M1—in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39–4·62, p<0·0001; reference model: 2·56, 1·85–3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified...

Objective.-The aim of this systematic review and meta-analysis (SR-MA) was to identify signaling molecule profiles and blood-derived biomarkers in migraine and cluster headache (CH) patients.Background.-Currently no migraine and CH valid biomarkers are available. Blood tests based on biomarker profiles have been used to gather information about the nervous system. Such tests have not yet been established within the primary headache field.Methods.-Case-control and case-crossover studies investigating whole blood, plasma, and serum were identified worldwide. The qualitative synthesis focused on 9 signaling molecules (serotonin [5-HT], calcitonin gene-related peptide [CGRP], endothelin-1 [ET-1], neurokinin A, neurokinin B, neuropeptide Y, pituitary adenylate cyclase-activating peptide 38 [PACAP-38], substance P (SP), and vasoactive intestinal peptide) and the quantitative synthesis on 5-HT and CGRP (≥5 comparisons available). The meta-analysis was conducted using standard and 3-level random effect models.Results.-Fifty-four eligible studies were identified (87.0% migraine, 9.3% CH, 3.7% migraine, and CH), and 2768 headache patients and 1165 controls included. Comparable fluctuations of 5-HT, CGRP, ET-1, PACAP-38, and SP in blood were generally observed between migraine and CH. Significant findings were observed for some subgroups and strata, for example, higher interictal and ictal 5-HT venous blood levels (ratio of means = 1.32, 95% CI: 1.08; 1.61; ratio of means = 1.23, 95% CI: 1.01; 1.49) in episodic migraine with aura with a female-dominated case group, higher interictal CGRP blood levels in episodic migraine (ratio of means = 1.63, 95% CI: 1.18; 2.26), and chronic migraine (ratio of means = 1.89, 95% CI: 1.33; 2.68), and higher ictal CGRP blood levels (ratio of means = 1.35, 95% CI: 1.09; 1.68) in episodic migraine were observed. In most subgroups, the quantitative synthesis revealed a high degree of heterogeneity between studies in part explained by the blood sampling site, specimen source, blood specimen, and sex distribution. Other potential confounders were age, aura, study quality, menstrual cycle, and methodology (eg, storage temperature).Conclusions.-Potential migraine and CH signaling molecule profiles and biomarkers were revealed. Nevertheless, the high degree of heterogeneity between studies impedes identification of valid biomarkers but allowed us to assess the presence of confounders. Consideration of the potential confounders identified in this SR-MA might be of importance in the experimental planning of future studies. This consideration could be incorporated through establishment of specific guidelines. Abbreviations: 2SD 2 times standard deviation, 5-HIAA 5-hydroxyindoleacetic acid, 5-HT serotonin, 5-HT 1B/1D serotonin receptor 1B/1D, CC, column chromatography, CC-RIA column chromatography and radioimmunoassay, CGRP calcitonin gene-related peptide, CH cluster headache, CI confidence interval, CSF cerebrospinal fluid, CTRL controls, CV coefficient of variation, D Cook's distance, EIA enzyme immun...

BackgroundExcessive summer heat is a serious environmental health problem in several European cities. Heat-related mortality and morbidity is likely to increase under climate change scenarios without adequate prevention based on locally relevant evidence.MethodsWe modelled the urban climate of Antwerp for the summer season during the period 1986–2015, and projected summer daily temperatures for two periods, one in the near (2026–2045) and one in the far future (2081–2100), under the Representative Concentration Pathway (RCP) 8.5. We then analysed the relationship between temperature and mortality, as well as with hospital admissions for the period 2009–2013, and estimated the projected mortality in the near future and far future periods under changing climate and population, assuming alternatively no acclimatization and acclimatization based on a constant threshold percentile temperature.ResultsDuring the sample period 2009–2013 we observed an increase in daily mortality from a maximum daily temperature of 26 °C, or the 89th percentile of the maximum daily temperature series. The annual average heat-related mortality in this period was 13.4 persons (95% CI: 3.8–23.4). No effect of heat was observed in the case of hospital admissions due to cardiorespiratory causes. Under a no acclimatization scenario, annual average heat-related mortality is multiplied by a factor of 1.7 in the near future (24.1 deaths/year CI 95%: 6.78–41.94) and by a factor of 4.5 in the far future (60.38 deaths/year CI 95%: 17.00–105.11). Under a heat acclimatization scenario, mortality does not increase significantly in the near or in the far future.ConclusionThese results highlight the importance of a long-term perspective in the public health prevention of heat exposure, particularly in the context of a changing climate, and the calibration of existing prevention activities in light of locally relevant evidence.

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The effect of school closure on the spread of COVID-19 has been discussed intensively in the literature and the news. To capture the interdependencies between children and adults, we consider daily age-stratified incidence data and contact patterns between age groups which change over time to reflect social distancing policy indicators. We fit a multivariate time-series endemic-epidemic model to such data from the Canton of Zurich, Switzerland and use the model to predict the age-specific incidence in a counterfactual approach (with and without school closures). The results indicate a 17% median increase of incidence in the youngest age group (0-14 year olds), whereas the relative increase in the other age groups drops to values between 2% and 3%. We argue that our approach is more informative to policy makers than summarising the effect of school closures with time-dependent effective reproduction numbers, which are difficult to estimate due to the sparsity of incidence counts within the relevant age groups.