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Eden Z Deng,1– 3 Daniel P Weikel,3 Katherine T Martucci1– 3 1Human Affect and Pain Neuroscience Laboratory, Department of Anesthesiology, Duke University Medical Center, Durham, NC, USA; 2Center for Translational Pain Medicine, Duke University Medical Center, Durham, NC, USA; 3Biostatistics Group, Department of Anesthesiology, Duke University School of Medicine, Durham, NC, USACorrespondence: Katherine T Martucci, Human Affect and Pain Neuroscience Laboratory, Duke University Medical Center, Durham, NC, 27710, USA, Tel +1 919-613-8023, Fax +1 919-684-2411, Email katherine.martucci@duke.eduPurpose: The cold pressor test (CPT) is a clinical pain research method used to measure cold pain tolerance. During this test, participants immerse an extremity (ie, hand or foot) into cold water for as long as tolerable. The duration of the test (traditionally up to an experimentally imposed cut-off at 2 minutes) indicates the amount of cold pain tolerance by the participant. Prior research studies have investigated cold pain tolerance in patients with chronic pain. However, few of these studies have used survival analysis, which allows for proper handling of data censoring and is therefore, an optimal statistical method for CPT data analysis. The goal of the present study was to use survival analysis to evaluate cold pain tolerance in patients with fibromyalgia. Furthermore, we aimed to model relationships between psychological and clinical variables as well as opioid medication use and cold pain tolerance.Patients and Methods: A total of 85 patients with fibromyalgia (42 who were taking opioids) and 47 healthy pain-free controls provided CPT and questionnaire data (collected across 2 study sites) for a case–control study. We used survival analysis using Cox regression to evaluate group differences (patients vs controls) in cold pain tolerance and to evaluate cold pain tolerance relationships with psychological, clinical, and medication use.Results: As compared to healthy controls, patients with fibromyalgia exhibited significantly lower CPT survival (HR = 2.17, 95% CI: [1.42, 3.31], p = 0.00035). As indicated by Cox regression models, the significant group difference in CPT survival did not relate to our selected psychological and clinical measures (p > 0.05). The groups of non-opioid-taking patients and healthy controls showed consistent CPT survival across study sites. However, patients taking opioid pain medications showed differences in CPT survival across study sites.Conclusion: By using survival analysis, an optimal method for time-to-event pain measures such as the CPT, we confirmed previously identified reductions in cold pain tolerance in patients with fibromyalgia. While our selected psychological and clinical measures were not significantly associated with cold pain tolerance, our data suggest that opioid medication use may impart greater cold pain tolerance in some patients.Keywords: chronic pain, opiate, cold pressor test, hyperalgesia

Background Ischemic lesions observed on diffusion-weighted imaging (DWI) magnetic resonance imaging are associated with poor outcomes after intracerebral hemorrhage (ICH). We evaluated the association between hyperglycemia, ischemic lesions, and functional outcomes after ICH. Methods and Results This was a retrospective observational analysis of 1167 patients who received magnetic resonance imaging in the ERICH (Ethnic and Racial Variations in Intracerebral Hemorrhage) study. A machine learning strategy using the elastic net regularization and selection procedure was used to perform automated variable selection to identify final multivariable logistic regression models. Sensitivity analyses with alternative model development strategies were performed, and predictive performance was compared. After covariate adjustment, white matter hyperintensity score, leukocyte count on admission, and non-Hispanic Black race (compared with non-Hispanic White race) were associated with the presence of DWI lesions. History of ICH and ischemic stroke, presence of DWI lesions, deep ICH location (versus lobar), ICH volume, age, lower Glasgow Coma Score on admission, and medical history of diabetes were associated with poor 6-month modified Rankin Scale outcome (4-6) after covariate adjustment. Inclusion of interactions between race and ethnicity and variables included in the final multivariable model for functional outcome improved model performance; a significant interaction between race and ethnicity and medical history of diabetes and serum blood glucose on admission was observed. Conclusions No measure of hyperglycemia or diabetes was associated with presence of DWI lesions. However, both medical history of diabetes and presence of DWI lesions were independently associated with poor functional outcomes after ICH.

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue., Competing Interests: The authors declare no competing interest., (Copyright © 2019 the Author(s). Published by PNAS.)

Recent epidemics of Zika, dengue, and chikungunya have heightened the need to understand the seasonal and geographic range of transmission by Aedes aegypti and Ae. albopictus mosquitoes. We use mechanistic transmission models to derive predictions for how the probability and magnitude of transmission for Zika, chikungunya, and dengue change with mean temperature, and we show that these predictions are well matched by human case data. Across all three viruses, models and human case data both show that transmission occurs between 18-34°C with maximal transmission occurring in a range from 26-29°C. Controlling for population size and two socioeconomic factors, temperature-dependent transmission based on our mechanistic model is an important predictor of human transmission occurrence and incidence. Risk maps indicate that tropical and subtropical regions are suitable for extended seasonal or year-round transmission, but transmission in temperate areas is limited to at most three months per year even if vectors are present. Such brief transmission windows limit the likelihood of major epidemics following disease introduction in temperate zones.