Acute pain pathways: protocol for a prospective cohort study.

INTRODUCTION: Opioid analgesics are often used to treat moderate-to-severe acute non-cancer pain; however, there is little high-quality evidence to guide clinician prescribing. An essential element to developing evidence-based guidelines is a better understanding of pain management and pain control among individuals experiencing acute pain for various common diagnoses. METHODS AND ANALYSIS: This multicentre prospective observational study will recruit 1550 opioid-naïve participants with acute pain seen in diverse clinical settings including primary/urgent care, emergency departments and dental clinics. Participants will be followed for 6 months with the aid of a patient-centred health data aggregating platform that consolidates data from study questionnaires, electronic health record data on healthcare services received, prescription fill data from pharmacies, and activity and sleep data from a Fitbit activity tracker. Participants will be enrolled to represent diverse races and ethnicities and pain conditions, as well as geographical diversity. Data analysis will focus on assessing patients' patterns of pain and opioid analgesic use, along with other pain treatments; associations between patient and condition characteristics and patient-centred outcomes including resolution of pain, satisfaction with care and long-term use of opioid analgesics; and descriptive analyses of patient management of leftover opioids. ETHICS AND DISSEMINATION: This study has received approval from IRBs at each site. Results will be made available to participants, funders, the research community and the public. TRIAL REGISTRATION NUMBER: NCT04509115.

Similar Breast Cancer Risk in Women Older Than 65 Years Initiating Glargine, Detemir, and NPH Insulins.

OBJECTIVE: To assess whether initiation of insulin glargine (glargine), compared with initiation of NPH or insulin detemir (detemir), was associated with an increased risk of breast cancer in women with diabetes. RESEARCH DESIGN AND METHODS: This was a retrospective new-user cohort study of female Medicare beneficiaries aged ≥65 years initiating glargine (203,159), detemir (67,012), or NPH (47,388) from September 2006 to September 2015, with follow-up through May 2017. Weighted Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% CIs for incidence of breast cancer according to ever use, cumulative duration of use, cumulative dose of insulin, length of follow-up time, and a combination of dose and length of follow-up time. RESULTS: Ever use of glargine was not associated with an increased risk of breast cancer compared with NPH (HR 0.97; 95% CI 0.88-1.06) or detemir (HR 0.98; 95% CI 0.92-1.05). No increased risk was seen with glargine use compared with either NPH or detemir by duration of insulin use, length of follow-up, or cumulative dose of insulin. No increased risk of breast cancer was observed in medium- or high-dose glargine users compared with low-dose users. CONCLUSIONS: Overall, glargine use was not associated with an increased risk of breast cancer compared with NPH or detemir in female Medicare beneficiaries.

Robust classification modeling on microarray data using misclassification penalized posterior.

MOTIVATION: Genome-wide microarray data are often used in challenging classification problems of clinically relevant subtypes of human diseases. However, the identification of a parsimonious robust prediction model that performs consistently well on future independent data has not been successful due to the biased model selection from an extremely large number of candidate models during the classification model search and construction. Furthermore, common criteria of prediction model performance, such as classification error rates, do not provide a sensitive measure for evaluating performance of such astronomic competing models. Also, even though several different classification approaches have been utilized to tackle such classification problems, no direct comparison on these methods have been made. RESULTS: We introduce a novel measure for assessing the performance of a prediction model, the misclassification-penalized posterior (MiPP), the sum of the posterior classification probabilities penalized by the number of incorrectly classified samples. Using MiPP, we implement a forward step-wise cross-validated procedure to find our optimal prediction models with different numbers of features on a training set. Our final robust classification model and its dimension are determined based on a completely independent test dataset. This MiPP-based classification modeling approach enables us to identify the most parsimonious robust prediction models only with two or three features on well-known microarray datasets. These models show superior performance to other models in the literature that often have more than 40-100 features in their model construction. AVAILABILITY: Our MiPP software program is available at the Bioconductor website (http://www.bioconductor.org).

Developing optimal prediction models for cancer classification using gene expression data.

Microarrays can provide genome-wide expression patterns for various cancers, especially for tumor sub-types that may exhibit substantially different patient prognosis. Using such gene expression data, several approaches have been proposed to classify tumor sub-types accurately. These classification methods are not robust, and often dependent on a particular training sample for modelling, which raises issues in utilizing these methods to administer proper treatment for a future patient. We propose to construct an optimal, robust prediction model for classifying cancer sub-types using gene expression data. Our model is constructed in a step-wise fashion implementing cross-validated quadratic discriminant analysis. At each step, all identified models are validated by an independent sample of patients to develop a robust model for future data. We apply the proposed methods to two microarray data sets of cancer: the acute leukemia data by Golub et al. and the colon cancer data by Alon et al. We have found that the dimensionality of our optimal prediction models is relatively small for these cases and that our prediction models with one or two gene factors outperforms or has competing performance, especially for independent samples, to other methods based on 50 or more predictive gene factors. The methodology is implemented and developed by the procedures in R and Splus. The source code can be obtained at http://hesweb1.med.virginia.edu/bioinformatics.