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Background: Prescription opioid use places a considerable economic burden on health care systems. Older patients undergoing surgical procedures for painful conditions commonly receive opioids pre- and postoperatively, and are susceptible to adverse reactions. This study explores predictors of prolonged postoperative opioid use among older patients after lumbar spine surgery and the consequences in terms of health care utilization and costs. Methods: We conducted a retrospective population-based cohort study using Ontario administrative data from older adults undergoing spine surgery between 2006 and 2017. Data were analyzed from 90 days preoperatively to 1 year after hospital discharge, with last postoperative opioid prescriptions stratified into 90-day increments. We used multivariable ordinal logistic regression to identify predictors of long-term opioid use and generalized linear modelling to examine resource utilization and health care costs (2021 Canadian dollars). Results: Of 15 109 patients included, 40.8% received preoperative opioid prescriptions. Preoperative opioid use strongly predicted prolonged postoperative use (odds ratio [OR] 4.47, 95% confidence interval [CI] 4.16-4.79), with 48.3% of patients who received preoperative opioids continuing to use opioids for longer than 9 months, relative to 12.7% of those without preoperative use. Several other risk factors for prolonged use were identified. Patients receiving long-term postoperative opioids incurred greater health care costs relative to those with opioids prescribed for fewer than 90 days (OR 1.49, 95% CI 1.44-1.54). Conclusion: Among older adults undergoing spine surgery, preoperative opioid use was a strong predictor of prolonged postoperative use, which was associated with increased health care costs. These results form an important baseline for future studies evaluating strategies to reduce opioid use targeting older surgical populations. Contexte : L'utilisation d'opio des vendus sur ordonnance impose un tr s lourd fardeau aux syst mes de sant . On les prescrit souvent en p riode pr - et postop ratoire aux malades g s qui subissent des interventions chirurgicales pour des maladies douloureuses et cela comporte des risques de r actions ind sirables. Cette tude explore les pr dicteurs d'une utilisation prolong e des opio des en p riode postop ratoire chez des personnes g es op r es la colonne vert brale et son impact sur l'utilisation des ressources sanitaires et les co ts associ s. M thodes : Nous avons proc d une tude de cohorte populationnelle r trospective partir des donn es administratives de l'Ontario concernant les adultes g s ayant subi une chirurgie de la colonne entre 2006 et 2017. Les donn es ont t analys es pour la p riode allant de 90 jours avant l'intervention 1 an suivant le cong hospitalier, et les derni res ordonnances postop ratoires d'opio des ont t stratifi es par tranches de 90 jours. Nous avons utilis l'analyse de r gression logistique multivari e ordinale pour identifier les pr dicteurs d'une utilisation prolong e des opio des et un mod le lin aire g n ralis pour examiner l'utilisation des ressources sanitaires et les co ts associ s (dollars canadiens de 2021). R sultats : Parmi les 15 109 personnes incluses, 40,8% avaient re u une ordonnance d'opio des avant leur intervention. L'utilisation d'opio des en p riode pr op ratoire s'est r v l tre un fort pr dicteur d'utilisation postop ratoire prolong e (rapport des cotes [RC] 4,47, intervalle de confiance [IC] de 95% 4,16-4,79), et 48,3% des malades ayant re u des opio des en p riode pr op ratoire ont continu de les utiliser pendant plus de 9 mois, contre 12,7% des malades qui n'en avaient pas pris. Plusieurs autres facteurs de risque d'utilisation prolong e ont t identifi s. Les malades qui ont re u des opio des ont engendr des d penses en soins de sant plus lev es que les malades qui on en avait prescrit pour moins de 90 jours (RC 1,49, IC de 95% 1,44-1,54). Conclusion : Chez les adultes g s soumis une chirurgie de la colonne, l'utilisation pr op ratoire d'opio des a t un fort pr dicteur d'utilisation postop ratoire prolong e, qui a t associ e une hausse des co ts de soins de sant . Ces r sultats constituent une importante base de connaissances pour les futures tudes qui valueront des strat gies de r duction du recours aux opio des l'intention de la patient le chirurgicale g e., Opioid use in North America has been declared a public health crisis, with Canada and the United States currently positioned as the 2 largest consumers of prescription opioids in the [...]

BackgroundWe introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment.ResultsThis open-source package includes multiple methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it suitable for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for integration with computational pipelines.ConclusionBPG provides a new approach for linking interactive and scripted data visualization and is available at http://labs.oicr.on.ca/boutros-lab/software/bpg or via CRAN at https://cran.r-project.org/web/packages/BoutrosLab.plotting.general.

BackgroundWe introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment.ResultsThis open-source package includes multiple methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it suitable for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for integration with computational pipelines.ConclusionBPG provides a new approach for linking interactive and scripted data visualization and is available at http://labs.oicr.on.ca/boutros-lab/software/bpg or via CRAN at https://cran.r-project.org/web/packages/BoutrosLab.plotting.general.

Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery.

The prediction of patient attendance in emergency departments (ED) is crucial for effective healthcare planning and resource allocation. This paper proposes an early warning system that can detect emerging trends in ED attendance, offering timely alerts for proactive operational planning. Over 13 years of historical ED attendance data (from January 2010 till December 2022) with 1,700,887 data points were used to develop and validate: (1) a Seasonal Autoregressive Integrated Moving Average with eXogenous factors (SARIMAX) forecasting model; (2) an Exponentially Weighted Moving Average (EWMA) surge prediction model, and (3) a trend persistence prediction model. Drift detection was achieved with the EWMA control chart, and the slopes of a kernel-regressed ED attendance curve were used to train various machine learning (ML) models to predict trend persistence. The EWMA control chart effectively detected significant COVID-19 events in Singapore. The surge prediction model generated preemptive signals on changes in the trends of ED attendance over the COVID-19 pandemic period from January 2020 until December 2022. The persistence of novel trends was further estimated using the trend persistence model, with a mean absolute error of 7.54 (95% CI: 6.77–8.79) days. This study advanced emergency healthcare management by introducing a proactive surge detection framework, which is vital for bolstering the preparedness and agility of emergency departments amid unforeseen health crises. [ABSTRACT FROM AUTHOR]

Background: Before the COVID‐19 pandemic, access to prenatal care was lower among some socio‐demographic groups. This pandemic caused disruptions to routine preventative care, which could have increased inequalities. Objectives: To investigate if the COVID‐19 pandemic increased inequalities in access to prenatal care among those who are younger, live in rural areas, have a lower socio‐economic situation (SES) and are recent immigrants. Methods: We used linked administrative datasets from ICES to identify a population‐based cohort of 455,245 deliveries in Ontario from January 2018 to December 2021. Our outcomes were first‐trimester prenatal visits, first‐trimester ultrasound and adequacy of prenatal care. We used joinpoint analysis to examine outcome time trends and identify trend change points. We stratified analyses by age, rural residence, SES and recent immigration, and examined risk differences (RD) with 95% confidence intervals (CI) between groups at the beginning and end of the study period. Results: For all outcomes, we noted disruptions to care beginning in March or April 2020 and returning to previous trends by November 2020. Inequalities were stable across groups, except recent immigrants. In July 2017, 65.0% and 69.8% of recent immigrants and non‐immigrants, respectively, received ultrasounds in the first trimester (RD −4.8%, 95% CI −8.0, −1.5). By October 2020, this had increased to 75.4%, with no difference with non‐immigrants (RD 0.4%, 95% CI −2.4, 3.2). Adequacy of prenatal care showed more intensive care as of November 2020, reflecting a higher number of visits. Conclusions: We found no evidence that inequalities between socio‐economic groups that existed prior to the pandemic worsened after March 2020. The pandemic may be associated with increased access to care for recent immigrants. The introduction of virtual visits may have resulted in a higher number of prenatal care visits. [ABSTRACT FROM AUTHOR]

BackgroundTumour samples containing distinct sub-populations of cancer and normal cells present challenges in the development of reproducible biomarkers, as these biomarkers are based on bulk signals from mixed tumour profiles. ISOpure is the only mRNA computational purification method to date that does not require a paired tumour-normal sample, provides a personalized cancer profile for each patient, and has been tested on clinical data. Replacing mixed tumour profiles with ISOpure-preprocessed cancer profiles led to better prognostic gene signatures for lung and prostate cancer.ResultsTo simplify the integration of ISOpure into standard R-based bioinformatics analysis pipelines, the algorithm has been implemented as an R package. The ISOpureR package performs analogously to the original code in estimating the fraction of cancer cells and the patient cancer mRNA abundance profile from tumour samples in four cancer datasets.ConclusionsThe ISOpureR package estimates the fraction of cancer cells and personalized patient cancer mRNA abundance profile from a mixed tumour profile. This open-source R implementation enables integration into existing computational pipelines, as well as easy testing, modification and extension of the model.

Ovarian carcinoma is the leading cause of gynecological malignancy, with the serous subtype being the most commonly presented subtype. Recent studies have demonstrated that grade does not yield significant prognostic information, independent of TNM staging. As such, several different grading systems have been proposed to reveal morphological characteristics of these tumors, however each yield different results. To help address this issue, we performed a rigorous computational analysis to better understand the molecular differences that fundamentally explain the different grades and grading systems. mRNA abundance levels were analyzed across 334 total patients and their association with each grade and grading system were assessed. Few molecular differences were observed between grade 2 and 3 tumors when using the International Federation of Gynecology and Obstetrics (FIGO) grading system, suggesting their molecular similarity. In contrast, grading by the Silverberg system reveals that grades 1-3 are molecularly equidistant from one another across a spectrum. Additionally, we have identified a few candidate genes with good prognostic information that could potentially be used for classifying cases with similar morphological appearances.

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