Implementation of the Mind Youth Questionnaire (MY-Q) for routine health-related quality of life screening of adolescents with type 1 diabetes in a large tertiary care center.

OBJECTIVES: Prevalence of diabetes distress and mental health comorbidities among adolescents with type 1 diabetes (T1D) is high. Despite recommendations for routine psychosocial risk assessment, there is little guidance for their implementation. This study aims to describe the implementation and baseline outcomes of the Mind Youth Questionnaire (MY-Q), a validated psychosocial screening tool for health-related quality of life (QoL) including mood, among adolescents living with T1D. METHODS: Adolescents aged 13-18 years completed the MY-Q from October 1, 2019-April 1, 2023. Baseline characteristics, MY-Q results including categories flagged positive (noting possible areas of concern), debrief duration, and frequency of social work or mental health referral were collected and analyzed using descriptive statistics. RESULTS: A total of 343 adolescents (mean age 15.3 years; 52 % female) completed a baseline MY-Q. Median overall MY-Q debrief time (IQR) was 10.0 min (6.0, 20.0). About 290 (84.5 %) adolescents had at least one of seven categories flagged, most commonly "Family" (61 %). About 30 % of adolescents had "Mood" flagged, and 2.9 % of adolescents were referred to mental health following debrief. CONCLUSIONS: Without the need for additional resources, implementation of the MY-Q in a pediatric tertiary care diabetes clinic successfully identified QoL issues and mental health concerns among adolescents with T1D.

Factors associated with the use of psychedelics, ketamine and MDMA among sexual and gender minority youths in Canada: a machine learning analysis.

BACKGROUND: Substance use is increasing among sexual and gender minority youth (SGMY). This increase may be due to changes in social norms and socialisation, or due to SGMY exploring the potential therapeutic value of drugs such as psychedelics. We identified predictors of psychedelics, MDMA and ketamine use. METHODS: Data were obtained from 1414 SGMY participants who completed the ongoing longitudinal 2SLGBTQ+ Tobacco Project in Canada between November 2020 to January 2021. We examined the association between 80 potential features (including sociodemographic factors, mental health-related factors and substance use-related factors) with the use of psychedelics, MDMA and ketamine in the past year. Random forest classifier was used to identify the predictors most associated with reported use of these drugs. RESULTS: 18.1% of participants have used psychedelics in the past year; 21.9% used at least one of the three drugs. Cannabis and cocaine use were the predictors most strongly associated with any of these drugs, while cannabis, but not cocaine use, was the one most associated with psychedelic use. Other mental health and 2SLGBTQ+ stigma-related factors were also associated with the use of these drugs. CONCLUSION: The use of psychedelics, MDMA and ketamine among 2SLGBTQ+ individuals appeared to be largely driven by those who used them together with other drugs. Depression scores also appeared in the top 10 factors associated with these illicit drugs, suggesting that there were individuals who may benefit from the potential therapeutic value of these drugs. These characteristics should be further investigated in future studies.

Evaluating the Utility and Privacy of Synthetic Breast Cancer Clinical Trial Data Sets.

PURPOSE: There is strong interest from patients, researchers, the pharmaceutical industry, medical journal editors, funders of research, and regulators in sharing clinical trial data for secondary analysis. However, data access remains a challenge because of concerns about patient privacy. It has been argued that synthetic data generation (SDG) is an effective way to address these privacy concerns. There is a dearth of evidence supporting this on oncology clinical trial data sets, and on the utility of privacy-preserving synthetic data. The objective of the proposed study is to validate the utility and privacy risks of synthetic clinical trial data sets across multiple SDG techniques. METHODS: We synthesized data sets from eight breast cancer clinical trial data sets using three types of generative models: sequential synthesis, conditional generative adversarial network, and variational autoencoder. Synthetic data utility was evaluated by replicating the published analyses on the synthetic data and assessing concordance of effect estimates and CIs between real and synthetic data. Privacy was evaluated by measuring attribution disclosure risk and membership disclosure risk. RESULTS: Utility was highest using the sequential synthesis method where all results were replicable and the CI overlap most similar or higher for seven of eight data sets. Both types of privacy risks were low across all three types of generative models. DISCUSSION: Synthetic data using sequential synthesis methods can act as a proxy for real clinical trial data sets, and simultaneously have low privacy risks. This type of generative model can be one way to enable broader sharing of clinical trial data.

Machine learning models for predicting seizure outcome after MR-guided laser interstitial thermal therapy in children.

OBJECTIVE: MR-guided laser interstitial thermal therapy (MRgLITT) is associated with lower seizure-free outcome but better safety profile compared to open surgery. However, the predictors of seizure freedom following MRgLITT remain uncertain. This study aimed to use machine learning to predict seizure-free outcome following MRgLITT and to identify important predictors of seizure freedom in children with drug-resistant epilepsy. METHODS: This multicenter study included children treated with MRgLITT for drug-resistant epilepsy at 13 epilepsy centers. The authors used clinical data, diagnostic investigations, and ablation features to predict seizure-free outcome at 1 year post-MRgLITT. Patients from 12 centers formed the training cohort, and patients in the remaining center formed the testing cohort. Five machine learning algorithms were developed on the training data by using 10-fold cross-validation, and model performance was measured on the testing cohort. The models were developed and tested on the complete feature set. Subsequently, 3 feature selection methods were used to identify important predictors. The authors then assessed performance of the parsimonious models based on these important variables. RESULTS: This study included 268 patients who underwent MRgLITT, of whom 44.4% had achieved seizure freedom at 1 year post-MRgLITT. A gradient-boosting machine algorithm using the complete feature set yielded the highest area under the curve (AUC) on the testing set (AUC 0.67 [95% CI 0.50-0.82], sensitivity 0.71 [95% CI 0.47-0.88], and specificity 0.66 [95% CI 0.50-0.81]). Logistic regression, random forest, support vector machine, and neural network yielded lower AUCs (0.58-0.63) compared to the gradient-boosting machine but the findings were not statistically significant (all p > 0.05). The 3 feature selection methods identified video-EEG concordance, lesion size, preoperative seizure frequency, and number of antiseizure medications as good prognostic features for predicting seizure freedom. The parsimonious models based on important features identified by univariate feature selection slightly improved model performance compared to the complete feature set. CONCLUSIONS: Understanding the predictors of seizure freedom after MRgLITT will assist with prognostication.

A Machine Learning Approach Reveals Distinct Predictors of Vaping Dependence for Adolescent Daily and Non-Daily Vapers in the COVID-19 Era.

Since 2016, there has been a substantial rise in e-cigarette (vaping) dependence among young people. In this prospective cohort study, we aimed to identify the different predictors of vaping dependence over 3 months among adolescents who were baseline daily and non-daily vapers. We recruited ever-vaping Canadian residents aged 16-25 years on social media platforms and asked them to complete a baseline survey in November 2020. A validated vaping dependence score (0-23) summing up their responses to nine questions was calculated at the 3-month follow-up survey. Separate lasso regression models were developed to identify predictors of higher 3-month vaping dependence score among baseline daily and non-daily vapers. Of the 1172 participants, 643 (54.9%) were daily vapers with a mean age of 19.6 ± 2.6 years and 76.4% (n = 895) of them being female. The two models achieved adequate predictive performance. Place of last vape purchase, number of days a pod lasts, and the frequency of nicotine-containing vaping were the most important predictors for dependence among daily vapers, while race, sexual orientation and reporting treatment for heart disease were the most important predictors in non-daily vapers. These findings have implications for vaping control policies that target adolescents at different stages of vape use.

Number of epilepsy surgeries has decreased despite an increase in pre-surgical evaluations at a tertiary pediatric epilepsy center in Ontario.

OBJECTIVE: A recent U.S. study reported that the number of epilepsy surgeries has remained stable or declined in recent years despite an increase in pre-surgical evaluation. This study aimed to evaluate trends in pre-surgical evaluation and epilepsy surgery from 2001 to 2019 and to determine whether these trends have changed in the later period (2014-2019) compared to earlier period (2001-2013). METHODS: This study evaluated trends in pre-surgical evaluation and epilepsy surgery at a tertiary pediatric epilepsy center. Children with drug resistant epilepsy who were evaluated for surgery were included. Clinical data, reasons for not undergoing surgery, and surgical characteristics of surgery patients were collected. Overall trends and trends in later period compared to earlier period for pre-surgical evaluation and epilepsy surgery were assessed. RESULTS: There were 1151 children who were evaluated for epilepsy surgery and 546 underwent surgery. There was an upward trend in pre-surgical evaluation in the earlier period (rate ratio [RR]=1.04 (95%CI:1.02-1.07), p<0.001) and the trajectory of presurgical evaluation in the later period was not significantly different to the earlier period (RR=1.00 [95%CI:0.95-1.06], p = 0.88). Among the reasons for not undergoing surgery, failure to localize the seizures occurred more frequently in later period than earlier period (22.6% vs. 17.1% respectively, p = 0.024). For number of surgeries, there was an upward trend between 2001 and 2013 (RR=1.08 [95%CI:1.05-1.11], p<0.001), and a decreasing trend in the later period compared to earlier period (RR=0.91 [95%CI:0.84-0.99], p = 0.029). CONCLUSION: Despite an increasing trend in pre-surgical evaluation, there was a decreasing trend in the number of epilepsy surgery in the later period as there was a larger proportion of patients in whom the seizures could not be localized. Trends in presurgical evaluation and epilepsy surgery will continue to evolve with introduction of technologies such as stereo-EEG and minimally invasive laser therapy.

Severe iron deficiency anemia in the paediatric emergency department: A retrospective study.

Background: Transfusion is discouraged in hemodynamically stable children with severe iron deficiency anemia (IDA). Intravenous (IV) iron sucrose (IS) could be an alternative for some patients; however, there is a paucity of data on its use in the paediatric emergency department (ED). Methods: We analyzed patients presenting with severe IDA at the Children's Hospital of Eastern Ontario (CHEO) ED between September 1, 2017, and June 1, 2021. We defined severe IDA as microcytic anemia <70 g/L and either a ferritin <12 ng/mL or a documented clinical diagnosis. Results: Of 57 patients, 34 (59%) presented with nutritional IDA and 16 (28%) presented with IDA secondary to menstrual bleeding. Fifty-five (95%) patients received oral iron. Thirteen (23%) patients additionally received IS and after 2 weeks, the average Hgb was similar to transfused patients. The median time for patients receiving IS without PRBC transfusion to increase their Hgb by at least 20 g/L was 7 days (95%CI 0.7 to 10.5 days). Of 16 (28%) children who were transfused with PRBC, there were three mild reactions, and one patient who developed transfusion associated circulatory overload (TACO). There were two mild and no severe reactions to IV iron. There were no return visits to the ED due to anemia in the following 30 days. Conclusions: Management of severe IDA with IS was associated with a rapid rise in Hgb without severe reactions or returns to ED. This study highlights a strategy for management of severe IDA in hemodynamically stable children that spares them the risks associated with PRBC transfusion. Paediatric specific guidelines and prospective studies are needed to guide the use of IV iron in this population.

Machine learning applications in tobacco research: a scoping review.

OBJECTIVE: Identify and review the body of tobacco research literature that self-identified as using machine learning (ML) in the analysis. DATA SOURCES: MEDLINE, EMABSE, PubMed, CINAHL Plus, APA PsycINFO and IEEE Xplore databases were searched up to September 2020. Studies were restricted to peer-reviewed, English-language journal articles, dissertations and conference papers comprising an empirical analysis where ML was identified to be the method used to examine human experience of tobacco. Studies of genomics and diagnostic imaging were excluded. STUDY SELECTION: Two reviewers independently screened the titles and abstracts. The reference list of articles was also searched. In an iterative process, eligible studies were classified into domains based on their objectives and types of data used in the analysis. DATA EXTRACTION: Using data charting forms, two reviewers independently extracted data from all studies. A narrative synthesis method was used to describe findings from each domain such as study design, objective, ML classes/algorithms, knowledge users and the presence of a data sharing statement. Trends of publication were visually depicted. DATA SYNTHESIS: 74 studies were grouped into four domains: ML-powered technology to assist smoking cessation (n=22); content analysis of tobacco on social media (n=32); smoker status classification from narrative clinical texts (n=6) and tobacco-related outcome prediction using administrative, survey or clinical trial data (n=14). Implications of these studies and future directions for ML researchers in tobacco control were discussed. CONCLUSIONS: ML represents a powerful tool that could advance the research and policy decision-making of tobacco control. Further opportunities should be explored.

A longitudinal analysis of early lung function trajectory in survivors of childhood Hodgkin lymphoma.

BACKGROUND: Childhood Hodgkin lymphoma survivors suffer from long-term effects decades after treatment completion with a prevalence of pulmonary dysfunction of up to 65.2%. AIMS: This study explored the early trajectory of pulmonary function in pediatric cancer patients with Hodgkin lymphoma who received pulmonary toxic therapy. METHODS AND RESULTS: In this single-center, 20-year retrospective cohort study, we included patients who were <18 years old at diagnosis of Hodgkin lymphoma between January 1994 and December 2014, and received bleomycin or thoracic radiation. We measured pulmonary function and reported on percent predicted values for forced expiratory volume in 1 s, total lung capacity, and diffusing capacity of the lungs. We used linear mixed models to identify the association of clinical factors with longitudinal changes in lung function at time points before and after treatment completion. Of 80 children who met inclusion criteria, all were treated with bleomycin, and 83.8% received thoracic radiation. More than half (51.2%) of patients had any abnormalities in lung function measures during the study observation period which averaged 24.2 months (±31.1SD). Females, younger age at diagnosis and treatment with radiation were associated with lower lung function measurements at various time points. While the majority of children experienced a recovery of their lung function within 1-2 years after treatment completion, some children with these risk factors did not. CONCLUSION: Pulmonary function abnormalities begin early in children treated for Hodgkin lymphoma. While the majority of children demonstrate a slow and continuous improvement in lung function back to baseline over time, we recommend routine asymptomatic screening of pulmonary function in certain childhood cancer survivors, particularly females, those diagnosed young and patients who received radiation therapy.

Seizure outcome of pediatric magnetic resonance-guided laser interstitial thermal therapy versus open surgery: A matched noninferiority cohort study.

OBJECTIVE: Minimally invasive magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) has been proposed as an alternative to open epilepsy surgery, to address concerns regarding the risk of open surgery. Our primary hypothesis was that seizure freedom at 1 year after MRgLITT is noninferior to open surgery in children with drug-resistant epilepsy (DRE). The secondary hypothesis was that MRgLITT has fewer complications and shorter hospitalization than surgery. The primary objective was to compare seizure outcome of MRgLITT to open surgery in children with DRE. The secondary objective was to compare complications and length of hospitalization of the two treatments. METHODS: This retrospective multicenter cohort study included children with DRE treated with MRgLITT or open surgery with 1-year follow-up. Exclusion criteria were corpus callosotomy, neurostimulation, multilobar or hemispheric surgery, and lesion with maximal dimension > 60 mm. MRgLITT patients were propensity matched to open surgery patients. The primary outcome was seizure freedom at 1 year posttreatment. The difference in seizure freedom was compared using noninferiority test, with noninferiority margin of -10%. The secondary outcomes were complications and length of hospitalization. RESULTS: One hundred eighty-five MRgLITT patients were matched to 185 open surgery patients. Seizure freedom at 1 year follow-up was observed in 89 of 185 (48.1%) MRgLITT and 114 of 185 (61.6%) open surgery patients (difference = -13.5%, one-sided 97.5% confidence interval = -23.8% to ∞, pNoninferiority  = .79). The lower confidence interval boundary of -23.8% was below the prespecified noninferiority margin of -10%. Overall complications were lower in MRgLITT compared to open surgery (10.8% vs. 29.2%, respectively, p < .001). Hospitalization was shorter for MRgLITT than open surgery (3.1 ± 2.9 vs. 7.2 ± 6.1 days, p < .001). SIGNIFICANCE: Seizure outcome of MRgLITT at 1 year posttreatment was inferior to open surgery. However, MRgLITT has the advantage of better safety profile and shorter hospitalization. The findings will help counsel children and parents on the benefits and risks of MRgLITT and contribute to informed decision-making on treatment options.

Developing the Breast Utility Instrument to Measure Health-Related Quality-of-Life Preferences in Patients with Breast Cancer: Selecting the Item for Each Dimension.

Introduction. Generic preference-based instruments inadequately measure breast cancer (BrC) health-related quality-of-life preferences given advances in therapy. Our overall purpose is to develop the Breast Utility Instrument (BUI), a BrC-specific preference-based instrument. This study describes the selection of the BUI items. Methods. A total of 408 patients from diverse BrC health states completed the EORTC QLQ-C30 and BR45 (breast module). For each of 10 dimensions previously assessed with confirmatory factor analysis, we evaluated data fit to the Rasch model based on global model and item fit, including threshold ordering, item residuals, infit and outfit, differential item functioning (age), and unidimensionality. Misfitting items were removed iteratively, and the model fit was reassessed. From items fitting the Rasch model, we selected 1 item per dimension based on high patient- and clinician-rated item importance, breadth of item thresholds, and clinical relevance. Results. Global model fit was good in 7 and borderline in 3 dimensions. Separation index was acceptable in 4 dimensions. Item selection criteria were maximized for the following items: 1) physical functioning (trouble taking a long walk), 2) emotional functioning (worry), 3) social functioning (interfering with social activities), 4) pain (having pain), 5) fatigue (tired), 6) body image (dissatisfied with your body), 7) systemic therapy side effects (hair loss), 8) sexual functioning (interest in sex), 9) breast symptoms (oversensitive breast), and 10) endocrine therapy symptoms (problems with your joints). Conclusions. We propose 10 items for the BUI. Our next steps include assessing the measurement properties prior to eliciting preference weights of the BUI. Highlights: A previous confirmatory factor analysis established 10 dimensions of the European Organisation for Research and Treatment of Cancer (EORTC) core quality of life questionnaire (QLQ-C30) and its breast module (BR45).In this study, we selected 1 item per dimension based on fit to the Rasch model, patient- and clinician-rated item importance, breadth of item thresholds, and clinical relevance.These items form the core of the future Breast Utility Instrument (BUI).The future BUI will be a novel breast cancer-specific preference-based instrument that potentially will better reflect women's preferences in clinical decision making and cost utility analyses.

Development and validation of machine learning models for prediction of seizure outcome after pediatric epilepsy surgery.

OBJECTIVE: There is substantial variability in reported seizure outcome following pediatric epilepsy surgery, and lack of individualized predictive tools that could evaluate the probability of seizure freedom postsurgery. The aim of this study was to develop and validate a supervised machine learning (ML) model for predicting seizure freedom after pediatric epilepsy surgery. METHODS: This is a multicenter retrospective study of children who underwent epilepsy surgery at five pediatric epilepsy centers in North America. Clinical information, diagnostic investigations, and surgical characteristics were collected, and used as features to predict seizure-free outcome 1 year after surgery. The dataset was split randomly into 80% training and 20% testing data. Thirty-five combinations of five feature sets with seven ML classifiers were assessed on the training cohort using 10-fold cross-validation for model development. The performance of the optimal combination of ML classifier and feature set was evaluated in the testing cohort, and compared with logistic regression, a classical statistical approach. RESULTS: Of the 801 patients included, 61.3% were seizure-free 1 year postsurgery. During model development, the best combination was XGBoost ML algorithm with five features from the univariate feature set, including number of antiseizure medications, magnetic resonance imaging lesion, age at seizure onset, video-electroencephalography concordance, and surgery type, with a mean area under the curve (AUC) of .73 (95% confidence interval [CI] = .69-.77). The combination of XGBoost and univariate feature set was then evaluated on the testing cohort and achieved an AUC of .74 (95% CI = .66-.82; sensitivity = .87, 95% CI = .81-.94; specificity = .58, 95% CI = .47-.71). The XGBoost model outperformed the logistic regression model (AUC = .72, 95% CI = .63-.80; sensitivity = .72, 95% CI = .63-.82; specificity = .66, 95% CI = .53-.77) in the testing cohort (p = .005). SIGNIFICANCE: This study identified important features and validated an ML algorithm, XGBoost, for predicting the probability of seizure freedom after pediatric epilepsy surgery. Improved prognostication of epilepsy surgery is critical for presurgical counseling and will inform treatment decisions.

Developing the Breast Utility Instrument, a preference-based instrument to measure health-related quality of life in women with breast cancer: Confirmatory factor analysis of the EORTC QLQ-C30 and BR45 to establish dimensions.

OBJECTIVES: Breast cancer (BrC) and its treatments impair health-related quality of life (HRQoL). Utility is a measure of HRQoL that includes preferences for health outcomes, used in treatment decision-making. Generic preference-based instruments lack BrC-specific concerns, indicating the need for a BrC-specific preference-based instrument. Our objective was to determine dimensions of the European Organisation for Research and Treatment of Cancer (EORTC) general cancer (QLQ-C30) and breast module (BR45) instruments, the first step in our development of the novel Breast Utility Instrument (BUI). METHODS: Patients (n = 408) attending outpatient BrC clinics at an urban cancer centre, and representing a spectrum of BrC health states, completed the QLQ-C30 and BR45. We performed confirmatory factor analysis of the combined QLQ-C30 and BR45 using mean-and variance-adjusted unweighted least squares estimation. The hypothesized factor model was based on clinical relevance, item distributions, missing data, item-importance, and internal reliability of dimensions. Models were evaluated based on global and item fit, local areas of strain, and likelihood ratio tests of nested models. RESULTS: Our final model had 10 dimensions: physical and role functioning, emotional functioning, social functioning, body image, pain, fatigue, systemic therapy side effects, sexual functioning and enjoyment, arm and breast symptoms, and endocrine therapy symptoms. Good overall model fit was achieved: χ2/df: 1.45, Tucker-Lewis index: 0.946, comparative fit index: 0.951, standardized root-mean-square residual: 0.069, root-mean-square error of approximation: 0.033 (0.030-0.037). All items had salient factor loadings (λ>0.4, p<0.001). CONCLUSIONS: We identified important BrC HRQoL dimensions to develop the BUI, a BrC-specific preference-based instrument.

Predictors of perceived success in quitting smoking by vaping: A machine learning approach.

Prior research has suggested that a set of unique characteristics may be associated with adult cigarette smokers who are able to quit smoking using e-cigarettes (vaping). In this cross-sectional study, we aimed to identify and rank the importance of these characteristics using machine learning. During July and August 2019, an online survey was administered to a convenience sample of 889 adult smokers (age ≥ 20) in Ontario, Canada who tried vaping to quit smoking in the past 12 months. Fifty-one person-level characteristics, including a Vaping Experiences Score, were assessed in a gradient boosting machine model to classify the status of perceived success in vaping-assisted smoking cessation. This model was trained using cross-validation and tested using the receiver operating characteristic (ROC) curve. The top five most important predictors were identified using a score between 0% and 100% that represented the relative importance of each variable in model training. About 20% of participants (N = 174, 19.6%) reported success in vaping-assisted smoking cessation. The model achieved relatively high performance with an area under the ROC curve of 0.865 and classification accuracy of 0.831 (95% CI [confidence interval] 0.780 to 0.874). The top five most important predictors of perceived success in vaping-assisted smoking cessation were more positive experiences measured by the Vaping Experiences Score (100%), less previously failed quit attempts by vaping (39.0%), younger age (21.9%), having vaped 100 times (16.8%), and vaping shortly after waking up (15.8%). Our findings provide strong statistical evidence that shows better vaping experiences are associated with greater perceived success in smoking cessation by vaping. Furthermore, our study confirmed the strength of machine learning techniques in vaping-related outcomes research based on observational data.

Evaluation of Fertility Preservation Counseling and Treatments for Female Oncology Patients in an Urban Pediatric Canadian Center.

Purpose: Several international organizations and guidelines have recommended implementation of structured fertility preservation (FP) discussions with patients and their families before initiation of chemotherapy and radiation treatments in children. This study aimed to identify current trends and rates in FP counseling and treatments at a Canadian pediatric tertiary care center. Objectives were to measure guideline adherence for FP counseling at our institution by determining (1) the frequency of FP counseling in pediatric female oncological patients at our institution, (2) the frequency of FP treatment in this study population, and (3) the factors associated with FP pre-treatment counseling. Methods: A retrospective chart review was performed, including all pediatric and adolescent female patients (age <18) seen in consultation by the oncology team. Demographic data, as well as documentation of FP counseling and referral to a reproductive endocrinology and infertility (REI) specialist and subsequent FP treatment were collected. Results: A total of 89 female pediatric patients were included in our study. Forty-two patients received fertility counseling (47.2%; 95% confidence interval [CI] 37.2-57.5). Only 29/42 (69.0%; 95% CI: 54-80.9) received counseling before onset of treatment. A 12/42 (41.4%; 95% CI: 25-59.3) of the patients who received FP counseling were referred to an REI specialist and 11/12 proceeded with FP treatment (37.9%, 95% CI: 22.7-56). Conclusion: This study presents contemporary data on the rates of FP counseling in Canadian pediatric female oncological patients and demonstrates low rates of FP counseling in our patient population.

Testing for non-inferior mortality: a systematic review of non-inferiority margin sizes and trial characteristics.

OBJECTIVE: To describe the size and variability of non-inferiority margins used in non-inferiority trials of medications with primary outcomes involving mortality, and to examine the association between trial characteristics and non-inferiority margin size. DESIGN: Systematic review. DATA SOURCES: Medline, Medline In Process, Medline Epub Ahead of Print and Embase Classic+Embase databases from January 1989 to December 2019. ELIGIBILITY CRITERIA: Prospective non-inferiority randomised controlled trials comparing pharmacological therapies, with primary analyses for non-inferiority and primary outcomes involving mortality alone or as part of a composite outcome. Trials had to prespecify non-inferiority margins as absolute risk differences or relative to risks of outcome and provide a baseline risk of primary outcome in the control intervention. RESULTS: 3992 records were screened, 195 articles were selected for full text review and 111 articles were included for analyses. 82% of trials were conducted in thrombosis, infectious diseases or oncology. Mortality was the sole primary outcome in 23 (21%) trials, and part of a composite primary outcome in 88 (79%) trials. The overall median non-inferiority margin was an absolute risk difference of 9% (IQR 4.2%-10%). When non-inferiority margins were expressed relative to the baseline risk of primary outcome in control groups, the median relative non-inferiority margin was 1.5 (IQR 1.3-1.7). In multivariable regression analyses examining the association between trial characteristics (medical specialty, inclusion of paediatric patients, mortality as a sole or part of a composite primary outcome, presence of industry funding) and non-inferiority margin size, only medical specialty was significantly associated with non-inferiority margin size. CONCLUSION: Absolute and relative non-inferiority margins used in published trials comparing medications are large, allowing conclusions of non-inferiority in the context of large differences in mortality. Accepting the potential for large increases in outcomes involving mortality while declaring non-inferiority is a challenging methodological issue in the conduct of non-inferiority trials.

Health care costs associated with chronic hepatitis C virus infection in Ontario, Canada: a retrospective cohort study.

BACKGROUND: High-quality estimates of health care costs are required to understand the burden of illness and to inform economic models. We estimated the costs associated with hepatitis C virus (HCV) infection from the public payer perspective in Ontario, Canada. METHODS: In this population-based retrospective cohort study, we identified patients aged 18-105 years diagnosed with chronic HCV infection in Ontario from 2003 to 2014 using linked administrative data. We allocated the time from diagnosis until death or the end of follow-up (Dec. 31, 2016) to 9 mutually exclusive health states using validated algorithms: no cirrhosis, no cirrhosis (RNA negative) (i.e., cured HCV infection), compensated cirrhosis, decompensated cirrhosis, hepatocellular carcinoma, both decompensated cirrhosis and hepatocellular carcinoma, liver transplantation, terminal (liver-related) and terminal (non-liver-related). We estimated direct medical costs (in 2018 Canadian dollars) per 30 days per health state and used regression models to identify predictors of the costs. RESULTS: We identified 48 239 patients with chronic hepatitis C, of whom 30 763 (63.8%) were men and 35 891 (74.4%) were aged 30-59 years at diagnosis. The mean 30-day costs were $798 (95% confidence interval [CI] $780-$816) (n = 43 568) for no cirrhosis, $661 (95% CI $630-$692) (n = 6422) for no cirrhosis (RNA negative), $1487 (95% CI $1375-$1599) (n = 4970) for compensated cirrhosis, $3659 (95% CI $3279-$4039) (n = 3151) for decompensated cirrhosis, $4238 (95% CI $3480-$4996) (n = 550) for hepatocellular carcinoma, $8753 (95% CI $7130-$10 377) (n = 485) for both decompensated cirrhosis and hepatocellular carcinoma, $4539 (95% CI $3746-$5333) (n = 372) for liver transplantation, $11 202 (95% CI $10 645-$11 760) (n = 3201) for terminal (liver-related) and $8801 (95% CI $8331-$9271) (n = 5278) for terminal (non-liver-related) health states. Comorbidity was the most significant predictor of total costs for all health states. INTERPRETATION: Our findings suggest that the financial burden of HCV infection is substantially higher than previously estimated in Canada. Our comprehensive, up-to-date cost estimates for clinically defined health states of HCV infection should be useful for future economic evaluations related to this disorder.

Does biomarker use in oncology improve clinical trial failure risk? A large-scale analysis.

PURPOSE: To date there has not been an extensive analysis of the outcomes of biomarker use in oncology. METHODS: Data were pooled across four indications in oncology drawing upon trial outcomes from www.clinicaltrials.gov: breast cancer, non-small cell lung cancer (NSCLC), melanoma and colorectal cancer from 1998 to 2017. We compared the likelihood drugs would progress through the stages of clinical trial testing to approval based on biomarker status. This was done with multi-state Markov models, tools that describe the stochastic process in which subjects move among a finite number of states. RESULTS: Over 10000 trials were screened, which yielded 745 drugs. The inclusion of biomarker status as a covariate significantly improved the fit of the Markov model in describing the drug trajectories through clinical trial testing stages. Hazard ratios based on the Markov models revealed the likelihood of drug approval with biomarkers having nearly a fivefold increase for all indications combined. A 12, 8 and 7-fold hazard ratio was observed for breast cancer, melanoma and NSCLC, respectively. Markov models with exploratory biomarkers outperformed Markov models with no biomarkers. CONCLUSION: This is the first systematic statistical evidence that biomarkers clearly increase clinical trial success rates in three different indications in oncology. Also, exploratory biomarkers, long before they are properly validated, appear to improve success rates in oncology. This supports early and aggressive adoption of biomarkers in oncology clinical trials.

The association between immune checkpoint or BRAF/MEK inhibitor therapy and uveitis in patients with advanced cutaneous melanoma.

BACKGROUND: Treatment with immune checkpoint and BRAF/MEK inhibitors has significantly improved the survival of patients with advanced cutaneous melanoma and other metastatic malignancies. Therapy-related uveitis is a rare ocular adverse event, which may potentially lead to legal blindness. The epidemiology of treatment-related uveitis is currently insufficiently known. PATIENTS AND METHODS: In this cohort study, we asked whether exposure to either immune checkpoint or BRAF/MEK inhibitors was associated with a higher risk of developing uveitis compared with the general population. Based on a Bayesian framework, we estimated the probability of developing uveitis with a right-censored, exponential survival model using data from the Zurich Melanoma Registry. The registry included all adult patients treated for advanced cutaneous melanoma between January 2008 and December 2018 at the University Hospital of Zurich, Switzerland. RESULTS: In total, 304 patients (64%) were treated with immune checkpoint and 186 patients (38%) with BRAF/MEK inhibitors. Median follow-up time was 74 days (interquartile range: 57-233 days). Eleven patients developed uveitis and 30 patients died. We estimated the probability of developing uveitis per year in the general population as 0.05% (95% credibility interval [CrI]: 0.02%-0.1%). Corresponding posterior probabilities of treatment-related uveitis were 3.48% (95% CrI: 0.93%-7.49%) and 5.04% (95% CrI: 2.07%-9.19%) for immune checkpoint or BRAF/MEK inhibitors (posterior probability for difference: 76%). CONCLUSIONS: Immune checkpoint and particularly BRAF/MEK inhibitor therapies are associated with an increase in the risk of developing uveitis. Treatment-related uveitis is not associated with systemic adverse events of immune checkpoint or BRAF/MEK inhibitors.

Understanding resource utilization and mortality in COPD to support policy making: A microsimulation study.

Chronic obstructive pulmonary disease (COPD) poses a significant but heterogeneous burden to individuals and healthcare systems. Policymakers develop targeted policies to minimize this burden but need personalized tools to evaluate novel interventions and target them to subpopulations most likely to benefit. We developed a platform to identify subgroups that are at increased risk of emergency department visits, hospitalizations and mortality and to provide stratified patient input in economic evaluations of COPD interventions. We relied on administrative and survey data from Ontario, Canada and applied a combination of microsimulation and multi-state modeling methods. We illustrated the functionality of the platform by quantifying outcomes across smoking status (current, former, never smokers) and by estimating the effect of smoking cessation on resource use and survival, by comparing outcomes of hypothetical cohorts of smokers who quit at diagnosis and smokers that continued to smoke post diagnosis. The cumulative incidence of all-cause mortality was 37.9% (95% CI: 34.9, 41.4) for never smokers, 34.7% (95% CI: 32.1, 36.9) for current smokers, and 46.4% (95% CI: 43.6, 49.0) for former smokers, at 14 years. Over 14 years, smokers who did not quit at diagnosis had 16.3% (95% CI: 9.6, 38.4%) more COPD-related emergency department visits than smokers who quit at diagnosis. In summary, we combined methods from clinical and economic modeling to create a novel tool that policymakers and health economists can use to inform future COPD policy decisions and quantify the effect of modifying COPD risk factors on resource utilization and morality.