PurposeOur objectives were to identify characteristics of response shift studies using patient-reported outcomes (PROMs) that explain variability in (1) the detection and (2) the magnitude of response shift effects.MethodsWe conducted a systematic review of quantitative studies published before June 2023. First, two-level multivariable logistic regression models (effect- and sample-levels) were used to explain variability in the probability of finding a response shift effect. Second, variability in effect sizes (standardized mean differences) was investigated with 3-level meta-regression models (participant-, effect- and sample-levels). Explanatory variables identified via the purposeful selection methodology included response shift method and type, and population-, study design-, PROM- and study-quality characteristics.ResultsFirst, logistic regression analysis of 5597 effects from 206 samples in 171 studies identified variables explaining 41.5% of the effect-level variance, while no variables explained sample-level variance. The average probability of response shift detection is 0.20 (95% CI: 0.17-0.28). Variation in detection was predominantly explained by response shift methods and type (recalibration vs. reprioritization/reconceptualization). Second, effect sizes were analyzed for 769 effects from 114 samples and 96 studies based on the then-test and structural equation modeling methods. Meta-regression analysis identified variables explaining 11.6% of the effect-level variance and 26.4% of the sample-level variance, with an average effect size of 0.30 (95% CI: 0.26-0.34).ConclusionResponse shift detection is influenced by study design and methods. Insights into the variables explaining response shift effects can be used to interpret results of other comparable studies using PROMs and inform the design of future response shift studies.

PurposeThe objective of this systematic review was to describe the prevalence and magnitude of response shift effects, for different response shift methods, populations, study designs, and patient-reported outcome measures (PROM)s.MethodsA literature search was performed in MEDLINE, PSYCINFO, CINAHL, EMBASE, Social Science Citation Index, and Dissertations & Theses Global to identify longitudinal quantitative studies that examined response shift using PROMs, published before 2021. The magnitude of each response shift effect (effect sizes, R-squared or percentage of respondents with response shift) was ascertained based on reported statistical information or as stated in the manuscript. Prevalence and magnitudes of response shift effects were summarized at two levels of analysis (study and effect levels), for recalibration and reprioritization/reconceptualization separately, and for different response shift methods, and population, study design, and PROM characteristics. Analyses were conducted twice: (a) including all studies and samples, and (b) including only unrelated studies and independent samples.ResultsOf the 150 included studies, 130 (86.7%) detected response shift effects. Of the 4868 effects investigated, 793 (16.3%) revealed response shift. Effect sizes could be determined for 105 (70.0%) of the studies for a total of 1130 effects, of which 537 (47.5%) resulted in detection of response shift. Whereas effect sizes varied widely, most median recalibration effect sizes (Cohen's d) were between 0.20 and 0.30 and median reprioritization/reconceptualization effect sizes rarely exceeded 0.15, across the characteristics. Similar results were obtained from unrelated studies.ConclusionThe results draw attention to the need to focus on understanding variability in response shift results: Who experience response shifts, to what extent, and under which circumstances?

Introduction Radon is an established lung carcinogen concentrating in indoor environments with importance for many workers worldwide. However, a systematic assessment of radon levels faced by all workers, not just those with direct uranium or radon exposure, has not previously been completed. The objective of this study was to estimate the prevalence of workers exposed to radon, and the level of exposure (>100–200 Bq/m3, 200–400 Bq/m3, 400–800 Bq/m3, and >800 Bq/m3) in a highly exposed country (Canada). Materials and Methods Exposures among underground workers were assessed using the CAREX Canada approach. Radon concentrations in indoor workplaces, obtained from two Canadian surveys, were modelled using lognormal distributions. Distributions were then applied to the susceptible indoor worker population to yield the number of exposed workers, by occupation, industry, province, and sex. CAREX Canada received an exemption from ethics approval for this study since all no personal data was used; all data on human subjects was publicly available. Results In total, an estimated 603,000 out of Canada's 18,268,120 workers are exposed to radon in Canada. An estimated 52% of exposed workers are women, even though they comprise only 48% of the labour force. The majority (68%) are exposed at a level of >100–200 Bq/m3. Workers are primarily exposed in educational services, professional, scientific and technical services, and health care and social assistance, but workers in mining, quarrying, and oil and gas extraction have the largest number of exposed workers at high levels (>800 Bq/m3). Overall, a significant number of workers are exposed to radon, many of whom are not adequately protected by existing guidelines. Conclusions Radon surveys across multiple industries and occupations are needed to better characterize occupational exposure. These results can be used to identify exposed workers, and to support lung cancer prevention programs within these groups.

Evidence supports the benefit of managing atrial fibrillation (AF) specific risk factors in secondary prevention of AF. However, a comprehensive summary of the effect of multifactorial risk factor interventions on outcomes of patients with AF over long-term is lacking. We searched MEDLINE, EMBASE, CINAHL, and Cochrane CENTRAL databases from inception to October 2021 for both randomized controlled trials (RCT) and observational studies comparing multifactorial risk factor interventions to usual care in patients with AF. Fifteen studies (10 RCT, 5 observational) with 3786 patients were included (mean age 63.8 years, 64.0% males). Follow-up ranged from 3 to 42 months. We found no significant effects of multifactorial risk factor interventions on AF recurrence [pooled relative risk (RR): 0.93, 95% CI: 0.74-1.16, P = 0.51, I2 = 54%], AF-related rehospitalization at 12 months (RR: 0.69, 95% CI: 0.43-1.11, P= 0.13, I2 = 0%), cardiovascular rehospitalization at 12 months (RR: 0.76, 95% CI: 0.53-1.09, P= 0.13, I2 = 53%), or AF-related adverse events at 12 and 15 months. However, multifactorial interventions were associated with reduced AF-related symptoms and improved health-related quality of life (HRQoL) at all studied time points. Current evidence does not support consistent associations between multifactorial risk factor interventions and AF recurrence after rhythm control therapy or AF-related or cardiovascular hospitalization in patients with AF. However, these interventions are associated with clinically relevant improvement in AF-related symptoms and HRQoL. Additional randomized studies are required to evaluate the impact of multifactorial risk factor interventions on patient-centered health outcomes.

Abstract Background In this longitudinal cohort study, we aimed to determine the association between average daily corticosteroid dose and time to first relapse among children with first presentation of nephrotic syndrome. Method We enrolled children (1–18 years) with nephrotic syndrome at the time of first presentation of nephrotic syndrome across 11 Canadian centers in the CHILDNEPH study between 2013–2019. Exposure was within-person average daily corticosteroid dose. Primary outcome was time to first relapse after entry into the study. Cox proportional hazards regression models were created to estimate the risk of relapse after adjusting for potential confounders. Results Among 328 children (129 females) with median age 4.3 (IQR 3.6) years, the unadjusted median time to first relapse in the study was 128 (42–707) days. The relationship between average daily corticosteroid dose and time to relapse was modified by age (HR: 0.87, 95% CI 0.82–0.93) with younger children having a higher risk of relapse compared to older children for the same corticosteroid dose. Relationships between time to first relapse, average corticosteroid dose, and age remained unchanged when including those children who entered the study at first or second relapse or those who relapsed while tapering the prednisone during the episode at entry in the study. Conclusion Younger children, despite receiving same average daily corticosteroid dose, experience faster time to relapse compared to older children. Further study is warranted to examine the optimal dosing of corticosteroids by age of the child.

Repeated measures discriminant analysis (RMDA) have been developed for distinguishing between two or more independent groups in multivariate repeated measures designs, in which multiple outcomes are repeatedly measured at two or more measurement occasions. However, these models, which are based on structured covariances, rely on the assumption of multivariate normality. Monte Carlo methods were used to compare the accuracy of RMDA procedures based on maximum likelihood estimators and robust maximum trimmed likelihood estimators under a variety of data analytic conditions. RMDA based on robust estimators are recommended for discriminating between population in multivariate repeated measures designs characterized by non-normal distributions.

PURPOSE:Between 33 and 59% of youth with chronic health conditions experience mental health conditions. Transition readiness, or the acquisition of knowledge and self-management skills, facilitates successful transition to adult care. Transition readiness among youth with co-occurring chronic health and mental health conditions has not been explored.DESIGN AND METHODS:This study used a sample of 201 patients (aged 16-21) with chronic conditions. All patients completed the Transition Readiness Assessment Questionniare (TRAQ) and were grouped into Cohort A: chronic health conditions only (n = 140), and Cohort B: co-occurring chronic health and mental health conditions (n = 61). A quantile regression at the 50th percentile was conducted to examine associations between TRAQ score and mental health comorbidity, age, gender and immigration status.RESULTS:The median TRAQ score for Cohort A was 3.87 (IQR 0.84) versus 4.00 (IQR 0.87) for Cohort B. Our analysis revealed that having a mental health comorbidity (b = 0.402, p = 0.034), being older in age (b = 0.540, p = 0.004) and being female (b = 0.388, p = 0.001) were associated with higher overall TRAQ score.CONCLUSIONS:The presence of a mental health comorbidity was associated with greater transition readiness as measured by the TRAQ in our sample. Future research should explore why youth with co-occurring chronic health and mental health conditions exhibit greater transition readiness.PRACTICE IMPLICATIONS:Youth with co-occurring chronic health and mental health conditions may develop transition readiness as a result of coping with mental health challenges. Practitioners could invite them to reflect on how their physical and mental health are related and affect their level of preparedness for adult care.

Background Variation in dose and duration of corticosteroids for childhood-onset steroid-sensitive nephrotic syndrome occurs worldwide, likely reflecting the evolving evidence on optimal dosing and variable severity of the disease observed between patients. We conducted a study to determine the associations between site, physician, and patient factors, and average daily corticosteroid dose and duration of therapy. Methods Data were derived from the Canadian Childhood Nephrotic Syndrome (CHILDNEPH) Project, an observational longitudinal study from 2013 to 2019 of children with nephrotic syndrome involving pediatric nephrologists in 11 sites across Canada. The primary outcome was average daily corticosteroid dose prescribed per episode of proteinuria, reported as mg/m(2) prednisone equivalents. Secondary outcome was duration of treatment for each episode of proteinuria in days. Exposure variables were categorized into site-, physician-, and patient-level variables. Results In total, 328 children, median age at enrollment of 4.3 years old (interquartile range [IQR], 3.6), participated and were followed for a median time of 2.62 years (IQR, 2.6). The observed variability in average daily corticosteroid dose and in duration of therapy was mostly attributed to the site where the patient was treated. Accounting for between patient, physician, and site differences, average daily corticosteroid dose decreased with increasing age (beta coefficient, -0.07; 95% confidence interval [95% CI], -0.09 to -0.05], P < 0.001). African and Indigenous ethnicity was associated with longer treatment duration compared with White patients (beta coefficient: African, 42.29, 95% CI, 7.85 to 76.73, P=0.02; Indigenous, 29.65, 95% CI, 2.79 to 56.52, P=0.03). Conclusions We found practice variation with respect to corticosteroid prescriptions across 11 Canadian sites, and that variation is mostly explained at the site level. Age and ethnicity are important factors to be considered, because they are significantly associated with the average corticosteroid dose and duration of therapy.

Discriminant analysis procedures that assume parsimonious covariance and/or means structures have been proposed for distinguishing between two or more populations in multivariate repeated measures designs. However, these procedures rely on the assumptions of multivariate normality which is not tenable in multivariate repeated measures designs which are characterized by binary, ordinal, or mixed types of response distributions. This study investigates the accuracy of repeated measures discriminant analysis (RMDA) based on the multivariate generalized estimating equations (GEE) framework for classification in multivariate repeated measures designs with the same or different types of responses repeatedly measured over time. Monte Carlo methods were used to compare the accuracy of RMDA procedures based on GEE, and RMDA based on maximum likelihood estimators (MLE) under diverse simulation conditions, which included number of repeated measure occasions, number of responses, sample size, correlation structures, and type of response distribution. RMDA based on GEE exhibited higher average classification accuracy than RMDA based on MLE especially in multivariate non-normal distributions. Three repeatedly measured responses namely severity of epilepsy, current number of anti-epileptic drugs, and parent-reported quality of life in children with epilepsy were used to demonstrate the application of these procedures.

Introduction In many practical situations, we are interested in the effect of covariates on correlated multiple responses. In this paper, we focus on estimation and variable selection in multi-response multiple regression models. Correlation among the response variables must be modeled for valid inference. Method We used an extension of the generalized estimating equation (GEE) methodology to simultaneously analyze binary, count, and continuous outcomes with nonlinear functions. Variable selection plays an important role in modeling correlated responses because of the large number of model parameters that must be estimated. We propose a penalized-likelihood approach based on the extended GEEs for simultaneous parameter estimation and variable selection. Results and conclusions We conducted a series of Monte Carlo simulations to investigate the performance of our method, considering different sample sizes and numbers of response variables. The results showed that our method works well compared to treating the responses as uncorrelated. We recommend using an unstructured correlation model with the Bayesian information criterion (BIC) to select the tuning parameters. We demonstrated our method using data from a concrete slump test.

Background and Purpose: Stroke-related functional risk scores are used to predict patients' functional outcomes following a stroke event. We evaluate the predictive accuracy of machine-learning algorithms for predicting functional outcomes in acute ischemic stroke patients after endovascular treatment. Methods: Data were from the Precise and Rapid Assessment of Collaterals with Multi-phase CT Angiography (PROVE-IT), an observational study of 614 ischemic stroke patients. Regression and machine learning models, including random forest (RF), classification and regression tree (CART), C5.0 decision tree (DT), support vector machine (SVM), adaptive boost machine (ABM), least absolute shrinkage and selection operator (LASSO) logistic regression, and logistic regression models were used to train and predict the 90-day functional impairment risk, which is measured by the modified Rankin scale (mRS) score > 2. The models were internally validated using split-sample cross-validation and externally validated in the INTERRSeCT cohort study. The accuracy of these models was evaluated using the area under the receiver operating characteristic curve (AUC), Matthews Correlation Coefficient (MCC), and Brier score. Results: Of the 614 patients included in the training data, 249 (40.5%) had 90-day functional impairment (i.e., mRS > 2). The median and interquartile range (IQR) of age and baseline NIHSS scores were 77 years (IQR = 69-83) and 17 (IQR = 11-22), respectively. Both logistic regression and machine learning models had comparable predictive accuracy when validated internally (AUC range = [0.65-0.72]; MCC range = [0.29-0.42]) and externally (AUC range = [0.66-0.71]; MCC range = [0.34-0.42]). Conclusions: Machine learning algorithms and logistic regression had comparable predictive accuracy for predicting stroke-related functional impairment in stroke patients.

Objective To examine whether cannabis use is associated with or mediates psychosocial health in people with epilepsy. Methods Consecutive adult epilepsy patients visiting the Calgary Comprehensive Epilepsy Programme clinic were administered validated patient-reported outcome measures (PROMs) including the Neurological Disorders Depression Inventory for Epilepsy (NDDI-E), Quality of Life in Epilepsy (QOLIE-10-P), EuroQOL five dimensions five level scale (EQ-5D-5L), Global Assessment of Severity of Epilepsy Scale, Global Assessment of Disability Associated with Seizures Scale and the Treatment Satisfaction Questionnaire for Medication scale. We used multiple regression analyses to investigate associations between cannabis use and PROMs. Mediation analyses were performed to determine the degree to which cannabis modulated the associations between current or past psychiatric disorders, monthly seizure frequency, and 1-year seizure freedom on psychosocial health. Results Of 337 consecutive patients, 71 (21%) reported cannabis use. Cannabis use was independently associated with depression (NDDI-E score >= 14; OR 3.90; 95% CI 2.01 to 7.59; p<0.001), lower quality of life (beta=-16.73, 95% CI - 26.26 to - 7.20; p=0.001), worse epilepsy-related disability (OR 2.23, 95% CI 1.19 to 4.17; p=0.01) and lower satisfaction with antiepileptic medication (OR 0.41, 95% CI 0.23 to 0.72; p=0.002). Cannabis use mediates 7%-12% of the effect of a psychiatric history on depression, worse quality of life and worse health valuation. Conclusions There is a strong and independent association between cannabis use and poor psychosocial health, and it partially mediates the deleterious effect of a psychiatric history on these same outcomes. Inclusion of PROMs in future cannabis trials is warranted.

Background and Purpose: The burden of stroke-related functional impairment remains high among stroke survivors. Clinical prediction models are commonly used to estimate patient functional impairment risk. However, these models have been principally developed based on regression models, which are sensitive to multicollinearity. This study investigates whether there is any advantage in using machine learning models to develop stroke-related functional impairment risk prediction tools. Methods: Using data from a multi-center hospital-based cohort study (n = 614). Modified Rankin Scale (mRS) score was used to assess 90-day functional impairment status. The accuracy of machine learning models was used to predict the risk of patient-specific risk of 90-day functional impairment. Area under the receiver operating characteristic curve (AUC) was used to assess the predictive accuracy of these models via internal cross-validation and external validation in the ESCAPE randomized controlled trial data. Results: Of the 614 patients included in the analyses, 348(56.7%) had some form of functional impairment (i.e., mRS > 1), 313 (50.9%) were males, while the median and interquartile range (IQR) of age and baseline NIHSS scores were 72 years (IQR = 63-80) and 12 (IQR = 6-19), respectively. Internal cross-validation shows that the AUC for regression models were 68.3% (95%CI = [63.9% - 76.5%]) and 70.1% (95%CI = [63.5% - 76.1%]) while the AUC for machine learning models ranged between 62.7% to 68.8%. But when these models were externally validated in the ESCAPE data, the AUC for regression models were 39.6% (95%CI = [36.1% - 47.5%]) and 35.8% (95%CI = [30.4% - 41.5%]) while the AUC for machine learning models ranged between 61.6% (95%CI = [58.2% - 67.3%]) and 66.7% (95%CI = [61.3% - 72.3%]). Conclusions: This study shows that while there were negligible differences between risk prediction models based on machine learning and regression-based models when internally validated, the former are more accurate than the latter in predicting stroke-related functional impairment in externally validated data. Future research will use Monte Carlo methods to develop recommendations for selecting machine learning models under a variety of data characteristics.

IntroductionThere has been an increased demand for more accurate prediction tools to aid clinical decision-making regarding disease diagnosis prognosis for coronary artery disease(CAD) patients. Patients undergoing CABG surgery are older and a larger number have had previous heart surgery. Consequently, mortality after CABG is expected to increase despite procedural advances. Objectives and ApproachThis study aims to compare the predictive performance of random forest(RF) and logistic regression(LR) classifiers for predicting 30-day and 1-year post-operative mortality risk in CAD patients who underwent CABG. Data was obtained by linking the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease(APPROACH) registry, a prospective longitudinal data of patients undergoing cardiac catheterization in Alberta, Canada, to vital statistics database. All patients who underwent first-time isolated CABG between January 1, 2007 and December 31, 2012 were included in the analysis. Area under the receiver operating curve(AUC) was used to compare the predictive performance of LR and RF regression. ResultsOf the 4,908 eligible subjects who underwent isolated CABG during the study period, mortality estimates of 30-day and 1-year post CABG surgery were 1.59% and 3.85%, respectively. Descriptive analysis revealed that age, sex, hypertension, dialysis, cerebrovascular disease, chronic obstructive pulmonary disease, and chronic heart failure were associated with 30-day and 1-year mortality. The accuracy of the LR and RF regression classifiers in predicting 30-day mortality were 74.1, and 99.7%, respectively. While the accuracy of the former and latter classifiers in predicting 1-year post CABG mortality were 74% and 97.4%, respectively. Conclusion/ImplicationsThis study shows that RF classifier results in better predictive accuracy than LR in predicting post-operating mortality risk in CAD patients. Machine learning models are potentially usefully for developing clinical prediction models that can be used to aid the monitoring of post-discharge outcomes in the management of cardiovascular diseases.

# 01. Laparoscopic splenectomy with management of intraoperative hemorrhage {#article-title-2} This video demonstrates a laparoscopic splenectomy for ITP with safe management of intraoperative hemorrhage. YouTube video link: [www.youtube.com/watch?v=6vYkOy3UePQ][1] # 02. Incisional hernia after

The diagnosis of epilepsy in children is known to impact the trajectory of their health-related quality of life (HRQOL) over time. However, there is limited knowledge about variations in longitudinal trajectories across multiple domains of HRQOL. This study aims to characterize the heterogeneity in HRQOL trajectories across multiple HRQOL domains and to evaluate predictors of differences among the identified trajectory groups in children with new-onset epilepsy. Data were obtained from the Health Related Quality of Life in Children with Epilepsy Study (HERQULES), a prospective multi-center study of 373 children newly diagnosed with new-onset epilepsy who were followed up over 2 years. Child HRQOL and family factors were reported by parents, and clinical characteristics were reported by neurologists. Group-based multi-trajectory modeling was adopted to characterize longitudinal trajectories of HRQOL as measured by the individual domains of cognitive, emotional, physical, and social functioning in the 55-item Quality of Life in Childhood Epilepsy Questionnaire (QOLCE-55). Multinomial logistic regression was used to assess potential factors that explain differences among the identified latent trajectory groups. Three distinct HRQOL trajectory subgroups were identified in children with new-onset epilepsy based on HRQOL scores: "High" (44.7%), "Intermediate" (37.0%), and "Low" (18.3%). While most trajectory groups exhibited increasing scores over time on physical and social domains, both flat and declining trajectories were noted on emotional and cognitive domains. Less severe epilepsy, an absence of cognitive and behavioral problems, lower parental depression scores, better family functioning, and fewer family demands were associated with a "Higher" or "Intermediate" HRQOL trajectory. The course of HRQOL over time in children with new-onset epilepsy appears to follow one of three different trajectories. Addressing the clinical and psychosocial determinants identified for each pattern can help clinicians provide more targeted care to these children and their families.