Response shift results of quantitative research using patient-reported outcome measures: a descriptive systematic review.

PURPOSE: The 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. METHODS: A 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. RESULTS: Of 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. CONCLUSION: The 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?

Association between transition readiness and mental health comorbidity in youth with chronic health conditions.

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.

Cannabis use is both independently associated with and mediates worse psychosocial health in patients with epilepsy.

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 (ß=-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.

Multivariate trajectories across multiple domains of health-related quality of life in children with new-onset epilepsy.

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 2years. 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.

Effect of Multifactorial Risk Factor Interventions on Atrial Fibrillation: A Systematic Review and Meta-Analysis.

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.

Repeated measures discriminant analysis using multivariate generalized estimation equations.

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.

Characteristics Associated with Variation in Corticosteroid Exposure in Children with Steroid-Sensitive Nephrotic Syndrome: Results from a Canadian Longitudinal Study.

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/m2 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.

Variable selection in multivariate multiple regression.

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.

Functional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression Models.

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.