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The true power of a trial is determined by design inputs (randomization scheme, sample size) and decision criteria (analysis model, type I error rate, etc.) which are chosen by the trial designer, in combination with parameters whose values are unknown and beyond the control of the trial designer (population characteristics, including the true benefit of the intervention). [...]we perform calculations for a target power using assumed values for these unknown parameters—for simplicity in the discussion, suppose the only one unknown is the true benefit. [...]this is not the motivating purpose of a sample size calculation, but it is a condition that we must believe to be satisfied (approximately) for the sample size calculation to have any worth—if it does not hold, the calculation misleads us about the true power, so we should never accept calculations in which we doubt that the assumed benefit is realistic. [...]consider a scenario where the MID is 5 and lies within the range of realistic estimates for the true benefit, 3 to 10, say.

Performing a sample size calculation for a randomized controlled trial requires specifying an assumed benefit (that is, the mean improvement in outcomes due to the intervention) and a target power. There is a widespread belief that judgments about the minimum important difference should be used when setting the assumed benefit and thus the sample size. This belief is misguided — when the purpose of the trial is to test the null hypothesis of no treatment benefit, the only role that the minimum important difference should be given is in determining whether the sample size should be zero, that is, whether the trial should be conducted at all. The true power of the trial depends on the true benefit, so the calculated sample size will result in a true power close to the target power used in the calculation only if the assumed benefit is close to the true benefit. Hence, the assumed benefit should be set to a value that is considered a realistic estimate of the true benefit. If a trial designed using a realistic value for the assumed benefit is unlikely to demonstrate that a meaningful benefit exists, the trial should not be conducted. Any attempt to reconcile discrepancies between the realistic estimate of benefit and the minimum important difference when setting the assumed benefit merely conflates a valid sample size calculation with one based on faulty inputs and leads to a true power that fails to match the target power. When calculating sample size, trial designers should focus efforts on determining reasonable estimates of the true benefit, not on what magnitude of benefit is judged important.

Mortality caused by septic shock may be determined by a systemic inflammatory response, independent of the inciting infection, but it may also be influenced by the anatomic source of infection. To determine the association between the anatomic source of infection and hospital mortality in critically ill patients who have septic shock. This was a retrospective, multicenter cohort study of 7,974 patients who had septic shock in 29 academic and community intensive care units in Canada, the United States, and Saudi Arabia from January 1989 to May 2008. Subjects were assigned 1 of 20 anatomic sources of infection based on clinical diagnosis and/or isolation of pathogens. The primary outcome was hospital mortality. Overall crude hospital mortality was 52% (21-85% across sources of infection). Variation in mortality remained after adjusting for year of admission, geographic source of admission, age, sex, comorbidities, community- versus hospital-acquired infection, and organism type. The source of infection with the highest standardized hospital mortality was ischemic bowel (75%); the lowest was obstructive uropathy-associated urinary tract infection (26%). Residual variation in adjusted hospital mortality was not explained by Acute Physiology and Chronic Health Evaluation II score, number of Day 1 organ failures, bacteremia, appropriateness of empiric antimicrobials, or adjunct therapies. In patients who received appropriate antimicrobials after onset of hypotension, source of infection was associated with death after adjustment for both predisposing and downstream factors. Anatomic source of infection should be considered in future trial designs and analyses, and in development of prognostic scoring systems.

In a cluster-randomized trial (CRT), the number of participants enrolled often varies across clusters. This variation should be considered during both trial design and data analysis to ensure statistical performance goals are achieved. Most methodological literature on the CRT design has assumed equal cluster sizes. This scoping review focuses on methodology for unequal cluster size CRTs. EMBASE, Medline, Google Scholar, MathSciNet and Web of Science databases were searched to identify English-language articles reporting on methodology for unequal cluster size CRTs published until March 2021. We extracted data on the focus of the paper (power calculation, Type I error etc.), the type of CRT, the type and the range of parameter values investigated (number of clusters, mean cluster size, cluster size coefficient of variation, intra-cluster correlation coefficient, etc.), and the main conclusions. Seventy-nine of 5032 identified papers met the inclusion criteria. Papers primarily focused on the parallel-arm CRT (p-CRT, n = 60, 76%) and the stepped-wedge CRT (n = 14, 18%). Roughly 75% of the papers addressed trial design issues (sample size/power calculation) while 25% focused on analysis considerations (Type I error, bias, etc.). The ranges of parameter values explored varied substantially across different studies. Methods for accounting for unequal cluster sizes in the p-CRT have been investigated extensively for Gaussian and binary outcomes. Synthesizing the findings of these works is difficult as the magnitude of impact of the unequal cluster sizes varies substantially across the combinations and ranges of input parameters. Limited investigations have been done for other combinations of a CRT design by outcome type, particularly methodology involving binary outcomes—the most commonly used type of primary outcome in trials. The paucity of methodological papers outside of the p-CRT with Gaussian or binary outcomes highlights the need for further methodological development to fill the gaps.

Background The COVID-19 pandemic has led to increased utilization of telemedicine services. Methods A retrospective analysis of all referral-based ambulatory telemedicine services in Ontario from November 2019 to June 2021 was collected from the Ontario Health Insurance Plan (OHIP) billing database. Only fee-for-service billings were included in the present analysis. Coincident COVID-19 cases were obtained from Public Health Ontario. Comparisons were made based on age bracket, sex, telemedicine and in-person care. Results Billings for telemedicine services in Ontario increased from $1.7 million CAD in November 2019 to $64 million CAD in April 2020 and the proportions reached a mean peak of 72% in April 2020 and declined to 46% in June 2021. A positive correlation was found between the use of telemedicine and COVID-19 cases (p = 0.05). The age group with the highest proportion of telemedicine use was the 10–20-year-olds, followed by the 20–50-year-olds (61 ± 9.0%, 55 ± 7.3%, p = 0.01). Both age groups remained above 50% telemedicine services at the end of the study period. There seemed to be higher utilization by females (females 54.2 ± 8.0%, males 47.9 ± 7.7%, ANCOVA p = 0.05) for all specialties, however, after adjusting for male to female ratio m:f of 0.952:1.0 according to the 2016 census, this was no longer significant. Conclusions The use of telemedicine services remained at a high level across groups, particularly the 10–50-year-olds. There were clear age preferences for using telemedicine. Studying these differences may provide insights into how the delivery of non-hospital-based medicine has changed during the COVID-19 pandemic.

Emergency department overcrowding has been associated with increased odds of hospital admission and mortality after discharge from the emergency department in predominantly adult cohorts. The objective of this study was to evaluate the association between crowding and the odds of several adverse outcomes among children seen at a pediatric emergency department. We conducted a retrospective cohort study involving all children visiting 8 Canadian pediatric emergency departments across 4 provinces between 2010 and 2014. We analyzed the association between mean departmental length of stay for each index visit and hospital admission within 7 days or death within 14 days of emergency department discharge, as well as hospital admission at index visit and return visits within 7 days, using mixed-effects logistic regression modelling. A total of 1 931 465 index visits occurred across study sites over the 5-year period, with little variation in index visit hospital admission or median length of stay. Hospital admission within 7 days of discharge and 14-day mortality were low across provinces (0.8%–1.5% and < 10 per 100 000 visits, respectively), and their association with mean departmental length of stay varied by triage categories and across sites but was not significant. There were increased odds of hospital admission at the index visit with increasing departmental crowding among visits triaged to Canadian Triage and Acuity Scale (CTAS) score 1–2 (odds ratios [ORs] ranged from 1.01 to 1.08) and return visits among patients with a CTAS score of 4–5 discharged at the index visit at some sites (ORs ranged from 1.00 to 1.06). Emergency department crowding was not significantly associated with hospital admission within 7 days of the emergency department visit or mortality in children. However, it was associated with increased hospital admission at the index visit for the sickest children, and with return visits to the emergency department for those less sick.

To determine which demographic characteristics are associated with moral distress in intensive care unit (ICU) professionals. We distributed a self-administered, validated survey to measure moral distress to all clinical personnel in 13 ICUs in British Columbia, Canada. Each respondent to the survey also reported their age, sex, and years of experience in the ICU where they were working. We used multivariate, hierarchical regression to analyze relationships between demographic characteristics and moral distress scores, and to analyze the relationship between moral distress and tendency to leave the workplace. Response rates to the surveys were the following: nurses—428/870 (49%); other health professionals (not nurses or physicians)—211/452 (47%); physicians—30/68 (44%). Nurses and other health professionals had higher moral distress scores than physicians. Highest ranked items associated with moral distress were related to cost constraints and end-of-life controversies. Multivariate analyses showed that age is inversely associated with moral distress, but only in other health professionals (rate ratio [95% confidence interval]: −7.3 [−13.4, −1.2]); years of experience is directly associated with moral distress, but only in nurses (rate ratio (95% confidence interval):10.8 [2.6, 18.9]). The moral distress score is directly related to the tendency to leave the ICU job, in both the past and present, but only for nurses and other non-physician health professionals. Moral distress is higher in ICU nurses and other non-physician professionals than in physicians, is lower with older age for other non-physician professionals but greater with more years of experience in nurses, and is associated with tendency to leave the job.

Rehabilitation is important for regaining mobility poststroke. Clinical practice guidelines suggest a high number of repetitive stepping activities to optimize subacute recovery especially when undertaken at intensities that challenge cardiovascular fitness. However, adherence to these guidelines is unclear. The objective of this study was to quantify aerobic minutes and step number in usual care inpatient stroke rehabilitation unit physical therapy sessions across Canada and identify characteristics of participants who met guideline aerobic intensity minutes at a session midpoint in their rehabilitation. To gain insight into usual care, we analyzed cross-sectional data from the usual care arm of the Walk 'n Watch implementation trial; trial sites included Canadian rehabilitation units that were not typically involved in research studies. To be included, medically stable patients were admitted for inpatient stroke rehabilitation, and able to take > 5 steps with a maximum of one person assisting. We assessed a midpoint physical therapy session with a wrist-based heart monitor (aerobic minutes) and ankle-based step counter (step number). Means, histograms, and correlations between aerobic minutes (> 40% heart rate reserve) and steps were calculated. There were 166 participants (69 females, age 69 standard deviation (SD)12 years) with stroke (138 Ischemic/ 27 Hemorrhagic) included. Participants had a mean of 10(SD11) aerobic minutes and 985(SD579) steps. The relationship between step number and aerobic minutes was negligible (R2 = 0.003). More participants with ≥20 aerobic minutes in a session were male, with lower 6 Minute Walk Test distance, and have a subcortical stroke location. The number of steps has increased, but aerobic minutes has not changed and remains extremely low compared to published reports in the past several years. Given that increasing activity levels are critical for stroke recovery, further investigation into the potential barriers to achieving targets set by guidelines is recommended. ClinicalTrials.gov NCT04238260.

Background In a cross-sectional stepped-wedge trial with unequal cluster sizes, attained power in the trial depends on the realized allocation of the clusters. This attained power may differ from the expected power calculated using standard formulae by averaging the attained powers over all allocations the randomization algorithm can generate. We investigated the effect of design factors and allocation characteristics on attained power and developed models to predict attained power based on allocation characteristics. Method Based on data simulated and analyzed using linear mixed-effects models, we evaluated the distribution of attained powers under different scenarios with varying intraclass correlation coefficient (ICC) of the responses, coefficient of variation (CV) of the cluster sizes, number of cluster-size groups, distributions of group sizes, and number of clusters. We explored the relationship between attained power and two allocation characteristics: the individual-level correlation between treatment status and time period, and the absolute treatment group imbalance. When computational time was excessive due to a scenario having a large number of possible allocations, we developed regression models to predict attained power using the treatment-vs-time period correlation and absolute treatment group imbalance as predictors. Results The risk of attained power falling more than 5% below the expected or nominal power decreased as the ICC or number of clusters increased and as the CV decreased. Attained power was strongly affected by the treatment-vs-time period correlation. The absolute treatment group imbalance had much less impact on attained power. The attained power for any allocation was predicted accurately using a logistic regression model with the treatment-vs-time period correlation and the absolute treatment group imbalance as predictors. Conclusion In a stepped-wedge trial with unequal cluster sizes, the risk that randomization yields an allocation with inadequate attained power depends on the ICC, the CV of the cluster sizes, and number of clusters. To reduce the computational burden of simulating attained power for allocations, the attained power can be predicted via regression modeling. Trial designers can reduce the risk of low attained power by restricting the randomization algorithm to avoid allocations with large treatment-vs-time period correlations.

To develop a whole-joint, unidimensional, irreversible, and fine-grained MRI knee osteoarthritis (OA) severity score, based on cartilage, osteophytes and meniscus (OA-COM), and to predict progression across different severity states using OA-COM as outcome and clinical variables as predictors. Optimal OA-COM thresholds were 12, 18, 24 and 30, for KL grades 1 to 4. Significant predictors of progression (depending on threshold) included physical exam effusion, malalignment and female sex, with other selected predictors age, BMI and crepitus. OA-COM (0-54 range) is a whole-joint, unidimensional, irreversible, and fine-grained MRI OA severity score reflecting cartilage, osteophytes and menisci. OA-COM scores 12, 18, 24 and 30 are equivalent to KL grades 1 to 4, while offering fine-grained differentiation of states between KL grades, and within pre-radiographic disease (KL = 0) or late-stage disease (KL = 4). In modeling, several clinical variables predicted progression across different states over 7 years.