Rating of Perceived Exertion: A Large Cross-Sectional Study Defining Intensity Levels for Individual Physical Activity Recommendations.

BACKGROUND: Physical inactivity is a growing risk factor worldwide, therefore getting people into sports is necessary. When prescribing physical activity, it is essential to recommend the correct training intensities. Cardiopulmonary exercise testing (CPX) enables precise determination of individuals' training intensities but is unavailable for a broad population. Therefore, the Borg scale allows individuals to assess perceived exertion and set their intensity easily and cost-efficiently. In order to transfer CPX to rating of perceived exertion (RPE), previous studies investigated RPE on specific physiological anchors, e.g. blood lactate (bLa) concentrations, but representativeness for a broad population is questionable. Some contradictory findings regarding individual factors influencing RPE occur, whereas univariable analysis has been performed so far. Moreover, a multivariable understanding of individual factors influencing RPE is missing. This study aims to determine RPE values at the individual anaerobic threshold (LT2) and defined bLa concentrations in a large cohort and to evaluate individual factors influencing RPE with multivariable analysis. METHODS: CPX with bicycle or treadmill ergometer of 6311 participants were analyzed in this cross-sectional study. RPE values at bLa concentrations 2 mmol/l, 3 mmol/l, 4 mmol/l, and LT2 (first rise in bLa over baseline + 1.5 mmol/l) were estimated by spline interpolation. Multivariable cumulative ordinal regression models were performed to assess the influence of sex, age, type of ergometry, VO2max, and duration of exercise testing on RPE. RESULTS: Median values [interquartile range (IQR)] of the total population were RPE 13 [11; 14] at 2 mmol/l, RPE 15 [13; 16] at 3 mmol/l, RPE 16 [15; 17] at 4 mmol/l, and RPE 15 [14; 16] at LT2. Main influence of individual factors on RPE were seen especially at 2 mmol/l: male sex (odds ratio (OR) [95%-CI]: 0.65 [0.587; 0.719]), treadmill ergometry (OR 0.754 [0.641; 0.886]), number of stages (OR 1.345 [1.300; 1.394]), age (OR 1.015 [1.012; 1.018]), and VO2max (OR 1.023 [1.015; 1.030]). Number of stages was the only identified influencing factor on RPE at all lactate concentrations/LT2 (3 mmol/l: OR 1.290 [1.244; 1.336]; 4 mmol/l: OR 1.229 [1.187; 1.274]; LT2: OR 1.155 [1.115; 1.197]). CONCLUSION: Our results suggest RPE ≤ 11 for light intensity, RPE 12-14 for moderate intensity, and RPE 15-17 for vigorous intensity, which slightly differs from the current American College of Sports Medicine (ACSM) recommendations. Additionally, we propose an RPE of 15 delineating heavy and severe intensity domain. Age, sex, type of ergometry, duration of exercise, and cardiopulmonary fitness should be considered when recommending individualized intensities with RPE, primarily at lower intensities. Therefore, this study can be used as a new guideline for prescribing individual RPE values in the clinical practice, predominantly for endurance type exercise.

Neurological affection and serum neurofilament light chain in wild type transthyretin amyloidosis.

In contrast to inherited transthyretin amyloidosis (A-ATTRv), neuropathy is not a classic leading symptom of wild type transthyretin amyloidosis (A-ATTRwt). However, neurological symptoms are increasingly relevant in A-ATTRwt as well. To better understand the role of neurological symptoms in A-ATTRwt, A-ATTRwt patients were prospectively characterized at Amyloidosis Center Charité Berlin (ACCB) between 2018 and 2023 using detailed neurological examination, quality of life questionnaires, and analysis of age- and BMI-adapted serum neurofilament light chain (NFL) levels. 16 out of 73 (21.9%) patients presented with a severe neuropathy which we defined by a Neuropathy Impairment Score (NIS) of 20 or more. In this group, quality of life was reduced, peripheral neuropathy was more severe, and spinal stenosis and joint replacements were frequent. Age- and BMI matched serum NFL levels were markedly elevated in patients with a NIS ≥ 20. We therefore conclude that highly abnormal values in neuropathy scores such as the NIS occur in A-ATTRwt, and have an important impact on quality of life. Both peripheral neuropathy and spinal canal stenosis are likely contributors. Serum NFL may serve as a biomarker for neurological affection in patients with A-ATTRwt. It will be important to consider neurological aspects of A-ATTRwt for diagnosis, clinical follow-up, and future treatment development.

Point estimation, confidence intervals, and P-values for optimal adaptive two-stage designs with normal endpoints.

Due to the dependency structure in the sampling process, adaptive trial designs create challenges in point and interval estimation and in the calculation of P-values. Optimal adaptive designs, which are designs where the parameters governing the adaptivity are chosen to maximize some performance criterion, suffer from the same problem. Various analysis methods which are able to handle this dependency structure have already been developed. In this work, we aim to give a comprehensive summary of these methods and show how they can be applied to the class of designs with planned adaptivity, of which optimal adaptive designs are an important member. The defining feature of these kinds of designs is that the adaptive elements are completely prespecified. This allows for explicit descriptions of the calculations involved, which makes it possible to evaluate different methods in a fast and accurate manner. We will explain how to do so, and present an extensive comparison of the performance characteristics of various estimators between an optimal adaptive design and its group-sequential counterpart.

Extension of a conditional performance score for sample size recalculation rules to the setting of binary endpoints.

BACKGROUND: Sample size calculation is a central aspect in planning of clinical trials. The sample size is calculated based on parameter assumptions, like the treatment effect and the endpoint's variance. A fundamental problem of this approach is that the true distribution parameters are not known before the trial. Hence, sample size calculation always contains a certain degree of uncertainty, leading to the risk of underpowering or oversizing a trial. One way to cope with this uncertainty are adaptive designs. Adaptive designs allow to adjust the sample size during an interim analysis. There is a large number of such recalculation rules to choose from. To guide the choice of a suitable adaptive design with sample size recalculation, previous literature suggests a conditional performance score for studies with a normally distributed endpoint. However, binary endpoints are also frequently applied in clinical trials and the application of the conditional performance score to binary endpoints is not yet investigated. METHODS: We extend the theory of the conditional performance score to binary endpoints by suggesting a related one-dimensional score parametrization. We moreover perform a simulation study to evaluate the operational characteristics and to illustrate application. RESULTS: We find that the score definition can be extended without modification to the case of binary endpoints. We represent the score results by a single distribution parameter, and therefore derive a single effect measure, which contains the difference in proportions [Formula: see text] between the intervention and the control group, as well as the endpoint proportion [Formula: see text] in the control group. CONCLUSIONS: This research extends the theory of the conditional performance score to binary endpoints and demonstrates its application in practice.

On the Effective Dissemination and Use of Learning Objectives Catalogs for Health Information Curricula Development.

Catalogs of competency-based learning objectives (CLO) were introduced and promoted as a prerequisite for high-quality, systematic curriculum development. While this is common in medicine, the consistent use of CLO is not yet well established in epidemiology, biometry, medical informatics, biomedical informatics, and nursing informatics especially in Germany. This paper aims to identify underlying obstacles and give recommendations in order to promote the dissemination of CLO for curricular development in health data and information sciences. To determine these obstacles and recommendations a public online expert workshop was organized. This paper summarizes the findings.

Optimization of the two-stage group sequential three-arm gold-standard design for non-inferiority trials.

If design parameters are chosen appropriately, group sequential trial designs are known to be able to reduce the expected sample size under the alternative hypothesis compared to single-stage designs. The same holds true for the so-called 'gold-standard' design for non-inferiority trials, a design involving an experimental group, an active control group, and a placebo group. However, choosing design parameters that maximize the advantages of a two-stage approach for the three-arm gold-standard design for non-inferiority trials is not a straightforward task. In particular, optimal choices of futility boundaries for this design have not been thoroughly discussed in existing literature. We present a variation of the hierarchical testing procedure, which allows for the incorporation of binding futility boundaries at interim analyses. We show that this procedure maintains strong control of the family-wise type I error rate. Within this framework, we consider the futility and efficacy boundaries as well as the sample size allocation ratios as optimization parameters. This allows the investigation of the efficiency gain from including the option to stop for futility in addition to the ability to stop for efficacy. To analyze the extended designs, optimality criteria that include the design's performance under the alternative as well as the null hypothesis are introduced. On top of this, we discuss methods to limit the allocation of placebo patients in the trial while maintaining relatively good operating characteristics. The results of our numerical optimization procedure are discussed and a comparison of different approaches to designing a three-arm gold-standard non-inferiority trial is provided.

Optimal unplanned design modification in adaptive two-stage trials.

Adaptive planning of clinical trials allows modifying the entire trial design at any time point mid-course. In this paper, we consider the case when a trial-external update of the planning assumptions during the ongoing trial makes an unforeseen design adaptation necessary. We take up the idea to construct adaptive designs with defined features by solving an optimization problem and apply it to the situation of unplanned design reassessment. By using the conditional error principle, we present an approach on how to optimally modify the trial design at an unplanned interim analysis while at the same time strictly protecting the type I error rate. This linking of optimal design planning and the conditional error principle allows sound reactions to unforeseen events that make a design reassessment necessary.

Optimization of adaptive designs with respect to a performance score.

Adaptive designs are an increasingly popular method for the adaptation of design aspects in clinical trials, such as the sample size. Scoring different adaptive designs helps to make an appropriate choice among the numerous existing adaptive design methods. Several scores have been proposed to evaluate adaptive designs. Moreover, it is possible to determine optimal two-stage adaptive designs with respect to a customized objective score by solving a constrained optimization problem. In this paper, we use the conditional performance score by Herrmann et al. (2020) as the optimization criterion to derive optimal adaptive two-stage designs. We investigate variations of the original performance score, for example, by assigning different weights to the score components and by incorporating prior assumptions on the effect size. We further investigate a setting where the optimization framework is extended by a global power constraint, and additional optimization of the critical value function next to the stage-two sample size is performed. Those evaluations with respect to the sample size curves and the resulting design's performance can contribute to facilitate the score's usage in practice.

Statistical model building: Background "knowledge" based on inappropriate preselection causes misspecification.

BACKGROUND: Statistical model building requires selection of variables for a model depending on the model's aim. In descriptive and explanatory models, a common recommendation often met in the literature is to include all variables in the model which are assumed or known to be associated with the outcome independent of their identification with data driven selection procedures. An open question is, how reliable this assumed "background knowledge" truly is. In fact, "known" predictors might be findings from preceding studies which may also have employed inappropriate model building strategies. METHODS: We conducted a simulation study assessing the influence of treating variables as "known predictors" in model building when in fact this knowledge resulting from preceding studies might be insufficient. Within randomly generated preceding study data sets, model building with variable selection was conducted. A variable was subsequently considered as a "known" predictor if a predefined number of preceding studies identified it as relevant. RESULTS: Even if several preceding studies identified a variable as a "true" predictor, this classification is often false positive. Moreover, variables not identified might still be truly predictive. This especially holds true if the preceding studies employed inappropriate selection methods such as univariable selection. CONCLUSIONS: The source of "background knowledge" should be evaluated with care. Knowledge generated on preceding studies can cause misspecification.

Selection, recombination, and the ancestral initiation graph.

Recently, the selection-recombination equation with a single selected site and an arbitrary number of neutral sites was solved by Alberti and Baake (2021) by means of the ancestral selection-recombination graph. Here, we introduce a more accessible approach, namely the ancestral initiation graph. The construction is based on a discretisation of the selection-recombination equation. We apply our method to systematically explain a long-standing observation concerning the dynamics of linkage disequilibrium between two neutral loci hitchhiking along with a selected one. In particular, this clarifies the nontrivial dependence on the position of the selected site.

Repercussions of the COVID-19 Response in Pregnant Women in Western Uganda: Knowledge, Behavior, and Emotional State after the First Lockdown in 2020.

Limited research exists on pregnant women's knowledge, attitudes, and behavior concerning COVID-19 in sub-Saharan Africa. We performed a cross-sectional study among 648 pregnant women in Fort Portal, Uganda, after the first lockdown starting in June 2020. Structured interviews were conducted at three different facilities during routine antenatal care, assessing sociodemographic background, knowledge of COVID-19, prevention behavior adherence, and psycho-emotional stress levels. We performed descriptive analyses and examined associated factors using multivariable logistic regression. In Fort Portal Region, 32.8% of pregnant women had a higher knowledge regarding the COVID-19 pandemic, while all women at least heard of COVID-19. 88.6% of the women showed low self-reported prevention behavior adherence. More than one third of the pregnant women experienced high psycho-emotional stress related to the pandemic (39.8%). The odds for psycho-emotional stress were increased among the age group 21-30 years (AOR 1.97; 95% CI 1.18-3.35) compared to women under the age of 21, and decreased in single or divorced women compared to women in partnerships (AOR 0.42; 0.22-0.77) and in women having less COVID-19-related knowledge (AOR 0.40; 0.27-0.58). In conclusion, prevention behavior adherence seemed challenging, and psycho-emotional stress was ubiquitous among our cohort. To avoid adverse consequences in maternal and neonatal health, campaigns for hygiene but also women's emotional state should be a major focus of community healthcare in exceptional times such as the SARS-CoV-2 pandemic.

Improving sample size recalculation in adaptive clinical trials by resampling.

Sample size calculations in clinical trials need to be based on profound parameter assumptions. Wrong parameter choices may lead to too small or too high sample sizes and can have severe ethical and economical consequences. Adaptive group sequential study designs are one solution to deal with planning uncertainties. Here, the sample size can be updated during an ongoing trial based on the observed interim effect. However, the observed interim effect is a random variable and thus does not necessarily correspond to the true effect. One way of dealing with the uncertainty related to this random variable is to include resampling elements in the recalculation strategy. In this paper, we focus on clinical trials with a normally distributed endpoint. We consider resampling of the observed interim test statistic and apply this principle to several established sample size recalculation approaches. The resulting recalculation rules are smoother than the original ones and thus the variability in sample size is lower. In particular, we found that some resampling approaches mimic a group sequential design. In general, incorporating resampling of the interim test statistic in existing sample size recalculation rules results in a substantial performance improvement with respect to a recently published conditional performance score.

Smoothing Corrections for Improving Sample Size Recalculation Rules in Adaptive Group Sequential Study Designs.

BACKGROUND: An adequate sample size calculation is essential for designing a successful clinical trial. One way to tackle planning difficulties regarding parameter assumptions required for sample size calculation is to adapt the sample size during the ongoing trial.This can be attained by adaptive group sequential study designs. At a predefined timepoint, the interim effect is tested for significance. Based on the interim test result, the trial is either stopped or continued with the possibility of a sample size recalculation. OBJECTIVES: Sample size recalculation rules have different limitations in application like a high variability of the recalculated sample size. Hence, the goal is to provide a tool to counteract this performance limitation. METHODS: Sample size recalculation rules can be interpreted as functions of the observed interim effect. Often, a "jump" from the first stage's sample size to the maximal sample size at a rather arbitrarily chosen interim effect size is implemented and the curve decreases monotonically afterwards. This jump is one reason for a high variability of the sample size. In this work, we investigate how the shape of the recalculation function can be improved by implementing a smoother increase of the sample size. The design options are evaluated by means of Monte Carlo simulations. Evaluation criteria are univariate performance measures such as the conditional power and sample size as well as a conditional performance score which combines these components. RESULTS: We demonstrate that smoothing corrections can reduce variability in conditional power and sample size as well as they increase the performance with respect to a recently published conditional performance score for medium and large standardized effect sizes. CONCLUSION: Based on the simulation study, we present a tool that is easily implemented to improve sample size recalculation rules. The approach can be combined with existing sample size recalculation rules described in the literature.

Optimal planning of adaptive two-stage designs.

Adaptive designs are playing an increasingly important role in the planning of clinical trials. While there exists various research on the optimal determination of a two-stage design, non-optimal versions still are frequently applied in clinical research. In this article, we strive to motivate the application of optimal adaptive designs and give guidance on how to determine them. It is demonstrated that optimizing a trial design with respect to particular objective criteria can have a substantial benefit over the application of conventional adaptive sample size recalculation rules. Furthermore, we show that in many practical situations, optimal group-sequential designs show an almost negligible performance loss compared to optimal adaptive designs. Finally, we illustrate how optimal designs can be tailored to specific operational requirements by customizing the underlying optimization problem.

Impact of the COVID-19 shutdown on orthopedic trauma numbers and patterns in an academic Level I Trauma Center in Berlin, Germany.

BACKGROUND: The COVID-19 pandemic led to the implementation of drastic shutdown measures worldwide. While quarantine, self-isolation and shutdown laws helped to effectively contain and control the spread of SARS-CoV-2, the impact of COVID-19 shutdowns on trauma care in emergency departments (EDs) remains elusive. METHODS: All ED patient records from the 35-day COVID-19 shutdown (SHUTDOWN) period were retrospectively compared to a calendar-matched control period in 2019 (CTRL) as well as to a pre (PRE)- and post (POST)-shutdown period in an academic Level I Trauma Center in Berlin, Germany. Total patient and orthopedic trauma cases and contacts as well as trauma causes and injury patterns were evaluated during respective periods regarding absolute numbers, incidence rate ratios (IRRs) and risk ratios (RRs). FINDINGS: Daily total patient cases (SHUTDOWN vs. CTRL, 106.94 vs. 167.54) and orthopedic trauma cases (SHUTDOWN vs. CTRL, 30.91 vs. 52.06) decreased during the SHUTDOWN compared to the CTRL period with IRRs of 0.64 and 0.59. While absolute numbers decreased for most trauma causes during the SHUTDOWN period, we observed increased incidence proportions of household injuries and bicycle accidents with RRs of 1.31 and 1.68 respectively. An RR of 2.41 was observed for injuries due to domestic violence. We further recorded increased incidence proportions of acute and regular substance abuse during the SHUTDOWN period with RRs of 1.63 and 3.22, respectively. CONCLUSIONS: While we observed a relevant decrease in total patient cases, relative proportions of specific trauma causes and injury patterns increased during the COVID-19 shutdown in Berlin, Germany. As government programs offered prompt financial aid during the pandemic to individuals and businesses, additional social support may be considered for vulnerable domestic environments.

Correction to: Optimality criteria for futility stopping boundaries for group sequential designs with a continuous endpoint.

An amendment to this paper has been published and can be accessed via the original article.

Optimality criteria for futility stopping boundaries for group sequential designs with a continuous endpoint.

BACKGROUND: In clinical trials with fixed study designs, statistical inference is only made when the trial is completed. In contrast, group sequential designs allow an early stopping of the trial at interim, either for efficacy when the treatment effect is significant or for futility when the treatment effect seems too small to justify a continuation of the trial. Efficacy stopping boundaries based on alpha spending functions have been widely discussed in the statistical literature, and there is also solid work on the choice of adequate futility stopping boundaries. Still, futility boundaries are often chosen with little or completely without theoretical justification, in particular in investigator initiated trails. Some authors contributed to fill this gap. In here, we rely on an idea of Schüler et al. (2017) who discuss optimality criteria for futility boundaries for the special case of trials with (multiple) time-to-event endpoints. Their concept can be adopted to define "optimal" futility boundaries (with respect to given performance indicators) for continuous endpoints. METHODS: We extend Schülers' definition for "optimal" futility boundaries to the most common study situation of a single continuous primary endpoint compared between two groups. First, we introduce the analytic algorithm to derive these futility boundaries. Second, the new concept is applied to a real clinical trial example. Finally, the performance of a study design with an "optimal" futility boundary is compared to designs with arbitrarily chosen futility boundaries. RESULTS: The presented concept of deriving futility boundaries allows to control the probability of wrongly stopping for futility, that means stopping for futility even if the treatment effect is promizing. At the same time, the loss in power is also controlled by this approach. Moreover, "optimal" futility boundaries improve the probability of correctly stopping for futility under the null hypothesis of no difference between two groups. CONCLUSIONS: The choice of futility boundaries should be thoroughly investigated at the planning stage. The sometimes met, arbitrary choice of futility boundaries can lead to a substantial negative impact on performance. Applying futility boundaries with predefined optimization criteria increases efficiency of group sequential designs. Other optimization criteria than proposed in here might be incorporated.

A new conditional performance score for the evaluation of adaptive group sequential designs with sample size recalculation.

In standard clinical trial designs, the required sample size is fixed in the planning stage based on initial parameter assumptions. It is intuitive that the correct choice of the sample size is of major importance for an ethical justification of the trial. The required parameter assumptions should be based on previously published results from the literature. In clinical practice, however, historical data often do not exist or show highly variable results. Adaptive group sequential designs allow a sample size recalculation after a planned unblinded interim analysis in order to adjust the sample size during the ongoing trial. So far, there exist no unique standards to assess the performance of sample size recalculation rules. Single performance criteria commonly reported are given by the power and the average sample size; the variability of the recalculated sample size and the conditional power distribution are usually ignored. Therefore, the need for an adequate performance score combining these relevant performance criteria is evident. To judge the performance of an adaptive design, there exist two possible perspectives, which might also be combined: Either the global performance of the design can be addressed, which averages over all possible interim results, or the conditional performance is addressed, which focuses on the remaining performance conditional on a specific interim result. In this work, we give a compact overview of sample size recalculation rules and performance measures. Moreover, we propose a new conditional performance score and apply it to various standard recalculation rules by means of Monte-Carlo simulations.

A variational approach to optimal two-stage designs.

Recalculating the sample size in adaptive two-stage designs is a well-established method to gain flexibility in a clinical trial. Jennison and Turnbull (2015) proposed an "optimal" adaptive two-stage design based on the inverse normal combination test, which minimizes a mixed criterion of expected sample size under the alternative and conditional power. We demonstrate that the use of a combination test is not necessary to control the type one error rate and use variational techniques to develop a general adaptive design that is globally optimal under predefined optimality criteria. This approach yields to more efficient designs and furthermore allows to investigate the efficiency of the inverse normal method and the relation between local (interim-based) recalculation rules and global (unconditional) optimality of adaptive two-stage designs.

Relevance of peripheral cholinesterase activity on postoperative delirium in adult surgical patients (CESARO): A prospective observational cohort study.

BACKGROUND: The cholinergic system is considered to play a key role in the development of postoperative delirium (POD), which is a common complication after surgery. OBJECTIVES: To determine whether peri-operative acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) activities are associated with the development of POD in in-hospital surgical patients, and raise hypotheses on cholinergic regulatory mechanisms in POD. DESIGN: A prospective multicentre observational study by the Peripheral Cholinesterase-activity on Neurocognitive Dysfunctions in Surgical Patients (CESARO) study group. SETTING: Nine German hospitals. PATIENTS: Patients of at least 18 years of age scheduled for inpatient elective surgery for a variety of surgical procedures. A total of 650 patients (mean age 61.5 years, 52.8% male) were included. METHODS: Clinical variables, and peripheral AChE and BuChE activities, were assessed throughout the peri-operative period using bedside point-of-care measurements (one pre-operative and two postoperative measurements). POD screening was conducted postoperatively for at least 24 h and up to the third postoperative day using a validated screening tool (nursing delirium screening scale). RESULTS: In all, 179 patients (27.5%) developed POD within the early postoperative phase. There was a lower BuChE activity in patients with delirium compared with patients without delirium pre-operatively (Cohen's r = 0.07, P = 0.091), on postoperative day 1 (Cohen's r = 0.12, P = 0.003) and on postoperative day 2 (Cohen's r = 0.12, P = 0.002). In contrast, there was a significantly higher AChE activity in patients with delirium compared with patients without delirium pre-operatively (Cohen's r = 0.10, P = 0.012), on postoperative day 1 (Cohen's r = 0.11, P = 0.004) and on postoperative day 2 (Cohen's r = 0.13, P = 0.002). After adjusting for covariates in multiple logistic regression, a significant association between both BuChE and AChE activities and POD was not found. However, in the multivariable analysis using the Generalized Estimating Equation, cholinesterase activities showed that a decrease of BuChE activity by 100 U L increased the risk of a delirium by approximately 2.1% (95% CI 1.6 to 2.8%) and for each 1 U g of haemoglobin increase in AChE activity, there was a 1.4% (95% CI 0.6 to 2.2%) increased risk of POD. CONCLUSION: Peri-operative peripheral cholinesterase activities may be related to the development of POD, but the clinical implications remain unclear. Further studies, in homogeneous patient groups with a strict protocol for measurement time points, are needed to investigate the relationship between cholinesterase activities and POD. TRIAL REGISTRATION: www.clinicaltrials.gov. Identifier NCT01964274.