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Abstract Background Low back pain is the leading cause of global disability for which exercise therapy is a widely recommended treatment. Research indicates that contextual factors may also influence treatment outcomes in low back pain. Examples include the patient-therapist relationship and other treatment-related circumstances that affect patient expectations. By focusing on the specific treatment effect, clinical trials often ignore the effect of contextual factors, thereby contributing to the so-called efficacy paradox. This means that treatment effects observed in clinical practice are often greater than those reported in clinical trials. This systematic review aims to investigate the proportion of improvement in pain and disability that can be attributed to contextual effects in the outcome of exercise therapy for patients with low back pain. Methods A meta-analysis was conducted. PubMed, Embase, and the Cochrane database were searched for eligible articles reporting randomized controlled trials that compared exercise therapy to placebo interventions. Risk of bias was assessed with the Revised Cochrane Risk of Bias Tool. Outcomes of interest were pain and disability. Meta-analysis was carried out to calculate the proportion attributable to contextual effects for both pain and disability. The body of evidence was assessed using the GRADE methodology. Results Eight studies met the inclusion criteria and were included in the meta-analysis. Five studies were rated as having a moderate risk of bias and two studies had a low risk of bias. Proportion attributable to contextual effects was 0.60 (95% CI 0.40–0.89) for pain and 0.69 (95% CI 0.48–1.00) for disability. Certainty of the evidence as assessed with the GRADE methodology was low. Conclusions A large extent of pain and disability improvement after exercise therapy in low back pain is attributable to contextual effects although this conclusion is based on low certainty evidence.

Background: Inhibitor eradication to restore factor (F)VIII efficacy is the treatment goal for persons with severe hemophilia A (HA) and inhibitors. Immune tolerance induction (ITI) is demanding and successful in about 70% of people. Until now, it has remained difficult to quantify the probability of ITI success or failure, complicating the decision to initiate or not initiate ITI. Estimating the individual chance of ITI success allows clinicians, patients, and their families to support shared decision-making. Objectives: We aimed to identify clinical predictors of ITI success and to develop a clinical prediction model to estimate the chance of successful ITI in persons with severe HA. Methods: This multicenter study included persons with severe HA who received ITI. Clinical data were collected. Successful ITI was defined by a negative inhibitor titer and an adequate response to FVIII concentrates. A multivariable logistic regression model was developed. Model performance and internal validation were performed. Results: Of 206 participants with a median age of 19.8 months (IQR, 12.1-38.8) at ITI start, 148 (71.8%) achieved ITI success. Our clinical prediction model included 4 predictors of ITI success: cumulative number of FVIII exposure days at inhibitor development, peak inhibitor titer, ethnicity, and F8 mutation type. The C statistic was 0.801 (95% CI, 0.70-0.87). Conclusion: In our study, including 206 people with severe HA and inhibitors, we developed a clinical prediction model to estimate the chance of successful ITI. After future external validation, this clinical prediction model may be useful for informing clinicians and families.

Objective: It is difficult for health care providers to diagnose structural spinal osteoarthritis (OA), because current guidelines recommend against imaging in patients with back pain. Therefore, the aim of this study was to develop and internally validate multivariable diagnostic prediction models based on a set of clinical and demographic features to be used for the diagnosis of structural spinal OA on lumbar radiographs in older patients with back pain. Design: Three diagnostic prediction models, for structural spinal OA on lumbar radiographs (i.e. multilevel osteophytes, multilevel disc space narrowing (DSN), and both combined), were developed and internally validated in the ‘Back Complaints in Older Adults’ (BACE) cohort (N ​= ​669). Model performance (i.e. overall performance, discrimination and calibration) and clinical utility (i.e. decision curve analysis) were assessed. Internal validation was performed by bootstrapping. Results: Mean age of the cohort was 66.9 years (±7.6 years) and 59% were female. All three models included age, gender, back pain duration and duration of spinal morning stiffness as predictors. The combined model additionally included restricted lateral flexion and spinal morning stiffness severity, and exhibited the best model performance (optimism adjusted c-statistic 0.661; good calibration with intercept −0.030 and slope of 0.886) and acceptable clinical utility. The other models showed suboptimal discrimination, good calibration and acceptable decision curves. Conclusion: All three models for structural spinal OA displayed lesuboptimal discrimination and need improvement. However, these internally validated models have potential to inform primary care clinicians about a patient with risk of having structural spinal OA on lumbar radiographs. External validation before implementation in clinical care is recommended.

Background and objective: Owing to the greater use of prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) in patients with biochemical recurrence (BCR) of prostate cancer (PCa) after robot-assisted radical prostatectomy (RARP), patient selection for local salvage radiation therapy (sRT) has changed. Our objective was to determine the short-term efficacy of sRT in patients with BCR after RARP, and to develop a novel nomogram predicting BCR-free survival after sRT in a nationwide contemporary cohort of patients who underwent PSMA PET/CT before sRT for BCR of PCa, without evidence of metastatic disease. Methods: All 302 eligible patients undergoing PCa sRT in four reference centers between September 2015 and August 2020 were included. We conducted multivariable logistic regression analysis using a backward elimination procedure to develop a nomogram for predicting biochemical progression of PCa, defined as prostate-specific antigen (PSA) ≥0.2 ng/ml above the post-sRT nadir within 1 yr after sRT. Key findings and limitations: Biochemical progression of disease within 1 yr after sRT was observed for 56/302 (19%) of the study patients. The final predictive model included PSA at sRT initiation, pathological grade group, surgical margin status, PSA doubling time, presence of local recurrence on PSMA PET/CT, and the presence of biochemical persistence (first PSA result ≥0.1 ng/ml) after RARP. The area under the receiver operating characteristic curve for this model was 0.72 (95% confidence interval 0.64–0.79). Using our nomogram, patients with a predicted risk of >20% had a 30.8% chance of developing biochemical progression within 1 yr after sRT. Conclusions: Our novel nomogram may facilitate better patient counseling regarding early oncological outcome after sRT. Patients with high risk of biochemical progression may be candidates for more extensive treatment. Patient summary: We developed a new tool for predicting cancer control outcomes of radiotherapy for patients with recurrence of prostate cancer after surgical removal of their prostate. This tool may help in better counseling of these patients with recurrent cancer regarding their early expected outcome after radiotherapy.

Abstract Background Convolutional neural networks (CNNs), applied to baseline [18F]-FDG PET/CT maximum intensity projections (MIPs), show potential for treatment outcome prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study is to investigate the robustness of CNN predictions to different image reconstruction protocols. Baseline [18F]FDG PET/CT scans were collected from 20 DLBCL patients. EARL1, EARL2 and high-resolution (HR) protocols were applied per scan, generating three images with different image qualities. Image-based transformation was applied by blurring EARL2 and HR images to generate EARL1 compliant images using a Gaussian filter of 5 and 7 mm, respectively. MIPs were generated for each of the reconstructions, before and after image transformation. An in-house developed CNN predicted the probability of tumor progression within 2 years for each MIP. The difference in probabilities per patient was then calculated between both EARL2 and HR with respect to EARL1 (delta probabilities or ΔP). We compared these to the probabilities obtained after aligning the data with ComBat using the difference in median and interquartile range (IQR). Results CNN probabilities were found to be sensitive to different reconstruction protocols (EARL2 ΔP: median = 0.09, interquartile range (IQR) = [0.06, 0.10] and HR ΔP: median = 0.1, IQR = [0.08, 0.16]). Moreover, higher resolution images (EARL2 and HR) led to higher probability values. After image-based and ComBat transformation, an improved agreement of CNN probabilities among reconstructions was found for all patients. This agreement was slightly better after image-based transformation (transformed EARL2 ΔP: median = 0.022, IQR = [0.01, 0.02] and transformed HR ΔP: median = 0.029, IQR = [0.01, 0.03]). Conclusion Our CNN-based outcome predictions are affected by the applied reconstruction protocols, yet in a predictable manner. Image-based harmonization is a suitable approach to harmonize CNN predictions across image reconstruction protocols.

Abstract Background Confounding is a common issue in epidemiological research. Commonly used confounder-adjustment methods include multivariable regression analysis and propensity score methods. Although it is common practice to assess the linearity assumption for the exposure-outcome effect, most researchers do not assess linearity of the relationship between the confounder and the exposure and between the confounder and the outcome before adjusting for the confounder in the analysis. Failing to take the true non-linear functional form of the confounder-exposure and confounder-outcome associations into account may result in an under- or overestimation of the true exposure effect. Therefore, this paper aims to demonstrate the importance of assessing the linearity assumption for confounder-exposure and confounder-outcome associations and the importance of correctly specifying these associations when the linearity assumption is violated. Methods A Monte Carlo simulation study was used to assess and compare the performance of confounder-adjustment methods when the functional form of the confounder-exposure and confounder-outcome associations were misspecified (i.e., linearity was wrongly assumed) and correctly specified (i.e., linearity was rightly assumed) under multiple sample sizes. An empirical data example was used to illustrate that the misspecification of confounder-exposure and confounder-outcome associations leads to bias. Results The simulation study illustrated that the exposure effect estimate will be biased when for propensity score (PS) methods the confounder-exposure association is misspecified. For methods in which the outcome is regressed on the confounder or the PS, the exposure effect estimate will be biased if the confounder-outcome association is misspecified. In the empirical data example, correct specification of the confounder-exposure and confounder-outcome associations resulted in smaller exposure effect estimates. Conclusion When attempting to remove bias by adjusting for confounding, misspecification of the confounder-exposure and confounder-outcome associations might actually introduce bias. It is therefore important that researchers not only assess the linearity of the exposure-outcome effect, but also of the confounder-exposure or confounder-outcome associations depending on the confounder-adjustment method used.

Abstract Objectives We aimed to develop a Service Capacity Index for Substance Use Disorders (SCI‐SUD) that would reflect the capacity of national health systems to provide treatment for alcohol and drug use disorders, in terms of the proportion of available service elements in a given country from a theoretical maximum. Methods Data were collected through the WHO Global Survey on Progress with Sustainable Development Goals (SDG) Health Target 3.5, conducted between December 2019 and July 2020 to produce the SCI‐SUD, based on 378 variables overall. Results The SCI‐SUD was directly derived for 145 countries. We used multiple imputation to produce comparable SCI‐SUD estimates for countries that did not submit data (40 countries) or had very high level of missingness (9 countries). The final SCI‐SUD demonstrates considerable consistency and internal stability and is strongly associated with the macro‐level economic, healthcare‐related and epidemiologic (such as prevalence rates) variables. Conclusion The presented methodology represents a step forward in monitoring the global situation in regard to the development of treatment systems for SU disorders, however, further work is warranted to improve the external validity of the measure (e.g., in‐depth data generation in countries) and ensure its feasibility for regular reporting (e.g., reducing the number of variables).

Abstract Aim Clinical prediction models need to be validated. In this study, we used simulation data to compare various internal and external validation approaches to validate models. Methods Data of 500 patients were simulated using distributions of metabolic tumor volume, standardized uptake value, the maximal distance between the largest lesion and another lesion, WHO performance status and age of 296 diffuse large B cell lymphoma patients. These data were used to predict progression after 2 years based on an existing logistic regression model. Using the simulated data, we applied cross-validation, bootstrapping and holdout (n = 100). We simulated new external datasets (n = 100, n = 200, n = 500) and simulated stage-specific external datasets (1), varied the cut-off for high-risk patients (2) and the false positive and false negative rates (3) and simulated a dataset with EARL2 characteristics (4). All internal and external simulations were repeated 100 times. Model performance was expressed as the cross-validated area under the curve (CV-AUC ± SD) and calibration slope. Results The cross-validation (0.71 ± 0.06) and holdout (0.70 ± 0.07) resulted in comparable model performances, but the model had a higher uncertainty using a holdout set. Bootstrapping resulted in a CV-AUC of 0.67 ± 0.02. The calibration slope was comparable for these internal validation approaches. Increasing the size of the test set resulted in more precise CV-AUC estimates and smaller SD for the calibration slope. For test datasets with different stages, the CV-AUC increased as Ann Arbor stages increased. As expected, changing the cut-off for high risk and false positive- and negative rates influenced the model performance, which is clearly shown by the low calibration slope. The EARL2 dataset resulted in similar model performance and precision, but calibration slope indicated overfitting. Conclusion In case of small datasets, it is not advisable to use a holdout or a very small external dataset with similar characteristics. A single small testing dataset suffers from a large uncertainty. Therefore, repeated CV using the full training dataset is preferred instead. Our simulations also demonstrated that it is important to consider the impact of differences in patient population between training and test data, which may ask for adjustment or stratification of relevant variables.

Radiotherapy (RT) is a cornerstone treatment strategy for brain tumours. Besides cytotoxicity, RT can cause disruption of the blood–brain barrier (BBB), resulting in an increased permeability into the surrounding brain parenchyma. Although this effect is generally acknowledged, it remains unclear how and to what extent different radiation schemes affect BBB integrity. The aim of this systematic review and meta-analysis is to investigate the effect of photon RT regimens on BBB permeability, including its reversibility, in clinical and preclinical studies. We systematically reviewed relevant clinical and preclinical literature in PubMed, Embase, and Cochrane search engines. A total of 69 included studies (20 clinical, 49 preclinical) were qualitatively and quantitatively analysed by meta-analysis and evaluated on key determinants of RT-induced BBB permeability in different disease types and RT protocols. Qualitative data synthesis showed that 35% of the included clinical studies reported BBB disruption following RT, whereas 30% were inconclusive. Interestingly, no compelling differences were observed between studies with different calculated biological effective doses based on the fractionation schemes and cumulative doses; however, increased BBB disruption was noted during patient follow-up after treatment. Qualitative analysis of preclinical studies showed RT BBB disruption in 78% of the included studies, which was significantly confirmed by meta-analysis (p

Abstract Background Depression and anxiety are common in visually impaired and blind adults, but often remain untreated in those who receive support from low vision service (LVS) organizations. This study aims to determine factors associated with discussing mental health by LVS workers. Methods A self-administered cross-sectional survey in one hundred LVS workers was performed. Data on current practice, symptom attribution, and determinants of the Integrated Change Model (i.e. predisposing and environmental factors, awareness, attitude, self-efficacy, social influence, confidence and barriers) were investigated. Multivariable logistic regression analysis was performed to determine predictors of discussing mental health problems in this population. Subsequently, internal validation was conducted using a bootstrapping method. Results Around 80% of the participants often discussed mental health with clients. Five factors were found to predict discussion of mental health: female gender (OR = 4.51; 95% confidence interval (CI) 0.98 to 21.61), higher education (OR = 3.39; CI 1.19 to 9.66), intention to discuss mental health problems (OR = 3.49; CI 1.20 to 10.15), higher self-efficacy (OR = 1.11; CI 1.02 to 1.20), and higher perceived social influence (OR = 1.15; CI 1.05 to 1.27). Good discrimination after internal validation was reflected by the area under the curve (0.850). Conclusions Previous studies indicate clients want healthcare providers to initiate discussions about mental health. However, still 20% of LVS workers do not discuss suspected depression or anxiety. In order to improve this, LVS organizations could address mental health as part of their care and provide training to ensure intention to discuss mental health problems, improve self-efficacy and create a supportive environment between colleagues.

Abstract Background Accurate identification of older persons at risk of unplanned hospital visits can facilitate preventive interventions. Several risk scores have been developed to identify older adults at risk of unplanned hospital visits. It is unclear whether risk scores developed in one country, perform as well in another. This study validates seven risk scores to predict unplanned hospital admissions and emergency department (ED) visits in older home care recipients from six countries. Methods We used the IBenC sample (n = 2446), a cohort of older home care recipients from six countries (Belgium, Finland, Germany, Iceland, Italy and The Netherlands) to validate four specific risk scores (DIVERT, CARS, EARLI and previous acute admissions) and three frailty indicators (CHESS, Fried Frailty Criteria and Frailty Index). Outcome measures were unplanned hospital admissions, ED visits or any unplanned hospital visits after 6 months. Missing data were handled by multiple imputation. Performance was determined by assessing calibration and discrimination (area under receiver operating characteristic curve (AUC)). Results Risk score performance varied across countries. In Iceland, for any unplanned hospital visits DIVERT and CARS reached a fair predictive value (AUC 0.74 [0.68–0.80] and AUC 0.74 [0.67–0.80]), respectively). In Finland, DIVERT had fair performance predicting ED visits (AUC 0.72 [0.67–0.77]) and any unplanned hospital visits (AUC 0.73 [0.67–0.77]). In other countries, AUCs did not exceed 0.70. Conclusions Geographical validation of risk scores predicting unplanned hospital visits in home care recipients showed substantial variations of poor to fair performance across countries. Unplanned hospital visits seem considerably dependent on healthcare context. Therefore, risk scores should be validated regionally before applied to practice. Future studies should focus on identification of more discriminative predictors in order to develop more accurate risk scores.

Abstract Background Mediation analysis methodology underwent many advancements throughout the years, with the most recent and important advancement being the development of causal mediation analysis based on the counterfactual framework. However, a previous review showed that for experimental studies the uptake of causal mediation analysis remains low. The aim of this paper is to review the methodological characteristics of mediation analyses performed in observational epidemiologic studies published between 2015 and 2019 and to provide recommendations for the application of mediation analysis in future studies. Methods We searched the MEDLINE and EMBASE databases for observational epidemiologic studies published between 2015 and 2019 in which mediation analysis was applied as one of the primary analysis methods. Information was extracted on the characteristics of the mediation model and the applied mediation analysis method. Results We included 174 studies, most of which applied traditional mediation analysis methods (n = 123, 70.7%). Causal mediation analysis was not often used to analyze more complicated mediation models, such as multiple mediator models. Most studies adjusted their analyses for measured confounders, but did not perform sensitivity analyses for unmeasured confounders and did not assess the presence of an exposure-mediator interaction. Conclusions To ensure a causal interpretation of the effect estimates in the mediation model, we recommend that researchers use causal mediation analysis and assess the plausibility of the causal assumptions. The uptake of causal mediation analysis can be enhanced through tutorial papers that demonstrate the application of causal mediation analysis, and through the development of software packages that facilitate the causal mediation analysis of relatively complicated mediation models.

Abstract Background In The Netherlands, low back pain patients can consult physicians specialized in musculoskeletal (MSK) medicine. Previous studies have reported on the characteristics of patients consulting MSK physicians, and the treatment options used. There are no studies yet reporting on the course of Low Back Pain (LBP) after treatment by musculoskeletal (MSK) physicians in The Netherlands. Methods In an observational cohort study MSK physicians recorded data about all low back pain patients presenting for a first consultation. At baseline they recorded age, gender, type and duration of the main complaint, and concomitant complaints. At the end of treatment they recorded the type of treatment and the number of treatment sessions. Patients were recruited to answer questionnaires at baseline, and at 6-weekly intervals during a follow-up period of six months. Patient questionnaires included information about previous medical consumption, together with PROMs measuring the level of pain and functional status. Latent Class Growth Analysis (LCGA) was used to classify patients into different groups according to their pain trajectories. Baseline variables were evaluated as predictors of a favourable trajectory using logistic regression analyses, and treatment variables were evaluated as possible confounders. Results A total of 1377 patients were recruited, of whom 1117 patients (81%) answered at least one follow-up measurement. LCGA identified three groups of patients with distinct pain trajectories. A first group (N = 226) with high pain levels showed no improvement, a second group (N = 578) with high pain levels showed strong improvement, and a third group (N = 313) with mild pain levels showed moderate improvement. The two groups of patients presenting with high baseline pain scores were compared, and a multivariable model was constructed with possible predictors of a favourable course. Male gender, previous specialist visit, previous pain clinic visit, having work, a shorter duration of the current episode, and a longer time since the complaints first started were predictors of a favourable course. The multivariable model showed a moderate area under the curve (0.68) and a low explained variance (0.09). Conclusions In low back pain patients treated by musculoskeletal physicians in The Netherlands three different pain trajectories were identified. Baseline variables were of limited value in predicting a favourable course.

Abstract Background Confounding bias is a common concern in epidemiological research. Its presence is often determined by comparing exposure effects between univariable- and multivariable regression models, using an arbitrary threshold of a 10% difference to indicate confounding bias. However, many clinical researchers are not aware that the use of this change-in-estimate criterion may lead to wrong conclusions when applied to logistic regression coefficients. This is due to a statistical phenomenon called noncollapsibility, which manifests itself in logistic regression models. This paper aims to clarify the role of noncollapsibility in logistic regression and to provide guidance in determining the presence of confounding bias. Methods A Monte Carlo simulation study was designed to uncover patterns of confounding bias and noncollapsibility effects in logistic regression. An empirical data example was used to illustrate the inability of the change-in-estimate criterion to distinguish confounding bias from noncollapsibility effects. Results The simulation study showed that, depending on the sign and magnitude of the confounding bias and the noncollapsibility effect, the difference between the effect estimates from univariable- and multivariable regression models may underestimate or overestimate the magnitude of the confounding bias. Because of the noncollapsibility effect, multivariable regression analysis and inverse probability weighting provided different but valid estimates of the confounder-adjusted exposure effect. In our data example, confounding bias was underestimated by the change in estimate due to the presence of a noncollapsibility effect. Conclusion In logistic regression, the difference between the univariable- and multivariable effect estimate might not only reflect confounding bias but also a noncollapsibility effect. Ideally, the set of confounders is determined at the study design phase and based on subject matter knowledge. To quantify confounding bias, one could compare the unadjusted exposure effect estimate and the estimate from an inverse probability weighted model.

The aim of this study was to externally validate a nomogram for side-specific extraprostatic extension (EPE) of prostate cancer (PCa) at robot-assisted radical prostatectomy (RARP). A prospectively maintained cohort of 1170 consecutive patients with PCa who underwent RARP at two high-volume RARP centres between 2018 and 2021 was retrospectively evaluated. Biopsies and magnetic resonance imaging (MRI) scans were centrally reviewed. The side-specific probability of EPE was calculated for each prostate side using prostate-specific antigen density, ipsilateral highest biopsy Gleason score, and ipsilateral MRI tumour stage. Model discrimination and calibration were analysed using the area under the receiver operating characteristic curve (AUC), calibration in the large, and calibration curves. The rate of side-specific EPE was 30% among 2254 prostate sides; the mean predicted rate was also 30%. The discriminatory value of the model was good, with an AUC of 80.4% (interquartile range 78.4–82.3%). The predicted probabilities matched the observed probabilities well (intercept −0.02, slope 1.053). There was slight underestimation of the observed probabilities from 70% upwards. In conclusion, an easy-to-use nomogram for side-specific EPE at RARP was externally validated and can be applied to virtually all PCa patients. Patient summary: A prediction model used to decide whether to spare the neurovascular bundles during removal of the prostate can be applied to virtually all prostate cancer patients.

Abstract Background Early identification of older cardiac patients at high risk of readmission or mortality facilitates targeted deployment of preventive interventions. In the Netherlands, the frailty tool of the Dutch Safety Management System (DSMS-tool) consists of (the risk of) delirium, falling, functional impairment, and malnutrition and is currently used in all older hospitalised patients. However, its predictive performance in older cardiac patients is unknown. Aim To estimate the performance of the DSMS-tool alone and combined with other predictors in predicting hospital readmission or mortality within 6 months in acutely hospitalised older cardiac patients. Methods An individual patient data meta-analysis was performed on 529 acutely hospitalised cardiac patients ≥70 years from four prospective cohorts. Missing values for predictor and outcome variables were multiply imputed. We explored discrimination and calibration of: (1) the DSMS-tool alone; (2) the four components of the DSMS-tool and adding easily obtainable clinical predictors; (3) the four components of the DSMS-tool and more difficult to obtain predictors. Predictors in model 2 and 3 were selected using backward selection using a threshold of p = 0.157. We used shrunk c-statistics, calibration plots, regression slopes and Hosmer-Lemeshow p-values (PHL) to describe predictive performance in terms of discrimination and calibration. Results The population mean age was 82 years, 52% were males and 51% were admitted for heart failure. DSMS-tool was positive in 45% for delirium, 41% for falling, 37% for functional impairments and 29% for malnutrition. The incidence of hospital readmission or mortality gradually increased from 37 to 60% with increasing DSMS scores. Overall, the DSMS-tool discriminated limited (c-statistic 0.61, 95% 0.56–0.66). The final model included the DSMS-tool, diagnosis at admission and Charlson Comorbidity Index and had a c-statistic of 0.69 (95% 0.63–0.73; PHL was 0.658). Discussion The DSMS-tool alone has limited capacity to accurately estimate the risk of readmission or mortality in hospitalised older cardiac patients. Adding disease-specific risk factor information to the DSMS-tool resulted in a moderately performing model. To optimise the early identification of older hospitalised cardiac patients at high risk, the combination of geriatric and disease-specific predictors should be further explored.

Abstract Background Lower protein intake in older adults is associated with loss of muscle mass and strength. The present study aimed to provide a pooled estimate of the overall prevalence of protein intake below recommended (according to different cut‐off values) among community‐dwelling older adults, both within the general older population and within specific subgroups. Methods As part of the PRevention Of Malnutrition In Senior Subjects in the EU (PROMISS) project, a meta‐analysis was performed using data from four cohorts (from the Netherlands, UK, Canada, and USA) and four national surveys [from the Netherlands, Finland (two), and Italy]. Within those studies, data on protein and energy intake of community‐dwelling men and women aged ≥55 years were obtained by either a food frequency questionnaire, 24 h recalls administered on 2 or 3 days, or food diaries administered on 3 days. Protein intake below recommended was based on the recommended dietary allowance of 0.8 g/kg body weight (BW)/d, by using adjusted BW (aBW) instead of actual BW. Cut‐off values of 1.0 and 1.2 were applied in additional analyses. Prevalences were also examined for subgroups according to sex, age, body mass index (BMI), education level, appetite, living status, and recent weight loss. Results The study sample comprised 8107 older persons. Mean ± standard deviation protein intake ranged from 64.3 ± 22.3 (UK) to 80.6 ± 23.7 g/d [the Netherlands (cohort)] or from 0.94 ± 0.38 (USA) to 1.17z ± 0.30 g/kg aBW/d (Italy) when related to BW. The overall pooled prevalence of protein intake below recommended was 21.5% (95% confidence interval: 14.0–30.1), 46.7% (38.3–55.3), and 70.8% (65.1–76.3) using the 0.8, 1.0, and 1.2 cut‐off value, respectively. A higher prevalence was observed among women, individuals with higher BMI, and individuals with poor appetite. The prevalence differed only marginally by age, education level, living status, and recent weight loss. Conclusions In community‐dwelling older adults, the prevalence of protein intake below the current recommendation of 0.8 g/kg aBW/d is substantial (14–30%) and increases to 65–76% according to a cut‐off value of 1.2 g/kg aBW/d. To what extent the protein intakes are below the requirements of these older people warrants further investigation.

Abstract Background Radiomics is aimed at image-based tumor phenotyping, enabling application within clinical-decision-support-systems to improve diagnostic accuracy and allow for personalized treatment. The purpose was to identify predictive 18-fluor-fluoro-2-deoxyglucose (18F-FDG) positron-emission tomography (PET) radiomic features to predict recurrence, distant metastasis, and overall survival in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy. Methods Between 2012 and 2018, 103 retrospectively (training cohort) and 71 consecutively included patients (validation cohort) underwent 18F-FDG-PET/CT imaging. The 434 extracted radiomic features were subjected, after redundancy filtering, to a projection resulting in outcome-independent meta-features (factors). Correlations between clinical, first-order 18F-FDG-PET parameters (e.g., SUVmean), and factors were assessed. Factors were combined with 18F-FDG-PET and clinical parameters in a multivariable survival regression and validated. A clinically applicable risk-stratification was constructed for patients’ outcome. Results Based on 124 retained radiomic features from 103 patients, 8 factors were constructed. Recurrence prediction was significantly most accurate by combining HPV-status, SUVmean, SUVpeak, factor 3 (histogram gradient and long-run-low-grey-level-emphasis), factor 4 (volume-difference, coarseness, and grey-level-non-uniformity), and factor 6 (histogram variation coefficient) (CI = 0.645). Distant metastasis prediction was most accurate assessing metabolic-active tumor volume (MATV)(CI = 0.627). Overall survival prediction was most accurate using HPV-status, SUVmean, SUVmax, factor 1 (least-axis-length, non-uniformity, high-dependence-of-high grey-levels), and factor 5 (aspherity, major-axis-length, inversed-compactness and, inversed-flatness) (CI = 0.764). Conclusions Combining HPV-status, first-order 18F-FDG-PET parameters, and complementary radiomic factors was most accurate for time-to-event prediction. Predictive phenotype-specific tumor characteristics and interactions might be captured and retained using radiomic factors, which allows for personalized risk stratification and optimizing personalized cancer care. Trial registration Trial NL3946 (NTR4111), local ethics commission reference: Prediction 2013.191 and 2016.498. Registered 7 August 2013, https://www.trialregister.nl/trial/3946

Abstract Background Societal expenditures on work-disability benefits is high in most Western countries. As a precursor of long-term work restrictions, long-term sickness absence (LTSA) is under continuous attention of policy makers. Different healthcare professionals can play a role in identification of persons at risk of LTSA but are not well trained. A risk prediction model can support risk stratification to initiate preventative interventions. Unfortunately, current models lack generalizability or do not include a comprehensive set of potential predictors for LTSA. This study is set out to develop and validate a multivariable risk prediction model for LTSA in the coming year in a working population aged 45–64 years. Methods Data from 11,221 working persons included in the prospective Study on Transitions in Employment, Ability and Motivation (STREAM) conducted in the Netherlands were used to develop a multivariable risk prediction model for LTSA lasting ≥28 accumulated working days in the coming year. Missing data were imputed using multiple imputation. A full statistical model including 27 pre-selected predictors was reduced to a practical model using backward stepwise elimination in a logistic regression analysis across all imputed datasets. Predictive performance of the final model was evaluated using the Area Under the Curve (AUC), calibration plots and the Hosmer-Lemeshow (H&L) test. External validation was performed in a second cohort of 5604 newly recruited working persons. Results Eleven variables in the final model predicted LTSA: older age, female gender, lower level of education, poor self-rated physical health, low weekly physical activity, high self-rated physical job load, knowledge and skills not matching the job, high number of major life events in the previous year, poor self-rated work ability, high number of sickness absence days in the previous year and being self-employed. The model showed good discrimination (AUC 0.76 (interquartile range 0.75–0.76)) and good calibration in the external validation cohort (H&L test: p = 0.41). Conclusions This multivariable risk prediction model distinguishes well between older workers with high- and low-risk for LTSA in the coming year. Being easy to administer, it can support healthcare professionals in determining which persons should be targeted for tailored preventative interventions.

Abstract Background Identifying those people at increased risk of early functional decline in activities of daily living (ADL) is essential for initiating preventive interventions. The aim of this study is to develop and validate a clinical prediction model for onset of functional decline in ADL in three years of follow-up in older people of 65–75 years old. Methods Four population-based cohort studies were pooled for the analysis: ActiFE-ULM (Germany), ELSA (United Kingdom), InCHIANTI (Italy), LASA (Netherlands). Included participants were 65–75 years old at baseline and reported no limitations in functional ability in ADL at baseline. Functional decline was assessed with two items on basic ADL and three items on instrumental ADL. Participants who reported at least some limitations at three-year follow-up on any of the five items were classified as experiencing functional decline. Multiple logistic regression analysis was used to develop a prediction model, with subsequent bootstrapping for optimism-correction. We applied internal-external cross-validation by alternating the data from the four cohort studies to assess the discrimination and calibration across the cohorts. Results Two thousand five hundred sixty community-dwelling people were included in the analyses (mean age 69.7 ± 3.0 years old, 47.4% female) of whom 572 (22.3%) reported functional decline at three-year follow-up. The final prediction model included 10 out of 22 predictors: age, handgrip strength, gait speed, five-repeated chair stands time (non-linear association), body mass index, cardiovascular disease, diabetes, chronic obstructive pulmonary disease, arthritis, and depressive symptoms. The optimism-corrected model showed good discrimination with a C statistic of 0.72. The calibration intercept was 0.06 and the calibration slope was 1.05. Internal-external cross-validation showed consistent performance of the model across the four cohorts. Conclusions Based on pooled cohort data analyses we were able to show that the onset of functional decline in ADL in three years in older people aged 65–75 years can be predicted by specific physical performance measures, age, body mass index, presence of depressive symptoms, and chronic conditions. The prediction model showed good discrimination and calibration, which remained stable across the four cohorts, supporting external validity of our findings.

Abstract Background Foot orthoses (FOs) are prescribed as an important conservative treatment option in patients with foot problems related to rheumatoid arthritis. However, a broad variation in FOs is used, both in clinical practice and in research. To date, there is no overview on the outcomes of the treatment with different kinds of FOs in patients with rheumatoid arthritis and a specific foot problem. The objectives of the present study were to summarize the comparative effectiveness of FOs in the treatment of various foot problems in patients with rheumatoid arthritis, on the primary outcomes foot function and foot pain, and the secondary outcomes physical functioning, health related quality of life, compliance, adverse events, the costs of FOs and patient satisfaction. Methods Studies comparing different kinds of FOs, with a presumed therapeutic effect, in the treatment of foot problems related to rheumatoid arthritis were included. A literature search was conducted in The Cochrane Central Registry for Controlled Trials (CENTRAL), PubMed, EMBASE and PEDro up to May 18th, 2018. Data was meta-analyzed, when this was not possible qualitative data analysis was performed. Results Ten studies were identified, with a total number of 235 patients. These studies made a comparison between different materials used (soft versus semi-rigid), types of FOs (custom-made versus ready-made; total-contact versus non-total contact), or modifications applied (metatarsal bars versus domes). Also, different techniques to construct custom-made FOs were compared (standard custom-molding techniques versus more sophisticated techniques). A medium effect for (immediate) reduction of forefoot plantar pressure was found in favor of treatment with soft FOs compared to semi-rigid FOs (SMD 0.60, 95% CI 0.07–1.14; P = 0.03; 28 participants). Other comparisons between FOs resulted in non-significant effects or inconclusive evidence for one kind of FOs over the other. Conclusions Foot orthoses made of soft materials may lead to more (immediate) forefoot plantar pressure reduction compared to foot orthoses constructed of semi-rigid materials. Definitive high quality RCTs, with adequate sample sizes and long-term follow-up, are needed to investigate the comparative (cost-) effectiveness of different kinds of foot orthoses for the treatment of foot problems related to rheumatoid arthritis.

Background: One in four patients with primary Clostridioides difficile infection (CDI) develops recurrent CDI (rCDI). With every recurrence, the chance of a subsequent CDI episode increases. Early identification of patients at risk for rCDI might help doctors to guide treatment. The aim of this study was to externally validate published clinical prediction tools for rCDI. Methods: The validation cohort consisted of 129 patients, diagnosed with CDI between 2018 and 2020. rCDI risk scores were calculated for each individual patient in the validation cohort using the scoring tools described in the derivation studies. Per score value, we compared the average predicted risk of rCDI with the observed number of rCDI cases. Discrimination was assessed by calculating the area under the receiver operating characteristic curve (AUC). Results: Two prediction tools were selected for validation (Cobo 2018 and Larrainzar-Coghen 2016). The two derivation studies used different definitions for rCDI. Using Cobo’s definition, rCDI occurred in 34 patients (26%) of the validation cohort: using the definition of Larrainzar-Coghen, we observed 19 recurrences (15%). The performance of both prediction tools was poor when applied to our validation cohort. The estimated AUC was 0.43 [95% confidence interval (CI); 0.32–0.54] for Cobo’s tool and 0.42 (95% CI; 0.28–0.56) for Larrainzar-Coghen’s tool. Conclusion: Performance of both prediction tools was disappointing in the external validation cohort. Currently identified clinical risk factors may not be sufficient for accurate prediction of rCDI.

Objectives: How to perform an intention to treat (ITT) analysis when a patient has a baseline value but no follow-up measurements is problematic. The purpose of this study was to compare different methods that deal with this problem, i.e. no imputation (standard and alternative mixed model analysis), single imputation (i.e. baseline value carried forward), and multiple imputation (selective and non-selective). Study design and setting: We used a simulation study with different scenarios regarding 1) the association between missingness and the baseline value, 2) whether the patients did or did not receive the treatment, and 3) the percentage of missing data, and two real life data sets. Results: Bias and coverage were comparable between the two mixed model analyses and multiple imputation in most situations including the real life data examples. Only in the situation when the patients in the treatment group were simulated not to have received the treatment, selective imputation using this information outperformed all other methods. Conclusions: In most situations a standard mixed model analysis without imputation is appropriate as ITT analysis. However, when patients with missing follow-up data allocated to the treatment group did not received treatment, it is advised to use selective imputation, using this information, although the results should be interpreted with caution.

Abstract Background Logistic regression is often used for mediation analysis with a dichotomous outcome. However, previous studies showed that the indirect effect and proportion mediated are often affected by a change of scales in logistic regression models. To circumvent this, standardization has been proposed. The aim of this study was to show the relative performance of the unstandardized and standardized estimates of the indirect effect and proportion mediated based on multiple regression, structural equation modeling, and the potential outcomes framework for mediation models with a dichotomous outcome. Methods We compared the performance of the effect estimates yielded by the three methods using a simulation study and two real-life data examples from an observational cohort study (n = 360). Results Lowest bias and highest efficiency were observed for the estimates from the potential outcomes framework and for the crude indirect effect ab and the proportion mediated ab/(ab + c’) based on multiple regression and SEM. Conclusions We advise the use of either the potential outcomes framework estimates or the ab estimate of the indirect effect and the ab/(ab + c’) estimate of the proportion mediated based on multiple regression and SEM when mediation analysis is based on logistic regression. Standardization of the coefficients prior to estimating the indirect effect and the proportion mediated may not increase the performance of these estimates.

Abstract Background In literature, not much emphasis has been placed on methods for analyzing repeatedly measured independent variables, even less so for the use in prediction modeling specifically. However, repeated measurements could especially be interesting for the construction of prediction models. Therefore, our objective was to evaluate different methods to model a repeatedly measured independent variable and a long-term fixed outcome variable into a prediction model. Methods Six methods to handle a repeatedly measured predictor were applied to develop prediction models. Methods were evaluated with respect to the models’ predictive quality (explained variance R 2 and the area under the curve (AUC)) and their properties were discussed. The models included overweight and BMI-standard deviation score (BMI-SDS) at age 10 years as outcome and seven BMI-SDS measurements between 0 and 5.5 years as longitudinal predictor. Methods for comparison encompassed developing models including: all measurements; a single (here: the last) measurement; a mean or maximum value of all measurements; changes between subsequent measurements; conditional measurements; and growth curve parameters. Results All methods, except for using the maximum or mean, resulted in prediction models for overweight of similar predictive quality, with adjusted Nagelkerke R 2 ranging between 0.230 and 0.244 and AUC ranging between 0.799 and 0.807. Continuous BMI-SDS prediction showed similar results. Conclusions The choice of method depends on hypothesized predictor-outcome associations, available data, and requirements of the prediction model. Overall, the growth curve method seems to be the most flexible method capable of incorporating longitudinal predictor information without loss in predictive quality.

Background/aims: Statistical mediation analysis is an often used method in trials, to unravel the pathways underlying the effect of an intervention on a particular outcome variable. Throughout the years, several methods have been proposed, such as ordinary least square (OLS) regression, structural equation modeling (SEM), and the potential outcomes framework. Most applied researchers do not know that these methods are mathematically equivalent when applied to mediation models with a continuous mediator and outcome variable. Therefore, the aim of this paper was to demonstrate the similarities between OLS regression, SEM, and the potential outcomes framework in three mediation models: 1) a crude model, 2) a confounder-adjusted model, and 3) a model with an interaction term for exposure-mediator interaction. Methods: Secondary data analysis of a randomized controlled trial that included 546 schoolchildren. In our data example, the mediator and outcome variable were both continuous. We compared the estimates of the total, direct and indirect effects, proportion mediated, and 95% confidence intervals (CIs) for the indirect effect across OLS regression, SEM, and the potential outcomes framework. Results: OLS regression, SEM, and the potential outcomes framework yielded the same effect estimates in the crude mediation model, the confounder-adjusted mediation model, and the mediation model with an interaction term for exposure-mediator interaction. Conclusions: Since OLS regression, SEM, and the potential outcomes framework yield the same results in three mediation models with a continuous mediator and outcome variable, researchers can continue using the method that is most convenient to them. Keywords: Mediation analysis, Indirect effect, Ordinary least square regression, Structural equation modeling, Potential outcomes framework, Cross-sectional data

Abstract Background Multiple imputation is a recommended method to handle missing data. For significance testing after multiple imputation, Rubin’s Rules (RR) are easily applied to pool parameter estimates. In a logistic regression model, to consider whether a categorical covariate with more than two levels significantly contributes to the model, different methods are available. For example pooling chi-square tests with multiple degrees of freedom, pooling likelihood ratio test statistics, and pooling based on the covariance matrix of the regression model. These methods are more complex than RR and are not available in all mainstream statistical software packages. In addition, they do not always obtain optimal power levels. We argue that the median of the p-values from the overall significance tests from the analyses on the imputed datasets can be used as an alternative pooling rule for categorical variables. The aim of the current study is to compare different methods to test a categorical variable for significance after multiple imputation on applicability and power. Methods In a large simulation study, we demonstrated the control of the type I error and power levels of different pooling methods for categorical variables. Results This simulation study showed that for non-significant categorical covariates the type I error is controlled and the statistical power of the median pooling rule was at least equal to current multiple parameter tests. An empirical data example showed similar results. Conclusions It can therefore be concluded that using the median of the p-values from the imputed data analyses is an attractive and easy to use alternative method for significance testing of categorical variables.

Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses.Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods.Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models.Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.

Background: Once recurrent or persistent locoregional tumour after radical chemoradiotherapy (CRT) for non-small cell lung cancer (NSCLC) is identified, few curative-intent treatment options are available. Selected patients might benefit from surgical salvage. We performed a systematic review of the available literature for this emerging treatment option. Methods: A systematic literature search was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. Publications about persistent or (locoregional) recurrent disease after radical/definitive CRT for locally advanced non-small cell lung cancer were identified. Results: Eight full papers were found, representing 158 patients. All were retrospective series and data were heterogeneous: definition and indication for salvage surgery varied and the median time from radiotherapy to surgery was 4.1–33 months. Complete resection (R0) was achieved in 85–100%, with vital tumour in 61–100%. A large number of pneumonectomies were performed, and additional structures were often resected. Where reported, 90-day mortality was 0–11.4%. Reported survival metrics varied but included median overall survival 9–46 months and 5-year survival 20–75%. Conclusion: There are limited, low-level, heterogeneous data in support of salvage surgery after radical CRT. Based on this, perioperative mortality appears acceptable and long-term survival is possible in (highly) selected patients. In suitable patients (fit, no distant metastases, tumour appears completely resectable and preferably with confirmed viable tumour), this treatment option should be discussed in an experienced multidisciplinary lung cancer team.

Introduction: Stereotactic body (or ablative) radiotherapy (SBRT/SABR) is now a guideline-recommended treatment for medically inoperable patients with peripherally-located, stage I non-small cell lung cancer (NSCLC), and for medically operable patients who decline surgery. The 5-year local failure rate after SBRT is about 10% and in highly selected patients, surgery has been used as a salvage therapy. We performed a systematic review to address the feasibility, safety, and outcome of salvage surgery for locally recurrent early stage NSCLC after SBRT. Methods: A systematic literature search was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. PubMed, Embase and Cochrane databases were searched and two authors independently assessed the articles. A total of seven eligible articles were identified. Results: All seven articles were retrospective case series, representing a total of 47 patients. Surgery was completed in all patients. Where reported in sufficient detail, morbidity (four studies) was between 29 and 50% (series of two patients) and 90-day mortality (six studies) was between 0% (four studies) and 11% ( n = 1, disease progression). Median ( n = 5)/mean ( n = 1) reported or calculated follow ups were 7–54.5/17.3 months. Median overall survival was reported in three studies and ranged between 13.6–82.7 months. Crude survival in three others was 2–35 months. Conclusion: Limited, low-level evidence prevents firm conclusions, but based on the existing data, salvage surgery after local recurrence of NSCLC following SBRT appears technically feasible, with acceptable morbidity and mortality in appropriately selected and counselled patients who are fit enough and who accept the risks (level of evidence 4, strength of recommendation C).

The aim of this study to create a comprehensive overview of factors that may influence motivation of ethnic minority students from their own perspective. A systematic review was conducted in PubMed, ERIC, and PsycINFO to find studies in which the motivation of ethnic minority students was studied. The articles reviewed were qualitatively synthesized by means of meta-ethnography, and were subjected to a quantitative meta-analysis where appropriate. Forty-five articles were included. Several factors were found to have either a positive or a negative influence on academic motivation, which can be classified into individual, family-related, school-related, and social factors. These factors should be taken into account when developing interventions aimed at enhancing motivation, which is expected to improve. However, evidence for the influence of most identified factors is weak, given that almost every factor was investigated in a single study only. Based on the outcomes of the current overview an integrative model, that provides a structure of the identified factors in relation to motivation which can be used for interventions, cannot be generated; thus, further research is needed.

This study aimed to determine the predictors of entering a hearing aid evaluation period (HAEP) using a prospective design drawing on the health belief model and the transtheoretical model. In total, 377 older persons who presented with hearing problems to an Ear, Nose, and Throat specialist ( n = 110) or a hearing aid dispenser ( n = 267) filled in a baseline questionnaire. After 4 months, it was determined via a telephone interview whether or not participants had decided to enter a HAEP. Multivariable logistic regression analyses were applied to determine which baseline variables predicted HAEP status. A priori, candidate predictors were divided into ‘likely’ and ‘novel’ predictors based on the literature. The following variables turned out to be significant predictors: more expected hearing aid benefits, greater social pressure, and greater self-reported hearing disability. In addition, greater hearing loss severity and stigma were predictors in women but not in men. Of note, the predictive effect of self-reported hearing disability was modified by readiness such that with higher readiness, the positive predictive effect became stronger. None of the ‘novel’ predictors added significant predictive value. The results support the notion that predictors of hearing aid uptake are also predictive of entering a HAEP. This study shows that some of these predictors appear to be gender specific or are dependent on a person’s readiness for change. After assuring the external validity of the predictors, an important next step would be to develop prediction rules for use in clinical practice, so that older persons’ hearing help-seeking journey can be facilitated.