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Background: Unplanned admissions to hospital represent a hazardous event for older people. Timely identification of high-risk individuals using a prediction tool may facilitate preventive interventions., Aim: To develop and validate an easy-to-use prediction model for unplanned admissions to hospital in community-dwelling older adults using readily available data to allow rapid bedside assessment by GPs., Design and Setting: This was a retrospective study using the general practice electronic health records of 243 324 community-dwelling adults aged ≥65 years linked with national administrative data to predict unplanned admissions to hospital within 6 months., Method: The dataset was geographically split into a development ( n = 142 791/243 324, 58.7%) and validation ( n = 100 533/243 324, 41.3%) sample to predict unplanned admissions to hospital within 6 months. The performance of three different models was evaluated with increasingly smaller selections of candidate predictors (optimal, readily available, and easy-to-use models). Logistic regression was used with backward selection for model development. The models were validated internally and externally. Predictive performance was assessed by area under the curve (AUC) and calibration plots., Results: In both samples, 7.6% (development cohort: n = 10 839/142 791, validation cohort: n = 7675/100 533) had ≥1 unplanned hospital admission within 6 months. The discriminative ability of the three models was comparable and remained stable after geographic validation. The easy-to-use model included age, sex, prior admissions to hospital, pulmonary emphysema, heart failure, and polypharmacy. Its discriminative ability after validation was AUC 0.72 (95% confidence interval = 0.71 to 0.72). Calibration plots showed good calibration., Conclusion: The models showed satisfactory predictive ability. Reducing the number of predictors and geographic validation did not have an impact on predictive performance, demonstrating the robustness of the model. An easy-to-use tool has been developed in this study that may assist GPs in decision making and with targeted preventive interventions., (© The Authors.)

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., Competing Interests: There were no competing interests of any of the authors., (© 2024 The Authors.)

Background: Prediction models can identify fall-prone individuals. Prediction models can be based on either data from research cohorts (cohort-based) or routinely collected data (RCD-based). We review and compare cohort-based and RCD-based studies describing the development and/or validation of fall prediction models for community-dwelling older adults., Methods: Medline and Embase were searched via Ovid until January 2023. We included studies describing the development or validation of multivariable prediction models of falls in older adults (60+). Both risk of bias and reporting quality were assessed using the PROBAST and TRIPOD, respectively., Results: We included and reviewed 28 relevant studies, describing 30 prediction models (23 cohort-based and 7 RCD-based), and external validation of two existing models (one cohort-based and one RCD-based). The median sample sizes for cohort-based and RCD-based studies were 1365 [interquartile range (IQR) 426-2766] versus 90 441 (IQR 56 442-128 157), and the ranges of fall rates were 5.4% to 60.4% versus 1.6% to 13.1%, respectively. Discrimination performance was comparable between cohort-based and RCD-based models, with the respective area under the receiver operating characteristic curves ranging from 0.65 to 0.88 versus 0.71 to 0.81. The median number of predictors in cohort-based final models was 6 (IQR 5-11); for RCD-based models, it was 16 (IQR 11-26). All but one cohort-based model had high bias risks, primarily due to deficiencies in statistical analysis and outcome determination., Conclusions: Cohort-based models to predict falls in older adults in the community are plentiful. RCD-based models are yet in their infancy but provide comparable predictive performance with no additional data collection efforts. Future studies should focus on methodological and reporting quality., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Geriatrics Society.)

Objective: Current risk algorithms do not accurately predict cardiovascular disease (CVD) risk in rheumatoid arthritis (RA). An area of interest is that of single-nucleotide polymorphisms (SNPs), of which several have been associated with CVD in the general population. We investigated whether these SNPs are associated with CVD in RA and whether SNPs could improve CVD risk prediction in RA., Methods: Sixty SNPs were genotyped in 353 patients with RA. Logistic and Cox regression analyses were performed to identify SNPs that were associated with CVD (n = 99). A prediction model with clinical variables was made. SNPs were added to investigate the additional predictive value. Both models were internally validated. External validation was done in a separate cohort (n = 297)., Results: rs3184504, rs4773144, rs12190287, and rs445925 were significantly associated with new CVD. The clinical prediction model consisted of age, sex, body mass index, systolic blood pressure, high-density lipoprotein cholesterol (HDLc), and creatinine, with an area under the curve (AUC) of 0.74 (P = 0.03). Internal validation resulted in an AUC of 0.76 (P < 0.01). A new model was made including SNPs and resulted in a model with rs17011666 and rs801426, age, total cholesterol, and HDLc, which performed slightly better with an AUC of 0.77 (P < 0.01). External validation resulted in a good fit for the clinical model, but a poor fit for the SNP model., Conclusion: Several SNPs were associated with CVD in RA. Risk prediction slightly improved after adding SNPs to the models, but the clinical relevance is debatable. However, larger studies are needed to determine more accurately the additional value of these SNPs to CVD risk prediction algorithms., (© 2024 The Author(s). Arthritis Care & Research published by Wiley Periodicals LLC on behalf of American College of Rheumatology.)

Abstract: Background : Necrotizing soft-tissue infections (NSTIs) present a surgical emergency of increasing incidence, which is often misdiagnosed and associated with substantial mortality and morbidity. A retrospective multicenter (11 hospitals) cohort study was initiated to identify the early predictors of misdiagnosis, mortality, and morbidity (skin defect size and amputation). Methods : Patients of all ages who presented with symptoms and were admitted for acute treatment of NSTIs between January 2013 and December 2017 were included. Generalized estimating equation analysis was used to identify early predictors (available before or during the first debridement surgery), with a significance level of P < 0.05. Results : The median age of the cohort (N = 216) was 59.5 (interquartile range = 23.6) years, of which 138 patients (63.9%) were male. Necrotizing soft-tissue infections most frequently originated in the legs (31.0%) and anogenital area (30.5%). More than half of the patients (n = 114, 54.3%) were initially misdiagnosed. Thirty-day mortality was 22.9%. Amputation of an extremity was performed in 26 patients (12.5%). Misdiagnosis was more likely in patients with a higher Charlson Comorbidity Index (β = 0.20, P = 0.001), and less likely when symptoms started in the anogenital area (β = -1.20, P = 0.003). Besides the established risk factors for mortality (septic shock and age), misdiagnosis was identified as an independent predictor of 30-day mortality (β = 1.03, P = 0.01). The strongest predictors of the final skin defect size were septic shock (β = 2.88, P < 0.001) and a skin-sparing approach to debridement (β = -1.79, P = 0.002). Conclusion : Recognition of the disease is essential for the survival of patients affected by NSTI, as is adequate treatment of septic shock. The application of a skin-sparing approach to surgical debridement may decrease morbidity., Competing Interests: The authors report no conflicts of interest., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Shock Society.)

Objective: Preterm birth is one of the most frequent complications of pregnancy in women with systemic lupus erythematosus. The high indicated preterm birth proportion due to hypertensive disorders of pregnancy and/or fetal growth restriction is well known, and preventive measures and screening for early detection are performed. The risk of spontaneous preterm birth is less well recognized. This study aimed to determine the proportions of spontaneous and indicated preterm birth in pregnancies of women with systemic lupus erythematosus., Data Sources: A systematic literature search using Pubmed, Embase, Web of Science, and Google Scholar was performed in June 2021., Study Eligibility Criteria: Studies in pregnant women with systemic lupus erythematosus reporting spontaneous and indicated preterm birth rates were selected. Original research articles published from 1995 to June 2021 were included., Methods: Quality and risk of bias of the included studies were assessed using the Newcastle-Ottawa quality assessment scale. To estimate the pooled event rates and 95% confidence intervals, meta-analysis of single proportions with a random-effects model was performed., Results: We included 21 articles, containing data of 8157 pregnancies in women with systemic lupus erythematosus. On average, 31% (95% prediction interval, 0.14-0.50) of the pregnancies resulted in preterm birth, including 14% (95% prediction interval, 0.04-0.27) spontaneous and 16% (95% prediction interval, 0.03-0.35) indicated preterm birth., Conclusion: In pregnant women with systemic lupus erythematosus, spontaneous and indicated preterm birth proportions are high. This information should be applied in (prepregnancy) counseling and management in pregnancy. The knowledge obtained by this meta-analysis paves the way for further research of associated risk factors and development of interventions to reduce spontaneous preterm birth in systemic lupus erythematosus pregnancies., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (© 2024 The Author(s).)

Background: Falls involve dynamic risk factors that change over time, but most studies on fall-risk factors are cross-sectional and do not capture this temporal aspect. The longitudinal clinical notes within electronic health records (EHR) provide an opportunity to analyse fall risk factor trajectories through Natural Language Processing techniques, specifically dynamic topic modelling (DTM). This study aims to uncover fall-related topics for new fallers and track their evolving trends leading up to falls., Methods: This case-cohort study utilised primary care EHR data covering information on older adults between 2016 and 2019. Cases were individuals who fell in 2019 but had no falls in the preceding three years (2016-18). The control group was randomly sampled individuals, with similar size to the cases group, who did not endure falls during the whole study follow-up period. We applied DTM on the clinical notes collected between 2016 and 2018. We compared the trend lines of the case and control groups using the slopes, which indicate direction and steepness of the change over time., Results: A total of 2,384 fallers (cases) and an equal number of controls were included. We identified 25 topics that showed significant differences in trends between the case and control groups. Topics such as medications, renal care, family caregivers, hospital admission/discharge and referral/streamlining diagnostic pathways exhibited a consistent increase in steepness over time within the cases group before the occurrence of falls., Conclusions: Early recognition of health conditions demanding care is crucial for applying proactive and comprehensive multifactorial assessments that address underlying causes, ultimately reducing falls and fall-related injuries., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Geriatrics Society.)

Objective: This study aimed to examine the presence of distinct trajectories of adherence to home-based exercise recommendations among people with low back pain (LBP). This study also aimed to identify differences in baseline characteristics among groups., Methods: This study was a secondary analysis of a prospective, multicenter cluster randomized controlled trial investigating the cost-effectiveness of a stratified blended physical therapist intervention compared to usual care physical therapy in patients with LBP. The intervention group received usual care with integrated support via a smartphone app. A total of 208 patients were recruited from 58 primary care physical therapist practices. Baseline data included patient characteristics, physical functioning, pain intensity, physical activity, fear avoidance, pain catastrophizing, self-efficacy, self-management ability, and health-related quality of life. The Exercise Adherence Scale (score range = 0-100) was used to measure adherence during each treatment session. Latent class growth analysis was used to estimate trajectories of adherence., Results: Adherence data were available from 173 out of 208 patients (83%). Data were collected during an average of 5.1 (standard deviation [SD] = 2.5) treatment sessions, with total treatment duration of 51 (SD = 41.7) days. Three trajectory classes were identified: "declining adherence" (12%), "stable adherence" (45%), and "increasing adherence" (43%). No differences in baseline characteristic were found between groups., Conclusion: Three adherence trajectories to exercise recommendations were identified in patients with LBP. However, baseline characteristics cannot identify a patient's trajectory group., Impact: Despite the presence of distinct trajectories of adherence in patients with LBP, physical therapists should not attempt to place a patient in a trajectory group at the start of treatment. Instead, adherence should be closely monitored as treatment progresses and supported when required as part of an ongoing process., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Physical Therapy Association.)

Objectives: Before being used in clinical practice, a prediction model should be tested in patients whose data were not used in model development. Previously, we developed the ADFICE&#95;IT models for predicting any fall and recurrent falls, referred as Any&#95;fall and Recur&#95;fall. In this study, we externally validated the models and compared their clinical value to a practical screening strategy where patients are screened for falls history alone., Design: Retrospective, combined analysis of 2 prospective cohorts., Setting and Participants: Data were included of 1125 patients (aged ≥65 years) who visited the geriatrics department or the emergency department., Methods: We evaluated the models' discrimination using the C-statistic. Models were updated using logistic regression if calibration intercept or slope values deviated significantly from their ideal values. Decision curve analysis was applied to compare the models' clinical value (ie, net benefit) against that of falls history for different decision thresholds., Results: During the 1-year follow-up, 428 participants (42.7%) endured 1 or more falls, and 224 participants (23.1%) endured a recurrent fall (≥2 falls). C-statistic values were 0.66 (95% CI 0.63-0.69) and 0.69 (95% CI 0.65-0.72) for the Any&#95;fall and Recur&#95;fall models, respectively. Any&#95;fall overestimated the fall risk and we therefore updated only its intercept whereas Recur&#95;fall showed good calibration and required no update. Compared with falls history, Any&#95;fall and Recur&#95;fall showed greater net benefit for decision thresholds of 35% to 60% and 15% to 45%, respectively., Conclusions and Implications: The models performed similarly in this data set of geriatric outpatients as in the development sample. This suggests that fall-risk assessment tools that were developed in community-dwelling older adults may perform well in geriatric outpatients. We found that in geriatric outpatients the models have greater clinical value across a wide range of decision thresholds compared with screening for falls history alone., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Preoperative assessment of the probability of pelvic lymph-node metastatic disease (pN1) is required to identify patients with prostate cancer (PCa) who are candidates for extended pelvic lymph-node dissection (ePLND)., Objective: To develop a novel intuitive prognostic nomogram for predicting pathological lymph-node (pN) status in contemporary patients with primary diagnosed localized PCa, using preoperative clinical and histopathological parameters, magnetic resonance imaging (MRI), and prostate-specific membrane antigen (PSMA) positron emission tomography (PET)., Design, Setting, and Participants: In total, 700 eligible patients who underwent robot-assisted radical prostatectomy and ePLND were included in the model-building cohort. The external validation cohort consisted of 305 surgically treated patients. Logistic regression with backward elimination was used to select variables for the Amsterdam-Brisbane-Sydney nomogram., Outcome Measurements and Statistical Analysis: Performance of the final model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision-curve analyses. Models were subsequently validated in an external population., Results and Limitations: The Amsterdam-Brisbane-Sydney nomogram included initial prostate-specific antigen value, MRI T stage, highest biopsy grade group (GG), biopsy technique, percentage of systematic cores with clinically significant PCa (GG ≥2), and lymph-node status on PSMA-PET. The AUC for predicting pN status was 0.81 (95% confidence interval [CI] 0.78-0.85) for the final model. On external validation, the Amsterdam-Brisbane-Sydney nomogram showed superior discriminative ability to the Briganti-2017 and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms (AUC 0.75 [95% CI 0.69-0.81] vs 0.67 [95% CI 0.61-0.74] and 0.65 [95% CI 0.58-0.72], respectively; p < 0.05), and similar discriminative ability to the Briganti-2019 nomogram (AUC 0.78 [95% CI 0.71-0.86] vs 0.80 [95% CI 0.73-0.86]; p = 0.76). The Amsterdam-Brisbane-Sydney nomogram showed excellent calibration on external validation, with an increased net benefit at a threshold probability of ≥4%., Conclusions: The validated Amsterdam-Brisbane-Sydney nomogram performs superior to the Briganti-2017 and MSKCC nomograms, and similar to the Briganti-2019 nomogram. Furthermore, it is applicable in all patients with newly diagnosed unfavorable intermediate- and high-risk PCa., Patient Summary: We developed and validated the Amsterdam-Brisbane-Sydney nomogram for the prediction of prostate cancer spread to lymph nodes before surgery. This nomogram performs similar or superior to all presently available nomograms., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Investigating prognostic factors in patients with relapsed or primary refractory classical Hodgkin lymphoma (R/R cHL) is essential to optimize risk-adapted treatment strategies. We built a prognostic model using baseline quantitative 18F-fluorodeoxyglucose positron emission tomography (PET) radiomics features and clinical characteristics to predict the progression-free survival (PFS) among patients with R/R cHL treated with salvage chemotherapy followed by autologous stem cell transplantation. Metabolic tumor volume and several novel radiomics dissemination features, representing interlesional differences in distance, volume, and standard uptake value, were extracted from the baseline PET. Machine learning using backward selection and logistic regression were applied to develop and train the model on a total of 113 patients from 2 clinical trials. The model was validated on an independent external cohort of 69 patients. In addition, we validated 4 different PET segmentation methods to calculate radiomics features. We identified a subset of patients at high risk for progression with significant inferior 3-year PFS outcomes of 38.1% vs 88.4% for patients in the low-risk group in the training cohort (P < .001) and 38.5% vs 75.0% in the validation cohort (P = .015), respectively. The overall survival was also significantly better in the low-risk group (P = .022 and P < .001). We provide a formula to calculate a risk score for individual patients based on the model. In conclusion, we developed a prognostic model for PFS combining radiomics and clinical features in a large cohort of patients with R/R cHL. This model calculates a PET-based risk profile and can be applied to develop risk-stratified treatment strategies for patients with R/R cHL. These trials were registered at www.clinicaltrials.gov as #NCT02280993, #NCT00255723, and #NCT01508312., (© 2023 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.)

Purpose: Healthcare providers often experience difficulties in discussing depression with adults with visual impairment (VI), obstructing timely referral. The purpose of this study was to examine predictors of routine discussions of depression with adults with VI from the perspective of different healthcare providers from different countries., Methods: Cross-sectional survey data from Welsh (N = 122), Australian (N = 94) and Dutch (N = 100) healthcare providers, that is eye care practitioners (ECPs) and low-vision care providers (LVCPs), were analysed. Multivariable logistic regression analysis was performed in the Welsh sample to determine predictors for discussing depression. Internal validation was conducted by using a bootstrap method, and the recalibrated model was externally validated in the Australian and Dutch sample., Results: Work experience in eye care services (OR 0.95; 95% confidence interval (CI) 0.92 to 0.99) and perceived barriers (OR 0.95; 95% CI 0.92 to 0.98) was found to predict discussing depression with patients. The area under the curve (AUC) of 0.73 reflected good discrimination of the model. The model showed a slightly better fit in the Australian sample (AUC = 0.77), but a poor fit in the Dutch sample., Conclusion: The final prediction model was not generalizable to Dutch healthcare providers. They perceived less barriers in depression management than Welsh and Australian healthcare providers. This could be explained by differences in ECPs and LVCPs roles and responsibilities, increased attention on mental health and differences in organizing health care. Differences between healthcare providers' responsibilities and support needs should be taken into account while creating a facilitating environment to discuss depression., (© 2023 The Authors. Acta Ophthalmologica published by John Wiley & Sons Ltd on behalf of Acta Ophthalmologica Scandinavica Foundation.)

Background: Convolutional neural networks (CNNs), applied to baseline [ 18 F]-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 [ 18 F]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., (© 2023. Springer-Verlag GmbH Germany, part of Springer Nature.)

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)., (© 2022 World Health Organization; licensed by John Wiley & Sons Ltd. International Journal of Methods in Psychiatric Research published by John Wiley & Sons Ltd.)

Introduction: For the analysis of clinical effects, multiple imputation (MI) of missing data were shown to be unnecessary when using longitudinal linear mixed-models (LLM). It remains unclear whether this also applies to trial-based economic evaluations. Therefore, this study aimed to assess whether MI is required prior to LLM when analyzing longitudinal cost and effect data., Methods: Two-thousand complete datasets were simulated containing five time points. Incomplete datasets were generated with 10, 25, and 50% missing data in follow-up costs and effects, assuming a Missing At Random (MAR) mechanism. Six different strategies were compared using empirical bias (EB), root-mean-squared error (RMSE), and coverage rate (CR). These strategies were: LLM alone (LLM) and MI with LLM (MI-LLM), and, as reference strategies, mean imputation with LLM (M-LLM), seemingly unrelated regression alone (SUR-CCA), MI with SUR (MI-SUR), and mean imputation with SUR (M-SUR)., Results: For costs and effects, LLM, MI-LLM, and MI-SUR performed better than M-LLM, SUR-CCA, and M-SUR, with smaller EBs and RMSEs as well as CRs closers to nominal levels. However, even though LLM, MI-LLM and MI-SUR performed equally well for effects, MI-LLM and MI-SUR were found to perform better than LLM for costs at 10 and 25% missing data. At 50% missing data, all strategies resulted in relatively high EBs and RMSEs for costs., Conclusion: LLM should be combined with MI when analyzing trial-based economic evaluation data. MI-SUR is more efficient and can also be used, but then an average intervention effect over time cannot be estimated., (© 2022. The Author(s).)

Purpose: Lumbar spinal fusion surgery (LSFS) is common for lumbar degenerative disorders. The objective was to develop clinical prediction rules to identify which patients are likely to have a favourable outcome to inform decisions regarding surgery and rehabilitation., Methods: A prospective observational study recruited 600 (derivation) and 600 (internal validation) consecutive adult patients undergoing LSFS for degenerative lumbar disorder through the British Spine Registry. Definition of good outcome (6 weeks, 12 months) was reduction in pain intensity (Numerical Rating Scale, 0-10) and disability (Oswestry Disability Index, ODI 0-50) > 1.7 and 14.3, respectively. Linear and logistic regression models were fitted and regression coefficients, Odds ratios and 95% CIs reported., Results: Lower BMI, higher ODI and higher leg pain pre-operatively were predictive of good disability outcome, higher back pain was predictive of good back pain outcome, and no previous surgery and higher leg pain were predictive of good leg pain outcome; all at 6 weeks. Working and higher leg pain were predictive of good ODI and leg pain outcomes, higher back pain was predictive of good back pain outcome, and higher leg pain was predictive of good leg pain outcome at 12 months. Model performance demonstrated reasonable to good calibration and adequate/very good discrimination., Conclusions: BMI, ODI, leg and back pain and previous surgery are important considerations pre-operatively to inform decisions for surgery. Pre-operative leg and back pain and work status are important considerations to inform decisions for management following surgery. Findings may inform clinical decision making regarding LSFS and associated rehabilitation., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: The faecal immunochemical test (FIT) suffers from suboptimal performance and participation in colorectal cancer (CRC) screening. Urinary volatile organic compounds (VOCs) may be a useful alternative. We aimed to determine the diagnostic potential of urinary VOCs for CRC/adenomas. By relating VOCs to known pathways, we aimed to gain insight into the pathophysiology of colorectal neoplasia., Methods: A systematic search was performed in PubMed, EMBASE and Web of Science. Original studies on urinary VOCs for CRC/adenoma detection with a control group were included. QUADAS-2 tool was used for quality assessment. Meta-analysis was performed by adopting a bivariate model for sensitivity/specificity. Fagan's nomogram estimated the performance of combined FIT-VOC. Neoplasm-associated VOCs were linked to pathways using the KEGG database., Results: Sixteen studies-involving 837 CRC patients and 1618 controls-were included; 11 performed chemical identification and 7 chemical fingerprinting. In all studies, urinary VOCs discriminated CRC from controls. Pooled sensitivity and specificity for CRC based on chemical fingerprinting were 84% (95% CI 73-91%) and 70% (95% CI 63-77%), respectively. The most distinctive individual VOC was butanal (AUC 0.98). The estimated probability of having CRC following negative FIT was 0.38%, whereas 0.09% following negative FIT-VOC. Combined FIT-VOC would detect 33% more CRCs. In total 100 CRC-associated urinary VOCs were identified; particularly hydrocarbons, carboxylic acids, aldehydes/ketones and amino acids, and predominantly involved in TCA-cycle or alanine/aspartate/glutamine/glutamate/phenylalanine/tyrosine/tryptophan metabolism, which is supported by previous research on (colorectal)cancer biology. The potential of urinary VOCs to detect precancerous adenomas or gain insight into their pathophysiology appeared understudied., Conclusion: Urinary VOCs hold potential for non-invasive CRC screening. Multicentre validation studies are needed, especially focusing on adenoma detection. Urinary VOCs elucidate underlying pathophysiologic processes., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objective: To develop prediction models for individual patient harm and benefit outcomes in elderly patients with RA and comorbidities treated with chronic low-dose glucocorticoid therapy or placebo., Methods: In the Glucocorticoid Low-dose Outcome in Rheumatoid Arthritis (GLORIA) study, 451 RA patients ≥65 years of age were randomized to 2 years 5 mg/day prednisolone or placebo. Eight prediction models were developed from the dataset in a stepwise procedure based on prior knowledge. The first set of four models disregarded study treatment and examined general predictive factors. The second set of four models was similar but examined the additional role of low-dose prednisolone. In each set, two models focused on harm [the occurrence of one or more adverse events of special interest (AESIs) and the number of AESIs per year) and two on benefit (early clinical response/disease activity and a lack of joint damage progression). Linear and logistic multivariable regression methods with backward selection were used to develop the models. The final models were assessed and internally validated with bootstrapping techniques., Results: A few variables were slightly predictive for one of the outcomes in the models, but none were of immediate clinical value. The quality of the prediction models was sufficient and the performance was low to moderate (explained variance 12-15%, area under the curve 0.67-0.69)., Conclusion: Baseline factors are not helpful in selecting elderly RA patients for treatment with low-dose prednisolone given their low power to predict the chance of benefit or harm., Trial Registration: https://clinicaltrials.gov; NCT02585258., (© The Author(s) 2022. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Background: Intrinsic capacity (IC) defined by the WHO refers to the composite of five domains of capacities. So far, developing and validating a standardized overall score of the concept have been challenging partly because its conceptual framework has been unclear. We consider that a person's IC is determined by its domain-specific indicators suggesting a formative measurement model., Aims: To develop an IC score applying a formative approach and assess its validity., Methods: The study sample (n = 1908) consisted of 57-88-year-old participants from the Longitudinal Aging Study Amsterdam (LASA). We used logistic regression models to select the indicators to the IC score with 6-year functional decline as an outcome. An IC score (range 0-100) was constructed for each participant. We examined the known-groups' validity of the IC score by comparing groups based on age and number of chronic diseases. The criterion validity of the IC score was assessed with 6-year functional decline and 10-year mortality as outcomes., Results: The constructed IC score included seven indicators covering all five domains of the construct. The mean IC score was 66.7 (SD 10.3). The scores were higher among younger participants and those who had lower number of chronic diseases. After adjustment for sociodemographic indicators, chronic diseases, and BMI, a one-point higher IC score was associated with a 7% decreased risk for 6-year functional decline and a 2% decreased risk for 10-year mortality., Conclusions: The developed IC score demonstrated discriminative ability according to age and health status and is associated with subsequent functional decline and mortality., (© 2023. The Author(s).)

Background: Falls in older people are common and morbid. Prediction models can help identifying individuals at higher fall risk. Electronic health records (EHR) offer an opportunity to develop automated prediction tools that may help to identify fall-prone individuals and lower clinical workload. However, existing models primarily utilise structured EHR data and neglect information in unstructured data. Using machine learning and natural language processing (NLP), we aimed to examine the predictive performance provided by unstructured clinical notes, and their incremental performance over structured data to predict falls., Methods: We used primary care EHR data of people aged 65 or over. We developed three logistic regression models using the least absolute shrinkage and selection operator: one using structured clinical variables (Baseline), one with topics extracted from unstructured clinical notes (Topic-based) and one by adding clinical variables to the extracted topics (Combi). Model performance was assessed in terms of discrimination using the area under the receiver operating characteristic curve (AUC), and calibration by calibration plots. We used 10-fold cross-validation to validate the approach., Results: Data of 35,357 individuals were analysed, of which 4,734 experienced falls. Our NLP topic modelling technique discovered 151 topics from the unstructured clinical notes. AUCs and 95% confidence intervals of the Baseline, Topic-based and Combi models were 0.709 (0.700-0.719), 0.685 (0.676-0.694) and 0.718 (0.708-0.727), respectively. All the models showed good calibration., Conclusions: Unstructured clinical notes are an additional viable data source to develop and improve prediction models for falls compared to traditional prediction models, but the clinical relevance remains limited., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Question: Can existing post-treatment prognostic models for predicting neck pain recovery (primarily in terms of disability and secondarily in terms of pain intensity and perceived improvement) be externally validated and updated at the end of the treatment period and at 6 and 12 weeks of follow-up in a new Dutch cohort of people with neck pain treated with guideline-based usual care physiotherapy?, Design: External validation and model updating in a new prospective cohort of three previously developed prognostic models., Participants: People with (sub)acute neck pain and registered for primary care physiotherapy treatment., Outcome Measures: Recovery of disability, pain intensity, and perceived recovery at 6 and 12 weeks and at the end of the treatment period., Results: Discriminative performance (c-statistic) of the disability model at 6 weeks was 0.73 (95% CI 0.69 to 0.77) and reasonably well calibrated after intercept recalibration. The disability model at 12 weeks and at the end of the treatment period showed discriminative c-statistic performance values of 0.69 (95% CI 0.64 to 0.73) and 0.68 (95% CI 0.63 to 0.72), respectively, and was well calibrated. Pain models and perceived recovery models did not reach acceptable performance. Cervical mobility added value to the disability models and pain catastrophising to the disability and pain models at 6 weeks., Discussion: Broad external validation of the disability model was successful in people with (sub)acute neck pain and clinicians may use this model in clinical practice with reasonable accuracy. Further research is required to assess the disability model's clinical impact and generalisability, and to identify additional valuable model predictors., Registration: https://osf.io/a6r3k/., (Copyright © 2023. Published by Elsevier B.V.)

This study is to determine whether the volume and contact surface area (CSA) of a tumour with an adjacent prostate capsule on MRI in a three-dimensional (3D) model that can predict side-specific extraprostatic extension (EPE) at radical prostatectomy (RP). Patients with localised prostate cancer (PCa) who underwent robot-assisted RP between July 2015 and March 2021 were included in this retrospective study. MRI-based 3D prostate models incorporating the PCa volume and location were reconstructed. The tumour volume and surface variables were extracted. For the prostate-to-tumour and tumour-to-prostate CSAs, the areas in which the distances were ≤ 1, ≤ 2, ≤ 3, ≤ 4, and ≤ 5 mm were defined, and their surface (cm 2 ) were determined. Differences in prostate sides with and without pathological EPE were analysed. Multivariable logistic regression analysis to find independent predictors of EPE. Overall, 75/302 (25%) prostate sides showed pathological EPE. Prostate sides with EPE had higher cT-stage, higher PSA density, higher percentage of positive biopsy cores, higher biopsy Gleason scores, higher radiological tumour stage, larger tumour volumes, larger prostate CSA, and larger tumour CSA (all p < 0.001). Multivariable logistic regression analysis showed that the radiological tumour stage (p = 0.001), tumour volume (p < 0.001), prostate CSA (p < 0.001), and tumour CSA (p ≤ 0.001) were independent predictors of pathological EPE. A 3D reconstruction of tumour locations in the prostate improves prediction of extraprostatic extension. Tumours with a higher 3D-reconstructed volume, a higher surface area of tumour in contact with the prostate capsule, and higher surface area of prostate capsule in contact with the tumour are at increased risk of side-specific extraprostatic extension., (© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Objective: To develop and internally validate a prognostic prediction model for development of a niche in the uterine scar after a first caesarean section (CS)., Study Design: Secondary analyses on data of a randomized controlled trial, performed in 32 hospitals in the Netherlands among women undergoing a first caesarean section. We used multivariable backward logistic regression. Missing data were handled using multiple imputation. Model performance was assessed by calibration and discrimination. Internal validation using bootstrapping techniques took place. The outcome was 'development of a niche in the uterus', defined as an indentation of ≥ 2 mm in the myometrium., Results: We developed two models to predict niche development: in the total population and after elective CS. Patient related risk factors were: gestational age, twin pregnancy and smoking, and surgery related risk factors were double-layer closure and less surgical experience. Multiparity and Vicryl suture material were protective factors. The prediction model in women undergoing elective CS revealed similar results. After internal validation, Nagelkerke R 2 ranged from 0.01 to 0.05 and was considered low; median area under the curve (AUC) ranged from 0.56 to 0.62, indicating failed to poor discriminative ability., Conclusions: The model cannot be used to accurately predict the development of a niche after a first CS. However, several factors seem to influence scar healing which indicates possibilities for future prevention such as surgical experience and suture material. The search for additional risk factors that play a role in development of a niche should be continued to improve the discriminative ability., Competing Interests: Declaration of Competing Interest Judith Huirne received a grant from ZonMw for the conduction of the study; she received a grant from Samsung, PlantTec, and a fee from Olympus, all outside the submitted work. Christianne de Groot received a grant from ZonMw outside the submitted work. The remaining authors report no conflict of interest., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

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., (© 2023. The Author(s).)

Aims: In immune-mediated inflammatory diseases (IMIDs), early symptom control is a key therapeutic goal. Methotrexate (MTX) is the first-line treatment across IMIDs. However, MTX is underutilized and suboptimally dosed, partly due to the inability of making individualized treatment decisions through therapeutic drug monitoring (TDM). To implement TDM in clinical practice, establishing a relationship between drug concentration and disease activity is paramount. In this meta-analysis, we investigated the relationship between concentrations of MTX polyglutamates (MTX-PG) in erythrocytes and efficacy as well as toxicity across IMIDs., Methods: Studies analysing MTX-PG in relation to disease activity and/or toxicity were included for inflammatory arthritis (rheumatoid [RA] and juvenile idiopathic arthritis [JIA]), inflammatory bowel disease (Crohn's and ulcerative colitis) and dermatitis (psoriasis and atopic dermatitis). Meta-analyses were performed resulting in several summary effect measures: regression coefficient (β), correlation coefficient and mean difference (of MTX-PG in responders vs. nonresponders) for IMIDs separately and collectively., Results: Twenty-five studies were included. In RA and JIA, higher MTX-PG was significantly associated with lower disease activity at 3 months (β: -0.002; 95% confidence interval [CI]: -0.004 to -0.001) and after 4 months of MTX use (β: -0.003; 95% CI: -0.005 to -0.002). Similarly, higher MTX-PG correlated with lower disease activity in psoriasis (R: -0.82; 95% CI: -0.976 to -0.102). Higher MTX-PG was observed in RA, JIA and psoriasis responders (mean difference: 5.2 nmol/L MTX-PG total ; P < .01)., Conclusion: We showed that higher concentrations of erythrocyte MTX-PG were associated with lower disease activity in RA, JIA and psoriasis. These findings are an important step towards implementation of TDM for MTX treatment across IMIDs., (© 2022 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Introduction: Although visual and quantitative assessments of [18F]FDG PET/CT studies typically rely on liver uptake value as a reference or normalisation factor, consensus or consistency in measuring [18F]FDG uptake is lacking. Therefore, we evaluate the variation of several liver standardised uptake value (SUV) measurements in lymphoma [18F]FDG PET/CT studies using different uptake metrics., Methods: PET/CT scans from 34 lymphoma patients were used to calculate SUVmax liver , SUVpeak liver and SUVmean liver as a function of (1) volume-of-interest (VOI) size, (2) location, (3) imaging time point and (4) as a function of total metabolic tumour volume (MTV). The impact of reconstruction protocol on liver uptake is studied on 15 baseline lymphoma patient scans. The effect of noise on liver SUV was assessed using full and 25% count images of 15 lymphoma scans., Results: Generally, SUVmax liver and SUVpeak liver were 38% and 16% higher compared to SUVmean liver . SUVmax liver and SUVpeak liver increased up to 31% and 15% with VOI size while SUVmean liver remained unchanged with the lowest variability for the largest VOI size. Liver uptake metrics were not affected by VOI location. Compared to baseline, liver uptake metrics were 15-18% and 9-18% higher at interim and EoT PET, respectively. SUV liver decreased with larger total MTVs. SUVmax liver and SUVpeak liver were affected by reconstruction protocol up to 62%. SUVmax and SUVpeak moved 22% and 11% upward between full and 25% count images., Conclusion: SUVmean liver was most robust against VOI size, location, reconstruction protocol and image noise level, and is thus the most reproducible metric for liver uptake. The commonly recommended 3 cm diameter spherical VOI-based SUVmean liver values were only slightly more variable than those seen with larger VOI sizes and are sufficient for SUVmean liver measurements in future studies., Trial Registration: EudraCT: 2006-005,174-42, 01-08-2008., (© 2022. The Author(s).)

The statistical quality of trial-based economic evaluations is often suboptimal, while a comprehensive overview of available statistical methods is lacking. Therefore, this review summarized and critically appraised available statistical methods for trial-based economic evaluations. A literature search was performed to identify studies on statistical methods for dealing with baseline imbalances, skewed costs and/or effects, correlated costs and effects, clustered data, longitudinal data, missing data and censoring in trial-based economic evaluations. Data was extracted on the statistical methods described, their advantages, disadvantages, relative performance and recommendations of the study. Sixty-eight studies were included. Of them, 27 (40%) assessed methods for baseline imbalances, 39 (57%) assessed methods for skewed costs and/or effects, 27 (40%) assessed methods for correlated costs and effects, 18 (26%) assessed methods for clustered data, 7 (10%) assessed methods for longitudinal data, 26 (38%) assessed methods for missing data and 10 (15%) assessed methods for censoring. All identified methods were narratively described. This review provides a comprehensive overview of available statistical methods for dealing with the most common statistical complexities in trial-based economic evaluations. Herewith, it can provide valuable input for researchers when deciding which statistical methods to use in a trial-based economic evaluation., (© 2022 The Authors. Health Economics published by John Wiley & Sons Ltd.)

Objectives: Longitudinal mediation effects can be estimated with mixed effects models. Mixed effects models are versatile, as they accommodate the estimation of contemporaneous, lagged, time-independent, and time-dependent effects. However, the inclusion of time lags and time interactions in mixed effects models for longitudinal mediation analysis has received little attention. This article demonstrates how time lags and time interactions in mixed effects models affect the interpretation of longitudinal mediation effect estimates., Study Design and Setting: We used a data example from the Amsterdam Growth and Health Longitudinal Study to illustrate how the inclusion of time lags and time interactions in mixed effects models may affect the size and interpretation of longitudinal mediation effect estimates., Results: The chosen time lags between the determinant, mediator, and outcome influenced the size and interpretation of the mediation effect estimates. Furthermore, time interactions can be used to model linear or nonlinear development of the mediation effects over time., Conclusion: The inclusion of time lags and time interactions should be considered when estimating longitudinal mediation effects based on mixed effects models, as this enables the estimation of lagged and time-dependent effects., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Because missing data are present in almost every study, it is important to handle missing data properly. First of all, the missing data mechanism should be considered. Missing data can be either completely at random (MCAR), at random (MAR), or not at random (MNAR). When missing data are MCAR, a complete case analysis can be valid. Also when missing data are MAR, in some situations a complete case analysis leads to valid results. However, in most situations, missing data imputation should be used. Regarding imputation methods, it is highly advised to use multiple imputations because multiple imputations lead to valid estimates including the uncertainty about the imputed values. When missing data are MNAR, also multiple imputations do not lead to valid results. A complication hereby is that it not possible to distinguish whether missing data are MAR or MNAR. Finally, it should be realized that preventing to have missing data is always better than the treatment of missing data., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Definitive concomitant cisplatin-based chemoradiotherapy (CRT) is the current gold standard for most patients with advanced stage head and neck squamous cell carcinoma (HNSCC) of the pharynx and larynx. Since previous meta-analysis on CRT outcomes in HNSCC have been reported, advances have been made in radiotherapy techniques and clinical management, while HPV-status has been identified as a strong confounding prognostic factor in oropharyngeal cancer. Here, we present real-world outcome data from a large multicenter cohort of HPV-negative advanced stage HNSCC treated with CRT using contemporary IMRT-based techniques., Method: Retrospective data were collected from a multicenter cohort of 513 patients treated with definitive concurrent platinum-based CRT with curative intent between January 2009 and August 2017. Only patients with HPV-negative advanced stage (III-IV) HNSCC were included. A prognostic model for outcome was developed based on clinical parameters and compared to TNM., Results: Nearly half of the 513 patients (49%) had an oropharyngeal tumor, often locally advanced (73.3% T3-T4b) and with involvement of the regional lymph nodes (84%). Most patients (84%) received cisplatin as single agent. In total 66% received the planned number of cycles and 75% reached a cumulative cisplatin dose of ≥200 mg/m 2 . Locoregional control was achieved in 324 (63%) patients during follow-up, and no association with tumor sites was observed (p = 0.48). Overall survival at 5 year follow-up was 47%, with a better survival for laryngeal cancer (p = 0.02) compared to other sites. A model with clinical variables (gender, high pre-treatment weight loss, N2c/N3-stage and <200 mg/m 2 dose of cisplatin) provided a noticeably stronger association with overall survival than TNM-staging (C- index 0.68 vs 0.55). Simultaneous Integrated Boosting (SIB) significantly outperformed Sequential Boosting (SEQ) to reduce the development of distant metastasis (SEQ vs SIB: OR 1.91 (1.11-3.26; p = 0.02)., Conclusion: Despite advances in clinical management, more than a third of patients with HPV-negative HNSCC do not complete CRT treatment protocols due to cisplatin toxicity. A model that consists of clinical variables and treatment parameters including cisplatin dose provided the strongest association with overall survival. Since cisplatin toxicity is a major obstacle in completing definitive CRT, the development of alternative and less toxic radiosensitizers is therefore warranted to improve treatment results. The association of RT-boost technique with distant metastasis is an important finding and requires further study., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: Early identification of older people at risk of falling is the cornerstone of fall prevention. Many fall prediction tools exist but their external validity is lacking. External validation is a prerequisite before application in clinical practice. Models developed with electronic health record (EHR) data are especially challenging because of the uncontrolled nature of routinely collected data. We aimed to externally validate our previously developed and published prediction model for falls, using a large cohort of community-dwelling older people derived from primary care EHR data., Design: Retrospective analysis of a prospective cohort drawn from EHR data., Setting and Participants: Pseudonymized EHR data were collected from individuals aged ≥65 years, who were enlisted in any of the participating 59 general practices between 2015 and 2020 in the Netherlands., Methods: Ten predictors were defined and obtained using the same methods as in the development study. The outcome was 1-year fall and was obtained from free text. Both reproducibility and transportability were evaluated. Model performance was assessed in terms of discrimination using the area under the receiver operating characteristic curve (ROC-AUC), and in terms of calibration, using calibration-in-the-large, calibration slope and calibration plots., Results: Among 39,342 older people, 5124 (13.4%) fell in the 1-year follow-up. The characteristics of the validation and the development cohorts were similar. ROC-AUCs of the validation and development cohort were 0.690 and 0.705, respectively. Calibration-in-the-large and calibration slope were 0.012 and 0.878, respectively. Calibration plots revealed overprediction for high-risk groups in a small number of individuals., Conclusions and Implications: Our previously developed prediction model for falls demonstrated good external validity by reproducing its predictive performance in the validation cohort. The implementation of this model in the primary care setting could be considered after impact assessment., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (© 2022. The Author(s).)

Objective: This study tested and validated an existing tool for its ability to predict the risk of long-term (ie, ≥6 weeks) sickness absence (LTSA) after four days of sick-listing., Methods: A 9-item tool is completed online on the fourth day of sick-listing. The tool was tested in a sample (N=13 597) of food retail workers who reported sick between March and May 2017. It was validated in a new sample (N=104 698) of workers (83% retail) who reported sick between January 2020 and April 2021. LTSA risk predictions were calibrated with the Hosmer-Lemeshow (H-L) test; non-significant H-L P-values indicated adequate calibration. Discrimination between workers with and without LTSA was investigated with the area (AUC) under the receiver operating characteristic (ROC) curve., Results: The data of 2203 (16%) workers in the test sample and 14 226 (13%) workers in the validation sample was available for analysis. In the test sample, the tool together with age and sex predicted LTSA (H-L test P=0.59) and discriminated between workers with and without LTSA [AUC 0.85, 95% confidence interval (CI) 0.83-0.87]. In the validation sample, LTSA risk predictions were adequate (H-L test P=0.13) and discrimination was excellent (AUC 0.91, 95% CI 0.90-0.92). The ROC curve had an optimal cut-off at a predicted 36% LTSA risk, with sensitivity 0.85 and specificity 0.83., Conclusion: The existing 9-item tool can be used to invite sick-listed retail workers with a ≥36% LTSA risk for expedited consultations. Further studies are needed to determine LTSA cut-off risks for other economic sectors.

Objective: Traditional methods to deal with non-linearity in regression analysis often result in loss of information or compromised interpretability of the results. A recommended but underutilized method for modeling non-linear associations in regression models is spline functions. We explain spline functions in a non-mathematical way and illustrate the application and interpretation to an empirical data example., Methods: Using data from the Amsterdam Growth and Health Longitudinal Study, we examined the non-linear relationship between the sum of four skinfolds and VO 2 max, which are measures of body fat and cardiorespiratory fitness, respectively. We compared traditional methods (i.e., quadratic regression and categorization) to spline methods [1- and 3-knot linear spline (LSP) models and a 3-knot restricted cubic spline (RCS) model] in terms of the interpretability of the results and their explained variance ( r a d j 2 )., Results: The spline models fitted the data better than the traditional methods. Increasing the number of knots in the LSP model increased the explained variance (from r a d j 2 = 0 . 578 for the 1-knot model to r a d j 2 = 0 . 582 for the 3-knot model). The RCS model fitted the data best ( r a d j 2 = 0 . 591 ), but results in regression coefficients that are harder to interpret., Conclusion: Spline functions should be considered more often as they are flexible and can be applied in commonly used regression analysis. RCS regression is generally recommended for prediction research (i.e., to obtain the predicted outcome for a specific exposure value), whereas LSP regression is recommended if one is interested in the effects in a population., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Schuster, Rijnhart, Twisk and Heymans.)

Purpose: Baseline metabolic tumor volume (MTV) is a promising biomarker in diffuse large B-cell lymphoma (DLBCL). Our aims were to determine the best statistical relationship between MTV and survival and to compare MTV with the International Prognostic Index (IPI) and its individual components to derive the best prognostic model., Methods: PET scans and clinical data were included from five published studies in newly diagnosed diffuse large B-cell lymphoma. Transformations of MTV were compared with the primary end points of 3-year progression-free survival (PFS) and overall survival (OS) to derive the best relationship for further analyses. MTV was compared with IPI categories and individual components to derive the best model. Patients were grouped into three groups for survival analysis using Kaplan-Meier analysis; 10% at highest risk, 30% intermediate risk, and 60% lowest risk, corresponding with expected clinical outcome. Validation of the best model was performed using four studies as a test set and the fifth study for validation and repeated five times., Results: The best relationship for MTV and survival was a linear spline model with one knot located at the median MTV value of 307.9 cm 3 . MTV was a better predictor than IPI for PFS and OS. The best model combined MTV with age as continuous variables and individual stage as I-IV. The MTV-age-stage model performed better than IPI and was also better at defining a high-risk group (3-year PFS 46.3% v 58.0% and 3-year OS 51.5% v 66.4% for the new model and IPI, respectively). A regression formula was derived to estimate individual patient survival probabilities., Conclusion: A new prognostic index is proposed using MTV, age, and stage, which outperforms IPI and enables individualized estimates of patient outcome.

Background: Use of fall prevention strategies requires detection of high-risk patients. Our goal was to develop prediction models for falls and recurrent falls in community-dwelling older adults and to improve upon previous models by using a large, pooled sample and by considering a wide range of candidate predictors, including medications., Methods: Harmonized data from 2 Dutch (LASA, B-PROOF) and 1 German cohort (ActiFE Ulm) of adults aged ≥65 years were used to fit 2 logistic regression models: one for predicting any fall and another for predicting recurrent falls over 1 year. Model generalizability was assessed using internal-external cross-validation., Results: Data of 5 722 participants were included in the analyses, of whom 1 868 (34.7%) endured at least 1 fall and 702 (13.8%) endured a recurrent fall. Positive predictors for any fall were: educational status, depression, verbal fluency, functional limitations, falls history, and use of antiepileptics and drugs for urinary frequency and incontinence; negative predictors were: body mass index (BMI), grip strength, systolic blood pressure, and smoking. Positive predictors for recurrent falls were: educational status, visual impairment, functional limitations, urinary incontinence, falls history, and use of anti-Parkinson drugs, antihistamines, and drugs for urinary frequency and incontinence; BMI was a negative predictor. The average C-statistic value was 0.65 for the model for any fall and 0.70 for the model for recurrent falls., Conclusion: Compared with previous models, the model for recurrent falls performed favorably while the model for any fall performed similarly. Validation and optimization of the models in other populations are warranted., (© The Author(s) 2022. Published by Oxford University Press on behalf of The Gerontological Society of America.)

Background: Currently used prediction tools have limited ability to identify community-dwelling older people at high risk for falls. Prediction models utilizing electronic health records (EHRs) provide opportunities but up to now showed limited clinical value as risk stratification tool, because of among others the underestimation of falls prevalence. The aim of this study was to develop a fall prediction model for community-dwelling older people using a combination of structured data and free text of primary care EHRs and to internally validate its predictive performance., Methods: We used EHR data of individuals aged 65 or older. Age, sex, history of falls, medications, and medical conditions were included as potential predictors. Falls were ascertained from the free text. We employed the Bootstrap-enhanced penalized logistic regression with the least absolute shrinkage and selection operator to develop the prediction model. We used 10-fold cross-validation to internally validate the prediction strategy. Model performance was assessed in terms of discrimination and calibration., Results: Data of 36 470 eligible participants were extracted from the data set. The number of participants who fell at least once was 4 778 (13.1%). The final prediction model included age, sex, history of falls, 2 medications, and 5 medical conditions. The model had a median area under the receiver operating curve of 0.705 (interquartile range 0.700-0.714)., Conclusions: Our prediction model to identify older people at high risk for falls achieved fair discrimination and had reasonable calibration. It can be applied in clinical practice as it relies on routinely collected variables and does not require mobility assessment tests., (© The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America.)

Purpose: To determine which demographic and clinical characteristics are predictive of vision-related quality of life (VrQoL) and quality of life (QoL) in patients with macular oedema receiving intravitreal anti-vascular endothelial growth factor (VEGF) treatment., Methods: Vision-related quality of life (VrQoL) and quality of life (QoL) were measured in 712 patients with retinal exudative disease receiving anti-VEGF treatment at baseline, 6 and 12 months. VrQoL was measured using an item-response theory based 47-question item bank (EyeQ), whereas QoL was measured using the EuroQol Five Dimensions (EQ-5D) questionnaire. The EQ-5D score was dichotomized into a perfect score of 1 and a suboptimal score of <1. Demographic and clinical patient characteristics were considered as possible predictors of (Vr)QoL. Prediction models for (Vr)QoL were created with linear mixed models and generalised estimating equations, using a forward selection procedure., Results: A worse VrQoL was predicted by poorer LogMAR visual acuity of the better eye, female sex, single civil status, older age, longer length of anti-VEGF treatment at baseline and the presence of non-ocular and ocular comorbidities. Suboptimal EQ-5D scores were predicted by poorer LogMAR visual acuity of the better eye, female sex, single civil status, older age, the presence of non-ocular comorbidities and a lower educational background., Conclusions: Along with visual acuity of the better eye, which is the main factor used in clinical decision making, other patient characteristics should also be considered for the risk assessment of (Vr)QoL, such as sex, age, civil status, comorbidities and length of anti-VEGF treatment., (© 2022 The Authors. Ophthalmic and Physiological Optics published by John Wiley & Sons Ltd on behalf of College of Optometrists.)

There is an increasing awareness that replication should become common practice in empirical studies. However, study results might fail to replicate for various reasons. The robustness of published study results can be assessed using the relatively new multiverse-analysis methodology, in which the robustness of the effect estimates against data analytical decisions is assessed. However, the uptake of multiverse analysis in empirical studies remains low, which might be due to the scarcity of guidance available on performing multiverse analysis. Researchers might experience difficulties in identifying data analytical decisions and in summarizing the large number of effect estimates yielded by a multiverse analysis. These difficulties are amplified when applying multiverse analysis to assess the robustness of the effect estimates from a mediation analysis, as a mediation analysis involves more data analytical decisions than a bivariate analysis. The aim of this paper is to provide an overview and worked example of the use of multiverse analysis to assess the robustness of the effect estimates from a mediation analysis. We showed that the number of data analytical decisions in a mediation analysis is larger than in a bivariate analysis. By using a real-life data example from the Longitudinal Aging Study Amsterdam, we demonstrated the application of multiverse analysis to a mediation analysis. This included the use of specification curves to determine the impact of data analytical decisions on the magnitude and statistical significance of the direct, indirect, and total effect estimates. Although the multiverse analysis methodology is still relatively new and future research is needed to further advance this methodology, this paper shows that multiverse analysis is a useful method for the assessment of the robustness of the direct, indirect, and total effect estimates in a mediation analysis and thereby to inform replication studies., (© 2021. The Author(s).)

We aimed to determine the added value of baseline metabolic tumor volume (MTV) and interim PET (I-PET) to the age-adjusted international prognostic index (aaIPI) to predict 2-y progression-free survival (PFS) in diffuse large B-cell lymphoma. Secondary objectives were to investigate optimal I-PET response criteria (using Deauville score [DS] or quantitative change in SUV max [ΔSUV max ] between baseline and I-PET4 [observational I-PET scans after 4 cycles of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone administered in 2-wk intervals with intensified rituximab in the first 4 cycles [R(R)-CHOP14]). Methods: I-PET4 scans in the HOVON-84 (Hemato-Oncologie voor Volwassenen Nederland [Haemato Oncology Foundation for Adults in the Netherlands]) randomized clinical trial (EudraCT 2006-005174-42) were centrally reviewed using DS (cutoff, 4-5). Additionally, ΔSUV max (prespecified cutoff, 70%) and baseline MTV were measured. Multivariable hazard ratio (HR), positive predictive value (PPV), and negative predictive value (NPV) were obtained for 2-y PFS. Results: In total, 513 I-PET4 scans were reviewed according to DS, and ΔSUV max and baseline MTV were available for 367 and 296 patients. The NPV of I-PET ranged between 82% and 86% for all PET response criteria. Univariate HR and PPV were better for ΔSUV max (4.8% and 53%, respectively) than for DS (3.1% and 38%, respectively). aaIPI and ΔSUV max independently predicted 2-y PFS (HR, 3.2 and 5.0, respectively); adding MTV brought about a slight improvement. Low or low-intermediate aaIPI combined with a ΔSUV max of more than 70% (37% of patients) yielded an NPV of 93%, and the combination of high-intermediate or high aaIPI and a ΔSUV max of 70% or less yielded a PPV of 65%. Conclusion: In this study on diffuse large B-cell lymphoma, I-PET after 4 cycles of R(R)-CHOP14 added predictive value to aaIPI for 2-y PFS, and both were independent response biomarkers in a multivariable Cox model. We externally validated that ΔSUV max outperformed DS in 2-y PFS prediction., (© 2022 by the Society of Nuclear Medicine and Molecular Imaging.)

Purpose: Marker-by-treatment analyses are promising new methods in internal medicine, but have not yet been implemented in orthopaedics. With this analysis, specific cut-off points may be obtained, that can potentially identify whether meniscal surgery or physical therapy is the superior intervention for an individual patient. This study aimed to introduce a novel approach in orthopaedic research to identify relevant treatment selection markers that affect treatment outcome following meniscal surgery or physical therapy in patients with degenerative meniscal tears., Methods: Data were analysed from the ESCAPE trial, which assessed the treatment of patients over 45 years old with a degenerative meniscal tear. The treatment outcome of interest was a clinically relevant improvement on the International Knee Documentation Committee Subjective Knee Form at 3, 12, and 24 months follow-up. Logistic regression models were developed to predict the outcome using baseline characteristics (markers), the treatment (meniscal surgery or physical therapy), and a marker-by-treatment interaction term. Interactions with p < 0.10 were considered as potential treatment selection markers and used these to develop predictiveness curves which provide thresholds to identify marker-based differences in clinical outcomes between the two treatments., Results: Potential treatment selection markers included general physical health, pain during activities, knee function, BMI, and age. While some marker-based thresholds could be identified at 3, 12, and 24 months follow-up, none of the baseline characteristics were consistent markers at all three follow-up times., Conclusion: This novel in-depth analysis did not result in clear clinical subgroups of patients who are substantially more likely to benefit from either surgery or physical therapy. However, this study may serve as an exemplar for other orthopaedic trials to investigate the heterogeneity in treatment effect. It will help clinicians to quantify the additional benefit of one treatment over another at an individual level, based on the patient's baseline characteristics., Level of Evidence: II., (© 2022. The Author(s).)

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 < 0.01). Of note, a high risk of bias, publication bias and a high heterogeneity across the studies was observed. This systematic review and meta-analysis sheds light on the impact of RT protocols on BBB integrity and opens the discussion for integrating this factor in the decision-making process of future RT, with better study of its occurrence and influence on concomitant or adjuvant therapies., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

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., (© 2022. The Author(s).)

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., (© 2022 The Author(s).)

Background and Purpose: Women have worse outcomes than men after stroke. Differences in presentation may lead to misdiagnosis and, in part, explain these disparities. We investigated whether there are sex differences in clinical presentation of acute stroke or transient ischemic attack., Methods: We conducted a systematic review and meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Inclusion criteria were (1) cohort, cross-sectional, case-control, or randomized controlled trial design; (2) admission for (suspicion of) ischemic or hemorrhagic stroke or transient ischemic attack; and (3) comparisons possible between sexes in ≥1 nonfocal or focal acute stroke symptom(s). A random-effects model was used for our analyses. We performed sensitivity and subanalyses to help explain heterogeneity and used the Newcastle-Ottawa Scale to assess bias., Results: We included 60 studies (n=582 844; 50% women). In women, headache (pooled odds ratio [OR], 1.24 [95% CI, 1.11-1.39]; I 2 =75.2%; 30 studies) occurred more frequently than in men with any type of stroke, as well as changes in consciousness/mental status (OR, 1.38 [95% CI, 1.19-1.61]; I 2 =95.0%; 17 studies) and coma/stupor (OR, 1.39 [95% CI, 1.25-1.55]; I 2 =27.0%; 13 studies). Aspecific or other neurological symptoms (nonrotatory dizziness and non-neurological symptoms) occurred less frequently in women (OR, 0.96 [95% CI, 0.94-0.97]; I 2 =0.1%; 5 studies). Overall, the presence of focal symptoms was not associated with sex (pooled OR, 1.03) although dysarthria (OR, 1.14 [95% CI, 1.04-1.24]; I 2 =48.6%; 11 studies) and vertigo (OR, 1.23 [95% CI, 1.13-1.34]; I 2 =44.0%; 8 studies) occurred more frequently, whereas symptoms of paresis/hemiparesis (OR, 0.73 [95% CI, 0.54-0.97]; I 2 =72.6%; 7 studies) and focal visual disturbances (OR, 0.83 [95% CI, 0.70-0.99]; I 2 =62.8%; 16 studies) occurred less frequently in women compared with men with any type of stroke. Most studies contained possible sources of bias., Conclusions: There may be substantive differences in nonfocal and focal stroke symptoms between men and women presenting with acute stroke or transient ischemic attack, but sufficiently high-quality studies are lacking. More studies are needed to address this because sex differences in presentation may lead to misdiagnosis and undertreatment.

Objectives: Development and internal validation of prognostic models for post-treatment and 1-year recovery in patients with neck pain in primary care., Design: Prospective cohort study., Setting: Primary care manual therapy practices., Participants: Patients with non-specific neck pain of any duration (n=1193)., Intervention: Usual care manual therapy., Outcome Measures: Recovery defined in terms of pain intensity, disability, and global perceived improvement directly post-treatment and at 1-year follow-up., Results: All post-treatment models exhibited acceptable discriminative performance after derivation (AUC≥0.7). The developed post-treatment disability model exhibited the best overall performance (R 2 =0.24; IQR, 0.22-0.26), discrimination (AUC=0.75; 95% CI, 0.63-0.84), and calibration (slope 0.92; IQR, 0.91-0.93). After internal validation and penalization, this model retained acceptable discriminative performance (AUC=0.74). The five other models, including those predicting 1-year recovery, did not reach acceptable discriminative performance after internal validation. Baseline pain duration, disability, and pain intensity were consistent predictors across models., Conclusion: A post-treatment prognostic model for disability was successfully developed and internally validated. This model has potential to inform primary care clinicians about a patient's individual prognosis after treatment, but external validation is required before clinical use can be recommended., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Neck pain is one of the leading causes of disability in most countries and it is likely to increase further. Numerous prognostic models for people with neck pain have been developed, few have been validated. In a recent systematic review, external validation of three promising models was advised before they can be used in clinical practice., Objective: The purpose of this study was to externally validate three promising models that predict neck pain recovery in primary care., Methods: This validation cohort consisted of 1311 patients with neck pain of any duration who were prospectively recruited and treated by 345 manual therapists in the Netherlands. Outcome measures were disability (Neck Disability Index) and recovery (Global Perceived Effect Scale) post-treatment and at 1-year follow-up. The assessed models were an Australian Whiplash-Associated Disorders (WAD) model (Amodel), a multicenter WAD model (Mmodel), and a Dutch non-specific neck pain model (Dmodel). Models' discrimination and calibration were evaluated., Results: The Dmodel and Amodel discriminative performance (AUC < 0.70) and calibration measures (slope largely different from 1) were poor. The Mmodel could not be evaluated since several variables nor their proxies were available., Conclusions: External validation of promising prognostic models for neck pain recovery was not successful and their clinical use cannot be recommended. We advise clinicians to underpin their current clinical reasoning process with evidence-based individual prognostic factors for recovery. Further research on finding new prognostic factors and developing and validating models with up-to-date methodology is needed for recovery in patients with neck pain in primary care., Competing Interests: Conflict of Interest The authors declare no conflicts of interest, (Copyright © 2021 The Authors. Publicado por Elsevier España, S.L.U. All rights reserved.)

Background: When resources are limited, occupational health survey participants are usually invited to consultations based on an occupational health provider's subjective considerations. This study aimed to find health survey participants at risk of long-term (i.e., ≥ 42 consecutive days) sickness absence (LTSA) for consultations with occupational health providers (OHPs)., Methods: The data of 64 011 non-sicklisted participants in occupational health surveys between 2010 and 2015 were used for the study. In a random sample of 40 000 participants, 27 survey variables were included in decision tree analysis (DTA) predicting LTSA at 1-year follow-up. The decision tree was transferred into a strategy to find participants for OHP consultations, which was then tested in the remaining 24 011 participants., Results: In the development sample, 1358 (3.4%) participants had LTSA at 1-year follow-up. DTA produced a decision tree with work ability as first splitting variable; company size and sleep problems were the other splitting variables. A strategy differentiating by company size would find 75% of the LTSA cases in small (≤99 workers) companies and 43% of the LTSA cases in medium-sized (100-499 workers) companies. For large companies (≥500 workers), case-finding was only 25%., Conclusions: In small and medium-sized companies, work ability and sleep problems can be used to find occupational health survey participants for OHP consultations aimed at preventing LTSA. Research is needed to further develop a case-finding strategy for large companies., (© The Author(s) 2021. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.)

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., (© 2021. The Author(s).)