Ferroelectric incommensurate spin crystals.

Ferroics, especially ferromagnets, can form complex topological spin structures such as vortices1 and skyrmions2,3 when subjected to particular electrical and mechanical boundary conditions. Simple vortex-like, electric-dipole-based topological structures have been observed in dedicated ferroelectric systems, especially ferroelectric-insulator superlattices such as PbTiO3/SrTiO3, which was later shown to be a model system owing to its high depolarizing field4-8. To date, the electric dipole equivalent of ordered magnetic spin lattices driven by the Dzyaloshinskii-Moriya interaction (DMi)9,10 has not been experimentally observed. Here we examine a domain structure in a single PbTiO3 epitaxial layer sandwiched between SrRuO3 electrodes. We observe periodic clockwise and anticlockwise ferroelectric vortices that are modulated by a second ordering along their toroidal core. The resulting topology, supported by calculations, is a labyrinth-like pattern with two orthogonal periodic modulations that form an incommensurate polar crystal that provides a ferroelectric analogue to the recently discovered incommensurate spin crystals in ferromagnetic materials11-13. These findings further blur the border between emergent ferromagnetic and ferroelectric topologies, clearing the way for experimental realization of further electric counterparts of magnetic DMi-driven phases.

Confounder Adjustment Using the Disease Risk Score: A Proposal for Weighting Methods.

Propensity score analysis is a common approach to addressing confounding in nonrandomized studies. Its implementation, however, requires important assumptions (e.g., positivity). The disease risk score (DRS) is an alternative confounding score that can relax some of these assumptions. Like the propensity score, the DRS summarizes multiple confounders into a single score, on which conditioning by matching allows the estimation of causal effects. However, matching relies on arbitrary choices for pruning out data (e.g., matching ratio, algorithm, and caliper width) and may be computationally demanding. Alternatively, weighting methods, common in propensity score analysis, are easy to implement and may entail fewer choices, yet none have been developed for the DRS. Here we present 2 weighting approaches: One derives directly from inverse probability weighting; the other, named target distribution weighting, relates to importance sampling. We empirically show that inverse probability weighting and target distribution weighting display performance comparable to matching techniques in terms of bias but outperform them in terms of efficiency (mean squared error) and computational speed (up to >870 times faster in an illustrative study). We illustrate implementation of the methods in 2 case studies where we investigate placebo treatments for multiple sclerosis and administration of aspirin in stroke patients.

Multiple imputation of incomplete multilevel data using Heckman selection models.

Missing data is a common problem in medical research, and is commonly addressed using multiple imputation. Although traditional imputation methods allow for valid statistical inference when data are missing at random (MAR), their implementation is problematic when the presence of missingness depends on unobserved variables, that is, the data are missing not at random (MNAR). Unfortunately, this MNAR situation is rather common, in observational studies, registries and other sources of real-world data. While several imputation methods have been proposed for addressing individual studies when data are MNAR, their application and validity in large datasets with multilevel structure remains unclear. We therefore explored the consequence of MNAR data in hierarchical data in-depth, and proposed a novel multilevel imputation method for common missing patterns in clustered datasets. This method is based on the principles of Heckman selection models and adopts a two-stage meta-analysis approach to impute binary and continuous variables that may be outcomes or predictors and that are systematically or sporadically missing. After evaluating the proposed imputation model in simulated scenarios, we illustrate it use in a cross-sectional community survey to estimate the prevalence of malaria parasitemia in children aged 2-10 years in five regions in Uganda.

Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines.

Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodology and reporting quality of individual-level patient data meta-analyses (IPD-MAs) conducted with non-randomized exposures, published in 2009, 2014, and 2019 that sought to estimate a causal relationship in medicine. We screened over 16,000 titles and abstracts, reviewed 45 full-text articles out of the 167 deemed potentially eligible, and included 29 into the analysis. Unfortunately, we found that causal methodologies were rarely implemented, and reporting was generally poor across studies. Specifically, only three of the 29 articles used quasi-experimental methods, and no study used G-methods to adjust for time-varying confounding. To address these issues, we propose stronger collaborations between physicians and methodologists to ensure that causal methodologies are properly implemented in IPD-MAs. In addition, we put forward a suggested checklist of reporting guidelines for IPD-MAs that utilize causal methods. This checklist could improve reporting thereby potentially enhancing the quality and trustworthiness of IPD-MAs, which can be considered one of the most valuable sources of evidence for health policy.

Acute clinical evaluation for the diagnosis of lateral ankle ligament injuries is useful: A comparison between the acute and delayed settings.

PURPOSE: To determine the diagnostic value of seven injury history variables, nine clinical tests (including the combination thereof) and overall clinical suspicion for complete discontinuity of the lateral ankle ligaments in the acute (0-2 days post-injury) and delayed setting (5-8 days post-injury). METHODS: All acute ankle injuries in adult athletes (≥18 years) presenting up to 2 days post-injury were assessed for eligibility. Athletes were excluded if imaging studies demonstrated a frank fracture or 3 T MRI could not be acquired within 10 days post-injury. Using standardized history variables and clinical tests, acute clinical evaluation was performed within 2 days post-injury. Delayed clinical evaluation was performed 5-8 days post-injury. Overall, clinical suspicion was recorded after clinical evaluation. MRI was used as the reference standard. RESULTS: Between February 2018 and February 2020, a total of 117 acute ankle injuries were screened for eligibility, of which 43 were included in this study. Complete discontinuity of lateral ankle ligaments was observed in 23 (53%) acute ankle injuries. In the acute setting, lateral swelling had 100% (95% confidence interval [CI]: 82-100) sensitivity, haematoma had 85% (95% CI: 61-96) specificity and the anterior drawer test had 100% (95% CI: 77-100) specificity. In the delayed setting, sensitivity for the presence of haematoma improved from 43% (95% CI: 24-65) to 91% (95% CI: 70-98; p < 0.01) and the sensitivity of the anterior drawer test improved from 21% (95% CI: 7-46) to 61% (95% CI: 39-80; p = 0.02). Clinical suspicion had a positive likelihood ratio (LR) of 4.35 (95% CI: 0.55-34.17) in the acute setting and a positive LR of 6.09 (95% CI: 1.57-23.60) in the delayed setting. CONCLUSIONS: In the acute setting, clinical evaluation can exclude complete discontinuity (e.g., absent lateral swelling) and identify athletes with a high probability of complete discontinuity (e.g., positive anterior drawer test) of the lateral ankle ligaments. In the delayed setting, the sensitivity of common clinical findings increases resulting in an improved diagnostic accuracy. In clinical practice, this study underlines the importance of meticulous clinical evaluation in the acute setting. LEVEL OF EVIDENCE: Level III.

Propensity-based standardization to enhance the validation and interpretation of prediction model discrimination for a target population.

External validation of the discriminative ability of prediction models is of key importance. However, the interpretation of such evaluations is challenging, as the ability to discriminate depends on both the sample characteristics (ie, case-mix) and the generalizability of predictor coefficients, but most discrimination indices do not provide any insight into their respective contributions. To disentangle differences in discriminative ability across external validation samples due to a lack of model generalizability from differences in sample characteristics, we propose propensity-weighted measures of discrimination. These weighted metrics, which are derived from propensity scores for sample membership, are standardized for case-mix differences between the model development and validation samples, allowing for a fair comparison of discriminative ability in terms of model characteristics in a target population of interest. We illustrate our methods with the validation of eight prediction models for deep vein thrombosis in 12 external validation data sets and assess our methods in a simulation study. In the illustrative example, propensity score standardization reduced between-study heterogeneity of discrimination, indicating that between-study variability was partially attributable to case-mix. The simulation study showed that only flexible propensity-score methods (allowing for non-linear effects) produced unbiased estimates of model discrimination in the target population, and only when the positivity assumption was met. Propensity score-based standardization may facilitate the interpretation of (heterogeneity in) discriminative ability of a prediction model as observed across multiple studies, and may guide model updating strategies for a particular target population. Careful propensity score modeling with attention for non-linear relations is recommended.

Measuring the performance of prediction models to personalize treatment choice.

When data are available from individual patients receiving either a treatment or a control intervention in a randomized trial, various statistical and machine learning methods can be used to develop models for predicting future outcomes under the two conditions, and thus to predict treatment effect at the patient level. These predictions can subsequently guide personalized treatment choices. Although several methods for validating prediction models are available, little attention has been given to measuring the performance of predictions of personalized treatment effect. In this article, we propose a range of measures that can be used to this end. We start by defining two dimensions of model accuracy for treatment effects, for a single outcome: discrimination for benefit and calibration for benefit. We then amalgamate these two dimensions into an additional concept, decision accuracy, which quantifies the model's ability to identify patients for whom the benefit from treatment exceeds a given threshold. Subsequently, we propose a series of performance measures related to these dimensions and discuss estimating procedures, focusing on randomized data. Our methods are applicable for continuous or binary outcomes, for any type of prediction model, as long as it uses baseline covariates to predict outcomes under treatment and control. We illustrate all methods using two simulated datasets and a real dataset from a trial in depression. We implement all methods in the R package predieval. Results suggest that the proposed measures can be useful in evaluating and comparing the performance of competing models in predicting individualized treatment effect.

Mesh, flap or combined repair of perineal hernia after abdominoperineal resection - A systematic review and meta-analysis.

AIM: The aim of this systematic review was to analyse recurrence rates after different surgical techniques for perineal hernia repair. METHOD: All original studies (n ≥ 2 patients) reporting recurrence rates after perineal hernia repair after abdominoperineal resection (APR) were included. The electronic database PubMed was last searched in December 2021. The primary outcome was recurrent perineal hernia. A weighted average of the logit proportions was determined by the use of the generic inverse variance method and random effects model. RESULTS: A total of 19 studies involving 172 patients were included. The mean age of patients was 64 ± 5.6 years and the indication for APR was predominantly cancer (99%, 170/172). The pooled percentage of recurrent perineal hernia was 39% (95% CI: 27%-52%) after biological mesh closure, 29% (95% CI: 21%-39%) after synthetic mesh closure, 37% (95% CI: 14%-67%) after tissue flap reconstruction only and 9% (95% CI: 1%-45%) after tissue flap reconstruction combined with mesh. CONCLUSION: Recurrence rates after mesh repair of perineal hernia are high, without a clear difference between biological and synthetic meshes. The addition of a tissue flap to mesh repair seemed to have a favourable outcome, which warrants further investigation.

Acute clinical evaluation for syndesmosis injury has high diagnostic value.

PURPOSE: To determine the diagnostic value of injury history, physical examination, six syndesmosis tests and overall clinical suspicion for syndesmosis injury. METHODS: All athletes (> 18 yrs) with an acute ankle injury presenting within 7 days post-injury were assessed for eligibility. Acute ankle injuries were excluded if imaging studies demonstrated a frank fracture or 3 T MRI could not be acquired within 10 days post-injury. Standardized injury history was recorded, and physical examination was performed by an Orthopaedic Surgeon or Sports Medicine Physician. Overall clinical suspicion was documented prior to MRI. Multivariate logistic regression was used to determine the association between independent predictors and syndesmosis injury. RESULTS: Between September 2016 and July 2019, a total of 150 acute ankle injuries were included. The median time from injury to acute clinical evaluation was 2 days (IQR 2). Prior to clinical evaluation, the median patient reported Visual Analog Scale for pain was 8/10 (IQR 2). Syndesmosis injury was present in 26 acute ankle injuries. An eversion mechanism of injury had a positive LR 3.47 (CI 95% 1.55-7.77). The squeeze tests had a positive LR of 2.20 (CI 95% 1.29-3.77) and a negative LR of 0.68 (CI 95% 0.48-0.98). Overall clinical suspicion had a sensitivity of 73% (CI 95% 52-88) and negative predictive value of 89% (CI 95% 78-95). Multivariate regression analyses demonstrated significant association for eversion mechanism of injury (OR 4.99; CI 95% 1.56-16.01) and a positive squeeze test (OR 3.25; CI 95% 1.24-8.51). CONCLUSIONS: In an acute clinical setting with patients reporting high levels of ankle pain, a negative overall clinical suspicion reduces the probability of syndesmosis injury. Eversion mechanism of injury and a positive squeeze test are associated with higher odds of syndesmosis injury. LEVEL OF EVIDENCE: Level III.

Prognostic models for chronic kidney disease: a systematic review and external validation.

BACKGROUND: Accurate risk prediction is needed in order to provide personalized healthcare for chronic kidney disease (CKD) patients. An overload of prognosis studies is being published, ranging from individual biomarker studies to full prediction studies. We aim to systematically appraise published prognosis studies investigating multiple biomarkers and their role in risk predictions. Our primary objective was to investigate if the prognostic models that are reported in the literature were of sufficient quality and to externally validate them. METHODS: We undertook a systematic review and appraised the quality of studies reporting multivariable prognosis models for end-stage renal disease (ESRD), cardiovascular (CV) events and mortality in CKD patients. We subsequently externally validated these models in a randomized trial that included patients from a broad CKD population. RESULTS: We identified 91 papers describing 36 multivariable models for prognosis of ESRD, 50 for CV events, 46 for mortality and 17 for a composite outcome. Most studies were deemed of moderate quality. Moreover, they often adopted different definitions for the primary outcome and rarely reported full model equations (21% of the included studies). External validation was performed in the Multifactorial Approach and Superior Treatment Efficacy in Renal Patients with the Aid of Nurse Practitioners trial (n = 788, with 160 events for ESRD, 79 for CV and 102 for mortality). The 24 models that reported full model equations showed a great variability in their performance, although calibration remained fairly adequate for most models, except when predicting mortality (calibration slope >1.5). CONCLUSIONS: This review shows that there is an abundance of multivariable prognosis models for the CKD population. Most studies were considered of moderate quality, and they were reported and analysed in such a manner that their results cannot directly be used in follow-up research or in clinical practice.

Diagnostic value of ultrasonography in acute lateral and syndesmotic ligamentous ankle injuries.

OBJECTIVES: To determine the diagnostic value of ultrasonography for complete discontinuity of the anterior talofibular ligament (ATFL), the calcaneofibular ligament (CFL) and the anterior inferior tibiofibular ligament (AITFL). METHODS: All acute ankle injuries in adult athletes (> 18 years old) presenting to the outpatient department of a specialised Orthopaedic and Sports Medicine Hospital within 7 days post-injury were assessed for eligibility. Using ultrasonography, one musculoskeletal radiologist assessed the ATFL, CFL and AITFL for complete discontinuity. Dynamic ultrasound measurements of the tibiofibular distance (mm) in both ankles (injured and contralateral) were acquired in the neutral position (N), during maximal external rotation (Max ER), and maximal internal rotation (Max IR). MR imaging was used as a reference standard. RESULTS: Between October 2017 and July 2019, 92 acute ankle injuries were included. Ultrasound diagnosed complete discontinuity of the ATFL with 87% (CI 74-95%) sensitivity and 69% (CI 53-82%) specificity. Discontinuity of the CFL was diagnosed with 29% (CI 10-56%) sensitivity and 92% (CI 83-97%) specificity. Ultrasound diagnosed discontinuity of the AITFL with 100% (CI 74-100%) sensitivity and 100% (CI 95-100%) specificity. Of the dynamic measurements, the side-to-side difference in external rotation had the highest diagnostic value for complete discontinuity of the AITFL (sensitivity 82%, specificity 86%; cut-off 0.93 mm). CONCLUSIONS: Ultrasound has a good to excellent diagnostic value for complete discontinuity of the ATFL and AITFL. Therefore, ultrasound can be used to screen for injury of the ATFL and AITFL. Compared with ultrasound, dynamic ultrasound has inferior diagnostic value for complete discontinuity of the AITFL. KEY POINTS: • Ultrasound has a good to excellent diagnostic value for complete discontinuity of the anterior talofibular ligament (ATFL) and anterior inferior tibiofibular ligament (AITFL). • Ultrasound can be used to screen for injury of the ATFL and AITFL. • Compared with ultrasound, dynamic ultrasound has inferior diagnostic value for complete discontinuity of the AITFL.

Developing more generalizable prediction models from pooled studies and large clustered data sets.

Prediction models often yield inaccurate predictions for new individuals. Large data sets from pooled studies or electronic healthcare records may alleviate this with an increased sample size and variability in sample characteristics. However, existing strategies for prediction model development generally do not account for heterogeneity in predictor-outcome associations between different settings and populations. This limits the generalizability of developed models (even from large, combined, clustered data sets) and necessitates local revisions. We aim to develop methodology for producing prediction models that require less tailoring to different settings and populations. We adopt internal-external cross-validation to assess and reduce heterogeneity in models' predictive performance during the development. We propose a predictor selection algorithm that optimizes the (weighted) average performance while minimizing its variability across the hold-out clusters (or studies). Predictors are added iteratively until the estimated generalizability is optimized. We illustrate this by developing a model for predicting the risk of atrial fibrillation and updating an existing one for diagnosing deep vein thrombosis, using individual participant data from 20 cohorts (N = 10 873) and 11 diagnostic studies (N = 10 014), respectively. Meta-analysis of calibration and discrimination performance in each hold-out cluster shows that trade-offs between average and heterogeneity of performance occurred. Our methodology enables the assessment of heterogeneity of prediction model performance during model development in multiple or clustered data sets, thereby informing researchers on predictor selection to improve the generalizability to different settings and populations, and reduce the need for model tailoring. Our methodology has been implemented in the R package metamisc.

Minimum sample size for external validation of a clinical prediction model with a binary outcome.

In prediction model research, external validation is needed to examine an existing model's performance using data independent to that for model development. Current external validation studies often suffer from small sample sizes and consequently imprecise predictive performance estimates. To address this, we propose how to determine the minimum sample size needed for a new external validation study of a prediction model for a binary outcome. Our calculations aim to precisely estimate calibration (Observed/Expected and calibration slope), discrimination (C-statistic), and clinical utility (net benefit). For each measure, we propose closed-form and iterative solutions for calculating the minimum sample size required. These require specifying: (i) target SEs (confidence interval widths) for each estimate of interest, (ii) the anticipated outcome event proportion in the validation population, (iii) the prediction model's anticipated (mis)calibration and variance of linear predictor values in the validation population, and (iv) potential risk thresholds for clinical decision-making. The calculations can also be used to inform whether the sample size of an existing (already collected) dataset is adequate for external validation. We illustrate our proposal for external validation of a prediction model for mechanical heart valve failure with an expected outcome event proportion of 0.018. Calculations suggest at least 9835 participants (177 events) are required to precisely estimate the calibration and discrimination measures, with this number driven by the calibration slope criterion, which we anticipate will often be the case. Also, 6443 participants (116 events) are required to precisely estimate net benefit at a risk threshold of 8%. Software code is provided.

Individual participant data meta-analysis for external validation, recalibration, and updating of a flexible parametric prognostic model.

Individual participant data (IPD) from multiple sources allows external validation of a prognostic model across multiple populations. Often this reveals poor calibration, potentially causing poor predictive performance in some populations. However, rather than discarding the model outright, it may be possible to modify the model to improve performance using recalibration techniques. We use IPD meta-analysis to identify the simplest method to achieve good model performance. We examine four options for recalibrating an existing time-to-event model across multiple populations: (i) shifting the baseline hazard by a constant, (ii) re-estimating the shape of the baseline hazard, (iii) adjusting the prognostic index as a whole, and (iv) adjusting individual predictor effects. For each strategy, IPD meta-analysis examines (heterogeneity in) model performance across populations. Additionally, the probability of achieving good performance in a new population can be calculated allowing ranking of recalibration methods. In an applied example, IPD meta-analysis reveals that the existing model had poor calibration in some populations, and large heterogeneity across populations. However, re-estimation of the intercept substantially improved the expected calibration in new populations, and reduced between-population heterogeneity. Comparing recalibration strategies showed that re-estimating both the magnitude and shape of the baseline hazard gave the highest predicted probability of good performance in a new population. In conclusion, IPD meta-analysis allows a prognostic model to be externally validated in multiple settings, and enables recalibration strategies to be compared and ranked to decide on the least aggressive recalibration strategy to achieve acceptable external model performance without discarding existing model information.

A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint.

Randomized trials typically estimate average relative treatment effects, but decisions on the benefit of a treatment are possibly better informed by more individualized predictions of the absolute treatment effect. In case of a binary outcome, these predictions of absolute individualized treatment effect require knowledge of the individual's risk without treatment and incorporation of a possibly differential treatment effect (ie, varying with patient characteristics). In this article, we lay out the causal structure of individualized treatment effect in terms of potential outcomes and describe the required assumptions that underlie a causal interpretation of its prediction. Subsequently, we describe regression models and model estimation techniques that can be used to move from average to more individualized treatment effect predictions. We focus mainly on logistic regression-based methods that are both well-known and naturally provide the required probabilistic estimates. We incorporate key components from both causal inference and prediction research to arrive at individualized treatment effect predictions. While the separate components are well known, their successful amalgamation is very much an ongoing field of research. We cut the problem down to its essentials in the setting of a randomized trial, discuss the importance of a clear definition of the estimand of interest, provide insight into the required assumptions, and give guidance with respect to modeling and estimation options. Simulated data illustrate the potential of different modeling options across scenarios that vary both average treatment effect and treatment effect heterogeneity. Two applied examples illustrate individualized treatment effect prediction in randomized trial data.

On the aggregation of published prognostic scores for causal inference in observational studies.

As real world evidence on drug efficacy involves nonrandomized studies, statistical methods adjusting for confounding are needed. In this context, prognostic score (PGS) analysis has recently been proposed as a method for causal inference. It aims to restore balance across the different treatment groups by identifying subjects with a similar prognosis for a given reference exposure ("control"). This requires the development of a multivariable prognostic model in the control arm of the study sample, which is then extrapolated to the different treatment arms. Unfortunately, large cohorts for developing prognostic models are not always available. Prognostic models are therefore subject to a dilemma between overfitting and parsimony; the latter being prone to a violation of the assumption of no unmeasured confounders when important covariates are ignored. Although it is possible to limit overfitting by using penalization strategies, an alternative approach is to adopt evidence synthesis. Aggregating previously published prognostic models may improve the generalizability of PGS, while taking account of a large set of covariates-even when limited individual participant data are available. In this article, we extend a method for prediction model aggregation to PGS analysis in nonrandomized studies. We conduct extensive simulations to assess the validity of model aggregation, compared with other methods of PGS analysis for estimating marginal treatment effects. We show that aggregating existing PGS into a "meta-score" is robust to misspecification, even when elementary scores wrongfully omit confounders or focus on different outcomes. We illustrate our methods in a setting of treatments for asthma.

Handling missing predictor values when validating and applying a prediction model to new patients.

Missing data present challenges for development and real-world application of clinical prediction models. While these challenges have received considerable attention in the development setting, there is only sparse research on the handling of missing data in applied settings. The main unique feature of handling missing data in these settings is that missing data methods have to be performed for a single new individual, precluding direct application of mainstay methods used during model development. Correspondingly, we propose that it is desirable to perform model validation using missing data methods that transfer to practice in single new patients. This article compares existing and new methods to account for missing data for a new individual in the context of prediction. These methods are based on (i) submodels based on observed data only, (ii) marginalization over the missing variables, or (iii) imputation based on fully conditional specification (also known as chained equations). They were compared in an internal validation setting to highlight the use of missing data methods that transfer to practice while validating a model. As a reference, they were compared to the use of multiple imputation by chained equations in a set of test patients, because this has been used in validation studies in the past. The methods were evaluated in a simulation study where performance was measured by means of optimism corrected C-statistic and mean squared prediction error. Furthermore, they were applied in data from a large Dutch cohort of prophylactic implantable cardioverter defibrillator patients.

Individual participant data meta-analysis to examine interactions between treatment effect and participant-level covariates: Statistical recommendations for conduct and planning.

Precision medicine research often searches for treatment-covariate interactions, which refers to when a treatment effect (eg, measured as a mean difference, odds ratio, hazard ratio) changes across values of a participant-level covariate (eg, age, gender, biomarker). Single trials do not usually have sufficient power to detect genuine treatment-covariate interactions, which motivate the sharing of individual participant data (IPD) from multiple trials for meta-analysis. Here, we provide statistical recommendations for conducting and planning an IPD meta-analysis of randomized trials to examine treatment-covariate interactions. For conduct, two-stage and one-stage statistical models are described, and we recommend: (i) interactions should be estimated directly, and not by calculating differences in meta-analysis results for subgroups; (ii) interaction estimates should be based solely on within-study information; (iii) continuous covariates and outcomes should be analyzed on their continuous scale; (iv) nonlinear relationships should be examined for continuous covariates, using a multivariate meta-analysis of the trend (eg, using restricted cubic spline functions); and (v) translation of interactions into clinical practice is nontrivial, requiring individualized treatment effect prediction. For planning, we describe first why the decision to initiate an IPD meta-analysis project should not be based on between-study heterogeneity in the overall treatment effect; and second, how to calculate the power of a potential IPD meta-analysis project in advance of IPD collection, conditional on characteristics (eg, number of participants, standard deviation of covariates) of the trials (potentially) promising their IPD. Real IPD meta-analysis projects are used for illustration throughout.

Return to play after surgery for isolated unstable syndesmotic ankle injuries (West Point grade IIB and III) in 110 male professional football players: a retrospective cohort study.

OBJECTIVES: To evaluate time to return to play following surgical stabilisation of isolated unstable syndesmosis injuries in a cohort of professional male football players. METHODS: All professional football players undergoing surgery for isolated unstable syndesmosis injury (West Point grade ≥IIB) at a specialised Orthopaedic and Sports Medicine Hospital were followed up until return to play (minimum ≥6 months). Players with a stable syndesmosis, injuries older than 6 weeks, concomitant medial or lateral malleolar fracture or previous ankle surgery were excluded. During rehabilitation, time required to return to sports-specific rehabilitation, team training and first match play, were recorded. RESULTS: Between January 2012 and December 2017, a total of 110 male professional football players were included. The mean time required to begin on field rehabilitation was 37±12 days, while the mean time to return to team training was 72±28 days. The first official match was played on average 103±28 days postoperatively. Multivariable analysis revealed that the severity of injury, the concomitant presence of talar cartilage injury and the age of the player were significantly associated (p<0.00001) with time to return to on field rehabilitation, team training and match play. CONCLUSION: In this cohort of professional football players, surgical stabilisation of isolated unstable syndesmosis injuries (West Point grade ≥IIB) allowed for relatively quick return to play. High grade injury (West Point grade III), concomitant cartilage injury and greater age were associated with longer return to play times. LEVEL OF EVIDENCE: Longitudinal observational cohort study (level II).

Performance of the Framingham risk models and pooled cohort equations for predicting 10-year risk of cardiovascular disease: a systematic review and meta-analysis.

BACKGROUND: The Framingham risk models and pooled cohort equations (PCE) are widely used and advocated in guidelines for predicting 10-year risk of developing coronary heart disease (CHD) and cardiovascular disease (CVD) in the general population. Over the past few decades, these models have been extensively validated within different populations, which provided mounting evidence that local tailoring is often necessary to obtain accurate predictions. The objective is to systematically review and summarize the predictive performance of three widely advocated cardiovascular risk prediction models (Framingham Wilson 1998, Framingham ATP III 2002 and PCE 2013) in men and women separately, to assess the generalizability of performance across different subgroups and geographical regions, and to determine sources of heterogeneity in the findings across studies. METHODS: A search was performed in October 2017 to identify studies investigating the predictive performance of the aforementioned models. Studies were included if they externally validated one or more of the original models in the general population for the same outcome as the original model. We assessed risk of bias for each validation and extracted data on population characteristics and model performance. Performance estimates (observed versus expected (OE) ratio and c-statistic) were summarized using a random effects models and sources of heterogeneity were explored with meta-regression. RESULTS: The search identified 1585 studies, of which 38 were included, describing a total of 112 external validations. Results indicate that, on average, all models overestimate the 10-year risk of CHD and CVD (pooled OE ratio ranged from 0.58 (95% CI 0.43-0.73; Wilson men) to 0.79 (95% CI 0.60-0.97; ATP III women)). Overestimation was most pronounced for high-risk individuals and European populations. Further, discriminative performance was better in women for all models. There was considerable heterogeneity in the c-statistic between studies, likely due to differences in population characteristics. CONCLUSIONS: The Framingham Wilson, ATP III and PCE discriminate comparably well but all overestimate the risk of developing CVD, especially in higher risk populations. Because the extent of miscalibration substantially varied across settings, we highly recommend that researchers further explore reasons for overprediction and that the models be updated for specific populations.