Five critical quality criteria for artificial intelligence-based prediction models

To raise the quality of clinical artificial intelligence (AI) prediction modelling studies in the cardiovascular health domain and thereby improve their impact and relevancy, the editors for digital health, innovation, and quality standards of the European Heart Journal propose five minimal quality criteria for AI-based prediction model development and validation studies: complete reporting, carefully defined intended use of the model, rigorous validation, large enough sample size, and openness of code and software

Impact of predictor measurement heterogeneity across settings on performance of prediction models: a measurement error perspective

It is widely acknowledged that the predictive performance of clinical prediction models should be studied in patients that were not part of the data in which the model was derived. Out-of-sample performance can be hampered when predictors are measured differently at derivation and external validation. This may occur, for instance, when predictors are measured using different measurement protocols or when tests are produced by different manufacturers. Although such heterogeneity in predictor measurement between deriviation and validation data is common, the impact on the out-of-sample performance is not well studied. Using analytical and simulation approaches, we examined out-of-sample performance of prediction models under various scenarios of heterogeneous predictor measurement. These scenarios were defined and clarified using an established taxonomy of measurement error models. The results of our simulations indicate that predictor measurement heterogeneity can induce miscalibration of prediction and affects discrimination and overall predictive accuracy, to extents that the prediction model may no longer be considered clinically useful. The measurement error taxonomy was found to be helpful in identifying and predicting effects of heterogeneous predictor measurements between settings of prediction model derivation and validation. Our work indicates that homogeneity of measurement strategies across settings is of paramount importance in prediction research.Comment: 32 pages, 4 figure

Are Off-Field Activities an Underestimated Risk for Hamstring Injuries in Dutch Male Amateur Soccer Players? An Exploratory Analysis of a Prospective Cohort Study

Purpose The purpose of this study was to explore what extent male amateur soccer players participate in off-field activities and whether these off-field activities are associated with the development of hamstring injuries. Methods Amateur soccer players (n = 399) from first-class selection teams (n = 32) filled out a baseline screening questionnaire concerning off-field activities (i.e., work and study type and hours, traveling time, sleep, energy costs, and time spent on other activities) and their history of hamstring injury as a part of a cluster-randomized controlled trial. Throughout one competition, the players reported weekly their hamstring injuries, which were verified by medical/technical staff. Multivariable Firth corrected logistic regression models were used to explore associations between off-field activities and hamstring injuries. Results Sixty-five hamstring injuries were recorded. Previous injury was significantly associated with hamstring injuries (OR ranging from 1.94 [95% CI 1.45–2.61] to 2.02 [95% CI 1.49–2.73]), but off-field activities were not. Conclusion Although amateur soccer players spent a relatively large amount of time on off-field activities, we did not find off-field activities measured at baseline to be associated with hamstring injuries in the subsequent competitive soccer season. In contrast, previous hamstring injury was found to be strongly associated with (recurrent) hamstring injuries

Three myths about risk thresholds for prediction models

Acknowledgments This work was developed as part of the international initiative of strengthening analytical thinking for observational studies (STRATOS). The objective of STRATOS is to provide accessible and accurate guidance in the design and analysis of observational studies (http://stratos-initiative.org/). Members of the STRATOS Topic Group ‘Evaluating diagnostic tests and prediction models’ are Gary Collins, Carl Moons, Ewout Steyerberg, Patrick Bossuyt, Petra Macaskill, David McLernon, Ben van Calster, and Andrew Vickers. Funding The study is supported by the Research Foundation-Flanders (FWO) project G0B4716N and Internal Funds KU Leuven (project C24/15/037). Laure Wynants is a post-doctoral fellow of the Research Foundation – Flanders (FWO). The funding bodies had no role in the design of the study, collection, analysis, interpretation of data, nor in writing the manuscript. Contributions LW and BVC conceived the original idea of the manuscript, to which ES, MVS and DML then contributed. DT acquired the data. LW analyzed the data, interpreted the results and wrote the first draft. All authors revised the work, approved the submitted version, and are accountable for the integrity and accuracy of the work.Peer reviewedPublisher PD

The Effects of Lower-Extremity Plyometric Training on Soccer-Specific Outcomes in Adult Male Soccer Players:A Systematic Review and Meta-Analysis

BACKGROUND: Plyometric training is a specific form of strength training that is used to improve the physical performance of athletes. An overview of the effects of plyometric training on soccer-specific outcomes in adult male soccer players is not available yet. PURPOSE: To systematically review and meta-analyze the effects of plyometric training on soccer-specific outcome measures in adult male soccer players and to identify which programs are most effective. METHODS: PubMed, Embase/Medline, Cochrane, PEDro, and Scopus were searched. Extensive quality and risk of bias assessments were performed using the Cochrane ROBINS 2.0 for randomized trials. A random effects meta-analysis was performed using Cochrane Review Manager 5.3. RESULTS: Seventeen randomized trials were included in the meta-analysis. The impact of plyometric training on strength, jump height, sprint speed, agility, and endurance was assessed. Only jump height, 20-m sprint speed, and endurance were significantly improved by plyometric training in soccer players. Results of the risk of bias assessment of the included studies resulted in overall scores of some concerns for risk of bias and high risk of bias. CONCLUSION: This review and meta-analysis showed that plyometric training improved jump height, 20-m sprint speed, and endurance, but not strength, sprint speed over other distances, or agility in male adult soccer players. However, the low quality of the included studies and substantial heterogeneity means that results need to be interpreted with caution. Future high-quality research should indicate whether or not plyometric training can be used to improve soccer-specific outcomes and thereby enhance performance

Mecor: An R package for measurement error correction in linear regression models with a continuous outcome

Measurement error in a covariate or the outcome of regression models is common, but is often ignored, even though measurement error can lead to substantial bias in the estimated covariate-outcome association. While several texts on measurement error correction methods are available, these methods remain seldomly applied. To improve the use of measurement error correction methodology, we developed mecor, an R package that implements measurement error correction methods for regression models with a continuous outcome. Measurement error correction requires information about the measurement error model and its parameters. This information can be obtained from four types of studies, used to estimate the parameters of the measurement error model: an internal validation study, a replicates study, a calibration study and an external validation study. In the package mecor, regression calibration methods and a maximum likelihood method are implemented to correct for measurement error in a continuous covariate in regression analyses. Additionally, methods of moments methods are implemented to correct for measurement error in the continuous outcome in regression analyses. Variance estimation of the corrected estimators is provided in closed form and using the bootstrap