An enhanced version of FREM (Fracture Risk Evaluation Model) using national administrative health data: analysis protocol for development and validation of a multivariable prediction model

Diagnostic and Prognostic Research

Protocol

Bo Abrahamsen¹  Michael Kriegbaum Skjødt¹  Katrine Hass Rubin²  Sören Möller²  Anne Clausen²  Simon Bang Kristensen³  Jens Søndergaard⁴ 

[1] Research Unit OPEN, Department of Clinical Research, University of Southern Denmark, Heden 16, 5000, Odense C, Denmark;Department of Medicine, Holbæk Hospital, Holbæk, Denmark;Research Unit OPEN, Department of Clinical Research, University of Southern Denmark, Heden 16, 5000, Odense C, Denmark;OPEN - Open Patient data Explorative Network, Odense University Hospital, Odense, Denmark;Research Unit OPEN, Department of Clinical Research, University of Southern Denmark, Heden 16, 5000, Odense C, Denmark;OPEN - Open Patient data Explorative Network, Odense University Hospital, Odense, Denmark;Department of Public Health, Aarhus University, Aarhus, Denmark;Research Unit of General Practice, Department of Public Health, University of Southern Denmark, Odense, Denmark;

关键词: Automated risk calculation;    Machine learning;    Prediction algorithm;    Decision support tool;    Osteoporotic fractures;    Register data;    Primary care;    General practice;    Decision aid;    LASSO regularization;    Gradient-boosted classification trees;   

DOI  :  10.1186/s41512-023-00158-w

 received in 2023-05-02, accepted in 2023-09-11,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundOsteoporosis poses a growing healthcare challenge owing to its rising prevalence and a significant treatment gap, as patients are widely underdiagnosed and consequently undertreated, leaving them at high risk of osteoporotic fracture. Several tools aim to improve case-finding in osteoporosis. One such tool is the Fracture Risk Evaluation Model (FREM), which in contrast to other tools focuses on imminent fracture risk and holds potential for automation as it relies solely on data that is routinely collected via the Danish healthcare registers. The present article is an analysis protocol for a prediction model that is to be used as a modified version of FREM, with the intention of improving the identification of subjects at high imminent risk of fracture by including pharmacological exposures and using more advanced statistical methods compared to the original FREM. Its main purposes are to document and motivate various aspects and choices of data management and statistical analyses.MethodsThe model will be developed by employing logistic regression with grouped LASSO regularization as the primary statistical approach and gradient-boosted classification trees as a secondary statistical modality. Hyperparameter choices as well as computational considerations on these two approaches are investigated by an unsupervised data review (i.e., blinded to the outcome), which also investigates and handles multicollinarity among the included exposures. Further, we present an unsupervised review of the data and testing of analysis code with respect to speed and robustness on a remote analysis environment. The data review and code tests are used to adjust the analysis plans in a blinded manner, so as not to increase the risk of overfitting in the proposed methods.DiscussionThis protocol specifies the planned tool development to ensure transparency in the modeling approach, hence improving the validity of the enhanced tool to be developed. Through an unsupervised data review, it is further documented that the planned statistical approaches are feasible and compatible with the data employed.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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