"ranges of continuous predictors, such as age). A good example of a prediction model that has been inadequately reported, making evaluations by independent investigators impossible [36,37], yet appears in numerous clinical guidelines [4,38] is the FRAX model for predicting the risk of osteoporotic fracture . "
[Show abstract][Hide abstract] ABSTRACT: Before considering whether to use a multivariable (diagnostic or prognostic) prediction model, it is essential that its performance be evaluated in data that were not used to develop the model (referred to as external validation). We critically appraised the methodological conduct and reporting of external validation studies of multivariable prediction models.
We conducted a systematic review of articles describing some form of external validation of one or more multivariable prediction models indexed in PubMed core clinical journals published in 2010. Study data were extracted in duplicate on design, sample size, handling of missing data, reference to the original study developing the prediction models and predictive performance measures.
11,826 articles were identified and 78 were included for full review, which described the evaluation of 120 prediction models. in participant data that were not used to develop the model. Thirty-three articles described both the development of a prediction model and an evaluation of its performance on a separate dataset, and 45 articles described only the evaluation of an existing published prediction model on another dataset. Fifty-seven percent of the prediction models were presented and evaluated as simplified scoring systems. Sixteen percent of articles failed to report the number of outcome events in the validation datasets. Fifty-four percent of studies made no explicit mention of missing data. Sixty-seven percent did not report evaluating model calibration whilst most studies evaluated model discrimination. It was often unclear whether the reported performance measures were for the full regression model or for the simplified models.
The vast majority of studies describing some form of external validation of a multivariable prediction model were poorly reported with key details frequently not presented. The validation studies were characterised by poor design, inappropriate handling and acknowledgement of missing data and one of the most key performance measures of prediction models i.e. calibration often omitted from the publication. It may therefore not be surprising that an overwhelming majority of developed prediction models are not used in practice, when there is a dearth of well-conducted and clearly reported (external validation) studies describing their performance on independent participant data.
BMC Medical Research Methodology 03/2014; 14(1):40. DOI:10.1186/1471-2288-14-40 · 2.27 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Osteoporosis poses a significant public health issue. In recent years, International and National Societies have developed Guidelines for the diagnosis and treatment of this disorder, with an effort of adapting specific tools for risk assessment on the peculiar characteristics of a given population. The Società Italiana dell'Osteoporosi, del Metabolismo Minerale e delle Malattie dello Scheletro (SIOMMMS) has recently revised the previously published Guidelines on the diagnosis, risk-assessment, prevention and management of idiopathic postmenopausal osteoporosis, also focusing on male and secondary osteoporosis. These recommendations are based on systematic reviews of the best available evidence and explicit consideration of cost effectiveness. When minimal evidence is available, recommendations are based on leading experts' experience and opinion, and on good clinical practice. Nonetheless, the practical management of osteoporosis is greatly influenced by economic reimbursement policies, particularly for secondary forms of osteoporosis. The refinement of risk assessment, the long-term treatment of osteoporosis and the prevention and management of disease-associated bone loss constitute open issues.
Clinical Cases in Mineral and Bone Metabolism 09/2012; 9(3):170-8.
[Show abstract][Hide abstract] ABSTRACT: Postmenopausal osteoporosis has a big impact on health care budget worldwide, which are expected to double by 2050. In spite of severe medical and socioeconomic consequences from fragility fractures, there are insufficient efforts in optimizing osteoporotic treatment and prevention. Undertreatment of osteoporosis is a well known phenomenon, particularly in elderly patients. Treatment rates remain low across virtually all patient, provider, and hospital-level characteristics, even after fragility fractures. In-hospital initiation is one of the options to increase treatment rates and improve osteoporosis management. However, multiple factors contribute to the failure of initiating appropriate treatment of osteoporosis in patients with fragility fractures. These include a lack of knowledge in osteoporosis and an absence of a comprehensive treatment guideline among family physicians and orthopedic surgeons. Furthermore, orthopedic surgeons are hardly willing to accept their responsibility for osteoporosis treatment due to the fact that they are usually not familiar with the initiation of specific drug treatments. The presented algorithm offers trauma surgeons and orthopedic surgeons a safe and simple guided pathway of treating osteoporosis in postmenopausal women appropriately after fragility fractures based on the current literature. From our point of view, this algorithm is useful for almost all cases and the user can expect treatment recommendations in more than 90 % of all cases. Nevertheless, some patients may require specialized review by an endocrinologist. The proposed algorithm may help to increase the rate of appropriate osteoporosis treatment hence reducing the rates of fragility fractures.
Archives of Orthopaedic and Trauma Surgery 05/2013; 133(8). DOI:10.1007/s00402-013-1774-x · 1.60 Impact Factor
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