Comparative Analysis for Computer-Based Decision Support: Case Study of Knee Osteoarthritis

McCabe, Philippa Grace; Olier, Iván; Ortega‐Martorell, Sandra; Jarman, Ian H.; Baltzopoulos, Vasilios; Lisboa, Paulo J. G.

doi:10.1007/978-3-030-33617-2_13

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“…The model used in the diagnostic analysis was logistic regression. The logistic regression approach was chosen after considering alternative analysis methods [29].…”

Section: Methodsmentioning

confidence: 99%

“…The self-reported data is made up from subject's answers to questionnaires relating to their symptoms and how they are impacted, recorded at the first presentation meeting, along with data made up of answers on questions about how much they take exercise and how this impacts them. An initial analysis looking at only the clinical and demographic work was conducted, and the subsequently presented at IDEAL 2019, showing the idea for the diagnostic model [29].…”

Section: Plos Onementioning

confidence: 99%

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Externally validated models for first diagnosis and risk of progression of knee osteoarthritis

et al. 2022

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Objective We develop and externally validate two models for use with radiological knee osteoarthritis. They consist of a diagnostic model for KOA and a prognostic model of time to onset of KOA. Model development and optimisation used data from the Osteoarthritis initiative (OAI) and external validation for both models was by application to data from the Multicenter Osteoarthritis Study (MOST). Materials and methods The diagnostic model at first presentation comprises subjects in the OAI with and without KOA (n = 2006), modelling with multivariate logistic regression. The prognostic sample involves 5-year follow-up of subjects presenting without clinical KOA (n = 1155), with modelling with Cox regression. In both instances the models used training data sets of n = 1353 and 1002 subjects and optimisation used test data sets of n = 1354 and 1003. The external validation data sets for the diagnostic and prognostic models comprised n = 2006 and n = 1155 subjects respectively. Results The classification performance of the diagnostic model on the test data has an AUC of 0.748 (0.721–0.774) and 0.670 (0.631–0.708) in external validation. The survival model has concordance scores for the OAI test set of 0.74 (0.7325–0.7439) and in external validation 0.72 (0.7190–0.7373). The survival approach stratified the population into two risk cohorts. The separation between the cohorts remains when the model is applied to the validation data. Discussion The models produced are interpretable with app interfaces that implement nomograms. The apps may be used for stratification and for patient education over the impact of modifiable risk factors. The externally validated results, by application to data from a substantial prospective observational study, show the robustness of models for likelihood of presenting with KOA at an initial assessment based on risk factors identified by the OAI protocol and stratification of risk for developing KOA in the next five years. Conclusion Modelling clinical KOA from OAI data validates well for the MOST data set. Both risk models identified key factors for differentiation of the target population from commonly available variables. With this analysis there is potential to improve clinical management of patients.

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“…The model used in the diagnostic analysis was logistic regression. The logistic regression approach was chosen after considering alternative analysis methods [29].…”

Section: Methodsmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%