# The influence of sex in diagnostic modelling of knee osteoarthritis

## TL;DR

This study compares generic and sex-specific diagnostic models for knee osteoarthritis and finds similar performance, but highlights sex-specific risk factors that could help early detection.

## Contribution

The study introduces sex-specific diagnostic models for knee osteoarthritis and identifies sex-specific risk factors.

## Key findings

- Generic and sex-specific models show comparable performance with overlapping confidence intervals.
- Sex-specific models include additional variables that are relevant for predicting KOA onset.
- Sex-specific risk factors could help identify females at higher risk of KOA earlier.

## Abstract

To compare diagnostic models for radiological KOA at KL2 + using sex-specific variables against a generic model with sex as an input. Data from the Osteoarthritis Initiative (OAI) was used for model development and optimisation.

Current models for diagnosis of knee osteoarthritis (KOA) at first presentation comprise subjects in the OAI dataset with and without KOA. We select subsets of the OAI data set for which additional sex-specific variables are available, resulting in male and female cohorts of size n = 1250 and n = 1442, respectively.

The classification performance of the previous diagnostic model on the test data has an area under the curve (AUC) of (95% CI 0.721–0.774) when only variables common to both sexes were entered for model selection and sex was a separate input. When tested separately on the male only and female cohort the test performance of the generic model gives baseline AUCs of (95% CI 0.689-0.770) and (95% CI 0.728-0.799) respectively. The sex-specific models for males and females yield AUCs of (95% CI 0.684-0.765) and (95% CI 0.731-0.803) respectively,

Fitting sex-specific models allows additional variables to be entered in the pool for model selection compared with a generic model with sex as a covariate. The focus of this study is whether the specificity of the additional data enhances their predictive power of logistic regression modelling for the diagnosis of incident radiological KOA in the OAI dataset, at first presentation. The performance of the generic and sex-specific models is comparable, since the confidence intervals for all of the models overlap. Nevertheless, some relevant variables after feature selection v are sex-specific, indicating that incidence of KOA at baseline presentation is associated with sex-specific attributes.

This specialisation of the sex-specific models indicates potential differences in the aetiology leading to disease onset and may provide greater utility to both clinicians and subjects. For instance, the risk factors identified by the specialised models provide quantitative indicators that useful for early identification of females at higher risk of KOA, prompting them to take proactive measures to improve joint health at an earlier stage in life.