Hypercortisolism is responsible for significant morbidity and mortality and is frequently a diagnostic challenge for clinicians. To treat Cushing's syndrome (CS) as soon as feasible, a rapid diagnosis is required.
A simple measure, the Cushing score, was created and internally validated in a new study to evaluate pre-test likelihood of hypercortisolism, and it has shown outstanding predictive value for the discriminating between people with and without a final diagnosis of Cushing's disease.
This study was conducted by Mirko Parasiliti-Caprino and team with the objective to create and evaluate a clinical model for estimating the pre-test likelihood of hypercortisolism in a high-risk group. The findings of this study were published in Frontiers in Endocrinology on 5th October, 2021.
Researchers performed a retrospective multicenter case-control study comprising five Italian Endocrinology referral facilities for this investigation (Turin, Messina, Naples, Padua and Rome). A total of 150 patients with Cushing's syndrome and 300 patients without hypercortisolism were included in the study. All patients were assessed for the possibility of hypercortisolism in accordance with current recommendations. The Cushing score was developed using multivariable logistic regression, with all major variables linked with a clinical suspicion of hypercortisolism included as potential predictors. A stepwise backward selection approach was utilized (final model AUC=0.873), followed by ten-fold cross-validation for internal validation.
The key findings of this study are:
1. The final estimation of the model performance revealed an average AUC=0.841, indicating a little overfitting impact.
2. The retrieved score was organized on a 17.5-point scale as follows: low-risk class (score value: 5.5, probability of disease=0.8%); intermediate-low-risk class (score value: 6-8.5, probability of disease=2.7%); intermediate-high-risk class (score value: 9-11.5, probability of disease=18.5%); and high-risk class (score value: 12, probability of disease=72.5%).
3. With an AUC of 0.873, clinical data demonstrated a great predictive value for distinguishing between participants with and without a final diagnosis of CS.
4. Obesity was maintained as a significant independent predictor of CS in this final multivariable model as well.
5. The accuracy of the Cushing score produced in our study is significantly higher than that of models.
In conclusion, this is the first research to describe an algorithm that is only based on clinical characteristics and can aid clinicians in differentiating instances with a low or high pre-test risk of CS. The derived Cushing score is a simple tool that could be widely adopted in clinical practice and could be of significant assistance in reducing the length and potential pitfalls in CS diagnostic work-up, with implications for patient health and health-care costs, particularly during the COVID-19 pandemic.
Reference:
Parasiliti-Caprino M, Bioletto F, Frigerio T, et al. A new clinical model to estimate the pre-test probability of Cushing's syndrome: The Cushing score. Front Endocrinol (Lausanne) Published Online October 5, 2021. doi:10.3389/fendo.2021.747549