Methods of determining optimal cut-point of diagnostic biomarkers with application of clinical data in ROC analysis: an update review

INTRODUCTION

An important application of ROC analysis is the determination of the optimal cut-point for biomarkers in diagnostic studies. This comprehensive review provides a framework of cut-point election for biomarkers in diagnostic medicine.

METHODS

Several methods were proposed for the selection of optional cut-points. The validity and precision of the proposed methods were discussed and the clinical application of the methods was illustrated with a practical example of clinical diagnostic data of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and malondialdehyde (MDA) for prediction of inflammatory bowel disease (IBD) patients using the NCSS software.

RESULTS

Our results in the clinical data suggested that for CRP and MDA, the calculated cut-points of the Youden index, Euclidean index, Product and Union index methods were consistent in predicting IBD patients, while for ESR, only the Euclidean and Product methods yielded similar estimates. However, the diagnostic odds ratio (DOR) method provided more extreme values for the optimal cut-point for all biomarkers analyzed.

CONCLUSION

Overall, the four methods including the Youden index, Euclidean index, Product, and IU can produce quite similar optimal cut-points for binormal pairs with the same variance. The cut-point determined with the Youden index may not agree with the other three methods in the case of skewed distributions while DOR does not produce valid informative cut-points. Therefore, more extensive Monte Carlo simulation studies are needed to investigate the conditions of test result distributions that may lead to inconsistent findings in clinical diagnostics.

Associations of glycated albumin and fructosamine with glycaemic status in urban black South Africans

BACKGROUND

The associations of glycated albumin (GA) and fructosamine (FA) as alternate tests for diabetes screening in South Africans were examined to overcome shortcomings with oral glucose tolerance tests (OGTTs).

METHODS

Based on OGTTs, glycaemic status included 1) normal glucose, 2) dysglycaemia (impaired fasting glucose, impaired glucose tolerance, newly diagnosed diabetes), and 3) known diabetes.

RESULTS

Among 1092 participants, >21 years, mean GA (16.9%) and FA (230.2 μmol/l) increased significantly by age and worsening glycaemic status and were significantly higher in women vs. men and BMI (kg/m²) ≥ 30 vs. <30. For dysglycaemia, correlations of GA and FA with fasting and 2-hour glucose levels were higher in obese (0.576 to 0.688) vs. non-obese (-0.010 to 0.522). Optimal GA threshold to identify dysglycaemia was 15.35% and comparable, but with lower sensitivity (0.54) and specificity (0.55), to Asian studies. For FA, the optimal cut-point of 227.0 μmol/l approximated that described in the literature. Dysglycaemia and known diabetes were associated with GA, while only known diabetes was related to FA, in models adjusted for age, gender and obesity.

CONCLUSIONS

Potential exists for GA and/or FA as alternative measures of dysglycaemia in clinical practice in Africans, but longitudinal studies are required to clearly elucidate their utility.

Exact confidence interval estimation for the Youden index and its corresponding optimal cut-point

In diagnostic studies, the receiver operating characteristic (ROC) curve and the area under the ROC curve are important tools in assessing the utility of biomarkers in discriminating between non-diseased and diseased populations. For classifying a patient into the non-diseased or diseased group, an optimal cut-point of a continuous biomarker is desirable. Youden's index (J), defined as the maximum vertical distance between the ROC curve and the diagonal line, serves as another global measure of overall diagnostic accuracy and can be used in choosing an optimal cut-point. The proposed approach is to make use of a generalized approach to estimate the confidence intervals of the Youden index and its corresponding optimal cut-point. Simulation results are provided for comparing the coverage probabilities of the confidence intervals based on the proposed method with those based on the large sample method and the parametric bootstrap method. Finally, the proposed method is illustrated via an application to a data set from a study on Duchenne muscular dystrophy (DMD).