Application of principal component analysis and logistic regression model in lupus nephritis patients with clinical hypothyroidism

Abstract

Background: Previous studies indicate that the prevalence of hypothyroidism is much higher in patients with lupus nephritis (LN) than in the general population, and is associated with LN's activity. Principal component analysis (PCA) and logistic regression can help determine relevant risk factors and identify LN patients at high risk of hypothyroidism; as such, these tools may prove useful in managing this disease. Methods: We carried out a cross-sectional study of 143 LN patients diagnosed by renal biopsy, all of whom had been admitted to Xiangya Hospital of Central South University in Changsha, China, between June 2012 and December 2016. The PCA-logistic regression model was used to determine the influential principal components for LN patients who have hypothyroidism. Results: Our PCA-logistic regression analysis results demonstrated that serum creatinine, blood urea nitrogen, blood uric acid, total protein, albumin, and anti-ribonucleoprotein antibody were important clinical variables for LN patients with hypothyroidism. The area under the curve of this model was 0.855. Conclusion: The PCA-logistic regression model performed well in identifying important risk factors for certain clinical outcomes, and promoting clinical research on other diseases will be beneficial. Using this model, clinicians can identify at-risk subjects and either implement preventative strategies or manage current treatments.