A nomogram predicting the histologic activity of lupus nephritis from clinical parameters.

BACKGROUND

The 2021 clinical guidelines of the Kidney Disease: Improving Global Outcomes emphasize the importance of the histological activity index (AI) in the management of lupus nephritis (LN). Patients with LN and a high AI have poor renal outcomes and high rates of nephritic relapse. In this study we constructed prediction models for the AI in LN.

METHODS

The study population comprised 337 patients diagnosed with LN using kidney biopsy. The participants were randomly divided into training and testing cohorts. They were further divided into high-activity (AI >2) and low-activity (AI ≤2) groups. This study developed two clinical prediction models using logistic regression and least absolute shrinkage and selection operator (LASSO) analyses with laboratory test results collected at the time of kidney biopsy. The performance of models was assessed using 5-fold cross-validation and validated in the testing cohort. A nomogram for individual assessment was constructed based on the preferable model.

RESULTS

Multivariate analysis showed that higher mean arterial pressure, lower estimated glomerular filtration rate, lower complement 3 level, higher urinary erythrocytes count and anti-double-stranded DNA seropositivity were independent risk factors for high histologic activity in LN. Both models performed well in the testing cohort regarding the discriminatory ability to identify patients with an AI >2. The average area under the curve of 5-fold cross-validation was 0.855 in the logistic model and 0.896 in the LASSO model. A webtool based on the LASSO model was created for clinicians to enter baseline clinical parameters to produce a probability score of an AI >2.

CONCLUSIONS

The established nomogram provides a quantitative auxiliary tool for distinguishing LN patients with a high AI and helps physicians make clinical decisions in their comprehensive assessment.