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Prediction models of treatment response in lupus nephritis

Ayoub, I.; Wolf, B.J.; Geng, L.; Song, H.; Khatiwada, A.; Tsao, B.P.; Oates, J.C.; Rovin, B.H.

Kidney International 101(2): 379-389

2022


ISSN/ISBN: 1523-1755
PMID: 34871620
Accession: 079487722

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In order to develop prediction models of one-year treatment response in lupus nephritis, an approach using machine learning to combine traditional clinical data and novel urine biomarkers was undertaken. Contemporary lupus nephritis biomarkers were identified through an unbiased PubMed search. Thirteen novel urine proteins contributed to the top 50% of ranked biomarkers and were selected for measurement at the time of lupus nephritis flare. These novel markers along with traditional clinical data were incorporated into a variety of machine learning algorithms to develop prediction models of one-year proteinuria and estimated glomerular filtration rate (eGFR). Models were trained on 246 individuals from four different sub-cohorts and validated on an independent cohort of 30 patients with lupus nephritis. Seven models were considered for each outcome. Three-quarters of these models demonstrated good predictive value with areas under the receiver operating characteristic curve over 0.7. Overall, prediction performance was the best for models of eGFR response to treatment. Furthermore, the best performing models contained both traditional clinical data and novel urine biomarkers, including cytokines, chemokines, and markers of kidney damage. Thus, our study provides further evidence that a machine learning approach can predict lupus nephritis outcomes at one year using a set of traditional and novel biomarkers. However, further validation of the utility of machine learning as a clinical decision aid to improve outcomes will be necessary before it can be routinely used in clinical practice to guide therapy.

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