Data-Driven Stratification Reveals 3 Distinct Clinicopathologic Phenotypes in Renal Thrombotic Microangiopathy

To define phenotypic subgroups of thrombotic microangiopathy (TMA) by integrating clinicopathologic data to identify patterns suggestive of pathogenic features and etiologies.

We retrospectively analyzed 283 patients with biopsy-confirmed renal TMA between January 1, 2010, and December 31, 2020. Sixty-four clinicopathologic variables were applied in consensus clustering.

Three distinct clusters were identified. Cluster 1 (n=78), predominantly women (n=50, 64%), was enriched for drug-induced (n=23, 29%), lupus-associated (n=9, 12%), and post-bone marrow transplant TMA (n=8, 10%). It showed wide immune-complex staining (IgM, C3, κ, and λ) and subendothelial deposits (n=16, 21%), suggestive of immune-associated injury. Cluster 2 (n=92) included the oldest patients (54±18 years) and was dominated by drug-induced TMA (n=21, 23%; one-third owing to vascular endothelial growth factor inhibitor bevacizumab) and monoclonal gammopathy-associated TMA (n=10, 11%). It showed marked endothelial damage (n=65, 71%) and mesangiolysis (n=66, 72%) with little immune staining, suggesting a toxic-related injury. Cluster 3 (n=113) exhibited the worst outcomes, with 80% (24/30) progressing to renal failure, compared with 31% (12/39) in cluster 1 and 34% (16/47) in cluster 2. This cluster was characterized by the youngest age (45±14 years), mostly males (n=67, 59%), severe hypertension (n=75, 66%), and low estimated glomerular filtration rate (10 mL/min/1.73 m²; interquartile range, 7-16). Biopsies showed acute vascular lesions-wrinkling of capillary tufts (n=74, 65%), fibrin thrombi (n=67, 59%), mucoid intimal edema (n=85, 75%), and onion-skin-type hyperplasia (n=55, 49%)-with minimal deposits (n=1, 0.9%), suggesting a severe vascular lesion process.

Integrative clustering of clinical and histologic data in TMA identified 3 clinicopathologic phenotypes that may suggest underlying immune-, toxic-, or severe vascular lesion-related drivers. These findings support data-driven classification that may help prioritize diagnostic and therapeutic considerations.