Citations

Abstract Background and Hypothesis Thrombotic microangiopathy (TMA) is increasingly recognized as a complication of both cancer and anti-cancer therapies. C5 complement inhibition is used in selected cases, but evidence supporting its effectiveness, particularly in kidney biopsy-proven disease, remains limited. Methods This retrospective cohort study included adult patients with biopsy-proven TMA attributed to anti-cancer therapies between 2015 and 2026 at Mayo Clinic, USA. Patients were stratified according to treatment with complement inhibition versus no complement inhibition therapy. The primary outcome was renal recovery; secondary outcomes included hematological recovery, composite outcome, overall survival, and longitudinal changes in hematologic and renal function parameters. Results A total of 31 patients with kidney biopsy-proven TMA related to anti-cancer therapy were included. Nine received complement inhibition, and 22 did not. A higher proportion of patients in the treated group achieved complete or partial renal recovery at 3 months (p = 0.008), hematologic recovery at 1 month (p = 0.007) and composite outcome (complete-partial renal plus hematologic recovery) (p = 0.041). During follow-up, platelet counts increased significantly at 1 month and 3 months (p = 0.045, both) in the treated group. Mixed-effects modeling showed significantly lower longitudinal estimated glomerular filtration rate (eGFR) values in the untreated group (β = −13.9 mL/min per 1.73 m², p = 0.028). In the gemcitabine-associated TMA subgroup, complete-partial renal recovery occurred only in treated patients (p = 0.021) and hematologic recovery was achieved in the whole cohort but occurred significantly faster in the complement inhibition group (p = 0.019). The higher rate of renal recovery persisted in sensitivity analysis (p = 0.021). Conclusions In this rigorously phenotyped cohort of patients with biopsy-proven, anti-cancer therapy-associated renal TMA, we observed a signal of benefit from C5 inhibition, most consistently for renal recovery and possibly extending to hematologic outcomes, particularly among patients with gemcitabine-associated TMA.

Abstract Complement-mediated thrombotic microangiopathy can be difficult to recognize when it presents in the setting of an apparent clinical trigger, such as pregnancy, severe hypertension, or kidney transplantation. These conditions can each cause endothelial injury directly but may also unmask complement dysregulation in susceptible individuals. In these situations, the critical question is not whether a trigger exists but whether it fully explains the syndrome and whether urgent complement blockade is warranted to prevent irreversible kidney injury. In this How I Treat article, we use 3 real-world cases to illustrate how we approach this decision in practice. We emphasize the bedside features that most influence our threshold to treat, including the pattern and severity of kidney injury, tempo of disease progression, and lack of improvement with supportive or trigger-directed care. We also address the interpretation of complement genetic findings, pregnancy- and transplant-specific risk stratification, and the individualization of treatment duration. Functional assays can support mechanistic assessment in selected cases but remain an adjunct to diagnosis and should not drive management decisions.

Complement-mediated hemolytic uremic syndrome (CM-HUS), commonly referred to as atypical HUS, is a rare thrombotic microangiopathy caused by uncontrolled activation of the alternative complement pathway, typically triggered by a “two-hit” mechanism. It is characterized by microangiopathic hemolytic anemia, thrombocytopenia, and end-organ damage, most commonly affecting the kidneys. While our understanding of the complement system has advanced significantly, CM-HUS remains a complex, heterogeneous disorder influenced by a spectrum of genetic variants, risk haplotypes, and acquired factors such as anti-factor H autoantibodies. This review highlights the current knowledge of CM-HUS pathogenesis, focusing on genetic variants in regulatory and activating proteins of the complement system. We also discuss the diagnostic complexity posed by incomplete penetrance, overlapping phenotypes, and limitations of genetic and functional assays. Emerging ex-vivo assays and complement biomarkers are explored as tools for refining diagnosis and risk stratification. The use of complement inhibitors such as eculizumab and ravulizumab has significantly improved renal outcomes and survival. This review provides a comprehensive, clinically grounded update on the genetics, pathophysiology, diagnostics, and therapeutic considerations in CM-HUS, aiming to provide clinicians and researchers with a deeper understanding of this complex, complement-driven disease.

Abstract Background Despite advances in the diagnosis and treatment of immune thrombotic thrombocytopenic purpura (iTTP), recent mortality estimates remain around 5–10%. Predicting iTTP mortality risk has been of interest, as it may help individualize treatment or potentially guide the use of more intensive or novel therapies based on disease severity. However, prior attempts at mortality prediction have yielded inconsistent results, and no individual variable has demonstrated independent or reliable predictive ability. In a previous analysis utilizing the USTMA iTTP registry, which included 419 patients from 15 TTP referral centers, we reported that mortality due to immune TTP was unpredictable (Blood. 2021;138[Suppl 1]:1047). Neither the French Thrombotic Microangiopathy (TMA) Reference Score (previously validated in the French TMA Registry), nor a previous logistic regression model attempt (developed by USTMA investigators in 2021) demonstrated sufficient accuracy in predicting iTTP mortality in the USTMA dataset (AUC = 0.63 and 0.69, respectively). We subsequently utilized gradient boosted machine, a machine learning approach capable of handling multiple continuous variables and the complex interactions between them in large datasets, and developed a novel prediction tool: the USTMA Mortality Index (Res Pract Thromb Haemost. 2024; 8(3):102388). It demonstrated good performance in predicting iTTP mortality (AUC = 0.77) in repeated 10-fold cross-validation. However, it has not yet undergone external validation. Objectives To externally validate the USTMA Mortality Index to predict mortality due to acute iTTP in a multicenter cohort. Methods We performed an external validation using an independent cohort of iTTP patients from 5 institutions: University of Utah, Case Western Reserve University, Rochester Regional Health, University of Illinois, and Mayo Clinic. Patients in the validation cohort were not part of the development cohort (the USTMA registry). We included patients with confirmed iTTP and defined acute iTTP mortality as death within 30 days of admission. USTMA Mortality Index variables were collected at initial presentation, which included (in order of highest to lowest relative influence): lactate dehydrogenase, serum creatinine, age, hemoglobin, platelet count, presence of stupor/coma, fever. The model's prediction accuracy was assessed using area under the receiver operating characteristic curve (AUC). We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) based on the previously defined cut-offs of 4.27% and 9.08% mortality risk in the original model, which categorized patients into standard risk, high risk, and very high risk of predicted mortality. Results The external validation cohort included 103 patients with confirmed iTTP and 11 deaths (10.7% mortality rate). The USTMA Mortality Index demonstrated poor performance with AUC of 0.69 (95% CI, 0.55–0.83) in the validation cohort. The wide confidence interval reflects substantial uncertainty in model performance, ranging from poor to excellent, and is likely due to the small number of deaths. At a risk threshold ≥4.27%:Sensitivity = 64%Specificity = 70%PPV = 20%NPV = 94% At a risk threshold ≥9.08%:Sensitivity = 18%Specificity = 87%PPV = 14%NPV = 90% Conclusion The USTMA Mortality Index demonstrated poor performance in the external validation cohort (AUC = 0.69, 95% CI, 0.55–0.83), contrasting with its previously favorable performance in repeated 10-fold cross-validation. This discrepancy likely reflects model overfitting, a known challenge in AI-based clinical modeling. Our study is limited by the small number of deaths, resulting in a wide confidence interval ranging from poor to excellent performance. Given that neither the French TMA Reference Score, nor our group's prior logistic regression model (2021), nor the USTMA Mortality Index (2024) could reliably predict mortality upon external validation in our cohorts, these findings collectively suggest that mortality in acute iTTP remains difficult to predict at this time, regardless of statistical methodology or current machine learning capabilities. Clinical decision-making, including the use of therapies such as caplacizumab, should not be guided solely by mortality risk predictions. Instead, efforts to reduce mortality and improve outcomes should prioritize individualized clinical assessment over model-based stratification at this time.