Race, rituximab, and relapse in TTP.

Immune-mediated thrombotic thrombocytopenic purpura (iTTP) is characterized by recurring episodes of thrombotic microangiopathy, causing ischemic organ impairment. Black patients are overrepresented in iTTP cohorts in the United States, but racial disparities in iTTP outcome and response to therapy have not been studied. Using the United States Thrombotic Microangiopathies Consortium iTTP Registry, we evaluated the impact of race on mortality and relapse-free survival (RFS) in confirmed iTTP in the United States from 1995 to 2020. We separately examined the impact of rituximab therapy and presentation with newly diagnosed (de novo) or relapsed iTTP on RFS by race. A total of 645 participants with 1308 iTTP episodes were available for analysis. Acute iTTP mortality did not differ by race. When all episodes of iTTP were included, Black race was associated with shorter RFS (hazard ratio [HR], 1.60; 95% CI, 1.16-2.21); the addition of rituximab to corticosteroids improved RFS in White (HR, 0.37; 95% CI, 0.18-0.73) but not Black patients (HR, 0.96; 95% CI, 0.71-1.31). In de novo iTTP, rituximab delayed relapse, but Black patients had shorter RFS than White patients, regardless of treatment. In relapsed iTTP, rituximab significantly improved RFS in White but not Black patients. Race affects overall relapse risk and response to rituximab in iTTP. Black patients may require closer monitoring, earlier retreatment, and alternative immunosuppression after rituximab treatment. How race, racism, and social determinants of health contribute to the disparity in relapse risk in iTTP deserves further study.

A machine learning approach to predict mortality due to immune-mediated thrombotic thrombocytopenic purpura.

Background: Mortality due to immune-mediated thrombotic thrombocytopenic purpura (iTTP) remains significant. Predicting mortality risk may potentially help individualize treatment. The French Thrombotic Microangiopathy (TMA) Reference Score has not been externally validated in the United States. Recent advances in machine learning technology can help analyze large numbers of variables with complex interactions for the development of prediction models. Objectives: To validate the French TMA Reference Score in the United States Thrombotic Microangiopathy (USTMA) iTTP database and subsequently develop a novel mortality prediction tool, the USTMA TTP Mortality Index. Methods: We analyzed variables available at the time of initial presentation, including demographics, symptoms, and laboratory findings. We developed our model using gradient boosting machine, a machine learning ensemble method based on classification trees, implemented in the R package gbm. Results: In our cohort (n = 419), the French score predicted mortality with an area under the receiver operating characteristic curve of 0.63 (95% CI: 0.50-0.77), sensitivity of 0.35, and specificity of 0.84. Our gradient boosting machine model selected 8 variables to predict acute mortality with a cross-validated area under the receiver operating characteristic curve of 0.77 (95% CI: 0.71-0.82). The 2 cutoffs corresponded to sensitivities of 0.64 and 0.50 and specificities of 0.76 and 0.87, respectively. Conclusion: The USTMA Mortality Index was acceptable for predicting mortality due to acute iTTP in the USTMA registry, but not sensitive enough to rule out death. Identifying patients at high risk of iTTP-related mortality may help individualize care and ultimately improve iTTP survival outcomes. Further studies are needed to provide external validation. Our model is one of many recent examples where machine learning models may show promise in clinical prediction tools in healthcare.

Using the exploration, preparation, implementation, sustainment (EPIS) framework to assess the cooperative re-engagement controlled trial (CoRECT).

Background: "Data to Care" (D2C) is a strategy which relies on a combination of public health surveillance data supplemented by clinic data to support continuity of HIV care. The Cooperative Re-Engagement Controlled Trial (CoRECT) was a CDC-sponsored randomized controlled trial of a D2C model, which provided an opportunity to examine the process of implementing an intervention for people with HIV (PWH) who are out-of-care across three public health department jurisdictions. Using the EPIS (Exploration, Preparation, Implementation, Sustainment) framework, we aimed to retrospectively describe the implementation process for each site to provide insights and guidance to inform future D2C activities implemented by public health agencies and their clinical and community partners. Methods: After completion of CoRECT, the three (Connecticut, Massachusetts, Philadelphia) trial sites reviewed study protocols and held iterative discussions to describe and compare their processes regarding case identification, interactions with partnering clinics and patients, and sustainability. The EPIS framework provided a structure for comparing key organizational and operational practices and was applied to the entire implementation process. Results: The trial sites varied in their implementation processes and the specific elements of the intervention. Factors including prior D2C experience, data management and analytic infrastructure, staff capacity, and relationships with clinic partners informed intervention development and implementation. Additionally, this review identified key lessons learned including to: (1) explore new supplemental sources for public health surveillance data; (2) work with stakeholders representing core functions/components in the early stages of the intervention design process; (3) build flexibility into all components of the follow-up activities; and (4) integrate data sharing, project management, and follow-up activities within existing DPH organizational structure. Conclusion: The CoRECT study provides a general blueprint and lessons learned for implementing a D2C intervention for re-engagement in HIV care. Interventions should be tailored to local operational and structural factors, and responsive to evolving clinical and public health practices.

Occult Metastases in Pelvic Lymphadenectomy Specimens From Patients With Urothelial Carcinoma of the Bladder.

OBJECTIVE: To identify occult metastases within lymph nodes (LNs) reported as negative by routine histologic evaluation. In patients with high-grade, muscle-invasive urothelial carcinoma (UC) of the bladder, pelvic lymphadenectomy during radical cystectomy demonstrates a survival advantage, increasing with the number of LNs removed, even if negative for metastatic disease. This finding may potentially be explained by the presence of occult metastases. MATERIALS AND METHODS: Radical cystectomy specimens with high-grade UC invading the perivesical tissue and negative LNs (pT3N0) between 2000 and 2014 were reviewed. Five levels were cut for each LN block. Two sections were cut per level: 1 stained for hematoxylin and eosin and 1 for AE1/AE3. Micrometastases were defined as tumor deposits >0.2 mm but <2 mm. Isolated tumor cells were defined as ≤0.2 mm. Medical records and survival data were reviewed. RESULTS: We identified 21 cases, consisting of 370 lymph nodes. Six of 21 patients (29%) had occult metastases, including 5 occult metastatic UC and 1 occult metastatic prostate adenocarcinoma. There were 10 positive LNs; 2 macrometastases, 2 micrometastases, and 6 with ITCs. Two of 6 patients (33%) had lymphovascular invasion identified in the primary tumor. Kaplan-Meier analysis showed no significant difference in overall survival between the group of patients who remained N0 versus those upstaged due to discovery of occult metastases (P-value = .42). CONCLUSION: In patients with pT3 UC undergoing cystectomy, we demonstrated the presence of occult metastases in 29% of patients. The high percentage of occult metastases present in these cases possibly explains the proven survival advantage of removing "negative" LNs. This finding might also have implications in the histologic evaluation of LNs.