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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.

Abstract Background Due to the high mortality of immune thrombotic thrombocytopenic purpura (iTTP), rapid clinical evaluation and early intervention are crucial. The PLASMIC score remains a valuable and widely used tool for predicting severe ADAMTS13 deficiency. However, recent studies have shown variable performance across different populations. In addition, its variables may often be missing during initial presentation. Refining the accuracy of early iTTP evaluation could improve timely recognition and treatment, while reducing unnecessary interventions in those eventually ruled out for iTTP. Machine learning advancements offer several advantages for enhancing clinical prediction models: the ability to assess continuous variables, capture complex interactions between them, and handle missing data. We previously utilized XGBoost (extreme gradient boosting) to develop a novel machine learning tool, TTP-14, which improved upon the PLASMIC score's predictive performance in repeat 10-fold cross-validation achieving an AUC of 0.91 (Blood. 2024;144[Suppl 1]:2617). TTP-14 generates a predicted probability for iTTP, expressed as a continuous percentage estimate of iTTP diagnostic likelihood, and is also able to do so with missing variables. However, it has not previously been externally validated. Objectives To externally validate the ability of TTP-14 to predict iTTP diagnosis and improve upon the PLASMIC score. Methods We performed an external validation of TTP-14 using an independent cohort of patients with suspected iTTP at the Mayo Clinic. These patients were not part of the original multi-center development cohort of 106 iTTP patients from the University of Utah, Case Western Reserve University, Rochester Regional Health, and University of Illinois. For the validation cohort, we identified patients who underwent ADAMTS13 activity testing between 2010 and 2025 for evaluation of thrombotic microangiopathy at the Mayo Clinic. iTTP was defined as ADAMTS13 activity <10%, and controls as ADAMTS13 activity >20%. TTP-14 variables were collected at initial presentation: platelet count, lactate dehydrogenase, creatinine, international normalized ratio, reticulocyte percentage, active cancer, indirect bilirubin, mean corpuscular volume, total bilirubin, haptoglobin, neurologic deficit, hemoglobin, age, and history of transplant. Receiver operating characteristic curve (AUC) was reported with 95% confidence intervals (CI). While the model provides a continuous estimate and is not categorical, we evaluated clinically meaningful probability thresholds to illustrate performance in decision-relevant zones. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were reported at these select thresholds and analyzed for patients with and without missing variables. Result We included 50 iTTP patients and 56 controls in the external validation cohort. 13 patients had missing PLASMIC variables. TTP-14 achieved an AUC of 0.96 (95% CI: 0.93–0.99), outperforming the PLASMIC score's AUC of 0.90 (95% CI: 0.83–0.96) in the same dataset, and improving upon TTP-14's previously reported cross-validated AUC of 0.91 (95% CI: 0.90–0.92) in the development cohort. Among patients with complete data (n = 93), applying select thresholds for clinical relevance demonstrated: Low risk cut-off (<5% predicted iTTP risk): Sensitivity = 98% Specificity = 71% PPV = 76% NPV = 97% Accuracy = 84% High risk cut-off (≥50% predicted iTTP risk): Sensitivity = 71% Specificity = 98% PPV = 97% NPV = 78% Accuracy = 85% In the full cohort (n = 106) that included those with missing PLASMIC variables, TTP-14 maintained excellent predictive performance with AUC = 0.95 (95% CI: 0.92–0.99). Performance metrics analyzed using the above thresholds also yielded similar results. Conclusion External validation of TTP-14 demonstrated high predictive accuracy for iTTP diagnosis (AUC = 0.96). TTP-14 outperformed the PLASMIC score in the validation cohort (AUC = 0.96 vs 0.90, respectively). TTP-14 offers more flexibility than the PLASMIC score by generating predictions even with incomplete data, and maintained excellent performance in the cohort that included those with missing variables (AUC = 0.95) Its refined accuracy and ability to predict iTTP despite missing data provide a key advantage, offering an alternative tool to improve rapid iTTP diagnosis. TTP-14 may help enhance early recognition and treatment of iTTP while reducing unnecessary interventions in non-TTP patients.

AbstractBackgroundImmune thrombocytopenia (ITP) and Evans syndrome (ES) are manifestations of immune dysregulation. Genetic variants in immune‐related genes have been identified in patients with ITP and especially ES. We aimed to explore familial autoimmunity in patients with ITP and ES to understand possible contributions to chronicity.ProcedureWe assessed family history in two ways: via patient report for ITP and ES and by population‐based analysis using the Utah Population Database (UPDB) for ITP. A total of 266 patients with ITP and 21 patients with ES were identified via chart review, and 252 of the 266 patients with ITP were also identified in the UPDB.ResultsChart review showed familial autoimmunity in 29/182 (15.9%) and 25/84 (29.8%) of patients with newly diagnosed+persistent (nd+p) ITP and chronic ITP (cITP), respectively, (p = .009). The UPDB analysis revealed that autoimmunity in relatives of patients with nd+pITP was higher than in relatives of controls (odds ratio [OR]: 1.69 [1.19–2.41], p = .004), but was not significantly increased in relatives of patients with cITP (OR 1.10 [0.63–1.92], p = .734). Incomplete family history in medical records likely contributed to the observed discrepancy.ConclusionsThe findings suggest that familial autoimmunity may have a stronger association with the development of ITP rather than its duration. Twelve (57.1%) patients with ES reported autoimmunity in their relatives. UPDB analysis was omitted due to the small number of patients with ES. The use of population databases offers a unique opportunity to assess familial health and may provide clues about contributors to immune dysregulation features within families.

Abstract Cholangiocarcinomas are rare but aggressive cancers, with an increasing incidence and a dismal 5-year survival rate of less than 5%. Mutations in the genes encoding isocitrate dehydrogenase, IDH1 and IDH2, have been reported in ∼70% of gliomas, ∼20% of myeloid leukemias, ∼56% of chondrosarcomas, and ∼10% of melanomas. We discovered IDH1 and IDH2 mutations in 29 of 305 (9%) intrahepatic cholangiocarcinomas. Tumors with mutations in IDH1 or IDH2 had lower 5-hydroxymethylcytosine and higher 5-methylcytosine levels, as well as increased dimethylation of histone H3K79. Mutations in IDH1 or IDH2 were independently associated with longer time to recurrence of cholangiocarcinoma in multivariate analysis (p = 0.046). We identified 2,267 genes that were significantly hypermethylated in tumors with mutations in IDH1 or IDH2. Hypermethylated CpG sites were significantly enriched in CpG shores and in the 1500 bp upstream of annotated transcription start sites, suggesting a global regulation of transcriptional potential. Gene expression profiling of 7 IDH1/2-mutant and 20 IDH1/2-wildtype tumors revealed increased expression of citric acid cycle and oxidative phosphorylation enzymes, as well as decreased expression of cytoskeleton organization genes. Integrated analyses of DNA methylation and gene expression identified interleukin-6 and several of its downstream targets as candidates for silencing by DNA methylation. Conclusions: Cholangiocarcinomas with mutations in IDH1 or IDH2 represent a molecular subclass with distinct signaling pathway alterations. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1058. doi:1538-7445.AM2012-1058