Kandula, Manasa

e23510 Background: There is minimal data comparing the prognostic models (PM) developed to predict recurrence of Gastrointestinal stromal tumors (GIST). Current PM include tumor size, location, and mitotic index (MI) but lack genotypic data. This analysis compares the performance of the National Institute of Health, Armed Forces Institute of Pathology, and Memorial Sloan Kettering Cancer Center models for predicting tumor recurrence with and without adjustment for c-kit Exon 11 mutation status (MS). Methods: This is an IRB-approved single-institution retrospective analysis. Adults with GIST mentioned in their medical records from 2014 – 2025 at the authors’ institution were identified. 330 patients with documented biopsy-proven GIST were included. Data was collected by manual review. PM were evaluated using multivariate analysis, adjusting for surgical resection, systemic therapy, and c-Kit exon 11 MS. Predictive performance was measured by area under the curve (AUC) and receiver operating characteristics (ROC) analysis. Results: Mean age at diagnosis was 64.6 years (SD =13.1 years). 46.7 % of patients were male. Primary GIST was most common in the stomach (63.9%) and small bowel (23.6%). Primary tumors were an average of 6.5 cm (SD 5.6 cm) with a MI of 6.6/50 HFU (SD = 13.4 50/HFU). Patients received resection (80.9%) and first-line systemic therapy with imatinib (43.3%). Recurrence occurred in 17.9% of patients. 19.4% of patients completed genetic testing, of which 51.6% were c-Kit exon 11 mutation positive. The predictive performance of all PM was not statistically different with (p value = 0.16) or without (p value = 0.18) adjustment for c-Kit exon 11 MS (Table 1). However, adjusting for c-Kit exon 11 MS improved AUC for all PM. Conclusions: Adjusting PM for c-KIT exon 11 status improved the predictive performance of all PM, although the results were not significant. This study found no significant difference in the performance of PM that predict GIST recurrence, which aligns with the one other study on this topic. Lack of significance may be due to a small sample size, which also limited the multivariate analysis. Including genotypes in PM that predict GIST recurrence should be further explored with larger multi-center datasets that can adjust for multiple mutations. Multivariate analysis comparing prognostic models for gastrointestinal stromal tumors at predicting recurrence with and without adjustment for c-KIT exon 11 genotypes using receiver operating characteristic. Without c-Kit exon 11 AUC (95% Confidence Interval) P value NIH 0.86 (0.81,0.90) 0.18 AFIP 0.87 (0.82,0.91) MSKCC 0.89 (0.84,0.94) With c-Kit exon 11 genotype NIH 0.80 (0.67,0.93) 0.16 AFIP 0.84 (0.73,0.95) MSKCC 0.90 (0.80,0.99) NIH: National Institute of Health, AFIP: Armed Forces Institute of Pathology, MSKCC: Memorial Sloan Kettering Cancer Centre. AUC: Area under the curve.

e16335 Background: Splanchnic vein thrombosis (SVT) is a rare but serious complication in gastrointestinal (GI) cancer patients. This study investigated its features and outcomes in U.S. hospitalizations. Methods: Hospitalizations (≥18 years old) with a GI malignancy (ICD-10-CM C15–C26) and portal vein thrombosis (PVT) (I81), hepatic vein thrombosis (HVT) (I82.0), or mesenteric/splenic vein thrombosis (MSVT) (I82.890 + K55) in any of the 40 diagnosis fields were found in 2022 National Inpatient Sample. Exclusions: non-GI cancers (C00-C14, C27-C96), thrombophilia (D68.5, D68.6, D45, D47.1, D59.5), or cirrhosis (K74, K70.3). Demographics and outcomes (in-hospital mortality, length of stay [LOS], and total hospital charges [TOTCHG]) were compared between SVT and non-SVT groups using unweighted descriptive statistics (medians, IQRs), χ², and Mann-Whitney U tests. Multivariable regression of mortality, LOS, and TOTCHG were adjusted for age, sex, race, income quartile, insurance type, hospital region and bed size, and comorbidity burden (elixsum). Stata 17.0 was used. Results: Among 51,940 GI cancer hospitalizations (mean age 68.37 ± 12.79 years; 44.46% female), 833 (1.60%) had SVT. SVT group was younger (66.60 ± 11.76 vs. 68.40 ± 12.80 years; p < 0.001). Sex distribution was similar (58.34% vs. 55.50% male; p = 0.101). Common cancers in SVT: liver (n=427; 51.26%), pancreas (n=310; 37.21%), and colorectal (n=42; 5.04%). SVT prevalence was highest in liver (7.10%), pancreatic (3.14%), gallbladder (2.65%), and biliary (2.13%) cancers. PVT was the predominant subtype (n=788; 94.60%), followed by HVT (n=21; 2.52%), MSVT (n=4; 0.48%), and multiple SVT sites (n=20; 2.40%). SVT subtype distribution was: PVT (n=788; 94.60%), HVT (n=21; 2.52%), MSVT (n=4; 0.48%), and multiple sites (n=20; 2.40%). SVT had longer median LOS (5 [3–10] vs. 4 [3–8] days; z = -6.666, p < 0.001), similar median charges ($65,053 [I33,560–130,026] vs. $66,433 [35,897–121,038]; z = -0.76, p = 0.447), and higher mortality (9.48% vs. 4.41%; χ² = 49.042, p < 0.001). In adjusted analyses, SVT had higher mortality (aOR = 2.23, 95% CI 1.74–2.85, p < 0.001), longer LOS by 1.50 days (95% CI 0.99–2.02, p < 0.001), and 14.63% higher TOTCHG (coefficient 0.136, 95% CI 0.051–0.222, p = 0.002). Conclusions: SVT affects ~1.6% of GI cancer hospitalizations, is most prevalent in liver cancer, and associated with longer stays and higher mortality, underscoring the need for proactive monitoring. Multivariable regression analyses of SVT on clinical outcomes (unweighted sample). Outcome SVT Effect 95% CI P-value Interpretation In-hospital mortality OR 2.23 1.74–2.85 <0.001 SVT doubles odds of in-hospital death Length of stay (days) +1.50 0.99–2.02 <0.001 SVT associated with ~1.5 extra hospital days Total hospital charges ($) +0.136 (log scale) 0.051–0.222 0.002 SVT increases hospital costs by ~14.63%

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.