Real-World Analysis of Clinical Characteristics, Treatment Outcomes, and the Novel Predictive Model for Patients with Thrombotic Thrombocytopenic Purpura (TTP) and TTP-Like Syndrome - Lv M; Hu X; Zhu L; Xu H; Chen E; Zhao N; Tong J; Zheng C (Vascular Health and Risk Management)
Mengya Lv,1 Xing Hu,2 Lijun Zhu,2 Hui Xu,2 Erling Chen,2 Na Zhao,2 Juan Tong,2 Changcheng Zheng1,2 1Department of Hematology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, People’s Republic of China; 2Department of Hematology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People’s Republic of ChinaCorrespondence: Changcheng Zheng, Department of Hematology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Lujiang Road No. 17, Hefei, 230001, People’s Republic of China, Tel/Fax +86-551-62282990, Email zhengchch1123@ustc.edu.cnPurpose: It is crucial to differentiate critically ill patients exhibiting thrombocytopenia and hemolytic anemia alongside organ damage to enable rapid identification of thrombotic thrombocytopenic purpura (TTP) and TTP-like syndrome, which allows for targeted emergency interventions such as plasma exchange.Patients and Methods: This study retrospectively analyzed clinical data from patients with TTP and TTP-like syndrome to further elucidate the potential differences between these conditions. We also established a new predictive model to facilitate early identification and differentiation between TTP and TTP-like syndrome. A new predictive model for diagnosing TTP was developed using five key indicators: reticulocyte percentage, platelet count, schistocyte percentage, LDH/ULN, and indirect bilirubin. The performance of this new model was compared with the traditional PLASMIC score by evaluating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).Results: Thirty-five patients were diagnosed with TTP and 42 were diagnosed with TTP-like syndrome. TTP is most commonly associated with autoimmune diseases (n=13, 37.14%), while TTP-like syndrome frequently arises from infections (n=23, 54.76%). The ADAMTS13 activity was significantly lower in the TTP group than in the TTP-like syndrome group (Mean 8.30% vs 46.12%). TTP-like syndrome patients had significantly higher levels of inflammatory markers. The new predictive model was developed for TTP with a predictive ability of 96.9%. Overall, 16 patients (20.77%) died, including 3 (8.57%) in the TTP group and 13 (30.95%) in the TTP-like syndrome group. Kaplan–Meier survival analysis showed significant differences in survival between TTP and TTP-like syndrome patients, with a 180-day overall survival (OS) rate of 90.6% vs 60.9% (p=0.009); and plasma exchange improved 180-day OS rate in the TTP group compared to the TTP-like syndrome group (90.6% vs 65.6%) (p=0.054).Conclusion: This study demonstrates that TTP and TTP-like syndrome are two distinct types of diseases. The new predictive model has shown good efficacy in distinguishing TTP and TTP-like syndrome. Plasma exchange significantly improves survival in TTP patients; however, its effect on TTP-like syndrome is minimal.Keywords: thrombotic thrombocytopenic purpura, TTP, TTP-like syndrome, systemic inflammatory response, TTP predictive model, plasma exchange