Thrombotic Microangiopathy (TMA) is a syndrome characterized by the presence of anemia, thrombocytopenia and organ damage and has multiple etiologies. The primary aim is to develop an algorithm to classify TMA (TMA-INSIGHT score). This was a single-center retrospective cohort study including hospitalized patients with TMA at a single center. We included all consecutive patients diagnosed with TMA between 2012 and 2021. TMA was defined based on the presence of anemia (hemoglobin level < 10 g/dL) and thrombocytopenia (platelet count < 150,000/µL), signs of hemolysis, and organ damage. We classified patients in eight categories: infections; Malignant Hypertension; Transplant; Malignancy; Pregnancy; Thrombotic Thrombocytopenic Purpura (TTP); Shiga toxin-mediated hemolytic uremic syndrome (STEC-SHU) and Complement Mediated TMA (aHUS). We fitted a model to classify patients using clinical characteristics, biochemical exams, and mean arterial pressure at presentation. We retrospectively retrieved TMA phenotypes using automatic strategies in electronic health records in almost 10 years (n = 2407). Secondary TMA was found in 97.5% of the patients. Primary TMA was found in 2.47% of the patients (TTP and aHUS). The best model was LightGBM with accuracy of 0.979, and multiclass ROC-AUC of 0.966. The predictions had higher accuracy in most TMA classes, although the confidence was lower in aHUS and STEC-HUS cases. Secondary conditions were the most common etiologies of TMA. We retrieved comorbidities, associated conditions, and mean arterial pressure to fit a model to predict TMA and define TMA phenotypic characteristics. This is the first multiclass model to predict TMA including primary and secondary conditions.

中文翻译：

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用于评估和分类血栓性微血管病患者的综合模型：TMA-INSIGHT 评分

血栓性微血管病（TMA）是一种以贫血、血小板减少和器官损伤为特征的综合征，有多种病因。主要目标是开发一种算法来对 TMA 进行分类（TMA-INSIGHT 评分）。这是一项单中心回顾性队列研究，包括单中心的 TMA 住院患者。我们纳入了 2012 年至 2021 年间诊断为 TMA 的所有连续患者。TMA 的定义是根据是否存在贫血（血红蛋白水平 < 10 g/dL）和血小板减少症（血小板计数 < 150,000/μL）、溶血迹象和器官损伤。我们将患者分为八类：感染；恶性高血压；移植; 恶性肿瘤；怀孕; 血栓性血小板减少性紫癜（TTP）；志贺毒素介导的溶血性尿毒症综合征 (STEC-SHU) 和补体介导的 TMA (aHUS)。我们建立了一个模型，利用临床特征、生化检查和就诊时的平均动脉压对患者进行分类。我们使用自动策略在电子健康记录中回顾性检索了近 10 年的 TMA 表型 (n = 2407)。97.5% 的患者发现继发性 TMA。2.47% 的患者（TTP 和 aHUS）发现原发性 TMA。最好的模型是 LightGBM，准确度为 0.979，多类 ROC-AUC 为 0.966。尽管 aHUS 和 STEC-HUS 案例的置信度较低，但大多数 TMA 类别的预测准确度较高。继发性疾病是 TMA 最常见的病因。我们检索了合并症、相关病症和平均动脉压，以拟合模型来预测 TMA 并定义 TMA 表型特征。这是第一个预测 TMA 的多类模型，包括主要和次要条件。