Thrombosis Journal (Nov 2023)

A comprehensive model for assessing and classifying patients with thrombotic microangiopathy: the TMA-INSIGHT score

  • Vanessa Vilani Addad,
  • Lilian Monteiro Pereira Palma,
  • Maria Helena Vaisbich,
  • Abner Mácola Pacheco Barbosa,
  • Naila Camila da Rocha,
  • Marilia Mastrocolla de Almeida Cardoso,
  • Juliana Tereza Coneglian de Almeida,
  • Monica AP de Paula de Sordi,
  • Juliana Machado-Rugolo,
  • Lucas Frederico Arantes,
  • Luis Gustavo Modelli de Andrade

DOI
https://doi.org/10.1186/s12959-023-00564-6
Journal volume & issue
Vol. 21, no. 1
pp. 1 – 12

Abstract

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Abstract Background 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). Methods 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. Results 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. Conclusion 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.

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