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

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.

Keywords: Atypical hemolytic uremic syndrome; Complement mediated TMA; Shiga toxin-mediated hemolytic uremic syndrome; Thrombotic Thrombocytopenic Purpura; Thrombotic microangiopathy.

Fig. 1

Flowchart Steps to Retrieve Patients with Thrombotic Microangiopathy (TMA)

Fig. 2

Thrombotic microangiopathy (TMA) by etiology in hospitalized patients

Fig. 3

A. Age in patients stratified by thrombotic microangiopathy (TMA). B. Mean Arterial Pressure (MAP) in patients stratified by thrombotic microangiopathy (TMA).

Fig. 4

Laboratory exams (delta creatinine, LDH, Hemoglobin, platelets) in patients stratified by thrombotic microangiopathy (TMA). (Values in log10 scale)

Fig. 5

Laboratory exams (AST, Bilirubin, TP and haptoglobin) in patients stratified by thrombotic microangiopathy (TMA). (Values in log10 scale)

Fig. 6

ROC AUC of LightGBM model to predict thrombotic microangiopathy (TMA) stratified by the predictions in each class

Fig. 7

Importance of Predictors of the LightGBM model to predict thrombotic microangiopathy (TMA).