RESUMO
Acquired thrombotic thrombocytopenic purpura (TTP) is an uncommon microangiopathic disorder that can have variable presentations and can be precipitated by a multitude of stressors to the body, most commonly sepsis. TTP is caused by a deficiency of ADAMTS13 leading to intravascular clotting causing thrombocytopenia and microangiopathic hemolytic anemia. TTP can be associated with various other pathologic conditions. One such rare association has been reported with diabetic ketoacidosis (DKA). Here, we present an even less appreciated presentation in association with DKA. A 62-year-old African American male with previously diagnosed prediabetes presented with DKA and developed hemodynamically significant bleeding. He was confirmed to have TTP that responded to plasmapheresis. TTP is a life-threatening illness if not treated urgently with plasmapheresis with or without rituximab. As acquired TTP most commonly occurs during stress on the body, it is important to treat the underlying stressor. Early identification and initiation of appropriate interventions are crucial to reducing mortality associated with TTP. Furthermore, we need to appreciate less commonly associated conditions such as DKA among patients.
RESUMO
PURPOSE: The purpose of this study was to evaluate the accuracy of a novel fully automated deep learning (DL) algorithm implementing a recurrent neural network (RNN) with long short-term memory (LSTM) for the detection of coronary artery calcium (CAC) from coronary computed tomography angiography (CCTA) data. MATERIALS AND METHODS: Under an IRB waiver and in HIPAA compliance, a total of 194 patients who had undergone CCTA were retrospectively included. Two observers independently evaluated the image quality and recorded the presence of CAC in the right (RCA), the combination of left main and left anterior descending (LM-LAD), and left circumflex (LCx) coronary arteries. Noncontrast CACS scans were allowed to be used in cases of uncertainty. Heart and coronary artery centerline detection and labeling were automatically performed. Presence of CAC was assessed by a RNN-LSTM. The algorithm's overall and per-vessel sensitivity, specificity, and diagnostic accuracy were calculated. RESULTS: CAC was absent in 84 and present in 110 patients. As regards CCTA, the median subjective image quality, signal-to-noise ratio, and contrast-to-noise ratio were 3.0, 13.0, and 11.4. A total of 565 vessels were evaluated. On a per-vessel basis, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 93.1% (confidence interval [CI], 84.3%-96.7%), 82.76% (CI, 74.6%-89.4%), and 86.7% (CI, 76.8%-87.9%), respectively, for the RCA, 93.1% (CI, 86.4%-97.7%), 95.5% (CI, 88.77%-98.75%), and 94.2% (CI. 90.2%-94.6%), respectively, for the LM-LAD, and 89.9% (CI, 80.2%-95.8%), 90.0% (CI, 83.2%-94.7%), and 89.9% (CI, 85.0%-94.1%), respectively, for the LCx. The overall sensitivity, specificity, and diagnostic accuracy were 92.1% (CI, 92.1%-95.2%), 88.9% (CI. 84.9%-92.1%), and 90.3% (CI, 88.0%-90.0%), respectively. When accounting for image quality, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 76.2%, 87.5%, and 82.2%, respectively, for poor-quality data sets and 93.3%, 89.2% and 90.9%, respectively, when data sets rated adequate or higher were combined. CONCLUSION: The proposed RNN-LSTM demonstrated high diagnostic accuracy for the detection of CAC from CCTA.
