A higher D-dimer threshold for predicting pulmonary embolism in patients with COVID-19: a retrospective study.

PURPOSE: COVID-19 raises D-dimer (DD) levels even in the absence of pulmonary embolism (PE), resulting in an increase in computed tomography pulmonary angiogram (CTPA) requests. Our purpose is to determine whether there are differences between DD values in PE-positive and PE-negative COVID-19 patients and, if so, to establish a new cutoff value which accurately determines when a CTPA is needed. METHODS: This study retrospectively analyzed all COVID-19 patients who underwent a CTPA due to suspected PE between March 1 and April 30, 2020, at Ramón y Cajal University Hospital, Madrid (Spain). DD level comparisons between PE-positive and PE-negative groups were made using Student's t test. The optimal DD cutoff value to predict PE risk in COVID-19 patients was calculated in the ROC curve. RESULTS: Two hundred forty-two patients were included in the study. One hundred fifty-one (62%) were men and the median age was 68 years (IQR 55-78). An increase of DD (median 3260; IQR 1203-9625 ng/mL) was detected in 205/242 (96%) patients. 73/242 (30%) of the patients were diagnosed with PE on CTPA. The DD median value was significantly higher (p < .001) in the PE-positive group (7872, IQR 3150-22,494 ng/mL) compared with the PE-negative group (2009, IQR 5675-15,705 ng/mL). The optimal cutoff value for DD to predict PE was 2903 ng/mL (AUC was 0.76 [CI 95% 0.69-0.83], sensitivity 81%). The overall mortality rate was 16% (39/242). CONCLUSION: A higher threshold (2903 ng/mL) for D-dimer could predict the risk of PE in COVID-19 patients with a sensitivity of 81%.

Using staged tree models for health data: Investigating invasive fungal infections by aspergillus and other filamentous fungi.

Machine learning models are increasingly used in the medical domain to study the association between risk factors and diseases to support practitioners in understanding health outcomes. In this paper, we showcase the use of machine-learned staged tree models for investigating complex asymmetric dependence structures in health data. Staged trees are a specific class of generative, probabilistic graphical models that formally model asymmetric conditional independence and non-regular sample spaces. An investigation of the risk factors in invasive fungal infections demonstrates the insights staged trees provide to support medical decision-making.

Incidental radiological finding of partial anomalous pulmonary vein drainage / Hallazgo radiológico incidental de un caso de drenaje venoso pulmonar anómalo parcial

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Síndrome de diferenciación en paciente con leucemia promielocítica aguda: importancia de la TC de tórax / Differentiation Syndrome in a Patient With Acute Promyelocytic Leukemia: Importance of Chest CT

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