Publications by authors named "Pedro-Ignacio Dorado-Diaz"
Article Synopsis
- Device-related thrombus (DRT) is rare after left atrial appendage (LAA) closure but poses a greater risk for blood clots, making the search for reliable predictors critical.
- A study involving 1150 patients compared traditional multivariable methods with machine learning techniques to determine which can better predict DRT.
- While machine learning showed higher predictive power without resampling (ROC curve of 0.7974), the results indicated no significant advantage over multivariable analysis when resampling was used, highlighting inconsistencies in previous research on DRT predictors.
Date of Admission during COVID-19 Pandemic Impacted Patient Outcomes in Addition to the Higher Efficacy of Tocilizumab Plus High-Dose Corticosteroid Therapy Compared to Tocilizumab Alone.
J Clin Med
December 2021
Background: The evidence for the efficacy of glucocorticoids combined with tocilizumab (TCZ) in COVID-19 comes from observational studies or subgroup analysis. Our aim was to compare outcomes between hospitalized COVID-19 patients who received high-dose corticosteroid pulse therapy and TCZ and those who received TCZ.
Methods: A retrospective single-center study was performed on consecutive hospitalized patients with severe COVID-19 between 1 March and 23 April 2020.
Background: Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management.
Methods: We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing.