Publications by authors named "Kanak Patel"
Article Synopsis
- The study analyzed a machine learning tool for early sepsis detection within a hospital's electronic medical records, aiming to assess its effectiveness and impact on care processes.
- Results showed low sensitivity for detecting sepsis (28.6% to 43.6%) and a positive predictive value of only 37.9%, indicating the tool was not effectively improving early sepsis recognition or mortality rates.
- The findings suggest that real-world application of machine learning models may differ from initial validations, highlighting the importance of context in evaluating such tools' clinical utility.
Critically ill patients often have deranged hemodynamics. Physical examination, central venous pressure, and pulmonary artery occlusion pressure ("wedge") have been shown to be unreliable at assessing volume status, volume responsiveness, and adequacy of cardiac output in critically ill patients. Thus, invasive and noninvasive cardiac output monitoring is a core feature of evaluating and managing a hemodynamically unstable patient.
View Article and Find Full Text PDFThis is a review of the current literature describing the different reported methods of managing a retrobulbar hemorrhage. The pathophysiology of the condition, together with its differing clinical presentations are described in length. Both the surgical and medical management are discussed.
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