Browsing by Author "Morad Hameed, S."

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    Acute care and emergency general surgery in patients with chronic liver disease: how can we optimize perioperative care? A review of the literature
    (2018-07-18) Bleszynski, Michael S; Bressan, Alexsander K; Joos, Emilie; Morad Hameed, S.; Ball, Chad G
    Abstract The increasing prevalence of advanced cirrhosis among operative candidates poses a major challenge for the acute care surgeon. The severity of hepatic dysfunction, degree of portal hypertension, emergency of surgery, and severity of patients’ comorbidities constitute predictors of postoperative mortality. Comprehensive history taking, physical examination, and thorough review of laboratory and imaging examinations typically elucidate clinical evidence of hepatic dysfunction, portal hypertension, and/or their complications. Utilization of specific scoring systems (Child-Pugh and MELD) adds objectivity to stratifying the severity of hepatic dysfunction. Hypovolemia and coagulopathy often represent major preoperative concerns. Resuscitation mandates judicious use of intravenous fluids and blood products. As a general rule, the most expeditious and least invasive operative procedure should be planned. Laparoscopic approaches, advanced energy devices, mechanical staplers, and topical hemostatics should be considered whenever applicable to improve safety. Precise operative technique must acknowledge common distortions in hepatic anatomy, as well as the risk of massive hemorrhage from porto-systemic collaterals. Preventive measures, as well as both clinical and laboratory vigilance, for postoperative hepatic and renal decompensation are essential.
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    Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care
    (2023-03-06) Hunter, Olivia F.; Perry, Frances; Salehi, Mina; Bandurski, Hubert; Hubbard, Alan; Ball, Chad G.; Morad Hameed, S.
    Abstract Artificial intelligence (AI) and machine learning describe a broad range of algorithm types that can be trained based on datasets to make predictions. The increasing sophistication of AI has created new opportunities to apply these algorithms within within trauma care. Our paper overviews the current uses of AI along the continuum of trauma care, including injury prediction, triage, emergency department volume, assessment, and outcomes. Starting at the point of injury, algorithms are being used to predict severity of motor vehicle crashes, which can help inform emergency responses. Once on the scene, AI can be used to help emergency services triage patients remotely in order to inform transfer location and urgency. For the receiving hospital, these tools can be used to predict trauma volumes in the emergency department to help allocate appropriate staffing. After patient arrival to hospital, these algorithms not only can help to predict injury severity, which can inform decision-making, but also predict patient outcomes to help trauma teams anticipate patient trajectory. Overall, these tools have the capability to transform trauma care. AI is still nascent within the trauma surgery sphere, but this body of the literature shows that this technology has vast potential. AI-based predictive tools in trauma need to be explored further through prospective trials and clinical validation of algorithms.