Journal of the American College of Surgeons
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Graves disease is the most common cause of hyperthyroidism in the US. Treatment with antithyroid drugs and radioactive iodine is more commonly used than surgical management with total thyroidectomy (TTx). However, incidentally discovered thyroid cancer (TC) has been described on surgical pathology from patients who underwent surgical treatment of Graves disease, which would be missed with these other treatment strategies. We sought to determine the incidence rate of TC among patients with surgically treated Graves disease. ⋯ Incidental TC was found on surgical pathology in 6.4% of patients undergoing TTx for Graves disease. Preoperative imaging with US and fine needle aspiration were often unreliable at predicting TC. The incidence of TC should not be underestimated when counseling patients on definitive management for Graves disease.
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Live donor kidney transplantation has been popularized to help mitigate the organ shortage crisis. At the time of living donor nephrectomy, living donors lose 50% of their kidney function or glomerular filtration rate (GFR). Studies have shown that in healthy living donors, the remaining kidney is able to adapt and recover 10% to 25% of postdonation lost GFR. GFR recovery is critical to long-term kidney health, particularly for Black Americans who disproportionately suffer from kidney disease with an incidence 2.5 times White Americans. To date, no study has examined whether health inequities in renal recovery postdonation exist. ⋯ Black living kidney donors were less likely to recover predonation eGFR, and time to renal recovery was significantly longer than their White counterparts. These data highlight the need for enhanced living kidney donor follow-up, particularly for Black living kidney donors who are at greatest future risk of end-stage kidney disease.
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Artificial intelligence (AI) tools created to enhance decision-making may have a significant impact on treatment algorithms for peripheral arterial disease (PAD). A Markov-based AI model was developed to predict optimal therapy based on maximization of calculated quality of life (cQoL), a patient-centered system of assessment designed to report outcomes directly linked to health-related quality of life. ⋯ AI can successfully predict treatment for PAD that maximizes patient quality of life in most cases. Future application of AI incorporating better estimates of patient anatomic and physiological risk factors and refinement of model structure should further enhance performance.