Predicting five-year mortality in soft-tissue sarcoma patients.

Aims: The aim of this study was to identify factors associated with five-year cancer-related mortality in patients with limb and trunk soft-tissue sarcoma (STS) and develop and validate machine learning algorithms in order to predict five-year cancer-related mortality in these patients. Methods: Demographic, clinicopathological, and treatment variables of limb and trunk STS patients in the Surveillance, Epidemiology, and End Results Program (SEER) database from 2004 to 2017 were analyzed. Multivariable logistic regression was used to determine factors significantly associated with five-year cancer-related mortality. Various machine learning models were developed and compared using area under the curve (AUC), calibration, and decision curve analysis. The model that performed best on the SEER testing data was further assessed to determine the variables most important in its predictive capacity. This model was externally validated using our institutional dataset. Results: A total of 13,646 patients with STS from the SEER database were included, of whom 35.9% experienced five-year cancer-related mortality. The random forest model performed the best overall and identified tumour size as the most important variable when predicting mortality in patients with STS, followed by M stage, histological subtype, age, and surgical excision. Each variable was significant in logistic regression. External validation yielded an AUC of 0.752. Conclusion: This study identified clinically important variables associated with five-year cancer-related mortality in patients with limb and trunk STS, and developed a predictive model that demonstrated good accuracy and predictability. Orthopaedic oncologists may use these findings to further risk-stratify their patients and recommend an optimal course of treatment.

Prediction of Non-Home Discharge Following Total Hip Arthroplasty in Geriatric Patients.

Introduction: The majority of total hip arthroplasty (THA) patients are discharged home postoperatively, however, many still require continued medical care. We aimed to identify important characteristics that predict nonhome discharge in geriatric patients undergoing THA using machine learning. We hypothesize that our analyses will identify variables associated with decreased functional status and overall health to be predictive of non-home discharge. Materials and Methods: Elective, unilateral, THA patients above 65 years of age were isolated in the NSQIP database from 2018-2020. Demographic, pre-operative, and intraoperative variables were analyzed. After splitting the data into training (75%) and validation (25%) data sets, various machine learning models were used to predict non-home discharge. The model with the best area under the curve (AUC) was further assessed to identify the most important variables. Results: In total, 19,840 geriatric patients undergoing THA were included in the final analyses, of which 5194 (26.2%) were discharged to a non-home setting. The RF model performed the best and identified age above 78 years (OR: 1.08 [1.07, 1.09], P < .0001), as the most important variable when predicting non-home discharge in geriatric patients with THA, followed by severe American Society of Anesthesiologists grade (OR: 1.94 [1.80, 2.10], P < .0001), operation time (OR: 1.01 [1.00, 1.02], P < .0001), anemia (OR: 2.20 [1.87, 2.58], P < .0001), and general anesthesia (OR: 1.64 [1.52, 1.79], P < .0001). Each of these variables was also significant in MLR analysis. The RF model displayed good discrimination with AUC = .831. Discussion: The RF model revealed clinically important variables for assessing discharge disposition in geriatric patients undergoing THA, with the five most important factors being older age, severe ASA grade, longer operation time, anemia, and general anesthesia. Conclusions: With the rising emphasis on patient-centered care, incorporating models such as these may allow for preoperative risk factor mitigation and reductions in healthcare expenditure.

No Difference in Short-Term Complications following Treatment of Closed Tibial Shaft Fractures with Intramedullary Nailing versus Plate Fixation.

Objectives: Tibial shaft fractures are treated with both intramedullary nailing (IMN) and plate fixation (ORIF). Using a large national database, we aimed to explore the differences in thirty-day complication rates between IMN and ORIF. Methods: Patients in the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database who had undergone either tibial IMN or ORIF for closed fractures from 2010 to 2018 were identified using current procedural terminology (CPT) codes. After excluding all patients with open fractures, the propensity score was matching. Univariate and multivariate logistic regressions were used to identify risk factors associated with the thirty-day incidence of complications in the two cohorts. Results: A total of 5,400 patients were identified with 3,902 (72.3%) undergoing IMN and 1,498 (27.7%) ORIF. After excluding any ICD-10 diagnosis codes not pertaining to closed, traumatic tibial shaft fractures, 2,136 IMN and 621 ORIF cases remained. After matching, the baseline demographics were not significantly different between the cohorts. Following matching, the rate of any adverse event (aae) did not differ significantly between the IMN (7.08% (n = 44)) and ORIF (8.86% (n = 55)) cohorts (p=0.13). There was also no significant difference in operative time (IMN = 98.5 min, ORIF = 100 min; p=0.3) or length of stay (IMN = 3.7 days, ORIF = 3.3 days; p=0.08) between the cohorts. Conclusion: There were no significant differences in short-term complications between cohorts. These are important data for the surgeon when considering surgical management of closed tibial shaft fractures.

The Limited Role of Glucagon for Ketogenesis During Fasting or in Response to SGLT2 Inhibition.

Glucagon is classically described as a counterregulatory hormone that plays an essential role in the protection against hypoglycemia. In addition to its role in the regulation of glucose metabolism, glucagon has been described to promote ketosis in the fasted state. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are a new class of glucose-lowering drugs that act primarily in the kidney, but some reports have described direct effects of SGLT2i on α-cells to stimulate glucagon secretion. Interestingly, SGLT2 inhibition also results in increased endogenous glucose production and ketone production, features common to glucagon action. Here, we directly test the ketogenic role of glucagon in mice, demonstrating that neither fasting- nor SGLT2i-induced ketosis is altered by interruption of glucagon signaling. Moreover, any effect of glucagon to stimulate ketogenesis is severely limited by its insulinotropic actions. Collectively, our data suggest that fasting-associated ketosis and the ketogenic effects of SGLT2 inhibitors occur almost entirely independent of glucagon.

Glucagon lowers glycemia when ß-cells are active.

Glucagon and insulin are commonly believed to have counteracting effects on blood glucose levels. However, recent studies have demonstrated that glucagon has a physiologic role to activate ß-cells and enhance insulin secretion. To date, the actions of glucagon have been studied mostly in fasting or hypoglycemic states, yet it is clear that mixed-nutrient meals elicit secretion of both glucagon and insulin, suggesting that glucagon also contributes to glucose regulation in the postprandial state. We hypothesized that the elevated glycemia seen in the fed state would allow glucagon to stimulate insulin secretion and reduce blood glucose. In fact, exogenous glucagon given under fed conditions did robustly stimulate insulin secretion and lower glycemia. Exogenous glucagon given to fed Gcgr:Glp1rßcell-/- mice failed to stimulate insulin secretion or reduce glycemia, demonstrating the importance of an insulinotropic glucagon effect. The action of endogenous glucagon to reduce glycemia in the fed state was tested with administration of alanine, a potent glucagon secretagogue. Alanine raised blood glucose in fasted WT mice or fed Gcgr:Glp1rßcell-/- mice, conditions where glucagon is unable to stimulate ß-cell activity. However, alanine given to fed WT mice produced a decrease in glycemia, along with elevated insulin and glucagon levels. Overall, our data support a model in which glucagon serves as an insulinotropic hormone in the fed state and complements rather than opposes insulin action to maintain euglycemia.