Objective:To determine the relationship between BC, specifically low skeletal muscle mass (sarcopenia) and poor muscle quality (myosteatosis) and outcomes in emergency laparotomy patients.Background:Emergency laparotomy has one of the highest morbidity and mortality rates of all surgical interventions. BC objectively identifies patients at risk of adverse outcomes in elective cancer cohorts, however, evidence is lacking in emergency surgery.Methods:An observational cohort study of patients undergoing emergency laparotomy at ten English hospitals was performed. BC analyses were performed at the third lumbar vertebrae level using preoperative computed tomography images to quantify skeletal muscle index (SMI) and skeletal muscle radiation attenuation (SM-RA). Sex-specific SMI and SM-RA were determined, with the lower tertile splits defining sarcopenia (low SMI) and myosteatosis (low SM-RA). Accuracy of mortality risk prediction, incorporating SMI and SM-RA variables into risk models was assessed with regression modeling.Results:Six hundred ten patients were included. Sarcopenia and myosteatosis were both associated with increased risk of morbidity (52.1% vs 45.1%, P = 0.028; 57.5% vs 42.6%, P = 0.014), 30-day (9.5% vs 3.6%, P = 0.010; 14.9% vs 3.4%, P < 0.001), and 1-year mortality (27.4% vs 11.5%, P < 0.001; 29.7% vs 12.5%, P < 0.001). Risk-adjusted 30-day mortality was significantly increased by sarcopenia [OR 2.56 (95% CI 1.12-5.84), P = 0.026] and myosteatosis [OR 4.26 (2.01-9.06), P < 0.001], similarly at 1-year [OR 2.66 (95% CI 1.57-4.52), P < 0.001; OR2.08 (95%CI 1.26-3.41), P = 0.004]. BC data increased discrimination of an existing mortality risk-prediction model (AUC 0.838, 95% CI 0.835–0.84).Conclusion:Sarcopenia and myosteatosis are associated with increased adverse outcomes in emergency laparotomy patients.
Introduction This paper assessed the association between operative approach and postoperative in-hospital mortality in elderly patients undergoing emergency abdominal surgery. Patients undergoing emergency laparotomy have high morbidity and mortality rates. One-third of patients requiring emergency surgery are over 75 years old, and their in-hospital mortality rate exceeds 17%. Fewer than 20% of emergency abdominal operations in the UK are attempted laparoscopically, and only 10% are completed laparoscopically. Little is known about how laparoscopic emergency surgery in the elderly might affect outcomes. Methods An observational UK study was performed using the prospectively maintained National Emergency Laparotomy Audit (NELA) database. Operative approach, NELA risk-prediction score and in-hospital mortality were recorded. The effect of operative approach on in-hospital mortality was analysed, both on a national basis and in a high-volume laparoscopic centre. Results A total of 47,667 patients were included in the study, of whom 15,068 were over 75 years of age. Nationally, surgery was completed by the laparoscopic approach in 7.8% of patients aged over 75; both crude mortality (9.2%) and risk-adjusted mortality (7.1%) were significantly reduced (p<0.0001). In our unit, surgery was completed laparoscopically in 48.4% of patients aged over 75; both crude mortality (6.6%) and risk-adjusted mortality (3.3%) were significantly reduced (p<0.0001). Conclusion Laparoscopy in emergency surgery has been shown in this study to significantly reduce in-hospital mortality in elderly patients and should be embraced in every centre dealing with emergency abdominal surgery.
Introduction Emergency laparotomy has a considerable mortality risk, with more than one in ten patients not surviving to discharge. Preoperative risk prediction using clinical tools is well established, however implemented variably. Preoperative CT is undertaken almost universally and contains granular data beyond diagnostics, including body composition, disease severity and other abstract features with the potential to enhance risk prediction. In this study we established the value of features extracted in an automated fashion from pre-operative CT in predicting 90-day post-surgery mortality. Method Anonymised CTs were collated from patients undergoing emergency laparotomy at ten hospitals in Southern England (2016–2017). For each case, axial portal venous abdominal/pelvic series were analysed using a pre-trained neural network, with each image converted into a matrix of numerical features. An elastic-net regression model to predict 90-day mortality was trained using these features and evaluated by bootstrapping with 1000 resampled datasets. Result A total of 136,709 images from 274 cases were available for analysis with a mean of 503 per case. Mortality within 90 days occurred in 34 cases (12.4%) with an average NELA mortality prediction of 8.5%. On internal (bootstrap) validation, the elastic net model derived from CT yielded excellent performance (AUC 0.903 95%CI 0.897–0.909), significantly in excess of the NELA risk calculator (AUC 0.809 95%CI 0.736–0.875), with a broader prediction range (0.01%-89.71%). Conclusion Artificial intelligence techniques applied to routinely performed cross-sectional imaging predicts emergency laparotomy mortality with greater accuracy than clinical data alone. Integration of these automated tools may be possible in the future. Take-home Message Automated analysis of CT can accurately predict risk of mortality after emergency laparotomy.
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