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Purpose: The study aimed to report the clinical manifestation and identify the risk factors for postoperative mortality in patients who were diagnosed with obstructive acute mesenteric ischemia (AMI) based on a survey of a hospital in western China., Patients and Methods: We reviewed clinical data of 108 patients with obstructive AMI at West China Hospital, Sichuan University, from 2011 to 2017. Clinical characteristic was described and compared. Factors affecting postoperative survival were analyzed., Results: A total of 108 obstructive AMI cases were included in this study, with an overall average age of 57.1 years, including 58 arterial occlusive mesenteric ischemia (AOMI) and 50 mesenteric venous thrombosis (MVT). AOMI patients were older and had a significantly higher frequency of these comorbidities, including heart disease, hypertension, and diabetes. In comparison, MVT had a significantly higher proportion of male patients and a higher frequency of liver disease. A total of 77 patients underwent laparotomy, and the 30-day postoperative mortality rate was 29.9%. Multivariate logistic regression analysis showed that the time interval from admission to surgery (adjust OR 1.19; 95% CI [1.07-1.34], P = 0.005), platelet count (adjust OR = 0.98; 95% CI [0.97-0.99], P = 0.008) and AOMI (adjust OR = 5.55; 95% CI [1.36-22.55], P = 0.017) were independent predictors of 30-day mortality after exploratory laparotomy for obstructive AMI. Further analysis of the 45 AOMI showed that the time interval from admission to surgery (adjustOR 1.22; 95% CI [1.01-1.47], P = 0.036) and platelet count (adjustOR = 0.98; 95% CI [0.97-0.99], P = 0.020) were independent risk factors for 30-day postoperative mortality of AOMI., Conclusion: Early identification of AOMI, improving the efficiency of enhanced CT examination, strengthening doctor-patient communication, active laparotomy, and optimize platelet management may also help reduce the overall short-term mortality of obstructive AMI. Building the multidisciplinary team model of diagnostic imaging, vascular intervention, and surgical treatment to manage obstructive AMI may be urgently needed in western China., Competing Interests: The authors report no conflicts of interest in this work., (© 2020 Wu et al.)

Purpose: This study aimed to use traditional statistics and machine learning to develop and validate prediction models for predicting hospital death in patients with AMI and compare these models' performance. Patients and Methods: Data were retrieved from the Medical Information Mart for Intensive Care (MIMIC III) electronic clinical database. A total of 338 eligible AMI patients were divided into a training cohort (n = 238) and a validation cohort (n = 100), and all patients were divided into survival groups and nonsurvival groups according to patients' hospital outcomes. The performance of the traditional statistics prediction model and the optimal machine learning prediction model was evaluated and compared with respect to discrimination, calibration, and clinical utility in the validation cohort. Results: Univariate and multivariate logistic regression analyses identified the following independent risk factors associated with hospital death for AMI in the training cohort, including diastolic blood pressure, blood lactate, blood creatinine, age, blood pH, and red blood cell distribution width. Both the nomogram (AUC = 77.0%, 67.9– 86.1%) and optimal machine learning model (AUC = 82.9%, 74.9– 91.0%) achieved good discrimination and calibration in the validation cohort. Decision curves analysis showed that the optimal machine learning model has a greater net benefit than that of nomogram in this study. Conclusion: The nomogram achieved a concise and relatively accurate prediction of hospital death in patients with AMI, the machine learning model also has good discrimination and seems to have better clinical utility. Traditional statistics may help infer the relationship between risk factors and hospital death, while machine learning may contribute to a more accurate prediction. Traditional statistics and machine learning are complementary in developing the prediction model for hospital death of AMI. Therefore, a combination of nomogram–machine learning (Nomo-ML) predictive model may improve care and help clinicians make AMI management-related decisions. [ABSTRACT FROM AUTHOR]