Objective: The aim of this study was to evaluate the efficacy of artificial neural networks (ANN) in predicting intra-abdominal infection in moderately severe (MASP) and severe acute pancreatitis (SAP) compared with that of a logistic regression model (LRM). Methods: Patients suffering from MSAP or SAP from July 2014 to June 2017 in three affiliated hospitals of the Army Medical University in Chongqing, China, were enrolled in this study. A univariate analysis was used to determine the different parameters between patients with and without intra-abdominal infection. Subsequently, these parameters were used to build LRM and ANN. Results: Altogether 263 patients with MSAP or SAP were enrolled in this retrospective study. A total of 16 parameters that differed between patients with and without intra-abdominal infection were used to construct both models. The sensitivity of ANN and LRM was 80.99% (95% confidence interval [CI] 72.63-87.33) and 70.25% (95% CI 61.15-78.04), respectively (P > 0.05), whereas the specificity was 89.44% (95% CI 82.89-93.77) and 77.46% (95% CI 69.54-83.87), respectively (P < 0.05). ANN predicted the risk of intra-abdominal infection better than LRM (area under the receiver operating characteristic curve: 0.923 [0.883-0.952] vs 0.802 [0.749-0.849], P < 0.001). Conclusions: ANN accurately predicted intra-abdominal infection in MSAP and SAPand is an ideal tool for predicting intra-abdominal infection in such patients. Coagulation parameters played an important role in such prediction.intra-abdominal infection, logistic regression, neural network, pancreatitis Male sex, n (%) 165 (62.7) Age, y (median [IQR]) 47 (39-59) History of smoking, n (%) 76 (28.9) History of alcohol consumption, n (%) 85 (32.3) History of hypertension, n (%) 58 (22.1) History of diabetes, n (%) 31 (11.8) Admission to different departments, n (%) Department of Gastroenterology 127 (48.3) Department of Emergency 67 (25.5) Intensive care unit 69 (26.2) Etiology, n (%) Biliary 106 (40.3) Hypertriglyceridemia 92 (35.0) Alcoholic 24 (9.1) Others 41 (15.6) BMI, kg/m 2 (median [IQR]) 25.71 (23.53-27.92) Obese (BMI ≥25 kg/m 2 ), n (%) 146 (55.5) SIRS, n (%) 200 (76.0) Intra-abdominal infection, n (%) 121 (46.0) APACHE II score, median (IQR) 10 (8-13) Abbreviations: APACHE, acute physiology and chronic health evaluation; BMI, body mass index; IQR, interquartile range; SIRS, systemic inflammatory response syndrome.

Background Since December 2019, COVID-19 has spread throughout the world. Clinical outcomes of COVID-19 patients vary among infected individuals. Therefore, it is vital to identify patients at high risk of disease progression. Methods In this retrospective, multicenter cohort study, COVID-19 patients from Huoshenshan Hospital and Taikang Tongji Hospital (Wuhan, China) were included. Clinical features showing significant differences between the severe and nonsevere groups were screened out by univariate analysis. Then, these features were used to generate classifier models to predict whether a COVID-19 case would be severe or nonsevere based on machine learning. Two test sets of data from the two hospitals were gathered to evaluate the predictive performance of the models. Results A total of 455 patients were included, and 21 features showing significant differences between the severe and nonsevere groups were selected for the training and validation set. The optimal subset, with eleven features in the k –nearest neighbor model, obtained the highest area under the curve (AUC) value among the four models in the validation set. D-dimer, CRP, and age were the three most important features in the optimal-feature subsets. The highest AUC value was obtained using a support vector–machine model for a test set from Huoshenshan Hospital. Software for predicting disease progression based on machine learning was developed. Conclusion The predictive models were successfully established based on machine learning, and achieved satisfactory predictive performance of disease progression with optimal-feature subsets.

Objective The effects of low molecular weight heparin (LMWH) on severe acute pancreatitis (SAP) have been controversial. We aimed to evaluate the efficacy of LMWH on prognosis of SAP by systematic review and meta‐analysis. Methods We searched relevant studies published up to March 2019 in five databases (MEDLINE/PubMed, EMBASE, the Cochrane Central Register of Controlled Trials in Cochrane Library, China National Knowledge Infrastructure, and the Chinese Journal of Science and Technology of VIP database). Results Sixteen randomized controlled trials with 1625 patients were included in the final analysis. Most studies were from China. In analysis of laboratory parameters and clinical scores, SAP patients receiving LMWH treatment had lower white blood cell counts, C‐reactive protein level, Acute Physiology and Chronic Health Evaluation II score, and computed tomography severity index. In clinical outcomes, SAP patients who received LMWH treatment had shorter hospital stay (pooled mean difference [95% confidence interval; CI] −8.79 [−11.18, −6.40], P < .01), lower mortality (pooled risk ratio [RR] [95% CI] 0.33 [0.24‐0.44], P < .01), lower incidences of multiple organ failure (pooled RR [95% CI] 0.34 [0.23‐0.52], P < .01), pancreatic pseudocyst (pooled RR [95% CI] 0.49 [0.27‐0.90], P = .02), and operation rate (pooled RR [95% CI] 0.39 [0.31‐0.50], P < .01). Conclusions LMWH could improve the prognosis of SAP, and has a potential role in reducing hospital stay, mortality, incidences of multiple organ failure, pancreatic pseudocyst, and operation rate.