Objective: To comprehensively evaluate and analyze the transfusion outcomes of patients with acute upper gastrointestinal bleeding (UGIB). Methods: The transfusion management system and hospital information system (HIS) were used to retrospectively collect clinical data of 230 patients with UGIB admitted to Zhejiang Provincial People's Hospital and its branches from June 2018 to June 2021. 101 cases were screened and categorized into transfusion group (n=56) and non-transfusion group (n=45) based on transfusion outcomes. The cohort comprised 68 males and 33 females. A univariate model based on the AIMS65 score, a logistic multiple regression model, and multivariate transfusion models using machine learning methods (including Random Forest, Support Vector Machine, and Artificial Neural Network) were established. The sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curves of each model were compared. Results: For the univariate model based on the AIMS65 scoring, the optimal threshold was 1.5. This model demonstrated a sensitivity of 0.446, a specificity of 0.822, an AUC of 0.67, an accuracy (ACC) of 0.614, a Kappa value of 0.256, and an F1-score of 0.655. For logistics regression model (optimal critical probability: 0.459), the sensitivity was 0.929, specificity was 0.889, AUC was 0.96, ACC was 0.911, Kappa was 0.819, and F1-score was 0.899. For the Random Forest model (optimal critical probability: 0.458), the sensitivity was 0.964, specificity was 0.956, AUC was 0.99, ACC was 0.960, Kappa was 0.920, and F1-score was 0.956. For the Support Vector Machine model (optimal critical probability: 0.474), the sensitivity was 0.875, specificity was 0.933, AUC was 0.94, ACC was 0.901, Kappa was 0.801, and F1-score was 0.894. For the Artificial Neural Network model (optimal critical probability: 0.797), the sensitivity was 0.804, specificity was 0.956, AUC was 0.96, ACC was 0.871, Kappa was 0.745, and F1-score was 0.869. Ten-fold cross validation also confirmed the reliability of the results. Conclusion: Based on integrated various clinical test indicators of patients, we could establish logistic regression model and multiple machine learning models. These models hold significant value for predicting the need for blood transfusion in patients, indicating a promising application prospect for machine learning algorithms in transfusion prediction.

【Objective】 To establish a blood transfusion outcome prediction model for comprehensivel evaluation of coagulation function of patients with upper gastrointestinal bleeding by thrombelastogram (TEG) and blood coagulation indicators. 【Methods】 The data of 101 patients with upper gastrointestinal hemorrhage, admitted to the Department of Gastroenterology of Zhejiang Provincial People′s Hospital and its Chun′an Branch from June 2018 to June 2021, were collected through Tongshuo blood transfusion management system and His system. Those patients were divided into blood transfusion group (n=56) and non-transfusion group (n=45), and into cirrhosis group (n=74) and non-cirrhosis group (n=27), and 40 patients, with non-upper gastrointestinal bleeding, were enrolled as the control. The results of TEG indicators (R, K, α, MA), coagulation function (PT, INR, APTT, TT, Fib), blood routine (Hb, Plt, WBC, NEUT%) and biochemical detection(Alb, SCr, ALT, AST, GGT) before transfusion were compared between groups and the correlation between TEG indicators and traditional coagulation parameters was analyzed. Single-factor and multi-factor analysis were used to screen blood transfusion-related factors to establish a predictive model. 【Results】 The comparisons of paremeters between transfusion and non-transfusion group were as follows, K (min), α (°), and MA (mm) was 3.86±3.12 vs 2.50±1.47, 54.00±14.08 vs 61.05±10.88, and 51.12±13.37 vs 58.26±11.08, respectively (P<0.01); PT (s) and Fib (g) was 16.36±7.45 vs 13.44±1.50 and 1.59±0.87 vs 2.35±1.09 (P<0.01); NEUT% and Hb (g/L) was 0.75 ±0.13 vs 0.66±0.15 and 68.04±14.49 vs 100.73±22.92 (P<0.01); Alb (g/L) and SCr (nmol/L) was 29.73±6.08 vs 33.73±7.19 and 99.50±53.55 vs 76.25±19.28 (P<0.01). Correlation analysis showed that APTT was positively correlated with R and K values, and negatively correlated with α and MA. Fib was negatively correlated with K values, and positively correlated with α and MA. Plt was negatively correlated with K values, and positively correlated with α and MA (P<0.01). Eight pre-transfusion indicators as K, MA, PT, Fib, NEUT%, Hb, Alb, and SCr were subjected to Logistic regression to establish a blood transfusion prediction model. The optimal ROC curve of blood transfusion threshold (blood transfusion predictive value of patients), sensitivity, specificity and AUC were 0.448, 92.9%, 88.9%, and 0.969, respectively. 【Conclusion】 The establishment of Logistic regression model by integrating detection indicators of TEG, coagulation function, blood routine and biochemistry in patients with upper gastrointestinal bleeding have showed significant correlation with blood transfusion prediction, and good clinical practicability.

Objective: To analyze the etiology and epidemiological characteristics of influenza in Wenzhou from 2009 to 2014, so as to provide the scientific basis for control and prevention of influenza. Methods: Throat swab specimens of influenza like illness (ILI) were collected from national influenza surveillance sentinel hospitals for nucleic acid detection with real-time PCR and virus isolation, culture and sequencing, and the results were analyzed with statistical methods. Results: During the 8 years, a total of 10 577 089 cases from outpatient and emergency department were monitored in sentinel hospitals. There were 337 896 ILI cases with an average ILI treatment rate of 3.19%. A total of 4 046 ILI samples were detected in children, 511 were positive for influenza, the positive rate was 12.63%. Among the detected influenza types, type B had the highest proportion, followed by H3N2. Among the 6 age groups, the number of flu patients was the highest in 0-3 years old group, the positive rate in 10-12 years old group was the highest (35.03%). There were 28 and 45 amino acid sequence mutations of HA fragment in influenza A and B, respectively, which included multiple mutation of 391 and 145 amino acids. The phylogenetic analysis showed that the strains of type B were different in different years, and Yamagata evolved into Y1 and Y2 two branches. Conclusions The prevalence peaks of influenza in children occurred in winter and spring in Wenzhou city, accompanied by small peaks in summer. Three subtypes of serotypes B, H3N2 and A(H1N1) dominated alternatively in Wenzhou during the 8 years. We should focus on strengthening the prevention and control of influenza in preschool children and primary and secondary school students.