Objective To investigate the predictive efficacy of complete blood count(CBC)parameters for the prognosis of acute pancreatitis(AP),and to construct a machine learning model.Methods The clinical data of 120 patients with AP admitted from January 2021 to December 2024 were retrospectively analyzed.Based on the prognostic outcomes within 28 d of treatment,they were divided into the poor prognosis group and the good prognosis group.On the first day of admission,CBC parameters[red blood cell count,hemoglobin,hematocrit,white blood cell count,neutrophil count,lymphocyte count,monocyte count,red blood cell distribution width(RDW),platelet count,mean platelet volume,platelet distribution width,neutrophil/lymphocyte ratio(NLR),platelet/lymphocyte ratio(PLR),lymphocyte/monocyte ratio(LMR)were detected in the two groups.The general data and CBC parameters of the two groups were compared.The clinical variables were screened using the Lasso regression equation.Four models,namely Support Vector Machine(SVM),logistic regression(LR),deep neural network(DNN),and Random Forest(RF),were constructed for external validation.The predictive efficacy of the four models for the prognosis of AP was evaluated by using the receiver operating characteristic(ROC)curve,the precision-recall(PR)curve,the calibration curve and the clinical decision curve.Results The APACHE Ⅱ score,bedside severity index score,proportion of vasoactive drug use,and proportion of severe hypoproteinemia in the poor prognosis group were all higher than those in the good prognosis group(P<0.01).The levels of RDW,NLR and PLR in the poor prognosis group were all higher than those in the good prognosis group,while the level of LMR was lower than that in the good prognosis group(P<0.01).The Lasso regression equation ultimately screened out four non-zero coefficient variables:NLR,RDW,APACHE Ⅱ score,and severe hypoproteinemia.Based on the above variables,SVM,LR,RF and DNN machine learning models were constructed.The RF model had the highest area under the ROC curve(AUC),PR AUC,accuracy rate and F1 score for predicting the prognosis of AP,and had the optimal comprehensive performance.The calibration curve showed that the curve of the RF model for predicting the prognosis of AP was relatively close to the ideal curve.The decision curve showed that when the threshold probability value of the RF model was 15%to 100%,it had a significance clinical net benefit rate.External validation also showed that the RF model had the optimal predictive efficacy.The calibration curve indicated that the predictive curve of the RF model for the prognosis of AP highly coincided with the actually observed curve.The decision curve showed that the RF model provided clinical net benefits to patients across the decision threshold range of 0 to 100%.Conclusion Machine learning models constructed based on CBC parameters can make relatively accurate predictions about the prognosis of AP.Among them,the RF model has the optimal predictive efficiency,which can be used as an auxiliary tool for clinical prediction of AP prognosis,and can also provide reference for clinical treatment.

Objective To investigate the predictive efficacy of complete blood count(CBC)parameters for the prognosis of acute pancreatitis(AP),and to construct a machine learning model.Methods The clinical data of 120 patients with AP admitted from January 2021 to December 2024 were retrospectively analyzed.Based on the prognostic outcomes within 28 d of treatment,they were divided into the poor prognosis group and the good prognosis group.On the first day of admission,CBC parameters[red blood cell count,hemoglobin,hematocrit,white blood cell count,neutrophil count,lymphocyte count,monocyte count,red blood cell distribution width(RDW),platelet count,mean platelet volume,platelet distribution width,neutrophil/lymphocyte ratio(NLR),platelet/lymphocyte ratio(PLR),lymphocyte/monocyte ratio(LMR)were detected in the two groups.The general data and CBC parameters of the two groups were compared.The clinical variables were screened using the Lasso regression equation.Four models,namely Support Vector Machine(SVM),logistic regression(LR),deep neural network(DNN),and Random Forest(RF),were constructed for external validation.The predictive efficacy of the four models for the prognosis of AP was evaluated by using the receiver operating characteristic(ROC)curve,the precision-recall(PR)curve,the calibration curve and the clinical decision curve.Results The APACHE Ⅱ score,bedside severity index score,proportion of vasoactive drug use,and proportion of severe hypoproteinemia in the poor prognosis group were all higher than those in the good prognosis group(P<0.01).The levels of RDW,NLR and PLR in the poor prognosis group were all higher than those in the good prognosis group,while the level of LMR was lower than that in the good prognosis group(P<0.01).The Lasso regression equation ultimately screened out four non-zero coefficient variables:NLR,RDW,APACHE Ⅱ score,and severe hypoproteinemia.Based on the above variables,SVM,LR,RF and DNN machine learning models were constructed.The RF model had the highest area under the ROC curve(AUC),PR AUC,accuracy rate and F1 score for predicting the prognosis of AP,and had the optimal comprehensive performance.The calibration curve showed that the curve of the RF model for predicting the prognosis of AP was relatively close to the ideal curve.The decision curve showed that when the threshold probability value of the RF model was 15%to 100%,it had a significance clinical net benefit rate.External validation also showed that the RF model had the optimal predictive efficacy.The calibration curve indicated that the predictive curve of the RF model for the prognosis of AP highly coincided with the actually observed curve.The decision curve showed that the RF model provided clinical net benefits to patients across the decision threshold range of 0 to 100%.Conclusion Machine learning models constructed based on CBC parameters can make relatively accurate predictions about the prognosis of AP.Among them,the RF model has the optimal predictive efficiency,which can be used as an auxiliary tool for clinical prediction of AP prognosis,and can also provide reference for clinical treatment.

OBJECTIVEThis study aimed to investigate the status of multiple sexual partners and unprotected sexual behaviors and related influencing factors among HIV-positive men who have sex with men (MSM).

METHODSHIV-positive men having sex with men aged 18 years or older, living in Chengdu, Chongqing or Guangzhou were recruited by using the " snowballing" sampling method. Participants completed the questionnaire on computers, after filling in the Informed Consent Form. Content of the study would include social demographic characteristics, number of sexual partners, sexual behaviors, and the symptoms assessment on depression and anxiety.χ(2)-test,t-test and non-conditional Multiple logistic Regression methods were used to examine the risky sexual behaviors with multiple sexual partners among the participants engaged in this project.

RESULTSMean age of the 501 participants was (30.24±7.70) years old. In the past 6 months, 17.4% (87/501) of them had engaged in unprotected sexual behavior with two or more sexual partners. Factors at risk would include: being married (OR=1.93, 95%CI: 0.77-4.84), divorced or widowed (OR=3.94, 95%CI: 1.66-9.36), having primary male sexual partners (OR=5.04, 95%CI: 1.08-23.54) and casual or commercial male sexual partners (OR=2.54, 95%CI: 1.34-4.80) in the past 6 months, drinking alcohol (OR=3.00, 95%CI: 1.37-6.62) or Rush (alkyl nitrite) (OR=3.53, 95%CI: 1.72-7.23) during sexual acts, sharing their HIV-infection status to their partly primary male sexual partners (OR=1.84, 95%CI:0.78-4.33) or not (OR=2.68, 95% CI: 1.25-5.73), and having high sexual sensation seeking scores (OR=1.09, 95%CI: 1.03-1.15).

CONCLUSIONSUnprotected sexual behaviors with multiple sexual partners among HIV-positive MSM played an important role in expediting the HIV transmission. Development of intervention programs to minimize the risk sexual behaviors and setting up efficient medical and biological measures in controlling the HIV transmission were in urgent need.

Adult ; Coitus ; Depression ; Depressive Disorder ; Family Characteristics ; Homosexuality, Male ; psychology ; statistics & numerical data ; Humans ; Infection ; epidemiology ; transmission ; Male ; Marriage ; Middle Aged ; Risk ; Risk-Taking ; Sexual Behavior ; Sexual Partners ; Surveys and Questionnaires ; Unsafe Sex ; psychology ; statistics & numerical data ; Young Adult