Objective:To explore the related risk factors of neonatal necrotizing enterocolitis (NEC) by constructing and comparing nine regression models.Methods:All NEC patients admitted to the neonatal internal medicine department, neonatal surgery department, and neonatal intensive care unit of Shanxi Provincial Children's Hospital (Shanxi Provincial Maternity and Child Health Center) from 2020 to 2022 were included as the case group. A control group consisted of children admitted during the same period based on the inclusion and exclusion criteria. The NEC data collected were used for feature selection by using the Boruta algorithm. Logistic regression, multi-decision tree gradient boosting, efficient gradient one-sided sampling, random forest, decision tree, gradient boosting decision tree (GBDT), neural network, support vector machine, and K-nearest neighbor models were constructed. The optimal model was selected through rigorous comparison and Shap explainable analysis was performed on the GBDT model.Results:Thirteen key factors were identified through screening for nine regression models construction. After strict comparison and analysis, the GBDT model showed higher stability compared with other eight regression models. In the validation set, the area under the receiver operating characteristic curve of the GBDT model was 0.958, with an accuracy of 0.925, and sensitivity and specificity of 0.827 and 0.950, respectively. Shap explainable analysis on the GBDT model revealed that suffering from anemia, non-invasive ventilator use, procalcitonin use, premature birth, and low birth weight increased the risk for NEC, while breastfeeding and probiotics decreased the risk for NEC.Conclusion:This study identified the risk factors and protective factors for NEC by using the GBDT model, which provided evidnce for the prevention and treatment of NEC.

Objective To systematically search,evaluate and summarise the best evidence for the management of swallowing disorders in senile dementia,and provide evidence for clinical practice.Methods Systematic search of guidelines,expert consensus,evidence summaries,systematic reviews on the management of swallowing disorders in dementia patients on databases and clinical decision support systems including BMJ Best Clinical Practices,UpToDate,International Guidelines Collaboration Network(IGCN),National Institute for Health and Care Excellence(NICE),Scottish intercollegiate guidelines network(SIGN),national guideline clearinghouse(NGC),Registered Nurses Association of Ontario(RNAO),New Zealand Guidelines Group(NZGG),World Health Organization(WHO),Medlive,PubMed,Cochrane Library,Embase,Web of Science,Joanna Briggs Institute(JBI),CNKI,Wanfang,VIP,SinoMed,the website of the European Society for Swallowing Disorders(ESSD)and Japanese Society of Dysphagia Rehabilitation(JSDR).Clinical decisions,guidelines,expert consensus,evidence summaries,systematic reviews,and clinical trial studies on the management of swallowing disorders in dementia patients were searched.The search was from inception to 30th November,2023.Two researchers trained in evidence-based nursing systems conducted literature screening and data extraction,and determined appropriate evaluation tools to evaluate the quality of the included literature,extracted and graded the evidence,and finally summarised the best evidence suitable for the management of swallowing disorders in dementia patients.Results A total of 8 literature were included,covering 1 guideline,1 clinical decision,4 evidence summaries,and 2 systematic reviews.Finally,19 pieces of evidence were collected from 5 aspects,including screening and evaluation,feeding behaviour management,auxiliary training,education and training,and follow-up monitoring.Conclusion The best evidence summarised in this study provided evidence-based reference for the management of swallowing disorders in patients with dementia by healthcare workers.

Objective:To explore the related risk factors of neonatal necrotizing enterocolitis (NEC) by constructing and comparing nine regression models.Methods:All NEC patients admitted to the neonatal internal medicine department, neonatal surgery department, and neonatal intensive care unit of Shanxi Provincial Children's Hospital (Shanxi Provincial Maternity and Child Health Center) from 2020 to 2022 were included as the case group. A control group consisted of children admitted during the same period based on the inclusion and exclusion criteria. The NEC data collected were used for feature selection by using the Boruta algorithm. Logistic regression, multi-decision tree gradient boosting, efficient gradient one-sided sampling, random forest, decision tree, gradient boosting decision tree (GBDT), neural network, support vector machine, and K-nearest neighbor models were constructed. The optimal model was selected through rigorous comparison and Shap explainable analysis was performed on the GBDT model.Results:Thirteen key factors were identified through screening for nine regression models construction. After strict comparison and analysis, the GBDT model showed higher stability compared with other eight regression models. In the validation set, the area under the receiver operating characteristic curve of the GBDT model was 0.958, with an accuracy of 0.925, and sensitivity and specificity of 0.827 and 0.950, respectively. Shap explainable analysis on the GBDT model revealed that suffering from anemia, non-invasive ventilator use, procalcitonin use, premature birth, and low birth weight increased the risk for NEC, while breastfeeding and probiotics decreased the risk for NEC.Conclusion:This study identified the risk factors and protective factors for NEC by using the GBDT model, which provided evidnce for the prevention and treatment of NEC.

Objective To systematically search,evaluate and summarise the best evidence for the management of swallowing disorders in senile dementia,and provide evidence for clinical practice.Methods Systematic search of guidelines,expert consensus,evidence summaries,systematic reviews on the management of swallowing disorders in dementia patients on databases and clinical decision support systems including BMJ Best Clinical Practices,UpToDate,International Guidelines Collaboration Network(IGCN),National Institute for Health and Care Excellence(NICE),Scottish intercollegiate guidelines network(SIGN),national guideline clearinghouse(NGC),Registered Nurses Association of Ontario(RNAO),New Zealand Guidelines Group(NZGG),World Health Organization(WHO),Medlive,PubMed,Cochrane Library,Embase,Web of Science,Joanna Briggs Institute(JBI),CNKI,Wanfang,VIP,SinoMed,the website of the European Society for Swallowing Disorders(ESSD)and Japanese Society of Dysphagia Rehabilitation(JSDR).Clinical decisions,guidelines,expert consensus,evidence summaries,systematic reviews,and clinical trial studies on the management of swallowing disorders in dementia patients were searched.The search was from inception to 30th November,2023.Two researchers trained in evidence-based nursing systems conducted literature screening and data extraction,and determined appropriate evaluation tools to evaluate the quality of the included literature,extracted and graded the evidence,and finally summarised the best evidence suitable for the management of swallowing disorders in dementia patients.Results A total of 8 literature were included,covering 1 guideline,1 clinical decision,4 evidence summaries,and 2 systematic reviews.Finally,19 pieces of evidence were collected from 5 aspects,including screening and evaluation,feeding behaviour management,auxiliary training,education and training,and follow-up monitoring.Conclusion The best evidence summarised in this study provided evidence-based reference for the management of swallowing disorders in patients with dementia by healthcare workers.

Objective:To systematically retrieve, analyze and evaluate risk prediction models of postoperative urinary retention, so as to provide a basis for the application and optimization of the model.Methods:The research on the risk prediction model of postoperative urinary retention was electronically retrieved in PubMed, Web of Science, Embase, Cochrane Library, CINAHL, China National Knowledge Infrastructure, WanFang Data, VIP, China Biology Medicine disc and other databases. The language of the literature was Chinese or English. The search period was from database establishment to February 20, 2023. Two researchers independently conducted literature screening and data extraction, and independently evaluated the bias risk and applicability of the included literature using the Prediction Model Risk of Bias Assessment Tool.Results:A total of 10 articles were included, including 17 risk prediction models for postoperative urinary retention. The areas under the receiver operating characteristic curve of 17 models were 0.700 to 0.920. The five most common predictors included in the model were age, gender, postoperative analgesia, diabetes, and operation time. The applicability of the model was good among the 10 studies, but there was some bias, mainly due to insufficient sample size, neglect of missing data and processing methods, overfitting issues, conversion of continuous variables into binary variables, and use of single factor screening for predictive factors.Conclusions:The risk prediction model of postoperative urinary retention has good prediction performance, but there is a certain risk of bias. The clinical value of the model needs further verification. External validation and continuous optimization are required for existing prediction models. Prospective research should also be carried out to develop a universal prediction model with good prediction performance, so as to provide an accurate and practical tool for clinical evaluation of postoperative urinary retention.

Objective To screen variables through LASSO regression,conduct multifactor Logistic regression analysis based on the screening results,and construct a Bayesian network model using max-min hill-climbing(MMHC)algorithm to explore the related fac-tors of necrotizing enterocolitis(NEC)in newborns and the complex network relationships among factors.The study also aimed to compare the two models to find the optimal modeling tool.Methods All NEC patients admitted to the Department of Neonatology,Department of Neonatal Surgery,and NICU of Shanxi Children's Hospital(Shanxi Maternal and Child Health Hospital)from January 2020 to December 2023 were retrospectively studied.NEC investigation data were collected and variable screening was conducted using LASSO regression.Multifactor Logistic regression analysis was performed based on the screening results.The MMHC mixed algorithm was employed for struc-ture learning,and the maximum likelihood estimation method was used for parameter learning to construct the NEC Bayesian network model.Results After variable screening,10 factors including prematurity,low birth weight,feeding method,intrauter distress and post-natal asphyxia history,anemia,non-invasive ventilator,probiotics,gestational diabetes,C-reactive protein(CRP),and procalcitonin(PCT)were included in the model construction.The area under the receiver operating characteristic(ROC)curve of the Bayesian net-work model in the modeling group and validation group were 0.825 and 0.817,respectively,with accuracies of 89.78％and 90.43％,respectively.The AUC of the multifactor Logistic regression analysis in the modeling group and validation group were 0.777 and 0.741,respectively,with accuracies of 70.01％and 69.44％,respectively.The performance of the Bayesian network model was superior to that of multifactor Logistic regression analysis.Furthermore,the Bayesian network model showed that low birth weight,feeding method,probi-otics,and PCT were directly related to NEC,prematurity and non-invasive ventilator were indirectly related to NEC through low birth weight,and CRP was indirectly related to NEC through PCT.Conclusion By comparing the two models,it was found that the Bayesian network model is an effective tool for in-depth study of NEC and the network relationships among related factors.Through this model,the association strength between NEC and various factors can be accurately evaluated,providing a scientific basis for the prevention and treat-ment of NEC.

Objective To screen variables through LASSO regression,conduct multifactor Logistic regression analysis based on the screening results,and construct a Bayesian network model using max-min hill-climbing(MMHC)algorithm to explore the related fac-tors of necrotizing enterocolitis(NEC)in newborns and the complex network relationships among factors.The study also aimed to compare the two models to find the optimal modeling tool.Methods All NEC patients admitted to the Department of Neonatology,Department of Neonatal Surgery,and NICU of Shanxi Children's Hospital(Shanxi Maternal and Child Health Hospital)from January 2020 to December 2023 were retrospectively studied.NEC investigation data were collected and variable screening was conducted using LASSO regression.Multifactor Logistic regression analysis was performed based on the screening results.The MMHC mixed algorithm was employed for struc-ture learning,and the maximum likelihood estimation method was used for parameter learning to construct the NEC Bayesian network model.Results After variable screening,10 factors including prematurity,low birth weight,feeding method,intrauter distress and post-natal asphyxia history,anemia,non-invasive ventilator,probiotics,gestational diabetes,C-reactive protein(CRP),and procalcitonin(PCT)were included in the model construction.The area under the receiver operating characteristic(ROC)curve of the Bayesian net-work model in the modeling group and validation group were 0.825 and 0.817,respectively,with accuracies of 89.78％and 90.43％,respectively.The AUC of the multifactor Logistic regression analysis in the modeling group and validation group were 0.777 and 0.741,respectively,with accuracies of 70.01％and 69.44％,respectively.The performance of the Bayesian network model was superior to that of multifactor Logistic regression analysis.Furthermore,the Bayesian network model showed that low birth weight,feeding method,probi-otics,and PCT were directly related to NEC,prematurity and non-invasive ventilator were indirectly related to NEC through low birth weight,and CRP was indirectly related to NEC through PCT.Conclusion By comparing the two models,it was found that the Bayesian network model is an effective tool for in-depth study of NEC and the network relationships among related factors.Through this model,the association strength between NEC and various factors can be accurately evaluated,providing a scientific basis for the prevention and treat-ment of NEC.

Objectives:To construct a nomogram prediction model using common preoperative indicators for early weight loss (EWL) 1 year after laparoscopic sleeve gastrectomy (LSG).Methods:Relevant data of obese patients who had undergone LSG from January 2015 to May 2022 in Fujian Medical University Union Hospital and Quanzhou First Hospital Affiliated Fujian Medical University were analyzed. Patients with a history of major abdominal surgery, severe gastroesophageal reflux disease, pregnancy within 1 year after surgery, or who were lost to follow-up were excluded, resulting in a total of 200 patients in the study (190 from Fujian Medical University Union Hospital and 10 from Quanzhou First Hospital Affiliated Fujian Medical University). The participants were 51 men and 149 women of a mean age 29.9±8.2 years and a body mass index (BMI) 38.7±6.5 kg/m 2. All patients in this group underwent standardized LSG procedure. Achieving ideal weight (BMI≤25 kg/m 2) 1 year after LSG was defined as goal of EWL. Logistic regression analyses were performed to identify factors that independently influenced EWL. These factors were incorporated into the nomogram model. Receiver operating characteristic (ROC) curves (the larger the area under the curve [AUC], the better the predictive ability and accuracy of the model), likelihood ratio test (higher likelihood ratio indicates greater model homogeneity), decision curve analysis (higher net benefit indicates a better model), Akaike information criterion (AIC; smaller AIC indicates a better model), and Bayesian information criterion (BIC; smaller BIC indicates a better model) were used to validate the predictive ability of the column line diagram model. Results:In this study of 200 obese patients who underwent LSG surgery, 136 achieved EWL goal, whereas the remaining 64 did not. The rate of EWL goal achievement of the entire group was 68.0%. Compared with patients who did not achieve EWL goal, those who did had lower BMI, alanine transaminase, aspartate transaminase, triglycerides, and higher cholesterol. Additionally, the proportion of female was higher and the proportions of patients with fatty liver and hypertension lower in those who achieved EWL goal (all P<0.05). Univariate and multivariate logistic regression analysis revealed that preoperative BMI (OR=0.852, 95%CI: 0.796-0.912, P<0.001), alanine transaminase (OR=0.992, 95%CI: 0.985-0.999, P=0.024), presence of fatty liver (OR=0.185, 95%CI: 0.038-0.887, P=0.035) and hypertension (OR=0.374, 95%CI: 0.144-0.969, P=0.043) were independently associated with failure to achieve EWL goal. Cholesterol (OR=1.428, 95%CI: 1.052-1.939, P=0.022) was independently associated with achieving EWL goal. We used the above variables to establish an EWL nomogram model. ROC analysis, the likelihood ratio test, decision curve analysis, and AIC all revealed that the predictive value of the model was better than that of BMI alone (nomogram model vs. BMI: area under the curve 0.840 vs. 0.798, P=0.047; likelihood ratio: 58.785 vs. 36.565, AIC: 193.066 vs. 207.063, BIC: 212.856 vs. 213.660). Conclusion:Our predictive model is more accurate in predicting EWL after LSG compared with using BMI.

Objective:To investigate the construction and application value of a predictive model for prolonged surgical duration in Da Vinci robotic radical gastrectomy for gastric cancer.Methods:The retrospective cohort study was conducted. The clinicopathological data of 534 patients who underwent Da Vinci robotic radical gastrectomy for gastric cancer in the Fujian Medical University Union Hospital from August 2016 to August 2021 were collected. There were 389 males and 145 females, aged (60±11)years. All 534 patients were randomly divided into the training dataset of 374 cases and the validation dataset of 160 cases with a ratio of 7∶3 based on random number method in the SPSS 25.0 software. Observation indicators: (1) incidence of prolonged surgical duration; (2) intraoperative and postoperative conditions in patients with prolonged surgical duration and without prolonged surgical duration; (3) complications in patients with prolonged surgical duration and without prolonged surgical duration; (4) analysis of risk factors influencing prolonged surgical duration; (5) construction and evaluation of an artificial neural network predictive model for pro-longed surgical duration. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the independent sample t test. Measurement data with skewed distribution were represented as M( Q1, Q3), and comparison between groups was conducted using the Mann-Whitney U test. Count data were described as absolute numbers or per-centages, and comparison between groups was conducted using the chi-square test or Fisher exact probability. Comparison of ordinal data was analyzed using the nonparametric test. Univariate and multivariate analyses were conducted using the Logistic regression model. Based on the results of univariate analysis, a multilayer perceptron was employed to train an artificial neural network pre-dictive model for prolonged surgical duration. The receiver operating characteristic (ROC) curve was drawn, and the area under curve (AUC), the accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to assess the model′s performance. Results:(1) Incidence of prolonged surgical duration. Of 534 patients, 284 cases underwent total gastrectomy, and 250 cases underwent distal gastrectomy, with operation time of (206±42)minutes and (187±36)minutes, res-pectively. Cases with prolonged surgical duration and without prolonged surgical duration who under-went total gastrectomy were 41 and 243, and cases with prolonged surgical duration and without prolonged surgical duration who underwent distal gastrectomy were 40 and 210. The gender (male, female), age, body mass index (BMI), tumor diameter, tumor location (upper stomach, middle stomach, lower stomach, mixed type), cases with neoadjuvant therapy, cases with preoperative American Society of Anesthesiologists (ASA) score as 1, 2, 3, cases with clinical T staging as stage T1, stage T2, stage T3, stage T4a, cases with clinical N staging as stage N0, stage N1, stage N2, stage N3, cases with clinical TNM staging as stage Ⅰ, stage Ⅱ, stage Ⅲ, cases with surgical resection scope as total gastrec-tomy or distal gastrectomy, cases with digestive tract reconstruction method as Billroth-Ⅰ anasto-mosis, Billroth-Ⅱ anastomosis, Roux-en-Y anastomosis, cases with surgeon experiences as ≤20 cases or >20 cases were 61,20, (61±9)years, (24±3)kg/m2, 4.0(2.5, 5.0)cm, 34, 10, 33, 4, 1, 3, 73, 5, 3, 6, 26, 46, 14, 41, 19, 7, 5, 13, 63, 41, 40, 1, 33, 47, 5, 76 in the 81 patients with prolonged surgical duration, versus 328, 125, (60±11)years, (23±3)kg/m2, 3.5(2.0, 5.0)cm, 129, 71, 227, 26, 6, 45, 382, 26, 73, 100, 118, 162, 211, 180, 52, 10, 138, 108,207, 243, 210, 13,200, 240, 15, 438 in the 453 patients without prolonged surgical duration, showing significant differences in the BMI, clinical T staging, clinical N staging, clinical TNM staging ( t=-3.68, Z=-4.63, -5.53, -5.56, P<0.05), and no significant difference in the gender, age, tumor diameter, tumor location, preoperative ASA score, surgical resec-tion scope, digestive tract reconstruction method, and surgeon experiences ( χ2=0.29, t=-0.95, Z=-1.27, χ2=5.92, Z=-1.46, χ2=0.25, 1.35, 0.87, P>0.05). There was no significant difference in cases with neoadjuvant therapy between them ( P>0.05). (2) Intraoperative and postoperative conditions in patients with prolonged surgical duration and without prolonged surgical duration. The operation time, volume of intraoperative blood loss, the number of lymph nodes dissected, time to postopera-tive first ambulation, time to postoperative anal exhaust, time to postoperative first intake of liquid diet, time to postoperative first intake of semi-liquid diet, duration of postoperative hospital stay were (261±34)minutes, 50(30, 50)mL, 39±15, (2.3±0.6)days, (3.4±0.9)days, (4.1±1.2)days, (5.7±1.2)days, 8.0(7.0, 9.0)days in the 81 patients with prolonged surgical duration, versus (186±29)minutes, 30(20,50)mL, 42±14, (2.2±0.6)days, (3.4±0.8)days, (4.1±1.1)days, (5.7±1.4)days, 8.0(7.0, 9.0)days in the 453 patients without prolonged surgical duration, showing significant differences in operation time, volume of intraoperative blood loss ( t=-20.46, Z=-3.32, P<0.05), and no significant difference in the number of lymph nodes dissected, time to postoperative first ambulation, time to postopera-tive anal exhaust, time to postoperative first intake of liquid diet, time to first intake of semi-liquid diet, duration of postoperative hospital stay ( t=1.87, -0.87, -0.16, 0.28, 0.03, Z=-1.45, P>0.05). (3) Complications in patients with prolonged surgical duration and without prolonged surgical duration. The overall incidence of complications, incidence of surgical complications (abdominal infection, anastomotic fistula, abdominal bleeding, incision-related complications, intestinal obstruction, lymphatic fistula), incidence of medical complications (pulmonary infection, liver-related complications) were 22.22%(18/81), 0, 0, 2.47%(2/81), 0, 8.64%(7/81), 1.23%(1/81), 12.35%(10/81), 1.23%(1/81) in the 81 patients with prolonged surgical duration, versus 13.47%(61/453), 2.65%(12/453), 0.44%(2/453), 1.77%(8/453), 0.44%(2/453), 3.31%(15/453), 0, 7.28%(33/453), 1.55%(7/453) in the 453 patients without prolonged surgical duration, showing a significant difference in the overall incidence of complications ( χ2=4.18, P<0.05), and no significant difference in the incidence of abdo-minal infection, anastomotic fistula, abdominal bleeding, incision-related complications, intestinal obstruction, lymphatic fistula, liver-related complications ( P>0.05). There was no significant difference in the incidence of pulmonary infection between them ( χ2=2.38, P>0.05). (4) Analysis of risk factors influencing prolonged surgical duration. Results of univariate analysis showed that BMI ≥25 kg/m2, tumor located in the lower stomach, clinical T3-T4a stage, clinical N1-N3 stage were correlated factors influencing prolonged surgical duration in Da Vinci robotic radical gastrectomy for gastric cancer ( odds ratio=1.88, 0.40, 6.24, 6.51, 3.08, 3.39, 17.15, 95% confidence interval as 1.03-3.42, 0.21-0.76, 1.40-27.76, 1.50-28.30, 1.43-6.60, 1.29-8.92, 4.84-60.74, P<0.05). Results of multivariate analysis showed that BMI ≥25 kg/m2, clinical T3 stage, clinical N3 stage were independent risk factors influencing prolonged surgical duration in Da Vinci robotic radical gastrectomy for gastric cancer ( odds ratio=2.31, 4.97, 11.08, 95% confidence interval as 1.19-4.46, 1.05-23.55, 2.72-45.13, P<0.05). (5) Construction and evaluation of an artificial neural network predictive model for pro-longed surgical duration. The BMI, tumor location, clinical T staging, and clinical N staging were incorporated into a multilayer perceptron to construct an artificial neural network predictive model for prolonged surgical duration. Results of ROC curve showed that the AUC, accuracy, sensitivity, specificity, positive predictive value, negative predictive value of the predictive model in the training dataset were 0.73 (95% confidence interval as 0.68-0.78), 91.4%, 68.1%, 94.8%, 65.3%, 95.4%. The above indicators of the predictive model in the validation dataset 0.72 (95% confidence interval as 0.65-0.79), 88.1%, 67.6%, 93.7%, 74.2%, 91.5%. Conclusions:BMI ≥25 kg/m2, clinical T3 stage, clinical N3 stage are independent risk factors influencing prolonged surgical duration in Da Vinci robotic radical gastrectomy for gastric cancer. The artificial neural network predictive model con-structed based on BMI, tumor location, clinical T staging, and clinical N staging can effectively predict patients at high risk of prolonged surgical duration in Da Vinci robotic radical gastrectomy for gastric cancer.

Objectives:To construct a nomogram prediction model using common preoperative indicators for early weight loss (EWL) 1 year after laparoscopic sleeve gastrectomy (LSG).Methods:Relevant data of obese patients who had undergone LSG from January 2015 to May 2022 in Fujian Medical University Union Hospital and Quanzhou First Hospital Affiliated Fujian Medical University were analyzed. Patients with a history of major abdominal surgery, severe gastroesophageal reflux disease, pregnancy within 1 year after surgery, or who were lost to follow-up were excluded, resulting in a total of 200 patients in the study (190 from Fujian Medical University Union Hospital and 10 from Quanzhou First Hospital Affiliated Fujian Medical University). The participants were 51 men and 149 women of a mean age 29.9±8.2 years and a body mass index (BMI) 38.7±6.5 kg/m 2. All patients in this group underwent standardized LSG procedure. Achieving ideal weight (BMI≤25 kg/m 2) 1 year after LSG was defined as goal of EWL. Logistic regression analyses were performed to identify factors that independently influenced EWL. These factors were incorporated into the nomogram model. Receiver operating characteristic (ROC) curves (the larger the area under the curve [AUC], the better the predictive ability and accuracy of the model), likelihood ratio test (higher likelihood ratio indicates greater model homogeneity), decision curve analysis (higher net benefit indicates a better model), Akaike information criterion (AIC; smaller AIC indicates a better model), and Bayesian information criterion (BIC; smaller BIC indicates a better model) were used to validate the predictive ability of the column line diagram model. Results:In this study of 200 obese patients who underwent LSG surgery, 136 achieved EWL goal, whereas the remaining 64 did not. The rate of EWL goal achievement of the entire group was 68.0%. Compared with patients who did not achieve EWL goal, those who did had lower BMI, alanine transaminase, aspartate transaminase, triglycerides, and higher cholesterol. Additionally, the proportion of female was higher and the proportions of patients with fatty liver and hypertension lower in those who achieved EWL goal (all P<0.05). Univariate and multivariate logistic regression analysis revealed that preoperative BMI (OR=0.852, 95%CI: 0.796-0.912, P<0.001), alanine transaminase (OR=0.992, 95%CI: 0.985-0.999, P=0.024), presence of fatty liver (OR=0.185, 95%CI: 0.038-0.887, P=0.035) and hypertension (OR=0.374, 95%CI: 0.144-0.969, P=0.043) were independently associated with failure to achieve EWL goal. Cholesterol (OR=1.428, 95%CI: 1.052-1.939, P=0.022) was independently associated with achieving EWL goal. We used the above variables to establish an EWL nomogram model. ROC analysis, the likelihood ratio test, decision curve analysis, and AIC all revealed that the predictive value of the model was better than that of BMI alone (nomogram model vs. BMI: area under the curve 0.840 vs. 0.798, P=0.047; likelihood ratio: 58.785 vs. 36.565, AIC: 193.066 vs. 207.063, BIC: 212.856 vs. 213.660). Conclusion:Our predictive model is more accurate in predicting EWL after LSG compared with using BMI.