Analysis of risk factors leading to anxiety and depression in patients with prostate cancer after castration and the construction of a risk prediction model

BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis.Therefore,attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment.AIM To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model.METHODS A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022.The patient cohort was divided into a training group(n=84)and a validation group(n=36)at a ratio of 7:3.The patients’anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale(SAS)and the Selfrating Depression Scale(SDS),respectively.Logistic regression was used to analyze the risk factors affecting negative mood,and a risk prediction model was constructed.RESULTS In the training group,35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50,respectively.Based on the scores,we further subclassified patients into two groups:a bad mood group(n=35)and an emotional stability group(n=49).Multivariate logistic regression analysis showed that marital status,castration scheme,and postoperative Visual Analogue Scale(VAS)score were independent risk factors affecting a patient's bad mood(P<0.05).In the training and validation groups,patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients(P<0.0001).The area under the curve(AUC)of the risk prediction model for predicting bad mood in the training group was 0.743,the specificity was 70.96%,and the sensitivity was 66.03%,while in the validation group,the AUC,specificity,and sensitivity were 0.755,66.67%,and 76.19%,respectively.The Hosmer-Lemeshow test showed aχ^(2) of 4.2856,a P value of 0.830,and a C-index of 0.773(0.692-0.854).The calibration curve revealed that the predicted curve was basically consistent with the actual curve,and the calibration curve showed that the prediction model had good discrimination and accuracy.Decision curve analysis showed that the model had a high net profit.CONCLUSION In PC patients,marital status,castration scheme,and postoperative pain(VAS)score are important factors affecting postoperative anxiety and depression.The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.

Machine learning for carbonate formation drilling: Mud loss prediction using seismic attributes and mud loss records

Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model.Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.

A male-ABCD algorithm for hepatocellular carcinoma risk prediction in HBs Ag carriers

Objective: Hepatocellular carcinoma(HCC) development among hepatitis B surface antigen(HBs Ag) carriers shows gender disparity, influenced by underlying liver diseases that display variations in laboratory tests. We aimed to construct a risk-stratified HCC prediction model for HBs Ag-positive male adults.Methods: HBs Ag-positive males of 35-69 years old(N=6,153) were included from a multi-center populationbased liver cancer screening study. Randomly, three centers were set as training, the other three centers as validation. Within 2 years since initiation, we administrated at least two rounds of HCC screening using Bultrasonography and α-fetoprotein(AFP). We used logistic regression models to determine potential risk factors,built and examined the operating characteristics of a point-based algorithm for HCC risk prediction.Results: With 2 years of follow-up, 302 HCC cases were diagnosed. A male-ABCD algorithm was constructed including participant's age, blood levels of GGT(γ-glutamyl-transpeptidase), counts of platelets, white cells,concentration of DCP(des-γ-carboxy-prothrombin) and AFP, with scores ranging from 0 to 18.3. The area under receiver operating characteristic was 0.91(0.90-0.93), larger than existing models. At 1.5 points of risk score,26.10% of the participants in training cohort and 14.94% in validation cohort were recognized at low risk, with sensitivity of identifying HCC remained 100%. At 2.5 points, 46.51% of the participants in training cohort and 33.68% in validation cohort were recognized at low risk with 99.06% and 97.78% of sensitivity, respectively. At 4.5 points, only 20.86% of participants in training cohort and 23.73% in validation cohort were recognized at high risk,with positive prediction value of 22.85% and 12.35%, respectively.Conclusions: Male-ABCD algorithm identified individual's risk for HCC occurrence within short term for their HCC precision surveillance.

Risk prediction models for hepatocellular carcinoma in different populations

Hepatocellular carcinoma （HCC） is a malignant disease with limited therapeutic options due to its aggressive progression. It places heaW burden on most low and middle income countries to treat HCC patients. Nowadays accurate HCC risk predictions can help making decisions on the need for HCC surveillance and antiviral therapy. HCC risk prediction models based on major risk factors of HCC are useful and helpful in providing adequate surveillance strategies to individuals who have different risk levels. Several risk prediction models among cohorts of different populations for estimating HCC incidence have been presented recently by using simple, efficient, and ready-to-use parameters. Moreover, using predictive scoring systems to assess HCC development can provide suggestions to improve clinical and public health approaches, making them more cost-effective and effort-effective, for inducing personalized surveillance programs according to risk stratification. In this review, the features of risk prediction models of HCC across different populations were summarized, and the perspectives of HCC risk prediction models were discussed as well.

A risk prediction score model for predicting occurrence of post-PCI vasovagal reflex syndrome： a single center study in Chinese population

Background The vasovagal reflex syndrome （VVRS） is common in the patiems undergoing percutaneous coronary intervemion （PCI） However, prediction and prevention of the risk for the VVRS have not been completely fulfilled. This study was conducted to develop a Risk Prediction Score Model to identify the determinants of VVRS in a large Chinese population cohort receiving PCI. Methods From the hos- pital electronic medical database, we idemified 3550 patients who received PCI （78.0% males, mean age 60 years） in Chinese PLA General Hospital from January 1, 2000 to August 30, 2016. The multivariate analysis and receiver operating characteristic 01OC） analysis were performed. Results The adverse events of VVRS in the patients were significantly increased after PCI procedure than before the operation （all P 〈 0.001）. The rate of VVRS [95% confidence interval （CI）] in patients receiving PCI was 4.5% （4.1%-5.6%）. Compared to the patients suffering no VVRS, incidence of VVRS involved the following factors, namely female gender, primary PCI, hypertension, over two stems im- plantation in the left anterior descending （LAD）, and the femoral puncture site. The multivariate analysis suggested that they were independ- ent risk factors for predicting the incidence of VVRS （all P 〈 0.001）. We developed a risk prediction score model for VVRS. ROC analysis showed that the risk prediction score model was effectively predictive of the incidence of VVRS in patients receiving PCI （c-statistic 0.76, 95% CI： 0.72-0.79, P 〈 0.001）. There were decreased evems of VVRS in the patients receiving PCI whose diastolic blood pressure dropped by more than 30 mmHg and heart rate reduced by 10 times per minute （AUC： 0.84, 95% CI： 0.81-0.87, P 〈 0.001）. Conclusion The risk prediction score is quite efficient in predicting the incidence of VVRS in patients receiving PCI. In which, the following factors may be in- volved, the femoral puncture site, female gender, hypertension, primary PCI, and over 2 stents implanted in LAD.

Prediction of presence and severity of coronary artery disease using prediction for atherosclerotic cardiovascular disease risk in China scoring system

BACKGROUND Coronary artery disease(CAD)is one of the leading causes of death and disease burden in China and worldwide.A practical and reliable prediction scoring system for CAD risk and severity evaluation is urgently needed for primary prevention.AIM To examine whether the prediction for atherosclerotic cardiovascular disease risk in China(China-PAR)scoring system could be used for this purpose.METHODS A total of 6813 consecutive patients who underwent diagnostic coronary angiography were enrolled.The China-PAR score was calculated for each patient and CAD severity was assessed by the Gensini score(GS).RESULTS Correlation analysis demonstrated a significant relationship between China-PAR and GS(r=0.266,P<0.001).In receiver operating characteristic curve analysis,the cut-off values of China-PAR for predicting the presence and the severity of CAD were 7.55%with a sensitivity of 55.8%and specificity of 71.8%[area under the curve(AUC)=0.693,95%confidence interval:0.681 to 0.706,P<0.001],and 7.45%with a sensitivity of 58.8%and specificity of 67.2%(AUC=0.680,95%confidence interval:0.665 to 0.694,P<0.001),respectively.CONCLUSION The China-PAR scoring system may be useful in predicting the presence and severity of CAD.

A Readmission Risk Prediction Model for Elderly Patients with Coronary Heart Disease

Objective:To analyze the independent risk factors and establish a risk prediction model by investigating the readmission of elderly patients with coronary heart disease(CHD)within 1 year after discharge.Methods:A total of 480 CHD patients,who were hospitalized in the Affiliated Hospital of Hebei University from October 2019 to December 2020,were included in this study.A general data scale,mental health status scale,the Clinical Frailty Scale,Pittsburgh Sleep Quality Index,as well as the Family Adaptability and Cohesion Evaluation Scale were used to collect data.According to the number of readmissions due to CHD within 1 year after discharge,the patients were divided into two groups:the readmission group(n=212)and the no readmission group(n=268).General data,laboratory examination indicators,frailty,mental health status,sleep status,as well as family intimacy and adaptability were compared between the two groups.Logistic regression was used to analyze the independent risk factors for the readmission of these patients,and R software was used to construct a line diagram model for predicting readmission of elderly patients with CHD.Results:Five factors including body mass index(OR=1.045),low density lipoprotein(OR=1.123),frailty(OR=1.946),mental health(OR=1.099),as well as family intimacy and adaptability(OR=0.928)were included to construct the risk prediction model for the readmission of elderly patients with CHD within 1 year after discharge.The ROC curve showed that the area under the curve for predicting readmission of elderly patients with CHD was 0.816;Hosmer-Lemeshow goodness of fit test showed X2=1.456 and P=0.989;the maximum Youden index corresponding to the predicted value of risk was 0.526.The results showed that the model could accurately predict the risk of readmission in elderly patients with CHD within 1 year after discharge.Conclusion:This study constructed a line diagram model based on five independent risk factors of the readmission of elderly patients with CHD:body mass index,low density lipoprotein,frailty,mental health status,as well as family intimacy and adaptability.This model has good discrimination,accuracy,and predictive efficiency,providing reference for the early prevention and intervention of readmission in elderly patients with CHD recurrence.

Development and Validation of a Prediction Model on Adult Emergency Department Patients for Early Identification of Fulminant Myocarditis

Objective It is difficult to predict fulminant myocarditis at an early stage in the emergency department.The objective of this study was to construct and validate a simple prediction model for the early identification of fulminant myocarditis.Methods A total of 61 patients with fulminant myocarditis and 160 patients with acute myocarditis were enrolled in the training and internal validation cohorts.LASSO regression and multivariate logistic regression were selected to develop the prediction model.The selection of the model was based on overall performance and simplicity.A nomogram based on the optimal model was built,and its clinical usefulness was evaluated by decision curve analysis.The predictive model was further validated in an external validation group.Results The resulting prediction model was based on 4 factors:systolic blood pressure,troponin I,left ventricular ejection fraction,and ventricular wall motion abnormality.The Brier scores of the final model were 0.078 in the training data set and 0.061 in the internal testing data set,respectively.The C-indexes of the training data set and the testing data set were 0.952 and 0.968,respectively.Decision curve analysis showed that the nomogram model developed based on the 4 predictors above had a positive net benefit for predicting probability thresholds.In the external validation cohort,the model also showed good performance(Brier score=0.007,and C-index=0.989).Conclusion We developed and validated an early prediction model consisting of 4 clinical factors(systolic blood pressure,troponin I,left ventricular ejection fraction,and ventricular wall motion abnormality)to identify potential fulminant myocarditis patients in the emergency department.

Risk Prediction of Aortic Dissection Operation Based on Boosting Trees

During the COVID-19 pandemic,the treatment of aortic dissection has faced additional challenges.The necessary medical resources are in serious shortage,and the preoperative waiting time has been significantly prolonged due to the requirement to test for COVID-19 infection.In this work,we focus on the risk prediction of aortic dissection surgery under the influence of the COVID-19 pandemic.A general scheme of medical data processing is proposed,which includes five modules,namely problem definition,data preprocessing,data mining,result analysis,and knowledge application.Based on effective data preprocessing,feature analysis and boosting trees,our proposed fusion decision model can obtain 100%accuracy for early postoperative mortality prediction,which outperforms machine learning methods based on a single model such as LightGBM,XGBoost,and CatBoost.The results reveal the critical factors related to the postoperative mortality of aortic dissection,which can provide a theoretical basis for the formulation of clinical operation plans and help to effectively avoid risks in advance.

Deep Learning-Based Prediction of Traffic Accidents Risk for Internet of Vehicles

With the increasing number of vehicles,traffic accidents pose a great threat to human lives.Hence,aiming at reducing the occurrence of traffic accidents,this paper proposes an algorithm based on a deep convolutional neural network and a random forest to predict accident risks.Specifically,the proposed algorithm includes a feature extractor and a feature classifier,where the former extracts key features using a convolutional neural network and the latter outputs a probability value of traffic accidents using a random forest with multiple decision trees,which indicates the degree of accident risks.Simulations show that the proposed algorithm can achieve higher performance in terms of the Area Under the Curve(AUC)of the Receiver Characteristic Operator as well as accuracy than the existing algorithms based on the Adaboost or the pure convolutional neural networks.

Development and application of hepatocellular carcinoma risk prediction model based on clinical characteristics and liver related indexes

BACKGROUND Hepatocellular carcinoma(HCC)is difficult to diagnose with poor therapeutic effect,high recurrence rate and has a low survival rate.The survival of patients with HCC is closely related to the stage of diagnosis.At present,no specific serolo-gical indicator or method to predict HCC,early diagnosis of HCC remains a challenge,especially in China,where the situation is more severe.AIM To identify risk factors associated with HCC and establish a risk prediction model based on clinical characteristics and liver-related indicators.METHODS The clinical data of patients in the Affiliated Hospital of North Sichuan Medical College from 2016 to 2020 were collected,using a retrospective study method.The results of needle biopsy or surgical pathology were used as the grouping criteria for the experimental group and the control group in this study.Based on the time of admission,the cases were divided into training cohort(n=1739)and validation cohort(n=467).Using HCC as a dependent variable,the research indicators were incorporated into logistic univariate and multivariate analysis.An HCC risk prediction model,which was called NSMC-HCC model,was then established in training cohort and verified in validation cohort.RESULTS Logistic univariate analysis showed that,gender,age,alpha-fetoprotein,and protein induced by vitamin K absence or antagonist-II,gamma-glutamyl transferase,aspartate aminotransferase and hepatitis B surface antigen were risk factors for HCC,alanine aminotransferase,total bilirubin and total bile acid were protective factors for HCC.When the cut-off value of the NSMC-HCC model joint prediction was 0.22,the area under receiver operating characteristic curve(AUC)of NSMC-HCC model in HCC diagnosis was 0.960,with sensitivity 94.40%and specificity 95.35%in training cohort,and AUC was 0.966,with sensitivity 90.00%and specificity 94.20%in validation cohort.In early-stage HCC diagnosis,the AUC of NSMC-HCC model was 0.946,with sensitivity 85.93%and specificity 93.62%in training cohort,and AUC was 0.947,with sensitivity 89.10%and specificity 98.49%in validation cohort.CONCLUSION The newly NSMC-HCC model was an effective risk prediction model in HCC and early-stage HCC diagnosis.

Risk prediction of common bile duct stone recurrence based on new common bile duct morphological subtypes

Stones in the common bile duct(CBD) are reported worldwide, and this condition is majorly managed through endoscopic retrograde cholangiopancreatography(ERCP). CBD stone recurrence is an important issue after endoscopic stone removal. Therefore, it is essential to identify its risk factors to determine the necessity of regular follow-up in patients who underwent endoscopic removal of CBD stones. The authors identified that the S and polyline morphological subtypes of CBD were associated with increased stone recurrence. New morphological subtypes of CBD presented by the authors can be important risk predictors of recurrence after endoscopic stone removal. Furthermore, the new morphological subtypes of CBD may predict the risk of residual CBD stones or technical difficulty in CBD stone removal. Further studies with a large sample size and longer follow-up durations are warranted to examine the usefulness of the newly identified morphological subtypes of CBD in predicting the outcomes of ERCP for CBD stone removal.

Optimization of the Classic Transfer-Stacking Model Migration Algorithm: A Way to Solve Time-Varying Performance Degradation of Acute Kidney Injury Clinical Prediction Model

Acute Kidney Injury (AKI) is one of the most common acute and critical illnesses in general wards and intensive care units. Its high morbidity and high fatality rate have become a major global public health problem. There are often serious lags in clinical diagnosis of AKI. Early diagnosis and timely intervention and effective care become critical. The use of electronic medical record data to build an AKI risk prediction model has been proven to help prevent the occurrence of AKI. However, in actual clinical applications, the distribution of historical data and new data will continue to vary over time, resulting in a significant decrease in the performance of the model. How to solve the problem of model performance degradation over time will be a core challenge for the long-term use of predictive models in clinical applications. Aiming at the above problems, this paper studies the classic Transfer-Stacking model migration algorithm. Aiming at the lack of this algorithm, such as the loss of a large amount of feature information of the target domain and poor fit when integrating the model of the target domain, the Accumulate-Transfer-Stacking algorithm is proposed to improve it. Improvements include: 1) Optimize the input vector and model integration algorithm of Transfer-Stacking’s target domain model. 2) Optimize Transfer-Stacking from a single-source domain model to a multi-source domain model. The experimental results show that for the improved algorithm proposed in this paper when the data is sufficient and insufficient, the average AUC value of the model on the data of subsequent years is 0.89 and 0.87, and the average F1 Score value is 0.45 and 0.36. Moreover, this method is significantly better than the unimproved Transfer-Stacking algorithm and baseline method, and can effectively overcome the problem of data distribution heterogeneity caused by time factors.

An Interpretable Prediction Model for Stroke Based on XGBoost and SHAP

Objective:To establish a stroke prediction and feature analysis model integrating XGBoost and SHAP to aid the clinical diagnosis and prevention of stroke.Methods:Based on the open data set on Kaggle,with the help of data preprocessing and grid parameter optimization,an interpretable stroke risk prediction model was established by integrating XGBoost and SHAP and an explanatory analysis of risk factors was performed.Results:The XGBoost model’s accuracy,sensitivity,specificity,and area under the receiver operating characteristic(ROC)curve(AUC)were 96.71%,93.83%,99.59%,and 99.19%,respectively.Our explanatory analysis showed that age,type of residence,and history of hypertension were key factors affecting the incidence of stroke.Conclusion:Based on the data set,our analysis showed that the established model can be used to identify stroke,and our explanatory analysis based on SHAP increases the transparency of the model and facilitates medical practitioners to analyze the reliability of the model.

Validation and performance of three scoring systems for predicting primary non-function and early allograft failure after liver transplantation

Background: Primary non-function(PNF) and early allograft failure(EAF) after liver transplantation(LT) seriously affect patient outcomes. In clinical practice, effective prognostic tools for early identifying recipients at high risk of PNF and EAF were urgently needed. Recently, the Model for Early Allograft Function(MEAF), PNF score by King's College(King-PNF) and Balance-and-Risk-Lactate(BAR-Lac) score were developed to assess the risks of PNF and EAF. This study aimed to externally validate and compare the prognostic performance of these three scores for predicting PNF and EAF. Methods: A retrospective study included 720 patients with primary LT between January 2015 and December 2020. MEAF, King-PNF and BAR-Lac scores were compared using receiver operating characteristic(ROC) and the net reclassification improvement(NRI) and integrated discrimination improvement(IDI) analyses. Results: Of all 720 patients, 28(3.9%) developed PNF and 67(9.3%) developed EAF in 3 months. The overall early allograft dysfunction(EAD) rate was 39.0%. The 3-month patient mortality was 8.6% while 1-year graft-failure-free survival was 89.2%. The median MEAF, King-PNF and BAR-Lac scores were 5.0(3.5–6.3),-2.1(-2.6 to-1.2), and 5.0(2.0–11.0), respectively. For predicting PNF, MEAF and King-PNF scores had excellent area under curves(AUCs) of 0.872 and 0.891, superior to BAR-Lac(AUC = 0.830). The NRI and IDI analyses confirmed that King-PNF score had the best performance in predicting PNF while MEAF served as a better predictor of EAD. The EAF risk curve and 1-year graft-failure-free survival curve showed that King-PNF was superior to MEAF and BAR-Lac scores for stratifying the risk of EAF. Conclusions: MEAF, King-PNF and BAR-Lac were validated as practical and effective risk assessment tools of PNF. King-PNF score outperformed MEAF and BAR-Lac in predicting PNF and EAF within 6 months. BAR-Lac score had a huge advantage in the prediction for PNF without post-transplant variables. Proper use of these scores will help early identify PNF, standardize grading of EAF and reasonably select clinical endpoints in relative studies.

Predicting Users’ Latent Suicidal Risk in Social Media: An Ensemble Model Based on Social Network Relationships

Suicide has become a critical concern,necessitating the development of effective preventative strategies.Social media platforms offer a valuable resource for identifying signs of suicidal ideation.Despite progress in detecting suicidal ideation on social media,accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge.To tackle this,we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships(TCNN-SN).This model enhances predictive performance by leveraging social network relationship features and applying correction factors within a weighted linear fusion framework.It is specifically designed to identify key individuals who can help uncover hidden suicidal users and clusters.Our model,assessed using the bespoke dataset and benchmarked against alternative classification approaches,demonstrates superior accuracy,F1-score and AUC metrics,achieving 88.57%,88.75%and 94.25%,respectively,outperforming traditional TextCNN models by 12.18%,10.84%and 10.85%.We assert that our methodology offers a significant advancement in the predictive identification of individuals at risk,thereby contributing to the prevention and reduction of suicide incidences.

Application value of machine learning models in predicting intraoperative hypothermia in laparoscopic surgery for polytrauma patients

BACKGROUND Hypothermia during laparoscopic surgery in patients with multiple trauma is a significant concern owing to its potential complications.Machine learning models offer a promising approach to predict the occurrence of intraoperative hypothermia.AIM To investigate the value of machine learning model to predict hypothermia during laparoscopic surgery in patients with multiple trauma.METHODS This retrospective study enrolled 220 patients who were admitted with multiple injuries between June 2018 and December 2023.Of these,154 patients were allocated to a training set and the remaining 66 were allocated to a validation set in a 7:3 ratio.In the training set,53 cases experienced intraoperative hypothermia and 101 did not.Logistic regression analysis was used to construct a predictive model of intraoperative hypothermia in patients with polytrauma undergoing laparoscopic surgery.The area under the curve(AUC),sensitivity,and specificity were calculated.RESULTS Comparison of the hypothermia and non-hypothermia groups found significant differences in sex,age,baseline temperature,intraoperative temperature,duration of anesthesia,duration of surgery,intraoperative fluid infusion,crystalloid infusion,colloid infusion,and pneumoperitoneum volume(P<0.05).Differences between other characteristics were not significant(P>0.05).The results of the logistic regression analysis showed that age,baseline temperature,intraoperative temperature,duration of anesthesia,and duration of surgery were independent influencing factors for intraoperative hypothermia during laparoscopic surgery(P<0.05).Calibration curve analysis showed good consistency between the predicted occurrence of intraoperative hypothermia and the actual occurrence(P>0.05).The predictive model had AUCs of 0.850 and 0.829 for the training and validation sets,respectively.CONCLUSION Machine learning effectively predicted intraoperative hypothermia in polytrauma patients undergoing laparoscopic surgery,which improved surgical safety and patient recovery.

Development and validation of a nomogram model for predicting the risk of pre-hospital delay in patients with acute myocardial infarction

BACKGROUND Acute myocardial infarction(AMI)is a severe cardiovascular disease caused by the blockage of coronary arteries that leads to ischemic necrosis of the myocardium.Timely medical contact is critical for successful AMI treatment,and delays increase the risk of death for patients.Pre-hospital delay time(PDT)is a significant challenge for reducing treatment times,as identifying high-risk patients with AMI remains difficult.This study aims to construct a risk prediction model to identify high-risk patients and develop targeted strategies for effective and prompt care,ultimately reducing PDT and improving treatment outcomes.AIM To construct a nomogram model for forecasting pre-hospital delay(PHD)likelihood in patients with AMI and to assess the precision of the nomogram model in predicting PHD risk.METHODS A retrospective cohort design was employed to investigate predictive factors for PHD in patients with AMI diagnosed between January 2022 and September 2022.The study included 252 patients,with 180 randomly assigned to the development group and the remaining 72 to the validation group in a 7:3 ratio.Independent risk factors influencing PHD were identified in the development group,leading to the establishment of a nomogram model for predicting PHD in patients with AMI.The model's predictive performance was evaluated using the receiver operating characteristic curve in both the development and validation groups.RESULTS Independent risk factors for PHD in patients with AMI included living alone,hyperlipidemia,age,diabetes mellitus,and digestive system diseases(P<0.05).A characteristic curve analysis indicated area under the receiver operating characteristic curve values of 0.787(95%confidence interval:0.716–0.858)and 0.770(95%confidence interval:0.660-0.879)in the development and validation groups,respectively,demonstrating the model's good discriminatory ability.The Hosmer–Lemeshow goodness-of-fit test revealed no statistically significant disparity between the anticipated and observed incidence of PHD in both development and validation cohorts(P>0.05),indicating satisfactory model calibration.CONCLUSION The nomogram model,developed with independent risk factors,accurately forecasts PHD likelihood in AMI individuals,enabling efficient identification of PHD risk in these patients.

Nomogram for predicting the risk of anxiety and depression in patients with non-mild burns

BACKGROUND Post-burn anxiety and depression affect considerably the quality of life and recovery of patients;however,limited research has demonstrated risk factors associated with the development of these conditions.AIM To predict the risk of developing post-burn anxiety and depression in patients with non-mild burns using a nomogram model.METHODS We enrolled 675 patients with burns who were admitted to The Second Affiliated Hospital,Hengyang Medical School,University of South China between January 2019 and January 2023 and met the inclusion criteria.These patients were randomly divided into development(n=450)and validation(n=225)sets in a 2:1 ratio.Univariate and multivariate logistic regression analyses were conducted to identify the risk factors associated with post-burn anxiety and depression dia-gnoses,and a nomogram model was constructed.RESULTS Female sex,age<33 years,unmarried status,burn area≥30%,and burns on the head,face,and neck were independent risk factors for developing post-burn anxiety and depression in patients with non-mild burns.The nomogram model demonstrated predictive accuracies of 0.937 and 0.984 for anxiety and 0.884 and 0.923 for depression in the development and validation sets,respectively,and good predictive per-formance.Calibration and decision curve analyses confirmed the clinical utility of the nomogram.CONCLUSION The nomogram model predicted the risk of post-burn anxiety and depression in patients with non-mild burns,facilitating the early identification of high-risk patients for intervention and treatment.