Association between Fish Consumption and Stroke Incidence Across Different Predicted Risk Populations:A Prospective Cohort Study from China

Objective The relationship between fish consumption and stroke is inconsistent,and it is uncertain whether this association varies across predicted stroke risks.Methods A cohort study comprising 95,800 participants from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China project was conducted.A standardized questionnaire was used to collect data on fish consumption.Participants were stratified into low-and moderate-to-high-risk categories based on their 10-year stroke risk prediction scores.Hazard ratios(HRs)and 95%confidence intervals(CIs)were estimated using Cox proportional hazard models and additive interaction by relative excess risk due to interaction(RERI),attributable proportion(AP),and synergy index(SI).Results During 703,869 person-years of follow-up,2,773 incident stroke events were identified.Higher fish consumption was associated with a lower risk of stroke,particularly among moderate-to-high-risk individuals(HR=0.53,95%CI:0.47-0.60)than among low-risk individuals(HR=0.64,95%CI:0.49-0.85).A significant additive interaction between fish consumption and predicted stroke risk was observed(RERI=4.08,95%CI:2.80-5.36;SI=1.64,95%CI:1.42-1.89;AP=0.36,95%CI:0.28-0.43).Conclusion Higher fish consumption was associated with a lower risk of stroke,and this beneficial association was more pronounced in individuals with moderate-to-high stroke risk.

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.

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.

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.

Construction and validation of a risk prediction model for depressive symptoms in a middle-aged and elderly arthritis population

BACKGROUND Arthritis is a prevalent and debilitating condition that affects a significant proportion of middle-aged and older adults worldwide.Characterized by chronic pain,inflammation,and joint dysfunction,arthritis can severely impact physical function,quality of life,and mental health.The overall burden of arthritis is further compounded in this population due to its frequent association with depression.As the global population both the prevalence and severity of arthritis are anticipated to increase.AIM To investigate depressive symptoms in the middle-aged and elderly arthritic population in China,a risk prediction model was constructed,and its effectiveness was validated.METHODS Using the China Health and Retirement Longitudinal Study 2018 data on middleaged and elderly arthritic individuals,the population was randomly divided into a training set(n=4349)and a validation set(n=1862)at a 7:3 ratio.Based on 10-fold cross-validation,least absolute shrinkage and selection regression was used to screen the model for the best predictor variables.Logistic regression was used to construct the nomogram model.Subject receiver operating characteristic and calibration curves were used to determine model differentiation and accuracy.Decision curve analysis was used to assess the net clinical benefit.RESULTS The prevalence of depressive symptoms in the middle-aged and elderly arthritis population in China was 47.1%,multifactorial logistic regression analyses revealed that gender,age,number of chronic diseases,number of pain sites,nighttime sleep time,education,audiological status,health status,and place of residence were all predictors of depressive symptoms.The area under the curve values for the training and validation sets were 0.740(95%confidence interval:0.726-0.755)and 0.731(95%confidence interval:0.709-0.754),respectively,indicating good model differentiation.The calibration curves demonstrated good prediction accuracy,and the decision curve analysis curves demonstrated good clinical utility.CONCLUSION The risk prediction model developed in this study has strong predictive performance and is useful for screening and assessing depression symptoms in middle-aged and elderly arthritis patients.

Risk assessment of coronary artery occlusion based on integrated Chinese and western medicine data

Objective To develop an integrated risk model for coronary artery occlusion based on data of both traditional Chinese medicine(TCM)and western medicine data,and to evaluate the contribution of TCM-specific indicators to conventional coronary heart disease(CHD)risk prediction.Methods Data of TCM indicators(tongue,facial,and pulse diagnostics)and clinical parame-ters from patients diagnosed with CHD at the Cardiology Department of Shanghai Baoshan Hospital of Integrated Traditional Chinese and Western Medicine,from October 3,2023 to March 15,2024,were collected.Important variables were identified using importance screen-ing and correlation analysis with CHD risk factors and laboratory markers.Six machine learn-ing models including logistic regression(LR),decision tree(DT),support vector machine(SVM),k-nearest neighbors(KNN),random forest(RF),and gradient boosting(GB),were ap-plied to evaluate the risk of coronary artery obstruction by combining clinical and TCM data of CHD.Model performance was assessed using metrics such as accuracy,precision,and re-call,with reliability validated through ten-fold cross-validation.Results A total of 288 patients were included in the study.Fifteen clinical risk factors,includ-ing body mass index(BMI),myoglobin,and alcohol consumption history,were incorporated into the diagnostic models.The KNN model showed good performance when combining clin-ical data with tongue and facial data.The SVM model performed well when clinical data was combined with pulse data.Among all the models,the KNN model with 10-fold cross-valida-tion,which integrates the three types of TCM diagnostic data(tongue,face,and pulse)with clinical data,performs the best(accuracy:0.837,precision:0.814,and recall:0.809).Conclusion Incorporating TCM diagnostic data can enhance the accuracy of coronary artery obstruction risk assessment.The KNN prediction model that integrate tongue,facial,and pulse data performs the best and can be recommended as a clinical decision support tool.

Navigating breast cancer brain metastasis:Risk factors,prognostic indicators,and treatment perspectives

In this editorial,we comment on the article by Chen et al.We specifically focus on the risk factors,prognostic factors,and management of brain metastasis(BM)in breast cancer(BC).BC is the second most common cancer to have BM after lung cancer.Independent risk factors for BM in BC are:HER-2 positive BC,triplenegative BC,and germline BRCA mutation.Other factors associated with BM are lung metastasis,age less than 40 years,and African and American ancestry.Even though risk factors associated with BM in BC are elucidated,there is a lack of data on predictive models for BM in BC.Few studies have been made to formulate predictive models or nomograms to address this issue,where age,grade of tumor,HER-2 receptor status,and number of metastatic sites(1 vs>1)were predictive of BM in metastatic BC.However,none have been used in clinical practice.National Comprehensive Cancer Network recommends screening of BM in advanced BC only when the patient is symptomatic or suspicious of central nervous system symptoms;routine screening for BM in BC is not recommended in the guidelines.BM decreases the quality of life and will have a significant psychological impact.Further studies are required for designing validated nomograms or predictive models for BM in BC;these models can be used in the future to develop treatment approaches to prevent BM,which improves the quality of life and overall survival.

Construction and verification of a model for predicting fall risk in patients with maintenance hemodialysis

Objective:To construct a risk prediction model for fall in patients with maintenance hemodialysis(MHD)and to verify the prediction effect of the model.Methods:From June 2020 to December 2020,307 patients who underwent MHD in a tertiary hospital in Chengdu were divided into a fall group(32 cases)and a non-fall group(275 cases).Logistic regression analysis model was used to establish the influencing factors of the subjects.Hosmer–Lemeshow and receiver operating characteristic(ROC)curve were used to test the goodness of fit and predictive effect of the model,and 104 patients were again included in the application research of the model.Results:The risk factors for fall were history of falls in the past year(OR=3.951),dialysis-related hypotension(OR=6.949),time up and go(TUG)test(OR=4.630),serum albumin(OR=0.661),frailty(OR=7.770),and fasting blood glucose(OR=1.141).Hosmer–Lemeshow test was P=0.475;the area under the ROC curve was 0.907;the Youden index was 0.642;the sensitivity was 0.843;and the specificity was 0.799.Conclusions:The risk prediction model constructed in this study has a good effect and can provide references for clinical screening of fall risks in patients with MHD.

Predictive Analytics for Project Risk Management Using Machine Learning

Risk management is relevant for every project that which seeks to avoid and suppress unanticipated costs, basically calling for pre-emptive action. The current work proposes a new approach for handling risks based on predictive analytics and machine learning (ML) that can work in real-time to help avoid risks and increase project adaptability. The main research aim of the study is to ascertain risk presence in projects by using historical data from previous projects, focusing on important aspects such as time, task time, resources and project results. t-SNE technique applies feature engineering in the reduction of the dimensionality while preserving important structural properties. This process is analysed using measures including recall, F1-score, accuracy and precision measurements. The results demonstrate that the Gradient Boosting Machine (GBM) achieves an impressive 85% accuracy, 82% precision, 85% recall, and 80% F1-score, surpassing previous models. Additionally, predictive analytics achieves a resource utilisation efficiency of 85%, compared to 70% for traditional allocation methods, and a project cost reduction of 10%, double the 5% achieved by traditional approaches. Furthermore, the study indicates that while GBM excels in overall accuracy, Logistic Regression (LR) offers more favourable precision-recall trade-offs, highlighting the importance of model selection in project risk management.

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.

Oral microbiome and risk of malignant esophageal lesions in a high-risk area of China:A nested case-control study

Objective:We aimed to prospectively evaluate the association of oral microbiome with malignant esophageal lesions and its predictive potential as a biomarker of risk.Methods:We conducted a case-control study nested within a population-based cohort with up to 8 visits of oral swab collection for each subject over an 11-year period in a high-risk area for esophageal cancer in China.The oral microbiome was evaluated with 16 S ribosomal RNA(rRNA)gene sequencing in 428 pre-diagnostic oral specimens from 84 cases with esophageal lesions of severe squamous dysplasia and above(SDA)and 168 matched healthy controls.DESeq analysis was performed to identify taxa of differential abundance.Differential oral species together with subject characteristics were evaluated for their potential in predicting SDA risk by constructing conditional logistic regression models.Results:A total of 125 taxa including 37 named species showed significantly different abundance between SDA cases and controls(all P<0.05&false discovery rate-adjusted Q<0.10).A multivariate logistic model including 11 SDA lesion-related species and family history of esophageal cancer provided an area under the receiver operating characteristic curve(AUC)of 0.89(95%CI,0.84-0.93).Cross-validation and sensitivity analysis,excluding cases diagnosed within 1 year of collection of the baseline specimen and their matched controls,or restriction to screenendoscopic-detected or clinically diagnosed case-control triads,or using only bacterial data measured at the baseline,yielded AUCs>0.84.Conclusions:The oral microbiome may play an etiological and predictive role in esophageal cancer,and it holds promise as a non-invasive early warning biomarker for risk stratification for esophageal cancer screening programs.

Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma

BACKGROUND Surgical resection is the primary treatment for hepatocellular carcinoma(HCC).However,studies indicate that nearly 70%of patients experience HCC recurrence within five years following hepatectomy.The earlier the recurrence,the worse the prognosis.Current studies on postoperative recurrence primarily rely on postoperative pathology and patient clinical data,which are lagging.Hence,developing a new pre-operative prediction model for postoperative recurrence is crucial for guiding individualized treatment of HCC patients and enhancing their prognosis.AIM To identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC.METHODS The demographic and clinical data of 371 HCC patients were collected for this retrospective study.These data were randomly divided into training and test sets at a ratio of 8:2.The training set was analyzed,and key feature variables with predictive value for early HCC recurrence were selected to construct six different machine learning prediction models.Each model was evaluated,and the bestperforming model was selected for interpreting the importance of each variable.Finally,an online calculator based on the model was generated for daily clinical practice.RESULTS Following machine learning analysis,eight key feature variables(age,intratumoral arteries,alpha-fetoprotein,preoperative blood glucose,number of tumors,glucose-to-lymphocyte ratio,liver cirrhosis,and pre-operative platelets)were selected to construct six different prediction models.The XGBoost model outperformed other models,with the area under the receiver operating characteristic curve in the training,validation,and test datasets being 0.993(95%confidence interval:0.982-1.000),0.734(0.601-0.867),and 0.706(0.585-0.827),respectively.Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value.CONCLUSION The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence.This model may guide surgical strategies and postoperative individualized medicine.

Risk factors for cardiovascular disease in the Chinese population:recent progress and implications

Cardiovascular disease(CVD)is the leading cause of death in both urban and rural areas of China.The current evidence regarding CVD risk factors was primarily established in Western countries,with limited generalizability to the Chinese population.In China,a growing number of population-based prospective cohort studies have emerged that have yielded substantial research data on CVD risk factors in the past five years.The research studies have covered biological risk factors(e.g.,blood lipids,blood pressure,blood glucose,adiposity),lifestyle risk factors(e.g.,smoking,alcohol,diet,physical activity),environmental risk factors(e.g.,ambient and indoor air pollution),and risk prediction.This study aimed to systematically review the research progress on CVD risk factors in the Chinese population in the past five years.Prospective studies in China have identified biological,lifestyle,and environmental risk factors for CVD and its main subtypes,along with some protective factors unique to the Chinese(e.g.,spicy food and green tea).This review aimed to provide high-quality evidence for achieving the Outline of Healthy China 2030,developing disease prevention guidelines and measures,and deepening efforts for popularization of health knowledge.

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.

Nomograms and risk score models for predicting survival in rectal cancer patients with neoadjuvant therapy

BACKGROUND Colorectal cancer is a common digestive cancer worldwide.As a comprehensive treatment for locally advanced rectal cancer(LARC),neoadjuvant therapy(NT)has been increasingly used as the standard treatment for clinical stage II/III rectal cancer.However,few patients achieve a complete pathological response,and most patients require surgical resection and adjuvant therapy.Therefore,identifying risk factors and developing accurate models to predict the prognosis of LARC patients are of great clinical significance.AIM To establish effective prognostic nomograms and risk score prediction models to predict overall survival(OS)and disease-free survival(DFS)for LARC treated with NT.METHODS Nomograms and risk factor score prediction models were based on patients who received NT at the Cancer Hospital from 2015 to 2017.The least absolute shrinkage and selection operator regression model were utilized to screen for prognostic risk factors,which were validated by the Cox regression method.Assessment of the performance of the two prediction models was conducted using receiver operating characteristic curves,and that of the two nomograms was conducted by calculating the concordance index(C-index)and calibration curves.The results were validated in a cohort of 65 patients from 2015 to 2017.RESULTS Seven features were significantly associated with OS and were included in the OS prediction nomogram and prediction model:Vascular_tumors_bolt,cancer nodules,yN,body mass index,matchmouth distance from the edge,nerve aggression and postoperative carcinoembryonic antigen.The nomogram showed good predictive value for OS,with a C-index of 0.91(95%CI:0.85,0.97)and good calibration.In the validation cohort,the C-index was 0.69(95%CI:0.53,0.84).The risk factor prediction model showed good predictive value.The areas under the curve for 3-and 5-year survival were 0.811 and 0.782.The nomogram for predicting DFS included ypTNM and nerve aggression and showed good calibration and a C-index of 0.77(95%CI:0.69,0.85).In the validation cohort,the C-index was 0.71(95%CI:0.61,0.81).The prediction model for DFS also had good predictive value,with an AUC for 3-year survival of 0.784 and an AUC for 5-year survival of 0.754.CONCLUSION We established accurate nomograms and prediction models for predicting OS and DFS in patients with LARC after undergoing NT.

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.

Predicting Pressure Ulcer Risk with the Braden Q Scale in Chinese Pediatric Patients in ICU

Objective: The purpose of this study was to: ( 1 ) observe the value of the score of Braden Q scale in predicting pressure ulcers in pediatric Intensive Care Unit ( ICU) patients in China, ( 2) determine the critical cutoff point for classifying patient risk, and ( 3) describe the pressure ulcer incidence. Methods: A prospective cohort descriptive study with a convenience sample of 198 patients bed-ridden for at least 24 hours without pre-existing pressure ulcers enrolled from a pediatric intensive care unit ( PICU) . The Braden Q score and skin assessment were independently rated, and data collectors were blinded to the other measures. Patients were observed for up to 3 times per week for 2 weeks and once a week thereafter until PICU discharge. Results: Fourteen patients ( 7. 1%) developed pressure ulcers; 12 ( 85. 7%) were Stage I pres-sure ulcers, 2 ( 14. 3%) were Stage II, and there were no Stage III or IV pressure ulcers. Most pressure ulcers ( 64. 3%) were present at the first observation. The Braden Q Scale has an overall cumulative variance contribution rate of 69. 599%. Using Stage I+ pressure ulcer data obtained during the first observation, a Receiver Operator Characteristic ( ROC) curve for each possible score of the Braden Q Scale was constructed. The area under the curve ( AUC) was 0. 57, and the 95% confidence interval was 0. 50-0. 62. At a cutoff score of 19, the sensitivity was 0. 71, and the specificity was 0. 53. The AUC of each item of the Braden Q Scale was 0. 543-0. 612. Conclusions: PICU patients are susceptible to pressure ulcers. The value of the Braden Q Scale in the studied pediatric population was relatively poor, and it should be optimized before it is used in Chinese pediatric patients.

A Fault Risk Warning Method of Integrated Energy Systems Based on RelieF-Softmax Algorithm

The integrated energy systems,usually including electric energy,natural gas and thermal energy,play a pivotal role in the energy Internet project,which could improve the accommodation of renewable energy through multienergy complementary ways.Focusing on the regional integrated energy system composed of electrical microgrid and natural gas network,a fault risk warning method based on the improved RelieF-softmax method is proposed in this paper.The raw data-set was first clustered by the K-maxmin method to improve the preference of the random sampling process in the RelieF algorithm,and thereby achieved a hierarchical and non-repeated sampling.Then,the improved RelieF algorithm is used to identify the feature vectors,calculate the feature weights,and select the preferred feature subset according to the initially set threshold.In addition,a correlation coefficient method is applied to reduce the feature subset,and further eliminate the redundant feature vectors to obtain the optimal feature subset.Finally,the softmax classifier is used to obtain the early warnings of the integrated energy system.Case studies are conducted on an integrated energy system in the south of China to demonstrate the accuracy of fault risk warning method proposed in this paper.

A Mortality Risk Assessment Approach on ICU Patients Clinical Medication Events Using Deep Learning

ICU patients are vulnerable to medications,especially infusion medications,and the rate and dosage of infusion drugs may worsen the condition.The mortality prediction model can monitor the real-time response of patients to drug treatment,evaluate doctors’treatment plans to avoid severe situations such as inverse Drug-Drug Interactions(DDI),and facilitate the timely intervention and adjustment of doctor’s treatment plan.The treatment process of patients usually has a time-sequence relation(which usually has the missing data problem)in patients’treatment history.The state-of-the-art method to model such time-sequence is to use Recurrent Neural Network(RNN).However,sometimes,patients’treatment can last for a long period of time,which RNN may not fit for modelling long time sequence data.Therefore,we propose to use the heterogeneous medication events driven LSTM to predict the outcome of the patient,and the Natural Language Processing and Gaussian Process(GP),which can handle noisy,incomplete,sparse,heterogeneous and unevenly sampled patients’medication records.In our work,we emphasize the semantic meaning of each medication event and the sequence of the medication events on patients,while also handling the missing value problem using kernel-based Gaussian process.We compare the performance of LSTM and Phased-LSTM on modelling the outcome of patients’treatment and data imputation using kernel-based Gaussian process and conduct an empirical study on different data imputation approaches.