Optimizing prediction models for pancreatic fistula after pancreatectomy:Current status and future perspectives

Postoperative pancreatic fistula(POPF)is a frequent complication after pancre-atectomy,leading to increased morbidity and mortality.Optimizing prediction models for POPF has emerged as a critical focus in surgical research.Although over sixty models following pancreaticoduodenectomy,predominantly reliant on a variety of clinical,surgical,and radiological parameters,have been documented,their predictive accuracy remains suboptimal in external validation and across diverse populations.As models after distal pancreatectomy continue to be pro-gressively reported,their external validation is eagerly anticipated.Conversely,POPF prediction after central pancreatectomy is in its nascent stage,warranting urgent need for further development and validation.The potential of machine learning and big data analytics offers promising prospects for enhancing the accuracy of prediction models by incorporating an extensive array of variables and optimizing algorithm performance.Moreover,there is potential for the development of personalized prediction models based on patient-or pancreas-specific factors and postoperative serum or drain fluid biomarkers to improve accuracy in identifying individuals at risk of POPF.In the future,prospective multicenter studies and the integration of novel imaging technologies,such as artificial intelligence-based radiomics,may further refine predictive models.Addressing these issues is anticipated to revolutionize risk stratification,clinical decision-making,and postoperative management in patients undergoing pancre-atectomy.

Rapid prediction models for 3D geometry of landslide dam considering the damming process

The geometry of a landslide dam plays a critical role in its stability and failure mode,and is influenced by the damming process.However,there is a lack of understanding of the factors that affect the 3D geometry of a landslide dam.To address this gap,we conducted a study using the smoothed particle hydrodynamics numerical method to investigate the evolution of landslide dams.Our study included 17 numerical simulations to examine the effects of several factors on the geometry of landslide dams,including valley inclination,sliding angle,landslide velocity,and landslide mass repose angle.Based on this,three rapid prediction models were established for calculating the maximum height,the minimum height,and the maximum width of a landslide dam.The results show that the downstream width of a landslide dam remarkably increases with the valley inclination.The position of the maximum dam height along the valley direction is independent of external factors and is always located in the middle of the landslide width area.In contrast,that position of the maximum dam height across the valley direction is significantly influenced by the sliding angle and landslide velocity.To validate our models,we applied them to three typical landslide dams and found that the calculated values of the landslide dam geometry were in good agreement with the actual values.The findings of the current study provide a better understanding of the evolution and geometry of landslide dams,giving crucial guidance for the prediction and early warning of landslide dam disasters.

Prediction models for recurrence in patients with small bowel bleeding

Obscure gastrointestinal bleeding(OGIB)has traditionally been defined as gastrointestinal bleeding whose source remains unidentified after bidirectional endoscopy.OGIB can present as overt bleeding or occult bleeding,and small bowel lesions are the most common causes.The small bowel can be evaluated using capsule endoscopy,device-assisted enteroscopy,computed tomography enterography,or magnetic resonance enterography.Once the cause of smallbowel bleeding is identified and targeted therapeutic intervention is completed,the patient can be managed with routine visits.However,diagnostic tests may produce negative results,and some patients with small bowel bleeding,regardless of diagnostic findings,may experience rebleeding.Predicting those at risk of rebleeding can help clinicians form individualized surveillance plans.Several studies have identified different factors associated with rebleeding,and a limited number of studies have attempted to create prediction models for recurrence.This article describes prediction models developed so far for identifying patients with OGIB who are at greater risk of rebleeding.These models may aid clinicians in forming tailored patient management and 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.

Equivalency and unbiasedness of grey prediction models

In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction models, the equivalence and unbiasedness of grey prediction models are analyzed and verified. The results show that all the grey prediction models that are strictly derived from x^（0）（k） ＋az^（1）（k） = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homogeneous exponential sequence can be accomplished. However, the models derived from dx^（1）/dt ＋ ax^（1）= b are only close to those derived from x^（0）（k） ＋ az^（1）（k） = b provided that ｜a｜ has to satisfy｜a｜ 0.1; neither could the unbiased simulation for the homogeneous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples.

Speed prediction models for car and sports utility vehicleat locations along four-lane median divided horizontal curves

Sites with varying geometric features were analyzed to develop the 85 th percentile speed prediction models for car and sports utility vehicle(SUV) at 50 m prior to the point of curvature(PC), PC, midpoint of a curve(MC), point of tangent(PT) and 50 m beyond PT on four-lane median divided rural highways. The car and SUV speed data were combined in the analysis as they were found to be normally distributed and not significantly different. Independent parameters representing geometric features and speed at the preceding section were logically selected in stepwise regression analyses to develop the models. Speeds at various locations were found to be dependent on some combinations of curve length, curvature and speed in the immediately preceding section of the highway. Curve length had a significant effect on the speed at locations 50 m prior to PC, PC and MC. The effect of curvature on speed was observed only at MC. The curve geometry did not have a significant effect on speed from PT onwards. The speed at 50 m prior to PC and curvature is the most significant parameter that affects the speed at PC and MC, respectively. Before entering a horizontal curve, drivers possibly perceive the curve based on its length. Longer curve encourages drivers to maintain higher speed in the preceding tangent section. Further, drivers start experiencing the effect of curvature only after entering the curve and adjust speed accordingly. Practitioners can use these findings in designing consistent horizontal curve for vehicle speed harmony.

Comparison of various prediction models in the effect of laparoscopic sleeve gastrectomy on type 2 diabetes mellitus in the Chinese population 5 years after surgery

Background:The effect of bariatric surgery on type 2 diabetes mellitus(T2DM)control can be assessed based on predictive models of T2DM remission.Various models have been externally verified internationally.However,long-term validated results after laparoscopic sleeve gastrectomy(LSG)surgery are lacking.The best model for the Chinese population is also unknown.Methods:We retrospectively analyzed Chinese population data 5 years after LSG at Beijing Shijitan Hospital in China between March 2009 and December 2016.The independent t-test,Mann–Whitney U test,and chi-squared test were used to compare characteristics between T2DM remission and non-remission groups.We evaluated the predictive efficacy of each model for longterm T2DM remission after LSG by calculating the area under the curve(AUC),sensitivity,specificity,Youden index,positive predictive value(PPV),negative predictive value(NPV),and predicted-to-observed ratio,and performed calibration using Hosmer–Lemeshow test for 11 prediction models.Results:We enrolled 108 patients,including 44(40.7%)men,with a mean age of 35.5 years.The mean body mass index was 40.3±9.1 kg/m^(2),the percentage of excess weight loss(%EWL)was(75.9±30.4)%,and the percentage of total weight loss(%TWL)was(29.1±10.6)%.The mean glycated hemoglobin A1c(HbA1c)level was(7.3±1.8)%preoperatively and decreased to(5.9±1.0)%5 years after LSG.The 5-year postoperative complete and partial remission rates of T2DM were 50.9%[55/108]and 27.8%[30/108],respectively.Six models,i.e.,"ABCD",individualized metabolic surgery(IMS),advanced-DiaRem,DiaBetter,Dixon et al’s regression model,and Panunzi et al’s regression model,showed a good discrimination ability(all AUC>0.8).The"ABCD"(sensitivity,74%;specificity,80%;AUC,0.82[95%confidence interval[CI]:0.74–0.89]),IMS(sensitivity,78%;specificity,84%;AUC,0.82[95%CI:0.73–0.89]),and Panunzi et al’s regression models(sensitivity,78%;specificity,91%;AUC,0.86[95%CI:0.78–0.92])showed good discernibility.In the Hosmer–Lemeshow goodness-of-fit test,except for DiaRem(P<0.01),DiaBetter(P<0.01),Hayes et al(P=0.03),Park et al(P=0.02),and Ramos-Levi et al’s(P<0.01)models,all models had a satifactory fit results(P>0.05).The P values of calibration results of the"ABCD"and IMS were 0.07 and 0.14,respectively.The predicted-to-observed ratios of the"ABCD"and IMS were 0.87 and 0.89,respectively.Conclusion:The prediction model IMS was recommended for clinical use because of excellent predictive performance,good statistical test results,and simple and practical design features.

Advancing Malaria Prediction in Uganda through AI and Geospatial Analysis Models

The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R2 of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.

Integrated Machine Learning and Deep Learning Models for Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.

Statistical Time Series Forecasting Models for Pandemic Prediction

COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease progression.In this case,COVID-19 data is a time-series dataset that can be projected using different methodologies.Thus,this work aims to gauge the spread of the outbreak severity over time.Furthermore,data analytics and Machine Learning(ML)techniques are employed to gain a broader understanding of virus infections.We have simulated,adjusted,and fitted several statistical time-series forecasting models,linearML models,and nonlinear ML models.Examples of these models are Logistic Regression,Lasso,Ridge,ElasticNet,Huber Regressor,Lasso Lars,Passive Aggressive Regressor,K-Neighbors Regressor,Decision Tree Regressor,Extra Trees Regressor,Support Vector Regressions(SVR),AdaBoost Regressor,Random Forest Regressor,Bagging Regressor,AuoRegression,MovingAverage,Gradient Boosting Regressor,Autoregressive Moving Average(ARMA),Auto-Regressive Integrated Moving Averages(ARIMA),SimpleExpSmoothing,Exponential Smoothing,Holt-Winters,Simple Moving Average,Weighted Moving Average,Croston,and naive Bayes.Furthermore,our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods.A third stage in the proposed system is to examine three different implementations to determine which model delivers the best performance.Then,this best method is used for future forecasts,and consequently,we can collect the most accurate and dependable predictions.

Prediction of treatment response to antipsychotic drugs for precision medicine approach to schizophrenia:randomized trials and multiomics analysis

Background:Choosing the appropriate antipsychotic drug(APD)treatment for patients with schizophrenia(SCZ)can be challenging,as the treatment response to APD is highly variable and difficult to predict due to the lack of effective biomarkers.Previous studies have indicated the association between treatment response and genetic and epigenetic factors,but no effective biomarkers have been identified.Hence,further research is imperative to enhance precision medicine in SCZ treatment.Methods:Participants with SCZ were recruited from two randomized trials.The discovery cohort was recruited from the CAPOC trial(n=2307)involved 6 weeks of treatment and equally randomized the participants to the Olanzapine,Risperidone,Quetiapine,Aripiprazole,Ziprasidone,and Haloperidol/Perphenazine(subsequently equally assigned to one or the other)groups.The external validation cohort was recruited from the CAPEC trial(n=1379),which involved 8 weeks of treatment and equally randomized the participants to the Olanzapine,Risperidone,and Aripiprazole groups.Additionally,healthy controls(n=275)from the local community were utilized as a genetic/epigenetic reference.The genetic and epigenetic(DNA methylation)risks of SCZ were assessed using the polygenic risk score(PRS)and polymethylation score,respectively.The study also examined the genetic-epigenetic interactions with treatment response through differential methylation analysis,methylation quantitative trait loci,colocalization,and promoteranchored chromatin interaction.Machine learning was used to develop a prediction model for treatment response,which was evaluated for accuracy and clinical benefit using the area under curve(AUC)for classification,R^(2) for regression,and decision curve analysis.Results:Six risk genes for SCZ(LINC01795,DDHD2,SBNO1,KCNG2,SEMA7A,and RUFY1)involved in cortical morphology were identified as having a genetic-epigenetic interaction associated with treatment response.The developed and externally validated prediction model,which incorporated clinical information,PRS,genetic risk score(GRS),and proxy methylation level(proxyDNAm),demonstrated positive benefits for a wide range of patients receiving different APDs,regardless of sex[discovery cohort:AUC=0.874(95%CI 0.867-0.881),R^(2)=0.478;external validation cohort:AUC=0.851(95%CI 0.841-0.861),R^(2)=0.507].Conclusions:This study presents a promising precision medicine approach to evaluate treatment response,which has the potential to aid clinicians in making informed decisions about APD treatment for patients with SCZ.Trial registration Chinese Clinical Trial Registry(https://www.chictr.org.cn/),18 Aug 2009 retrospectively registered:CAPOC-ChiCTR-RNC-09000521(https://www.chictr.org.cn/showproj.aspx?proj=9014),CAPEC-ChiCTRRNC-09000522(https://www.chictr.org.cn/showproj.aspx?proj=9013).

Quantitative prediction model for the depth limit of oil accumulation in the deep carbonate rocks:A case study of Lower Ordovician in Tazhong area of Tarim Basin

With continuous hydrocarbon exploration extending to deeper basins,the deepest industrial oil accumulation was discovered below 8,200 m,revealing a new exploration field.Hence,the extent to which oil exploration can be extended,and the prediction of the depth limit of oil accumulation(DLOA),are issues that have attracted significant attention in petroleum geology.Since it is difficult to characterize the evolution of the physical properties of the marine carbonate reservoir with burial depth,and the deepest drilling still cannot reach the DLOA.Hence,the DLOA cannot be predicted by directly establishing the relationship between the ratio of drilling to the dry layer and the depth.In this study,by establishing the relationships between the porosity and the depth and dry layer ratio of the carbonate reservoir,the relationships between the depth and dry layer ratio were obtained collectively.The depth corresponding to a dry layer ratio of 100%is the DLOA.Based on this,a quantitative prediction model for the DLOA was finally built.The results indicate that the porosity of the carbonate reservoir,Lower Ordovician in Tazhong area of Tarim Basin,tends to decrease with burial depth,and manifests as an overall low porosity reservoir in deep layer.The critical porosity of the DLOA was 1.8%,which is the critical geological condition corresponding to a 100%dry layer ratio encountered in the reservoir.The depth of the DLOA was 9,000 m.This study provides a new method for DLOA prediction that is beneficial for a deeper understanding of oil accumulation,and is of great importance for scientific guidance on deep oil drilling.

A method for establishing a bearing residual life prediction model for process enhancement equipment based on rotor imbalance response analysis

A rotating packed bed is a typical chemical process enhancement equipment that can strengthen micromixing and mass transfer.During the operation of the rotating packed bed,the nonreactants and products irregularly adhere to the wire mesh packing in the rotor,thus resulting in an imbalance in the vibration of the rotor,which may cause serious damage to the bearing and material leakage.This study proposes a model prediction for estimating the bearing residual life of a rotating packed bed based on rotor imbalance response analysis.This method is used to determine the influence of the mass on the imbalance in the vibration of the rotor on bearing damage.The major influence on rotor vibration was found to be exerted by the imbalanced mass and its distribution radius,as revealed by the results of orthogonal experiments.Through implementing finite element analysis,the imbalance response curve for the rotating packed bed rotor was obtained,and a correlation among rotor imbalance mass,distribution radius of imbalance mass,and bearing residue life was established via data fitting.The predicted value of the bearing life can be used as the reference basis for an early safety warning of a rotating packed bed to effectively avoid accidents.

Bayesian network-based survival prediction model for patients having undergone post-transjugular intrahepatic portosystemic shunt for portal hypertension

BACKGROUND Portal hypertension(PHT),primarily induced by cirrhosis,manifests severe symptoms impacting patient survival.Although transjugular intrahepatic portosystemic shunt(TIPS)is a critical intervention for managing PHT,it carries risks like hepatic encephalopathy,thus affecting patient survival prognosis.To our knowledge,existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes.Consequently,the development of an innovative modeling approach is essential to address this limitation.AIM To develop and validate a Bayesian network(BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS.METHODS The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed.Variables were selected using Cox and least absolute shrinkage and selection operator regression methods,and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT.RESULTS Variable selection revealed the following as key factors impacting survival:age,ascites,hypertension,indications for TIPS,postoperative portal vein pressure(post-PVP),aspartate aminotransferase,alkaline phosphatase,total bilirubin,prealbumin,the Child-Pugh grade,and the model for end-stage liver disease(MELD)score.Based on the above-mentioned variables,a BN-based 2-year survival prognostic prediction model was constructed,which identified the following factors to be directly linked to the survival time:age,ascites,indications for TIPS,concurrent hypertension,post-PVP,the Child-Pugh grade,and the MELD score.The Bayesian information criterion was 3589.04,and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16.The model’s accuracy,precision,recall,and F1 score were 0.90,0.92,0.97,and 0.95 respectively,with the area under the receiver operating characteristic curve being 0.72.CONCLUSION This study successfully developed a BN-based survival prediction model with good predictive capabilities.It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT.

Construction and validation of a risk-prediction model for anastomotic leakage after radical gastrectomy: A cohort study in China

Objectives:Anastomotic leakage(AL)stands out as a prevalent and severe complication following gastric cancer surgery.It frequently precipitates additional serious complications,significantly influencing the overall survival time of patients.This study aims to enhance the risk-assessment strategy for AL following gastrectomy for gastric cancer.Methods:This study included a derivation cohort and validation cohort.The derivation cohort included patients who underwent radical gastrectomy at Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,from January 1,2015 to December 31,2020.An evidence-based predictor questionnaire was crafted through extensive literature review and panel discussions.Based on the questionnaire,inpatient data were collected to form a model-derivation cohort.This cohort underwent both univariate and multivariate analyses to identify factors associated with AL events,and a logistic regression model with stepwise regression was developed.A 5-fold cross-validation ensured model reliability.The validation cohort included patients from August 1,2021 to December 31,2021 at the same hospital.Using the same imputation method,we organized the validation-queue data.We then employed the risk-prediction model constructed in the earlier phase of the study to predict the risk of AL in the subjects included in the validation queue.We compared the predictions with the actual occurrence,and evaluated the external validation performance of the model using model-evaluation indicators such as the area under the receiver operating characteristic curve(AUROC),Brier score,and calibration curve.Results:The derivation cohort included 1377 patients,and the validation cohort included 131 patients.The independent predictors of AL after radical gastrectomy included age65 y,preoperative albumin<35 g/L,resection extent,operative time240 min,and intraoperative blood loss90 mL.The predictive model exhibited a solid AUROC of 0.750(95%CI:0.694e0.806;p<0.001)with a Brier score of 0.049.The 5-fold cross-validation confirmed these findings with a calibrated C-index of 0.749 and an average Brier score of 0.052.External validation showed an AUROC of 0.723(95%CI:0.564e0.882;p?0.006)and a Brier score of 0.055,confirming reliability in different clinical settings.Conclusions:We successfully developed a risk-prediction model for AL following radical gastrectomy.This tool will aid healthcare professionals in anticipating AL,potentially reducing unnecessary interventions.

Development and validation of a circulating tumor DNA-based optimization-prediction model for short-term postoperative recurrence of endometrial cancer

BACKGROUND Endometrial cancer(EC)is a common gynecological malignancy that typically requires prompt surgical intervention;however,the advantage of surgical management is limited by the high postoperative recurrence rates and adverse outcomes.Previous studies have highlighted the prognostic potential of circulating tumor DNA(ctDNA)monitoring for minimal residual disease in patients with EC.AIM To develop and validate an optimized ctDNA-based model for predicting shortterm postoperative EC recurrence.METHODS We retrospectively analyzed 294 EC patients treated surgically from 2015-2019 to devise a short-term recurrence prediction model,which was validated on 143 EC patients operated between 2020 and 2021.Prognostic factors were identified using univariate Cox,Lasso,and multivariate Cox regressions.A nomogram was created to predict the 1,1.5,and 2-year recurrence-free survival(RFS).Model performance was assessed via receiver operating characteristic(ROC),calibration,and decision curve analyses(DCA),leading to a recurrence risk stratification system.RESULTS Based on the regression analysis and the nomogram created,patients with postoperative ctDNA-negativity,postoperative carcinoembryonic antigen 125(CA125)levels of<19 U/mL,and grade G1 tumors had improved RFS after surgery.The nomogram’s efficacy for recurrence prediction was confirmed through ROC analysis,calibration curves,and DCA methods,highlighting its high accuracy and clinical utility.Furthermore,using the nomogram,the patients were successfully classified into three risk subgroups.CONCLUSION The nomogram accurately predicted RFS after EC surgery at 1,1.5,and 2 years.This model will help clinicians personalize treatments,stratify risks,and enhance clinical outcomes for patients with EC.

Correction:Establishment of a prediction model for prehospital return of spontaneous circulation in out-of-hospital patients with cardiac arrest

This is an erratum to an already published paper named“Establishment of a prediction model for prehospital return of spontaneous circulation in out-ofhospital patients with cardiac arrest”.We found errors in the affiliated institution of the authors.We apologize for our unintentional mistake.Please note,these changes do not affect our results.

Analysis of risk factors of suicidal ideation in adolescent patients with depression and construction of prediction model

BACKGROUND Major depressive disorder is a common mental illness among adolescents and is the largest disease burden in this age group.Most adolescent patients with depression have suicidal ideation(SI);however,few studies have focused on the factors related to SI,and effective predictive models are lacking.AIM To construct a risk prediction model for SI in adolescent depression and provide a reference assessment tool for prevention.METHODS The data of 150 adolescent patients with depression at the First People's Hospital of Lianyungang from June 2020 to December 2022 were retrospectively analyzed.Based on whether or not they had SI,they were divided into a SI group(n=91)and a non-SI group(n=59).The general data and laboratory indices of the two groups were compared.Logistic regression was used to analyze the factors influencing SI in adolescent patients with depression,a nomogram prediction model was constructed based on the analysis results,and internal evaluation was performed.Receiver operating characteristic and calibration curves were used to evaluate the model’s efficacy,and the clinical application value was evaluated using decision curve analysis(DCA).RESULTS There were differences in trauma history,triggers,serum ferritin levels(SF),highsensitivity C-reactive protein levels(hs-CRP),and high-density lipoprotein(HDLC)levels between the two groups(P<0.05).Logistic regression analysis showed that trauma history,predisposing factors,SF,hs-CRP,and HDL-C were factors influencing SI in adolescent patients with depression.The area under the curve of the nomogram prediction model was 0.831(95%CI:0.763–0.899),sensitivity was 0.912,and specificity was 0.678.The higher net benefit of the DCA and the average absolute error of the calibration curve were 0.043,indicating that the model had a good fit.CONCLUSION The nomogram prediction model based on trauma history,triggers,ferritin,serum hs-CRP,and HDL-C levels can effectively predict the risk of SI in adolescent patients with depression.

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.

Prediction model for hepatitis B e antigen seroconversion in chronic hepatitis B with peginterferon-alfa treated based on a responseguided therapy strategy

BACKGROUND Models for predicting hepatitis B e antigen(HBeAg)seroconversion in patients with HBeAg-positive chronic hepatitis B(CHB)after nucleos(t)ide analog treatment are rare.AIM To establish a simple scoring model based on a response-guided therapy(RGT)strategy for predicting HBeAg seroconversion and hepatitis B surface antigen(HBsAg)clearance.METHODS In this study,75 previously treated patients with HBeAg-positive CHB underwent a 52-week peginterferon-alfa(PEG-IFNα)treatment and a 24-wk follow-up.Logistic regression analysis was used to assess parameters at baseline,week 12,and week 24 to predict HBeAg seroconversion at 24 wk post-treatment.The two best predictors at each time point were used to establish a prediction model for PEG-IFNαtherapy efficacy.Parameters at each time point that met the corresponding optimal cutoff thresholds were scored as 1 or 0.RESULTS The two most meaningful predictors were HBsAg≤1000 IU/mL and HBeAg≤3 S/CO at baseline,HBsAg≤600 IU/mL and HBeAg≤3 S/CO at week 12,and HBsAg≤300 IU/mL and HBeAg≤2 S/CO at week 24.With a total score of 0 vs 2 at baseline,week 12,and week 24,the response rates were 23.8%,15.2%,and 11.1%vs 81.8%,80.0%,and 82.4%,respectively,and the HBsAg clearance rates were 2.4%,3.0%,and 0.0%,vs 54.5%,40.0%,and 41.2%,respectively.CONCLUSION We successfully established a predictive model and diagnosis-treatment process using the RGT strategy to predict HBeAg and HBsAg seroconversion in patients with HBeAg-positive CHB undergoing PEG-IFNαtherapy.