Construction Of Prediction Model Research Articles

Construction of a nomogram risk prediction model for depressive symptoms in elderly breast cancer patients

The primary objective of this study was to identify the factors associated with the development of depressive symptoms in elderly breast cancer (BC) patients and to construct a nomogram model for predicting these symptoms. We recruited 409 patients undergoing BC treatment in the breast departments of two tertiary-level hospitals in Jiangsu Province from November 2023 to April 2024 as our study cohort. Participants were categorized into depressed and non-depressed groups based on their clinical outcomes. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors for depression among BC patients. Multivariate analysis revealed that monthly income, pain score, family support score, and physical activity score significantly influenced the onset of depression in older BC patients (P < 0.05).The risk prediction model, constructed using these identified factors, demonstrated excellent discriminatory power, as evidenced by an area under the ROC curve (AUC) of 0.824. The maximum Youden index was 0.627, with a sensitivity of 90.60%, specificity of 72.10%, and a diagnostic threshold value of 1.501. The results of the Hosmer-Lemeshow goodness-of-fit test (χ² = 3.181, P = 0.923) indicated that the model fit the data well. The calibration curve for the model closely followed the ideal curve, suggesting a strong fit and high predictive accuracy. Our nomogram model exhibited superior predictive performance, enabling healthcare professionals to identify high-risk patients early and implement preventative measures to mitigate the development of depressive symptoms. This study is a cross-sectional study that lacks longitudinal data and has a small sample size. Future research could involve larger samples, multicenter studies, and prospective designs to build better clinical predictive models.

Construction and Validation of a Prognostic Prediction Model for Gastric Mucinous Adenocarcinoma Based on the SEER Database

This study focused on the development and validation of a prognostic model to predict overall survival (OS) in patients with gastric mucinous adenocarcinoma (GMA), utilizing data from the SEER database. A cohort of 537 GMA patients was analyzed to identify factors affecting survival, using both univariate and multivariate Cox regression analyses on demographic and clinicopathological variables. The analysis led to the creation of a nomogram model. Findings revealed that TNM staging and the administration of radiotherapy or chemotherapy were significant factors impacting survival outcomes. Specifically, patients at stages T4, N3, and M1 faced a notably higher mortality risk, while those who received radiotherapy and chemotherapy experienced a significant reduction in mortality risk. The model's performance, as indicated by C-indices of 0.73 in the training set and 0.66 in the validation set, suggests robust predictive capability. The area under the curve (AUC) analysis confirmed high accuracy in predicting 1-year, 3-year, and 5-year survival rates, and calibration curves displayed strong agreement between predicted and actual outcomes. Decision curve analysis (DCA) demonstrated that the model exhibited significant clinical utility across various thresholds. Overall, this study presents a reliable prognostic tool for individualized risk stratification and clinical treatment in GMA patients. Future work should focus on enhancing the model by incorporating molecular biological data to increase its predictive accuracy and applicability.

Analysis of Risk Factors for Multidrug-Resistant Organism (MDRO) Infections and Construction of a Risk Prediction Model in a Cancer Specialty Hospital.

Aims/Background Patients receiving treatment in specialized cancer hospitals are particularly susceptible to multidrug-resistant organisms (MDRO) infections due to factors such as weakened immune systems caused by intensive treatments and prolonged hospital stays. This study aims to investigate the risk factors for MDRO infections in the cancer specialty hospital setting and to develop a corresponding risk prediction model. Methods Patients diagnosed with MDRO infections were selected for the MDRO infection group (n = 238), and those without for the non-MDRO infection group (n = 238). Non-parametric tests, chi-square tests, and multivariate logistic regression analysis were used to identify the primary risk factors for MDRO infections. With the aid of analysis utilizing R software 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria), we developed a nomogram prediction model, which was evaluated using the receiver operating characteristics (ROC) curve, calibration curve, and decision curve analysis (DCA). Results Age, antibiotic application time, and central venous catheterization were independent risk factors for MDRO infection (p < 0.05). The constructed nomogram prediction model for patients with MDRO infection has a C-index of 0.8640. The ROC curve results showed that the prediction model has a specificity of 0.7700, a sensitivity of 0.8800, and an area under the curve (AUC) of 0.8800. Conclusion This study identifies significant risk factors for MDRO infections in a cancer specialty hospital setting and offers a clinically useful prediction model, which may aid in targeted preventive measures and optimization of antibiotics usage, thereby potentially reducing the incidence and impact of these infections.

The rate of change in clinical indicators can predict the progression of hepatitis B virus-related acute-on-chronic preliver failure.

The objective of this study was to investigate the predictors and predictive model construction of the progression of HBV-Pre.Acute-on-chronic liver failure (ACLF), a total of 133 patients with HBV-Pre.ACLF was divided into the progressive group (52 patients) and the recovery group (81 patients) according to whether they progressed to ACLF or not. The clinical parameters N%, L%, PLT, ALT, TBiL, ALB, Cre, Na, NH3, CRP, AFP, prothrombin time (PT), international normalized ratio (INR), FIB, and their rate of change at baseline were analyzed in the 2 groups. The independent risk factors for HBV-Pre.ACLF progression was found by univariate and multivariate analyses, and a predictive model was constructed. The clinical parameters ALB, FIB, Na, combined alprostadil treatment and MELD, and MELD-Na scores at baseline were significantly different between the 2 groups (P <.05), while ALT, TBiL, Cre, CHE, NH3, N%, L%, PLT, INR, and PT were not significantly different (P >.05). The change rates of Na, CHE, PT, FIB, CRP, Cre, PLT, and the ratio after to before of N% were significantly different between the 2 groups (P <.05), while the change rates of ALT, TBIL, NH3, AFP, L%, and the ratio after to before of INR were not significantly different between the 2 groups (P >.05). Univariate and multivariate analyses showed that baseline ALB, Na, FIB, combined alprostadil therapy and the rate of change of Na and PLT were protective factors for disease progression, and the rate of change of PT, CRP, and the ratio after to before of N% were independent risk factors for disease progression. The novel model was LogitP = -6.051 + 4.049×ΔPT + 0.626×ΔCRP + 4.527×the ratio after to before N% and its area under the curve was 0.944 (95% confidence interval: 0.900-0.988) predicting progression of HBV-Pre.ACLF, and the best cutoff value was -0.22. The patients with a higher logitP score (> -0.22) had an increased risk for progression to ACLF (P <.05). The novel model logitP shows good predictive value for the disease progression of HBV-Pre.ACLF.

Risk factor analysis and prediction model construction for contralateral central lymph node metastasis in unilateral papillary thyroid carcinoma

ObjectiveTo investigate contralateral central lymph node metastasis (CCLNM) in patients with unilateral papillary thyroid carcinoma (UPTC). To provide a reference for clinical decision-making, a prediction model for the probability of CCLNM was established.MethodThe clinicopathological data of 221 UPTC patients who underwent surgical treatment were retrospectively analyzed. Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors for CCLNM according to clinicopathological characteristics, construct a prediction model to construct a visual nomogram, and evaluate the model.ResultsAccording to univariate and multivariate logistic regression analyses, sex (P = 0.01, OR: 3.790, 95% CI: 1.373–10.465), extrathyroidal tumor extension (ETE) (P = 0.040, OR: 6.364, 95% CI: 1.083–37.381), tumor diameter (P = 0.010, OR: 3.674, 95% CI: 1.372–9.839) and ipsilateral central lymph node metastasis (ICLNM) (P < 0.001, OR: 38.552, 95% CI: 2.675–27.342) were found to be independent risk factors for CCLNM and were used to construct a nomogram for internal verification. The ROC curve had an AUC of 0.852 in the training group and an AUC of 0.848 in the verification group, and the calibration curve indicated that the prediction probability of the model was consistent with the actual probability. Finally, the analysis of the decision curve showed that the model has good application value in clinical decision-making.ConclusionSex, ETE, tumor size, and ICLNM emerged as independent risk factors for CCLNM in UPTC patients. A predictive model was therefore developed, harnessing these variables to enable an objective, personalized estimation of CCLNM risk. This tool offers valuable insights to inform surgical planning and optimize treatment strategies for UPTC management.

Effect of Porosity and Gradient Parameters on Compressive Mechanical Properties of Sheet Gyroid Gradient Porous Structures and Construction of Mechanical Properties Prediction Model

A series of uniform porous structures and radial stepwise and radial linear gradient structures are designed based on sheet‐gyroid to explore the influence mechanism of porosity and gradient parameters on forming quality and compressive mechanical properties. On this basis, mathematical models for predicting mechanical properties of porous structures based on uniform, discrete gradient, and linear gradient porosity distribution are established. The volume fraction deviation of uniform porous structures increases gradually with the increase of porosity. The relative density of porous structures ranges from 96.78 to 98.79%. The influence of porosity on compressive mechanical properties is investigated, and a prediction model of the equivalent elastic modulus and yield strength of porous structures is constructed based on the generalized G‐A model. The elastic modulus of the radial stepwise gradient porous structures is predicted by combining the rule of mixing. The deviation between the predicted results and the experimental results ranges from 0.21 to 2.61%. At the same time, a prediction model of the compressive mechanical properties of the radial linear gradient porous structure is constructed, and it is found that the predicted value is somewhat different from the experimental value, which is related to the synergistic strengthening effect of the gradient porous structure.

Development and construction of a cataract risk prediction model based on biochemical indices: the National Health and Nutrition Examination Survey, 2005–2008

PurposeThe aim of this study is to develop and validate a novel multivariable prediction model capable of accurately estimating the probability of cataract development, utilizing parameters such as blood biochemical markers and age.DesignThis population-based cross-sectional study comprised 9,566 participants drawn from the National Health and Nutrition Examination Survey (NHANES) across the 2005–2008 cycles.MethodsDemographic information and laboratory test results from the patients were collected and analyzed using LASSO regression and multivariate logistic regression to accurately capture the influence of biochemical indicators on the outcomes. The SHAP (Shapley Additive Explanations) scale was employed to assess the importance of each clinical feature, excluding age. A multivariate logistic regression model was then developed and visualized as a nomogram. To assess the model’s performance, its discrimination, calibration, and clinical utility were evaluated using receiver operating characteristic (ROC) curves, 10-fold cross-validation, Hosmer-Lemeshow calibration curves, and decision curve analysis (DCA), respectively.ResultsLogistic regression analysis identified age, erythrocyte folate (nmol/L), blood glucose (mmol/L), and blood urea nitrogen (mmol/L) as independent risk factors for cataract, and these variables were incorporated into a multivariate logistic regression-based nomogram for cataract risk prediction. The area under the receiver operating characteristic (ROC) curve (AUC) for cataract risk prediction was 0.917 (95% CI: 0.9067–0.9273) in the training cohort, and 0.9148 (95% CI: 0.8979–0.9316) in the validation cohort. The Hosmer-Lemeshow calibration curve demonstrated a good fit, indicating strong model calibration. Ten-fold cross-validation confirmed the logistic regression model’s robust predictive performance and stability during internal validation. Decision curve analysis (DCA) demonstrated that the nomogram prediction model provided greater clinical benefit for predicting cataract risk when the patient’s threshold probability ranged from 0.10 to 0.90.ConclusionThis study identified blood urea nitrogen (mmol/L), serum glucose (mmol/L), and erythrocyte folate (mmol/L) as significant risk factors for cataract. A risk prediction model was developed, demonstrating strong predictive accuracy and clinical utility, offering clinicians a reliable tool for early and effective diagnosis. Cataract development may be delayed by reducing levels of blood urea nitrogen, serum glucose, and erythrocyte folate through lifestyle improvements and dietary modifications.

Establishing a Risk Model for Diabetic Nephropathy and Addressing the Therapeutic Effect of Combined Epalrestat- Dapagliflozin Regimen.

To explore the construction of a diagnostic prediction model of diabetic nephropathy (DN) in type 2 diabetic patients for prognostic risk prediction and observe the therapeutic effect of Epalrestat combined with Dapagliflozin on DN. The study consisted of two phases, phase I: A retrospective analysis was conducted on the case information and clinical treatment related data of a total of 460 patients who underwent kidney biopsy from June 2018 to June 2021. They were randomly divided into validation queue and training queue. The predictive factors of the diagnostic prediction model were obtained through multivariate logistic regression. An interventional study of 94 patients with DN admitted between January 2022 and August 2023 was conducted, and they were randomized into a control group (n = 47) receiving Dapagliflozin and a research group (n = 47) receiving Epalrestat combined with Dapagliflozin. The glucose metabolism, renal function, and treatment safety of the two groups before and after treatment were compared. In addition, the adverse reactions during the treatment of the two groups were counted. In the phase I of the study, the DN risk model established showed a good performance in the diagnosis and risk assessment of patients with DN and could provide certain reference opinions for future clinical practice. In the phase II of the study, the research group showed better glucose metabolism and renal function than the control group after treatment (P < .05), but no statistical difference was identified between groups in the incidence of adverse reactions (P > .05). Conclusion. Epalrestat combined with Dapagliflozin is significantly effective in the treatment of DN, which can effectively improve glucose metabolism and renal function in DN patients.

A Method for Predicting the Impact of Labor Education at Institutions of Higher Education Based on a Two-Stage Clustering Algorithm

In order to develop more accurate teaching plans and methods based on students’ characteristics and needs, and improve the effectiveness of labor education, a two-stage clustering algorithm based method for predicting the effectiveness of labor education in universities is studied. A two-stage clustering algorithm-based method for predicting the effectiveness of labor education in universities has been proposed. Through in-depth analysis of labor education data in universities, principal component analysis (PCA) was used to select 16 most representative variables from the original features. Subsequently, self-organizing map (SOM) neural network was used for preliminary clustering, and the optimal number of clusters was determined to be 8. Furthermore, the K-means clustering algorithm performs fine clustering based on the initial centers provided by SOM, significantly improving the stability and accuracy of clustering. At the same time, processing the outliers in the clustering results has reduced their impact on the clustering effect. Finally, the processed data was input into an improved XGBoost prediction model, which achieved a true positive rate (TPR) of over 85% on the ROC curve when predicting the effectiveness of labor education in universities. While maintaining a low false positive rate (FPR), the model outperformed other comparison methods significantly. This achievement not only validates the effectiveness of the proposed method in feature selection, clustering optimization, and prediction model construction, but also confirms its ability to accurately predict the labor education effectiveness of students in different grades, providing strong support for higher education institutions to optimize teaching plans and improve educational outcomes.

Construction of vulnerable plaque prediction model based on multimodal vascular ultrasound parameters and clinical risk factors

The rupture of vulnerable plaque (VP) are significant pathogenic factors leading to cardiovascular and cerebrovascular diseases. This study aims to construct a vulnerable plaque prediction model (VPPM) by combining multimodal vascular ultrasound parameters and clinical risk factors, and to validate it. A total of 196 atherosclerotic patients who underwent carotid endarterectomy (CEA) from January 2017 to December 2023 were collected and divided into a modeling group (n = 137) and a validation group (n = 59). Clinical information including: hypertension, diabetes, smoking history, and body mass index (BMI) was included in the analysis. All patients underwent carotid ultrasound and contrast-enhanced ultrasound (CEUS) examination after admission, with main ultrasound parameters including thickness, echogenicity types, stenosis degree, and CEUS neovascularization grading of plaques. Independent risk factors for VP in CEA patients were screened through binary Logistic regression analysis, and a prediction model was established along with a nomogram. The calibration curve, receiver-operating characteristic curve (ROC), and decision curve analysis (DCA) were employed to assess the calibration, diagnostic efficacy, and clinical utility of the VPPM model. There were no significant statistical differences in multimodal vascular ultrasound parameters and clinical risk factors between the modeling and validation groups (P > 0.05). Binary Logistic regression analysis identified plaque thickness, echo type, CEUS neovascularization grading, BMI, and smoking history as 5 variables entering the prediction model. The VPPM model showed good diagnostic efficacy, with an area under the ROC curve of 0.959 (95% CI 0.915–0.999). Using the nomogram with a VPPM risk assessment score of 135.42 as the diagnostic cutoff value in the modeling group, the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and Youden index were 88.1%, 94.1%, 14.98, 0.126, and 82.2%, respectively. In the DCA curve, the VPPM model curve was significantly better than two extreme lines, indicating good clinical utility. The VPPM model constructed by integrating multimodal ultrasound parameters and clinical key risk factors has high diagnostic efficacy and is expected to be an auxiliary tool for clinical diagnosis of vulnerable plaques.

Risk factor analysis and prediction model construction for severe adenovirus pneumonia in children

BackgroundSevere adenovirus pneumonia in children has a high mortality rate, but research on risk prediction models is lacking. Such models are essential as they allow individualized predictions and assess whether children will likely progress to severe disease.MethodsA retrospective analysis was performed on children with adenovirus pneumonia who were hospitalized at the Children’s Hospital of Nanjing Medical University from January 2017 to March 2024. The patients were grouped according to clinical factors, and the groups were compared using Ridge regression and multiple logistic regression to identify risk factors associated with severe adenovirus pneumonia. A prediction model was constructed, and its value in clinical application was evaluated.Results699 patients were included in the study, with 284 in the severe group and 415 in the general group. Through the screening of 44 variables, the final risk factors for severe adenovirus pneumonia in children as the levels of neutrophils (OR = 1.086, 95% CI: 1.054‒1.119, P < 0.001), D-dimer (OR = 1.005, 95% CI: 1.003‒1.007, P < 0.001), fibrinogen degradation products (OR = 1.341, 95% CI: 1.034‒1.738, P = 0.027), B cells (OR = 1.076, 95%CI: 1.046‒1.107, P < 0.001), and lactate dehydrogenase (OR = 1.008, 95% CI: 1.005‒1.011, P < 0.001). The value of the area under the receiver operating characteristic curve was 0.974, the 95% CI was 0.963–0.985, and the P-value of the Hosmer-Lemeshow test was 0.547 (P > 0.05), indicating that the model had strong predictive power.ConclusionIn this study, the clinical variables of children with adenovirus pneumonia were retrospectively analyzed to identify risk factors for severe disease. A prediction model for severe disease was constructed and evaluated, showing good application value.

Construction of Solid-Liquid Two-Phase Flow and Wear Rate Prediction Model in Multiphase Pump Based on Mixture Model-Discrete Phase Model Combination Method

Blade wear is the critical problem in the operation of multiphase pump. This paper presents a numerical study of the multiphase flow of multiphase pump. The trajectory of particles in the pump is calculated by the discrete phase model. Then, the simulation results are compared with the model test results of the pump to verify the correctness of the simulation method. The results show that the particles in the impeller domain are mainly near the hub, and the particles in the diffuser domain form a agglomerated area in the middle of the flow channel. The average wear rate of the impeller is more affected by the particle size than that of the diffuser. The maximum wear rate of blade surface increases first and then decreases with the increase of particle size. According to the wear data under different particle sizes, the regression model between particle size and wear rate is fitted to predict the wear of mixed transport pump in actual operation. The research results have important reference value for the prediction of the wear performance of the multiphase pump.

Enhancing the convenience of frailty index assessment for elderly Chinese people with machine learning methods.

Frailty is a state that is closely associated with adverse health outcomes in the aging process. The frailty index (FI), which measures frailty in terms of cumulative deficits, has been widely used for frailty assessment in elderly people, and its advantage of self-reported information collection makes it applicable to a broader group of elderly people. Our study aims to simplify the Frailty Index Assessment Scale, while maintaining its reliability and accuracy, to easily and quickly assess frailty in elderly people. In this study, participants (age ≥ 65 years) from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), which had 13,339, 372 and 1214 participants in 2008, 2011, and 2014, respectively, were used. The 2008 dataset was split into 80% for training and 20% for internal validation, and the data from 2011 to 2014 as external validation. In order to obtain effective predictors, we used Lasso regression, Boruta algorithm and random forest classifier score for feature selection. We used six models for predictive model construction and evaluated the models in the validation dataset. Model performance was measured by area under the curve (AUC), accuracy and F1 score. Logistic regression was found to be the best performing and most interpretable algorithm with AUC, accuracy and F1 of 0.974, 0.932 and 0.880 for the validation dataset, respectively. The AUCs for the external independent validation dataset were 0.963 and 0.977, respectively. Subgroup analysis showed that the model had good predictive power in both males and females. The predictive power was stronger among the elderly people over 80 years old, with AUC, accuracy and F1 of 0.973,0.914, and 0.893, respectively. The model also obtained good predictive power in the case of FI measured by different indicators. The model showed good robustness in the follow-up assessment of frailty status in elderly people, with the AUC remaining above 0.95 and accuracy above 0.9 over the long-term follow-up. Using machine learning techniques, we have successfully developed a simple frailty assessment prediction model based on 10 key features to shorten the frailty assessment scale with near full-scale accuracy. A user-friendly website was created to facilitate the application of this prediction model ( https://healthy-aging.shinyapps.io/Frailty_Assessment/ ).

Construction and validation of a nomogram prediction model for the catheter-related thrombosis risk of central venous access devices in patients with cancer: a prospective machine learning study.

Central venous access devices (CVADs) are integral to cancer treatment. However, catheter-related thrombosis (CRT) poses a considerable risk to patient safety. It interrupts treatment; delays therapy; prolongs hospitalisation; and increases the physical, psychological and financial burden of patients. Our study aims to construct and validate a predictive model for CRT risk in patients with cancer. It offers the possibility to identify independent risk factors for CRT and prevent CRT in patients with cancer. We prospectively followed patients with cancer and CVAD at Xiangya Hospital of Central South University from January 2021 to December 2022 until catheter removal. Patients with CRT who met the criteria were taken as the case group. Two patients with cancer but without CRT diagnosed in the same month that a patient with cancer and CRT was diagnosed were selected by using a random number table to form a control group. Data from patients with CVAD placement in Qinghai University Affiliated Hospital and Hainan Provincial People's Hospital (January 2023 to June 2023) were used for the external validation of the optimal model. The incidence rate of CRT in patients with cancer was 5.02% (539/10 736). Amongst different malignant tumour types, head and neck (9.66%), haematological (6.97%) and respiratory (6.58%) tumours had the highest risks. Amongst catheter types, haemodialysis (13.91%), central venous (8.39%) and peripherally inserted central (4.68%) catheters were associated with the highest risks. A total of 500 patients with CRT and 1000 without CRT participated in model construction and were randomly assigned to the training (n = 1050) or testing (n = 450) groups. We identified 11 independent risk factors, including age, catheterisation method, catheter valve, catheter material, infection, insertion history, D-dimer concentration, operation history, anaemia, diabetes and targeted drugs. The logistic regression model had the best discriminative ability amongst the three models. It had an area under the curve (AUC) of 0.868 (0.846-0.890) for the training group. The external validation AUC was 0.708 (0.618-0.797). The calibration curve of the nomogram model was consistent with the ideal curve. Moreover, the Hosmer-Lemeshow test showed a good fit (P > 0.05) and high net benefit value for the clinical decision curve. The nomogram model constructed in this study can predict the risk of CRT in patients with cancer. It can help in the early identification and screening of patients at high risk of cancer CRT.

Construction of a Risk Prediction Model for Ureteral Stricture after Ureteroscopic Holmium Laser Lithotripsy.

Introduction: To analyze the influencing factors of ureteral stenosis after ureteroscopic holmium laser lithotripsy. Methods: The clinical data of 427 patients treated with ureteroscopic holmium laser lithotripsy were selected, and the patients were divided into two groups based on the presence or absence of ureteral stenosis after the operation. Univariate and multivariate logistic regression were used to analyze the independent risk factors for postoperative ureteral stenosis, and R software and regression coefficients were used to construct a predictive model. Results: After a 1-year follow-up of 427 patients, 28 patients (6.56%) developed ureteral stenosis; univariate analysis showed that the occurrence of ureteral stenosis after subureteral holmium laser lithotripsy was related to stone diameter, stone incarceration, degree of hydronephrosis, holmium laser injury of mucosa, and operation time (p < 0.05); further logistic regression analysis showed that a large stone diameter, stone incarceration, and moderate to severe hydronephrosis were independent risk factors for ureteral stenosis after ureteroscopic holmium laser lithotripsy (p < 0.05); According to H-L deviation degree and area under receiver operating characteristic curve test, the results show that the model has high accuracy (χ2 = 2.475, p = 0.613) and differentiation (0.875 [95% confidence interval or CI: 0.817-0.919]), and the external verification of the nomogram prediction model was carried out by the verification group. The results showed that the prediction probability of the calibration curve was close to the actual probability and had a good consistency (area under the curve: 0.873 [95 CI: 0.822-0.914]). Conclusion: The established nomogram model exhibits high accuracy and discriminative ability. It can effectively identify high-risk groups, enabling timely prevention of ureteral stenosis and minimizing the risk of postoperative ureteral stenosis.

B-141 Analysis of risk factors and construction of prediction model for pneumonia in patients with pneumoconiosis based on the clinical laboratory indexes

Abstract Background Pneumoconiosis has been the leading industrial disease in China. Pneumonia is one of the common comorbidities for patients with pneumoconiosis, which may lead to the worse condition and poor prognosis. Timely and accurate diagnosis is necessary to retard the pneumonia progression in pneumoconiosis patients. Due to the similarity of clinical features between pneumoconiosis and pneumonia, clinical laboratory tests are needed for assessing the hazard of pneumonia occurrence in patients with pneumoconiosis. Therefore, we aim to explore the risk factors of pneumonia from laboratory test indexes for patients with pneumoconiosis, construct prediction model, and evaluate its feasibility. Methods A total of 243 patients with pneumoconiosis from the General Hospital of Xuzhou Mining Group from September 2021 to August 2023 were selected as the modeling set, which was further separated as the non-pneumonia group and the pneumonia group. Nineteen laboratory test indexes related to inflammation, tumor markers, renal function, and blood routine examination were compared between the two groups. The univariate and multivariate Logistic analysis was used to screen out risk factors and construct the prediction model. Two hundred and twenty-eight patients with pneumoconiosis from General Hospital of Xuzhou Mining Group (Yi'an Branch) were enrolled as the validation set. The predictive efficiency of the model on pneumonia for patients with pneumoconiosis was evaluated through Hosmer-Lemeshow(H-L)test and receiver operating characteristic (ROC) curve. Results C-reactive protein (CRP), interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-10 (IL-10), β2 microglobulin, creatinine, urea, neutrophil percentage, lymphocyte percentage, and lymphocyte count in pneumonia group were significant different from those in non-pneumonia group (&amp;lt;0.05). Logistic regression analysis suggested that CRP (OR=3.883, &amp;lt;0.001), IL-6 (OR=1.217, =0.003), IL-8 (OR=1.215, =0.002) and β2-microglobulin (OR=2.838, =0.002) were independent risk factors for pneumonia in patients with pneumoconiosis. The prediction model was constructed as Logit (P) =1.357×CRP+0.196×IL-6+0.195×IL-8+1.043×β2-microglobulin-9.772. The H-L test demonstrated that there was no significantly difference between predicted values and the true values. The areas under ROC curves of the model in the modeling set and the validation set were 0.909 and 0.848. The sensitivity was 86.8% and 75.6%, and accuracy 82.1% and 79.7%, respectively. Conclusions CRP, IL-6, IL-8, and β2-microglobulin were independent risk factors of pneumonia for pneumoconiosis patients. The risk model constructed by laboratory test indexes showed a good predictive performance, which might provide laboratory evidence for the pneumonia diagnosis in patients with pneumoconiosis.

Construction and verification of pancreatic fistula risk prediction model after pancreaticoduodenectomy based on ensemble machine learning

Objective: To construct an ensemble machine learning model for predicting the occurrence of clinically relevant postoperative pancreatic fistula (CR-POPF) after pancreaticoduodenectomy and evaluate its application value. Methods: This is a research on predictive model. Clinical data of 421 patients undergoing pancreaticoduodenectomy in the Department of Pancreatic Surgery,Union Hospital, Tongji Medical College,Huazhong University of Science and Technology from June 2020 to May 2023 were retrospectively collected. There were 241 males (57.2%) and 180 females (42.8%) with an age of (59.7±11.0)years (range: 12 to 85 years).The research objects were divided into training set (315 cases) and test set (106 cases) by stratified random sampling in the ratio of 3∶1. Recursive feature elimination is used to screen features,nine machine learning algorithms are used to model,three groups of models with better fitting ability are selected,and the ensemble model was constructed by Stacking algorithm for model fusion. The model performance was evaluated by various indexes,and the interpretability of the optimal model was analyzed by Shapley Additive Explanations(SHAP) method. The patients in the test set were divided into different risk groups according to the prediction probability (P) of the alternative pancreatic fistula risk score system (a-FRS). The a-FRS score was validated and the predictive efficacy of the model was compared. Results: Among 421 patients,CR-POPF occurred in 84 cases (20.0%). In the test set,the Stacking ensemble model performs best,with the area under the curve (AUC) of the subject's work characteristic curve being 0.823,the accuracy being 0.83,the F1 score being 0.63,and the Brier score being 0.097. SHAP summary map showed that the top 9 factors affecting CR-POPF after pancreaticoduodenectomy were pancreatic duct diameter,CT value ratio,postoperative serum amylase,IL-6,body mass index,operative time,albumin difference before and after surgery,procalcitonin and IL-10. The effects of each feature on the occurrence of CR-POPF after pancreaticoduodenectomy showed a complex nonlinear relationship. The risk of CR-POPF increased when pancreatic duct diameter<3.5 mm,CT value ratio<0.95,postoperative serum amylase concentration>150 U/L,IL-6 level>280 ng/L,operative time>350 minutes,and albumin decreased by more than 10 g/L. The AUC of a-FRS in the test set was 0.668,and the prediction performance of a-FRS was lower than that of the Stacking ensemble machine learning model. Conclusion: The ensemble machine learning model constructed in this study can predict the occurrence of CR-POPF after pancreaticoduodenectomy,and has the potential to be a tool for personalized diagnosis and treatment after pancreaticoduodenectomy.

Analysis of Influencing Factors and Construction of Predictive Model for Persistent Cough After Lung Cancer Resection Under Thoracoscopy.

This study aims to explore the influencing factors of cough after pulmonary resection (CAP) after thoracoscopic lung resection in lung cancer patients and to develop a predictive model. A total of 374 lung cancer patients who underwent lung resection in our hospital from March 2020 to October 2023 were randomly divided into a modeling group (n=262) and a validation group (n=112). Based on the occurrence of CAP in the modeling group, the patients were divided into a CAP group (n=85) and a non-CAP group (n=177). Multivariate Logistic regression analysis was used to identify the influencing factors of CAP in lung cancer patients. A nomogram model for predicting the risk of CAP was constructed using R4.3.1. The consistency of the model's predictions was evaluated, and a clinical decision curve (DCA) was drawn to assess the clinical utility of the nomogram. The predictive performance of the model was evaluated using ROC curves and the Hosmer-Lemeshow test. Multivariate Logistic regression analysis showed that smoking history (OR=6.285, 95% CI: 3.031-13.036), preoperative respiratory function training (OR=20.293, 95% CI: 7.518-54.779), surgical scope (OR=20.667, 95% CI: 7.734-55.228), and peribronchial lymph node dissection (OR=5.883, 95% CI: 2.829-12.235) were significant influencing factors of CAP in lung cancer patients (P<0.05). ROC curves indicated good discriminatory power of the model, and the Hosmer-Lemeshow test showed a high degree of agreement between predicted and actual probabilities. The DCA curve revealed that the nomogram model had high clinical value when the high-risk threshold was between 0.08 and 0.98. The nomogram model based on smoking history, preoperative respiratory function training, surgical scope, and peribronchial lymph node dissection has high predictive performance for CAP in lung cancer patients. It is useful for clinical prediction, guiding preoperative preparation, and postoperative care.

Analysis of risk factors and prediction model construction for varicella encephalitis in children: A retrospective cohort study.

This study aimed to analyze the risk factors for varicella encephalitis in children and establish a predictive model. This retrospective cohort study included a varicella encephalitis group (n = 75) and a varicella-non-encephalitis group (n = 135). Logistic regression analysis was employed to find risk factors for varicella encephalitis and create a predictive model. Older age, vomiting, poor mental status, and prolonged rash duration were independent risk factors for varicella encephalitis (p < .05). The predictive model for varicella encephalitis combined above four factors. The ROC curve of the predictive model showed an area under the curve of 0.955 (95% CI 0.925-0.986) for varicella encephalitis in children with a sensitivity of 94.7%, and a specificity of 86.0%. Children with varicella who are older, experience vomiting, exhibit poor mental status, or have a prolonged rash duration should be closely monitored clinically. The predictive model combining these four factors demonstrates good predictive efficiency.

Short-Term Prognostic Risk Factors and Nomogram Predictive Model Construction for Acute Ischaemic Stroke after Endovascular Treatment.

To investigate the factors influencing the severity of acute ischaemic stroke (AIS) and short-term prognosis after endovascular treatment. Observational study. Place and Duration of the Study: Department of Cerebrovascular Diseases, The First People's Hospital of Qujing City, Yunnan Province, China, from June 2020 to December 2022. An analysis was conducted on 160 AIS patients undergoing endovascular treatment, classified into atherosclerosis subtype (AS) and cardioembolic subtype (CE) based on the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification, each comprising 80 cases. The association among baseline and endovascular treatment parameters, clinical characteristics, postoperative complications, and short-term outcomes (risks of mortality and poor prognosis) was tested. Univariate analysis revealed significant positive correlations between increased fasting glucose, diastolic pressure, and the number of thrombectomy procedures with the frequency of cerebral haemorrhage in CE-type stroke. The time from femoral artery puncture to vessel opening was positively associated with the degree of brain oedema. In the AS subtype, univariate regression analysis demonstrated a significant association between heart rate, fasting glucose, cholesterol, time from femoral artery puncture to vessel opening, and degree of brain oedema with short-term prognosis. Fasting glucose and the number of thrombectomy procedures were significantly linked to short-term prognosis in CE-type AIS. A predictive model using line charts was developed for factors associated with postoperative complications and short-term prognosis, achieving predictive accuracies of 95.5% for the risk of death and 92.7% for poor prognosis (mRS >2), notably surpassing traditional prediction methods. Clinical characteristics and endovascular treatment-related factors are important for the short-term prognosis of AIS patients. Development of predictive models can efficiently identify high-risk patients at an early stage. Acute ischaemic stroke, Atherosclerosis, Cardioembolic, Severity, Prognosis.