Computer Simulation ; Models, Statistical

Objective To analyze the risk factors and build a clinical prediction model for hemodynamic depression (HD) after carotid artery stenting (CAS). Methods A total of 116 patients who received CAS in the Department of Vascular Surgery,Drum Tower Clinical College of Nanjing Medical University and the Department of Vascular Surgery,the Affiliated Suqian First People's Hospital of Nanjing Medical University from January 1,2016 to January 1,2022 were included in this study.The patients were assigned into a HD group and a non-HD group.The clinical baseline data and vascular disease characteristics of each group were collected,and multivariate Logistic regression was employed to identify the independent predictors of HD after CAS and build a clinical prediction model.The receiver operating characteristic (ROC) curve was drawn,and the area under the ROC curve (AUC) was calculated to evaluate the predictive performance of the model. Results The HD group had lower proportions of diabetes (P=0.014) and smoking (P=0.037) and higher proportions of hypertension (P=0.031),bilateral CAS (P=0.018),calcified plaque (P=0.001),eccentric plaque (P=0.003),and the distance<1 cm from the minimum lumen level to the carotid bifurcation (P=0.009) than the non-HD group.The age,sex,coronary heart disease,symptomatic carotid artery stenosis,degree of stenosis,and length of lesions had no statistically significant differences between the HD group and the non-HD group (all P>0.05).Based on the above predictive factors,a clinical prediction model was established,which showed the AUC of 0.807 and the 95% CI of 0.730-0.885 (P<0.001).The model demonstrated the sensitivity of 62.7% and the specificity of 87.7% when the best cut-off value of the model score reached 12.5 points. Conclusions Diabetes,smoking,calcified plaque,eccentric plaque,and the distance<1 cm from the minimum lumen level to the carotid bifurcation are independent predictors of HD after CAS.The clinical prediction model built based on the above factors has good performance in predicting the occurrence of HD after CAS.
Humans ; Carotid Stenosis ; Depression ; Models, Statistical ; Prognosis ; Stents ; Hemodynamics ; Plaque, Amyloid

OBJECTIVES: To construct a clinical prediction model for the impact of acupuncture on pregnancy outcomes in poor ovarian response (POR) patients, providing insights and methods for predicting pregnancy outcomes in POR patients undergoing acupuncture treatment. METHODS: Clinical data of 268 POR patients (2 cases were eliminated) primarily treated with "thirteen needle acupuncture for Tiaojing Cuyun (regulating menstruation and promoting pregnancy)" was collected from the international patient registry platform of acupuncture moxibustion (IPRPAM) from September 19, 2017 to April 30, 2023, involving 24 clinical centers including Acupuncture-Moxibustion Hospital of China Academy of Chinese Medical Sciences. LASSO and univariate Cox regression were used to screen factors influencing pregnancy outcomes, and a multivariate Cox regression model was established based on the screening results. The best model was selected using the Akaike information criterion (AIC), and a nomogram for clinical pregnancy prediction was constructed. The prediction model was evaluated using receiver operating characteristic (ROC) curves and calibration curves, and internal validation was performed using the Bootstrap method. RESULTS: (1) Age, level of anti-Müllerian hormone (AMH), and total treatment numbers of acupuncture were independent predictors of pregnancy outcomes in POR patients receiving acupuncture (P<0.05). (2) The AIC value of the best subset-Cox multivariate model (560.6) was the smallest, indicating it as the optimal model. (3) The areas under curve (AUCs) of the clinical prediction model after 6, 12, 24, and 36 months treatment were 0.627, 0.719, 0.770, and 0.766, respectively, and in the validation group, they were 0.620, 0.704, 0.759, and 0.765, indicating good discrimination and repeatability of the prediction model. (4) The calibration curve showed that the prediction curve of the clinical prediction model was close to the ideal model's prediction curve, indicating good calibration of the prediction model. CONCLUSIONS The clinical prediction model for the impact of acupuncture on pregnancy outcomes in POR patients based on the IPRPAM platform has good clinical application value and provides insights into predicting pregnancy outcomes in POR patients undergoing acupuncture treatment.
Pregnancy ; Female ; Humans ; Pregnancy Outcome ; Models, Statistical ; Prognosis ; Acupuncture Therapy ; Registries

Reconstructing three-dimensional (3D) models from two-dimensional (2D) images is necessary for preoperative planning and the customization of joint prostheses. However, the traditional statistical modeling reconstruction shows a low accuracy due to limited 3D characteristics and information loss. In this study, we proposed a new method to reconstruct the 3D models of femoral images by combining a statistical shape model with Laplacian surface deformation, which greatly improved the accuracy of the reconstruction. In this method, a Laplace operator was introduced to represent the 3D model derived from the statistical shape model. By coordinate transformations in the Laplacian system, novel skeletal features were established and the model was accurately aligned with its 2D image. Finally, 50 femoral models were utilized to verify the effectiveness of this method. The results indicated that the precision of the method was improved by 16.8%-25.9% compared with the traditional statistical shape model reconstruction. Therefore, the method we proposed allows a more accurate 3D bone reconstruction, which facilitates the development of personalized prosthesis design, precise positioning, and quick biomechanical analysis.
Imaging, Three-Dimensional/methods* ; Tomography, X-Ray Computed/methods* ; Femur/surgery* ; Models, Statistical ; Lower Extremity

Objective: To identify the clinically relevant factors of steroid-resistant nephrotic syndrome (SSNS) in children and establish a predictive model followed by verifying its feasibility. Methods: A retrospective analysis was performed in a total of 111 children with nephrotic syndrome admitted to Children's Hospital of ShanXi from January 2016 to December 2021. The clinical data of general conditions, manifestations, laboratory tests, treatment, and prognosis were collected. According to the steroid response, patients were divided into SSNS and steroid resistant nephrotic syndrome (SRNS) group. Single factor Logistic regression analysis was used for comparison between the 2 groups, and variables with statistically significant differences were included in multivariate Logistic regression analysis. The multivariate Logistic regression analysis was used to identify the related variables of children with SRNS. The area under the receiver operating characteristic curve (ROC), the calibration curve and the clinical decision curve were used to evaluate its effectiveness of the variables. Results: Totally 111 children with nephrotic syndrome was composed of 66 boys and 45 girls, aged 3.2 (2.0, 6.6) years. There were 65 patients in the SSNS group and 46 in the SRNS group.Univariate Logistic regression analysis showed that the 6 variables, including erythrocyte sedimentation rate, 25-hydroxyvitamin D, suppressor T cells, D-dimer, fibrin degradation products, β2-microglobulin, had statistically significant differences between SSNS and SRNS groups (85 (52, 104) vs. 105 (85, 120) mm/1 h, 18 (12, 39) vs. 16 (12, 25) nmol/L, 0.23 (0.19, 0.27) vs. 0.25 (0.20, 0.31), 0.7 (0.6, 1.1) vs. 1.1 (0.9, 1.7) g/L, 3.1 (2.3, 4.1) vs. 3.3 (2.7, 5.8) g/L, 2.3 (1.9,2.8) vs. 3.0 (2.5, 3.7) g/L, χ²=3.73, -2.42, 2.24, 3.38, 2.24,3.93,all P<0.05), were included in the multivariate Logistic regression analysis. Finally, we found that 4 variables including erythrocyte sedimentation rate, suppressor T cells, D-dimer and β2-microglobulin (OR=1.02, 1.12, 25.61, 3.38, 95%CI 1.00-1.04, 1.03-1.22, 1.92-341.04, 1.65-6.94, all P<0.05) had significant correlation with SRNS. The optimal prediction model was selected. The ROC curve cut-off=0.38, with the sensitivity of 0.83, the specificity of 0.77 and area under curve of 0.87. The calibration curve showed that the predicted probability of SRNS group occurrence was in good agreement with the actual occurrence probability, χ²=9.12, P=0.426. The clinical decision curve showed good clinical applicability. The net benefit is up to 0.2. Make the nomogram. Conclusions: The prediction model based on the 4 identified risk factors including erythrocyte sedimentation rate, suppressor T cells, D-dimer and β2-microglobulin was suitable for the early diagnosis and prediction of SRNS in children. The prediction effect was promising in clinical application.
Male ; Female ; Humans ; Child ; Nephrotic Syndrome/diagnosis* ; Retrospective Studies ; Models, Statistical ; Prognosis ; Steroids/therapeutic use*

UNLABELLED: AbstractObjective: To analysis the clinical data of patients after single-center hematopoietic stem cell transplantation (HSCT) and construct a predictive model for metabolic syndrome (MS) diagnosis. METHODS: Ninety-three hematology patients who underwent HSCT at the First Hospital of Lanzhou University from July 2015 to September 2022 were selected to collect basic data, transplantation status and postoperative data, the clinical characteristics of patients with and without MS after transplantation were compared and analyzed. Logistic regression model was used to analyze the influence fators of MS after transplantation, and a predictive model of HSCT-MS diagnosis was constructed under the influence of independent influence factors. The model was evaluated using the ceceiver operating characteristic curve (ROC curve). RESULTS: Metabolic syndrome occurred in 36 of 93 HSCT patients and did not occur in 57. Compared with non-HSCT-MS group, HSCT-MS had significantly higher fasting blood glucose (FBG) levels before transplantation, shorter course before transplantation, and higher bilirubin levels after transplantation (P<0.05). The statistically significant clinical indicators were subjected to multi-factor logistic regression analysis, and the results showed that pre-transplant high FBG, pre-transplant short disease course and post-transplant high bilirubin were independent influence factors for HSCT-MS. The standard error of predicting the occurrence of HSCT-MS based on the clinical model was 0.048, the area under the curve AUC=0.776, 95% CI :0.683-0.869, the optimal threshold was 0.58 based on the Jorden index at maximum, the sensitivity was 0.694, and the specificity was 0.772, which has certain accuracy. CONCLUSION A clinical prediction model for HSCT-MS based on logistic regression analysis is constructed through the analysis of clinical data, which has certain clinical value.
Humans ; Metabolic Syndrome ; Prognosis ; Models, Statistical ; Hematopoietic Stem Cell Transplantation ; ROC Curve ; Retrospective Studies

OBJECTIVE: To establish a predictive model for severe swallowing disorder after acute ischemic stroke based on nomogram model, and evaluate its effectiveness. METHODS: A prospective study was conducted. The patients with acute ischemic stroke admitted to Mianyang Central Hospital from October 2018 to October 2021 were enrolled. Patients were divided into severe swallowing disorder group and non-severe swallowing disorder group according to whether severe swallowing disorder occurred within 72 hours after admission. The differences in general information, personal history, past medical history, and clinical characteristics of patients between the two groups were compared. The risk factors of severe swallowing disorder were analyzed by multivariate Logistic regression analysis, and the relevant nomogram model was established. The bootstrap method was used to perform self-sampling internal validation on the model, and consistency index, calibration curve, receiver operator characteristic curve (ROC curve), and decision curve were used to evaluate the predictive performance of the model. RESULTS: A total of 264 patients with acute ischemic stroke were enrolled, and the incidence of severe swallowing disorder within 72 hours after admission was 19.3% (51/264). Compared with the non-severe swallowing disorder group, the severe swallowing disorder group had a higher proportion of patients aged of ≥ 60 years old, with severe neurological deficits [National Institutes of Health stroke scale (NIHSS) score ≥ 7], severe functional impairments [Barthel index, an activity of daily living functional status assessment index, < 40], brainstem infarction and lesions ≥ 40 mm (78.43% vs. 56.81%, 52.94% vs. 28.64%, 39.22% vs. 12.21%, 31.37% vs. 13.62%, 54.90% vs. 24.41%), and the differences were statistically significant (all P < 0.01). Multivariate Logistic regression analysis showed that age ≥ 60 years old [odds ratio (OR) = 3.542, 95% confidence interval (95%CI) was 1.527-8.215], NIHSS score ≥ 7 (OR = 2.741, 95%CI was 1.337-5.619), Barthel index < 40 (OR = 4.517, 95%CI was 2.013-10.136), brain stem infarction (OR = 2.498, 95%CI was 1.078-5.790) and lesion ≥ 40 mm (OR = 2.283, 95%CI was 1.485-3.508) were independent risk factors for severe swallowing disorder after acute ischemic stroke (all P < 0.05). The results of model validation showed that the consistency index was 0.805, and the trend of the calibration curve was basically consistent with the ideal curve, indicating that the model had good prediction accuracy. ROC curve analysis showed that the area under the ROC curve (AUC) predicted by nomogram model for severe swallowing disorder after acute ischemic stroke was 0.817 (95%CI was 0.788-0.852), indicating that the model had good discrimination. The decision curve showed that within the range of 5% to 90%, the nomogram model had a higher net benefit value for predicting the risk of severe swallowing disorder after acute ischemic stroke, indicating that the model had good clinical predictive performance. CONCLUSIONS The independent risk factors of severe swallowing disorder after acute ischemic stroke include age ≥ 60 years old, NIHSS score ≥ 7, Barthel index < 40, brainstem infarction and lesion size ≥ 40 mm. The nomogram model established based on these factors can effectively predict the occurrence of severe swallowing disorder after acute ischemic stroke.
United States ; Humans ; Aged ; Middle Aged ; Ischemic Stroke ; Deglutition Disorders ; Models, Statistical ; Nomograms ; Prognosis ; Prospective Studies ; Brain Stem Infarctions

Due to the latent characteristics of HIV infection, exceptionality of HIV high-risk population, social discrimination and insufficient awareness of AIDS prevention, timely testing and diagnosis of HIV infection is still a challenge worldwide. Until recently, it is difficult to exactly understand the overall HIV epidemic only using routine surveillance data. Therefore, epidemiological and statistical modeling is widely used to address this issue. Almost at the same time when AIDS was firstly discovered firstly, scientists also began to study the methods for the estimation and prediction of HIV infection epidemic. This article summarizes the development of global and domestic HIV epidemic estimation for the further understanding of its current performance and methods applied to provide reference for the future work.
Acquired Immunodeficiency Syndrome/epidemiology* ; Epidemics ; HIV Infections/epidemiology* ; Humans ; Models, Statistical

Reduced rank regression is an extended multivariate linear regression model with the function of dimension reduction. It has been more and more widely used in nutritional epidemiology research to understand people's dietary patterns in recent years. However, there has been no existing Stata package or command to implement reduced rank regression independently. Therefore, we developed a new user-written package named "rrr" for its implementation in Stata. This paper summarizes the methodology of reduced rank regression, the development and functions of the Stata rrr package and its application in the China Kadoorie Biobank dataset, with the aim of facilitating the future wide use of this statistical method in epidemiology and public health research.
China ; Humans ; Models, Statistical ; Public Health ; Regression Analysis

Objective: To introduce and compare four analysis methods of multiple parallel mediation model, including pure regression method, method based on inverse probability weighting, extended natural effect model method and weight-based imputation strategies. Methods: For the multiple parallel mediation model, the simulation experiments of three scenarios were carried out to compare the performance of different methods in estimating direct and indirect effects in different situations. Dataset from UK Biobank was then analyzed by using the four methods. Results: The estimation biases of the regression method and the inverse probability weighting method were relatively small, followed by the extended natural effect model method, and the estimation results of the weight-based imputation strategies were quite different from the other three methods. Conclusions: Different multiple parallel mediation analysis methods have different application situations and their own advantages and disadvantages. The regression method is more suitable for continuous mediator, and the inverse probability weighting method is more suitable for binary mediator. The extended natural effect model method has better performances when the residuals of two parallel mediators are positively correlated and the correlation degree is small. The weight-based imputation strategies might not be appropriate for parallel mediation analysis. Therefore, appropriate methods should be selected according to the specific situation in practice.
Bias ; Computer Simulation ; Humans ; Mediation Analysis ; Models, Statistical ; Probability ; Regression Analysis ; Research Design