Nomogram Model Research Articles

Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis.

Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration. We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO) were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis. Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 (RRM2), DLG-associated protein 5 (DLGAP5), and kinesin family member 11 (KIF11), were identified through WGCNA, LASSO, SVM-RFE, and RF algorithms. These hub genes correlated strongly with T cells, natural killer cells, and macrophage cells as indicated by immune cell infiltration analysis. RRM2, DLGAP5, and KIF11 could serve as potential diagnostic indicators and treatment targets for RA. The infiltration of immune cells offers additional insights into the underlying mechanisms involved in the progression of RA.

A Nomogram to Predict the Risk of Acute Ischemic Stroke in Patients with Maintenance Hemodialysis: A Retrospective Cohort Study

Introduction: Acute ischemic stroke (AIS) stands as a leading cause of death and disability globally. This study aimed to investigate the risk factors and relevance linked with AIS in patients undergoing maintenance hemodialysis (MHD) and to create and validate nomogram models. Methods: We examined the medical records of 314 patients with stage 5 chronic kidney disease (CKD 5) undergoing MHD, who sought neurology outpatient department consultation for suspected AIS symptoms between January 2018 and December 2023. These 314 patients were randomly divided into the training cohort (n = 222) and validation cohort (n = 92). The least absolute shrinkage selection operator (LASSO) regression model was employed for optimal feature selection in the AIS risk model. Subsequently, multivariable logistic regression analysis was used to construct a predictive model incorporating the features selected through LASSO. This predictive model’s performance was assessed using the C-index and the area under the receiver operating characteristic curve (AUC). Additionally, calibration and clinical utility were evaluated through calibration plots and decision curve analysis (DCA). The model’s internal validation was conducted using the validation cohort. Results: Predictors integrated into the prediction nomogram encompassed cardiovascular disease (CVD) (odds ratio [OR] 7.95, 95% confidence interval [CI] 2.400–29.979), smoking (OR 5.7, 95% CI: 1.661–21.955), dialysis time (OR: 5.91, 95% CI: 5.866–29.979), low-density lipoprotein (OR: 2.99, 95% CI: 0.751–13.007), and fibrin degradation products (OR: 5.47, 95% CI: 1.563–23.162). The model exhibited robust discrimination, with a C-index of 0.877 and 0.915 in the internal training and validation cohorts, respectively. The AUC for the training set was 0.857, and a similar AUC of 0.905 was achieved in the validation cohort. DCA demonstrated a positive net benefit within a threshold risk range of 2–96%. Conclusion: The proposed nomogram effectively identifies MHD patients at high risk of AIS at an early stage. This model holds the potential to aid clinicians in making preventive recommendations.

Hepatic Pecoma versus Hepatocellular Carcinoma In The Noncirrhotic Liver on Gd-EOB-DTPA-Enhanced MRI: A Diagnostic Challenge.

Hepatic perivascular epithelioid cell tumors (PEComa) often mimic hepatocellular carcinoma (HCC) in patients without cirrhosis. This study aimed to develop a nomogram using imaging characteristics on Gd-EOB-DTPA-enhanced MRI and to distinguish PEComa from HCC in a noncirrhotic liver. Forty patients with non-cirrhotic Gd-EOB-DTPA-enhanced magnetic resonance imaging(MRI) were included in our study. A multivariate logistic regression model was used to select significant variables to distinguish PEComa from HCC. A nomogram was developed based on the regression model. The performance of the nomogram was assessed with respect to the ROC curve and calibration curve. Decision curve analysis (DCA) was performed to evaluate the clinical usefulness of the nomogram. Two significant predictors were identified: the appearance of an early draining vein and the T1D value of tumors. The ROC curve showed that the area under the curve (AUC) of the model to predict the risk of PEComa was 0.91 (95% CI: 0.80~1) and showed that the model had high specificity (92.3%) and sensitivity (88.9%). The nomogram incorporating these two predictors showed favorable calibration, which was validated using 1000 resampling procedures, and the corrected C-index of this model was 0.90. Furthermore, DCA analysis showed that the model had clinical practicability. In conclusion, the nomogram model showed favorable predictive accuracy for distinguishing PEComa from HCC in non-cirrhotic patients and may aid in clinical decision-making.

Revealing the impact of autophagy-related genes in rheumatoid arthritis: Insights from bioinformatics

BackgroundRheumatoid arthritis is a systemic inflammatory autoimmune disease that severely impacts physical and mental health. Autophagy is a cellular process involving the degradation of cellular components in lysosomes. However, from a bioinformatics perspective, autophagy-related genes have not been comprehensively elucidated in rheumatoid arthritis. MethodsIn this study, we performed differential analysis of autophagy-related genes in rheumatoid arthritis patients using the GSE93272 dataset from the Gene Expression Omnibus database. Marker genes were screened by least absolute shrinkage and selection operator. Based on marker genes, we used unsupervised cluster analysis to elaborate different autophagy clusters, and further identified modules strongly associated with rheumatoid arthritis by weighted gene co-expression network analysis. In addition, we constructed four machine learning models, random forest model, support vector machine model, generalized linear model and extreme gradient boosting based on marker genes, and based on the optimal machine learning model, a nomogram model was constructed for distinguishing between normal individuals and rheumatoid arthritis patients. Finally, five external independent rheumatoid arthritis datasets were used for the validation of our results. ResultsThe results showed that autophagy-related genes had significant expression differences between normal individuals and osteoarthritis patients. Through least absolute shrinkage and selection operator screening, we identified 31 marker genes and found that they exhibited significant synergistic or antagonistic effects in rheumatoid arthritis, and immune cell infiltration analysis revealed significant changes in immune cell abundance. Subsequently, we elaborated different autophagy clusters (cluster 1 and cluster 2) using unsupervised cluster analysis. Next, further by weighted gene co-expression network analysis, we identified a brown module strongly associated with rheumatoid arthritis. In addition, we constructed a nomogram model for five marker genes (CDKN2A, TP53, ATG16L2, FKBP1A, and GABARAPL1) based on a generalized linear model (area under the curve = 1.000), and the predictive efficiency and accuracy of this nomogram model were demonstrated in the calibration curves, the decision curves and the five external independent datasets were validated. ConclusionThis study identified marker autophagy-related genes in rheumatoid arthritis and analyzed their impact on the disease, providing new perspectives for understanding the role of autophagy-related genes in rheumatoid arthritis and providing new directions for its individualized treatment.

MRI-based clinical-radiomics nomogram model for predicting microvascular invasion in hepatocellular carcinoma.

Preoperative microvascular invasion (MVI) of liver cancer is an effective method to reduce the recurrence rate of liver cancer. Hepatectomy with extended resection and additional adjuvant or targeted therapy can significantly improve the survival rate of MVI+ patients by eradicating micrometastasis. Preoperative prediction of MVI status is of great clinical significance for surgical decision-making and the selection of other adjuvant therapy strategies to improve the prognosis of patients. Established a radiomics machine learning model based on multimodal MRI and clinical data, and analyzed the preoperative prediction value of this model for microvascular invasion (MVI) of hepatocellular carcinoma (HCC). The preoperative liver MRI data and clinical information of 130 HCC patients who were pathologically confirmed to be pathologically confirmed were retrospectively studied. These patients were divided into MVI-positive group (MVI+) and MVI-negative group (MVI-) based on postoperative pathology. After a series of dimensionality reduction analysis, six radiomic features were finally selected. Then, linear support vector machine (linear SVM), support vector machine with rbf kernel function (rbf-SVM), logistic regression (LR), Random forest (RF) and XGBoost (XGB) algorithms were used to establish the MVI prediction model for preoperative HCC patients. Then, rbf-SVM with the best predictive performance was selected to construct the radiomics score (R-score). Finally, we combined R-score and clinical-pathology-image independent predictors to establish a combined nomogram model and corresponding individual models. The predictive performance of individual models and combined nomogram was evaluated and compared by receiver operating characteristic curve (ROC). Alpha-fetoprotein concentration, peritumor enhancement, maximum tumor diameter, smooth tumor margins, tumor growth pattern, presence of intratumor hemorrhage, and RVI were independent predictors of MVI. Compared with individual models, the final combined nomogram model (AUC: 0.968, 95% CI: 0.920-1.000) constructed by radiometry score (R-score) combined with clinicopathological parameters and apparent imaging features showed the optimal predictive performance. This multi-parameter combined nomogram model had a good performance in predicting MVI of HCC, and had certain auxiliary value for the formulation of surgical plan and evaluation of prognosis.

Retrospective analysis of predictive factors for AVF dysfunction in patients undergoing MHD.

To construct an early clinical prediction model for AVF dysfunction in patients undergoing Maintenance Hemodialysis (MHD) and perform internal and external verifications. We retrospectively examined clinical data from 150 patients diagnosed with MHD at Hefei Third People's Hospital from January 2014 to June 2023. Depending on arteriovenous fistula (AVF) functionality, patients were categorized into dysfunctional (n = 62) and functional (n = 88) cohorts. Using the least absolute shrinkage and selection operator(LASSO) regression model, variables potentially influencing AVF functionality were filtered using selected variables that underwent multifactorial logistic regression analysis. The Nomogram model was constructed using the R software, and the Area Under Curve(AUC) value was calculated. The model's accuracy was appraised through the calibration curve and Hosmer-Lemeshow test, with the model undergoing internal validation using the bootstrap method. There were 11 factors exhibiting differences between the group of patients with AVF dysfunction and the group with normal AVF function, including age, sex, course of renal failure, diabetes, hyperlipidemia, Platelet count (PLT), Calcium (Ca), Phosphorus, D-dimer (D-D), Fibrinogen (Fib), and Anastomotic width. These identified factors are included as candidate predictive variables in the LASSO regression analysis. LASSO regression identified age, sex, diabetes, hyperlipidemia, anastomotic diameter, blood phosphorus, and serum D-D levels as 7 predictive factors. Unconditional binary logistic regression analysis revealed that advanced age (OR = 4.358, 95% CI: 1.454-13.062), diabetes (OR = 4.158, 95% CI: 1.243-13.907), hyperlipidemia (OR = 3.651, 95% CI: 1.066-12.499), D-D (OR = 1.311, 95% CI: 1.063-1.616), and hyperphosphatemia (OR = 4.986, 95% CI: 2.513-9.892) emerged as independent risk factors for AVF dysfunction in MHD patients. The AUC of the predictive model was 0.934 (95% CI: 0.897-0.971). The Hosmer-Lemeshow test showed high consistency between the model's predictive results and actual clinical observations (χ2 = 1.553, P = .092). Internal validation revealed an AUC of 0.911 (95% CI: 0.866-0.956), with the Calibration calibration curve nearing the ideal curve. Advanced age, coexisting diabetes, hyperlipidemia, blood D-D levels, and hyperphosphatemia are independent risk factors for AVF dysfunction in patients undergoing MHD.

Development and validation of a novel nomogram model for predicting delayed graft function in deceased donor kidney transplantation based on pre-transplant biopsies

BackgroundDelayed graft function (DGF) is an important complication after kidney transplantation surgery. The present study aimed to develop and validate a nomogram for preoperative prediction of DGF on the basis of clinical and histological risk factors.MethodsThe prediction model was constructed in a development cohort comprising 492 kidney transplant recipients from May 2018 to December 2019. Data regarding donor and recipient characteristics, pre-transplantation biopsy results, and machine perfusion parameters were collected, and univariate analysis was performed. The least absolute shrinkage and selection operator regression model was used for variable selection. The prediction model was developed by multivariate logistic regression analysis and presented as a nomogram. An external validation cohort comprising 105 transplantation cases from January 2020 to April 2020 was included in the analysis.Results266 donors were included in the development cohort, 458 kidneys (93.1%) were preserved by hypothermic machine perfusion (HMP), 96 (19.51%) of 492 recipients developed DGF. Twenty-eight variables measured before transplantation surgery were included in the LASSO regression model. The nomogram consisted of 12 variables from donor characteristics, pre-transplantation biopsy results and machine perfusion parameters. Internal and external validation showed good discrimination and calibration of the nomogram, with Area Under Curve (AUC) 0.83 (95%CI, 0.78–0.88) and 0.87 (95%CI, 0.80–0.94). Decision curve analysis demonstrated that the nomogram was clinically useful.ConclusionA DGF predicting nomogram was developed that incorporated donor characteristics, pre-transplantation biopsy results, and machine perfusion parameters. This nomogram can be conveniently used for preoperative individualized prediction of DGF in kidney transplant recipients.

Explore the value of carotid ultrasound radiomics nomogram in predicting ischemic stroke risk in patients with type 2 diabetes mellitus.

Type 2 Diabetes Mellitus (T2DM) with insulin resistance (IR) is prone to damage the vascular endothelial, leading to the formation of vulnerable carotid plaques and increasing ischemic stroke (IS) risk. The purpose of this study is to develop a nomogram model based on carotid ultrasound radiomics for predicting IS risk in T2DM patients. 198 T2DM patients were enrolled and separated into study and control groups based on IS history. After manually delineating carotid plaque region of interest (ROI) from images, radiomics features were identified and selected using the least absolute shrinkage and selection operator (LASSO) regression to calculate the radiomics score (RS). A combinatorial logistic machine learning model and nomograms were created using RS and clinical features like the triglyceride-glucose index. The three models were assessed using area under curve (AUC) and decision curve analysis (DCA). Patients were divided into the training set and the testing set by the ratio of 0.7. 4 radiomics features were selected. RS and clinical variables were all statically significant in the training set and were used to create a combination model and a prediction nomogram. The combination model (radiomics + clinical nomogram) had the largest AUC in both the training set and the testing set (0.898 and 0.857), and DCA analysis showed that it had a higher overall net benefit compared to the other models. This study created a carotid ultrasound radiomics machine-learning-based IS risk nomogram for T2DM patients with carotid plaques. Its diagnostic performance and clinical prediction capabilities enable accurate, convenient, and customized medical care.

Use of serum KL-6 and chest radiographic severity grade to predict 28-day mortality in COVID-19 patients with pneumonia: a retrospective cohort study.

Coronavirus disease 2019 (COVID-19) has had a global social and economic impact. An easy assessment procedure to handily identify the mortality risk of inpatients is urgently needed in clinical practice. Therefore, the aim of this study was to develop a simple nomogram model to categorize patients who might have a poor short-term outcome. A retrospective cohort study of 189 COVID-19 patients was performed at Shanghai Ren Ji Hospital from December 12, 2022 to February 28, 2023. Chest radiography and biomarkers, including KL-6 were assessed. Risk factors of 28-day mortality were selected by a Cox regression model. A nomogram was developed based on selected variables by SMOTE strategy. The predictive performance of the derived nomogram was evaluated by calibration curve. In total, 173 patients were enrolled in this study. The 28-day mortality event occurred in 41 inpatients (23.7%). Serum KL-6 and radiological severity grade (RSG) were selected as the final risk factors. A nomogram model was developed based on KL-6 and RSG. The calibration curve suggested that the nomogram model might have potential clinical value. The AUCs for serum KL-6, RSG, and the combined score in the development group and validation group were 0.885 (95% CI: 0.804-0.952), 0.818 (95% CI: 0.711-0.899), 0.868 (95% CI: 0.776-0.942) and 0.932 (95% CI: 0.862-0.997), respectively. Our results suggested that the nomogram based on KL-6 and RSG might be a potential method for evaluating 28-day mortality in COVID-19 patients. A high combined score might indicate a poor outcome in COVID-19 patients with pneumonia.

CPE correlates with poor prognosis in gastric cancer by promoting tumourigenesis

AimsTo investigate the potential functions and mechanisms of tumourigenesis in carboxypeptidase E (CPE) and its prognostic value in gastric cancer, and to develop a predictive model for prognosis based on CPE. ResultsTranscriptome level variation and the prognostic value of CPE in different types of cancers were investigated using bioinformatics analyses. The association between CPE and clinicopathological characteristics was specifically explored in gastric cancer. Elevated CPE expression was associated with poor survival and recurrence prognosis and was found in cases with a later clinical stage of gastric cancer. The CPE was considered an independent prognostic factor, as assessed using Cox regression analysis. The prognostic value of CPE was further verified through immunohistochemistry and haematoxylin staining. Enrichment analysis provided a preliminary confirmation of the potential functions and mechanisms of CPE. Immune cell infiltration analysis revealed a significant correlation between CPE and macrophage infiltration. Eventually, a prognosis prediction nomogram model based on CPE was developed. ConclusionCPE was identified as an independent biomarker associated with poor prognosis in gastric cancer. This suggests that CPE overexpression promoted epithelial-mesenchymal transition via the activation of the Erk/Wnt pathways, leading to proliferation, invasion, and metastasis. Targeted therapeutic strategies for gastric cancer may benefit from these findings.

The prognostic nutritional index as a predictor of efficacy and early recurrence for adjuvant transarterial chemoembolization in hepatocellular carcinoma

Background and aimsPostoperative adjuvant transcatheter arterial chemoembolization (TACE) can prevent recurrence of hepatocellular carcinoma (HCC) in certain patients. This study aimed to identify the potential beneficiaries of adjuvant TACE. Methods477 patients who underwent curative resection for HCC were enrolled in this retrospectively cohort study. The trajectory of the prognostic nutritional index (PNI) during the perioperative period was fitted using a latent-class growth mixed model. The association between adjuvant TACE and recurrence-free survival in each PNI group was assessed using the Kaplan-Meier curve. Furthermore, Cox regression analysis was conducted to identify the risk factors for early recurrence after adjuvant TACE and develop a nomogram model. ResultsPatients in the PNI group III had a high risk of recurrence and could benefit from adjuvant TACE (P = 0.009). The prognostic prediction model for adjuvant TACE (PAT) incorporated eight variables (PNI, tumor size, tumor number, microvascular invasion, sex, aspartate aminotransferase, gamma-glutamyl transferase, and degree of differentiation). Patients with PAT score >330 and 235–330 had significantly higher recurrence rates than those with PAT score <235 (P < 0.001). ConclusionPNI may help guide the selection of adjuvant TACE beneficiaries. PAT demonstrated a high accuracy in predicting the prognosis of patients who underwent postoperative TACE.

TIMM17A overexpression in lung adenocarcinoma and its association with prognosis

Lung adenocarcinoma (LUAD), a leading cause of cancer-related mortality worldwide, demands a deeper understanding of its molecular mechanisms and the identification of reliable biomarkers for better diagnosis and targeted therapy. Leveraging data from the Cancer Genome Atlas (TCGA), the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and the Human Protein Atlas (HPA), we investigated the mRNA and protein expression profiles of TIMM17A and assessed its prognostic significance through Kaplan–Meier survival curves and Cox regression analysis. Through Gene Set Enrichment Analysis, we explored the regulatory mechanisms of TIMM17A in LUAD progression and demonstrated its role in modulating the proliferative capacity of A549 cells, a type of LUAD cell, via in vitro experiments. Our results indicate that TIMM17A is significantly upregulated in LUAD tissues, correlating with clinical staging, lymph node metastasis, overall survival, and progression-free survival, thereby establishing it as a critical independent prognostic factor. The construction of a nomogram model further enhances our ability to predict patient outcomes. Knockdown of TIMM17A inhibited the growth of LUAD cells. The potential of TIMM17A as a biomarker and therapeutic target for LUAD presents a promising pathway for improving patient diagnosis and treatment strategies.

Nomogram establishment for short-term survival prediction in ICU patients with aplastic anemia based on the MIMIC-IV database

ABSTRACT Objective To establish an efficient nomogram model to predict short-term survival in ICU patients with aplastic anemia (AA). Methods The data of AA patients in the MIMIC-IV database were obtained and randomly assigned to the training set and testing set in a ratio of 7:3. Independent prognosis factors were identified through univariate and multivariate Cox regression analyses. The variance inflation factor was calculated to detect the correlation between variables. A nomogram model was built based on independent prognostic factors and risk scores for factors were generated. Model performance was tested using C-index, receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and Kaplan-Meier curve. Results A total of 1,963 AA patients were included. A nomogram model with 7 variables was built, including SAPS II, chronic pulmonary obstructive disease, body temperature, red cell distribution width, saturation of peripheral oxygen, age and mechanical ventilation. The C-indexes in the training set and testing set were 0.642 and 0.643 respectively, indicating certain accuracy of the model. ROC curve showed favorable classification performance of nomogram. The calibration curve reflected that its probabilistic prediction was reliable. DCA revealed good clinical practicability of the model. Moreover, the Kaplan-Meier curve showed that receiving mechanical ventilation could improve the survival status of AA patients in the short term but did not in the later period. Conclusion The nomogram model of the short-term survival rate of AA patients was built based on clinical characteristics, and early mechanical ventilation could help improve the short-term survival rate of patients.

Genetic Susceptibility, Mendelian Randomization, and Nomogram Model Construction of Gestational Diabetes Mellitus.

Gestational diabetes mellitus (GDM) is a pregnancy-complicated disease that poses a risk to maternal and infant health. However, the etiology of the disease has been not yet elucidated. To detect the genetic susceptibility and construct a nomogram model with significantly associated polymorphisms and key clinical indicators for early prediction of GDM. Eleven functional single nucleotide polymorphisms (SNPs) screened by genome-wide association study were genotyped in 554 GDM cases and 641 healthy controls. Functional analysis of GDM positively associated SNPs, multivariate mendelian randomization (MVMR), and a GDM early predictive nomogram model construction were performed. rs1965211, rs3760675, and rs7814359 were significantly associated with genetic susceptibility to GDM after adjusting age and prepregnancy body mass index (pre-BMI). It seems that GDM-associated SNPs have effects on regulating target gene transcription factor binding, posttranscriptional splicing, and translation product structure. Besides, rs3760675 can be expression quantitative trait loci and increase the XAB2 mRNA expression level (P = .047). The MVMR analysis showed that the increase of clinical variables of BMI, hemoglobin A1c (HbA1c), and fasting plasma glucose (FPG) had significant causal effects on GDM (BMI-ORMVMR = 1.52, HbA1c-ORMVMR = 1.32, FPG-ORMVMR = 1.78), P < .05. A nomogram model constructed with pre-BMI, FPG, HbA1c, and genotypes of rs1965211, rs3760675, and rs7814359 showed an area under the receiver operating characteristic curve of 0.824. Functional polymorphisms can change women's susceptibility to GDM and the predictive nomogram model based on genetic and environmental factors can effectively distinguish individuals with different GDM risks in early stages of pregnancy.

Risk factors and a predictive model for the occurrence of adverse outcomes in patients with new-onset refractory status epilepsy.

To determine risk factors for the occurrence of adverse outcomes in patients with new-onset refractory status epilepsy (NORSE) and to construct a concomitant nomogram. Seventy-six adult patients with NORSE who were admitted to the Department of Neurology, First Affiliated Hospital of Sun Yat-sen University between January 2016 and December 2022 were enrolled for the study. Participants were divided into two-those with good and poor functional outcomes-and their pertinent data was obtained from the hospital medical recording system. Univariate analysis was used to identify potential causes of poor outcomes in both groups and a multivariate logistic regression model was used to identify risk factors for the occurrence of poor outcomes. Using the R programming language RMS package, a nomogram was created to predict the occurrence of poor outcomes. The NORSE risk of adverse outcome nomogram model included four predictors, namely duration of mechanical ventilation (OR = 4.370, 95% CI 1.221-15.640, p = 0.023), antiviral therapy (OR = 0.045, 95% CI 0.005-0.399, p = 0.005), number of anesthetics (OR = 13.428, 95% CI 2.16-83.48, p = 0.005) and neutrophil count/lymphocyte count ratio (NLR) (OR = 5.248, 95% CI 1.509-18.252, p = 0.009). The nomogram had good consistency and discrimination in predicting risk and can thus assist clinical care providers to assess outcomes for NORSE patients. Through ordinary bootstrap analyses, the results of the original set prediction were confirmed as consistent with those of the test set. The nomogram model of risk of adverse outcomes in NORSE adult patients developed in this study can facilitate clinicians to predict the risk of adverse outcomes in NORSE patients and make timely and reasonable interventions for patients at high risk of adverse outcomes.

Data-driven intelligent prediction of TBM surrounding rock and personalized evaluation of disaster-inducing factors

With the widespread application of tunnel boring machines (TBM) in underground tunnel construction, the accurate and real-time identification of geological indicators has become a key factor for ensuring construction safety. In this study, we introduced the nomogram method into the field of civil engineering for the first time to predict surrounding rock grades. Against the backdrop of a major water conservancy project in China, an in-depth analysis of construction data from the ascending phase was conducted to establish the relationship between surrounding rock grade predictions and key construction parameters. Compared with traditional models, the nomogram method has shown advantages in terms of prediction accuracy. Furthermore, by providing a personalized scoring mechanism for specific construction data, the nomogram method not only achieves the real-time prediction of rock grades but also clearly reveals the dynamic relationships between input parameters and rock grades, clarifying the entire prediction process. We identified the main factors related to rock grade prediction among TBM construction parameters, including the slurry conveyor belt speed, jacking pressure, cutterhead torque, cutterhead thrust, thrust pressure, and actual rotation speed. The training process revealed that the nomogram prediction model achieved optimal performance with seven input parameters, balancing accuracy and training time. According to the prediction results, the nomogram model’s area under the curve index is 0.92, providing greater accuracy than the traditional random forest model (0.89). Therefore, this study enhances the transparency and interpretability of model predictions by providing personalized assessments of construction parameters for predicting surrounding rock grades, overcoming the black-box issue of existing prediction methods and offering a novel and effective tool for rock grade prediction.

Construction of a nomogram for predicting compensated cirrhosis with Wilson’s disease based on non-invasive indicators

BackgroundWilson’s disease (WD) often leads to liver fibrosis and cirrhosis, and early diagnosis of WD cirrhosis is essential. Currently, there are few non-invasive prediction models for WD cirrhosis. The purpose of this study is to non-invasively predict the occurrence risk of compensated WD cirrhosis based on ultrasound imaging features and clinical characteristics.MethodsA retrospective analysis of the clinical characteristics and ultrasound examination data of 102 WD patients from November 2018 to November 2020 was conducted. According to the staging system for WD liver involvement, the patients were divided into a cirrhosis group (n = 43) and a non-cirrhosis group (n = 59). Multivariable logistic regression analysis was used to identify independent influencing factors for WD cirrhosis. A nomogram for predicting WD cirrhosis was constructed using R analysis software, and validation of the model’s discrimination, calibration, and clinical applicability was completed. Due to the low incidence of WD and the small sample size, bootstrap internal sampling with 500 iterations was adopted for validation to prevent overfitting of the model.ResultsAcoustic Radiation Force Impulse (ARFI), portal vein diameter (PVD), and serum albumin (ALB) are independent factors affecting WD cirrhosis. A nomogram for WD cirrhosis was constructed based on these factors. The area under the ROC curve (AUC) of the model’s predictive ability is 0.927 (95% CI: 0.88–0.978). As demonstrated by 500 Bootstrap internal sampling validations, the model has high discrimination and calibration. Clinical decision curve analysis shows that the model has high clinical practical value. ROC curve analysis of the model’s rationality indicates that the model’s AUC is greater than the AUC of using ALB, ARFI, and PVD alone.ConclusionThe nomogram model constructed based on ARFI, PVD, and ALB can serve as a non-invasive tool to effectively predict the risk of developing WD cirrhosis.

Construction and validation of a neovascular glaucoma nomogram in patients with diabetic retinopathy after pars plana vitrectomy

BACKGROUND Neovascular glaucoma (NVG) is likely to occur after pars plana vitrectomy (PPV) for diabetic retinopathy (DR) in some patients, thus reducing the expected benefit. Understanding the risk factors for NVG occurrence and building effective risk prediction models are currently required for clinical research. AIM To develop a visual risk profile model to explore factors influencing DR after surgery. METHODS We retrospectively selected 151 patients with DR undergoing PPV. The patients were divided into the NVG (NVG occurrence) and No-NVG (No NVG occurrence) groups according to the occurrence of NVG within 6 months after surgery. Independent risk factors for postoperative NVG were screened by logistic regression. A nomogram prediction model was established using R software, and the model’s prediction accuracy was verified internally and externally, involving the receiver operator characteristic curve and correction curve. RESULTS After importing the data into a logistic regression model, we concluded that a posterior capsular defect, preoperative vascular endothelial growth factor ≥ 302.90 pg/mL, glycosylated hemoglobin ≥ 9.05%, aqueous fluid interleukin 6 (IL-6) ≥ 53.27 pg/mL, and aqueous fluid IL-10 ≥ 9.11 pg/mL were independent risk factors for postoperative NVG in patients with DR (P &lt; 0.05). A nomogram model was established based on the aforementioned independent risk factors, and a computer simulation repeated sampling method was used to internally and externally verify the nomogram model. The area under the curve (AUC), sensitivity, and specificity of the model were 0.962 [95% confidence interval (95%CI): 0.932-0.991], 91.5%, and 82.3%, respectively. The AUC, sensitivity, and specificity of the external validation were 0.878 (95%CI: 0.746-0.982), 66.7%, and 95.7%, respectively. CONCLUSION A nomogram constructed based on the risk factors for postoperative NVG in patients with DR has a high prediction accuracy. This study can help formulate relevant preventive and treatment measures.

Prognostic analysis of related factors of adverse reactions to immunotherapy in advanced gastric cancer and establishment of a nomogram model.

Immunotherapy for advanced gastric cancer has attracted widespread attention in recent years. However, the adverse reactions of immunotherapy and its relationship with patient prognosis still need further study. In order to determine the association between adverse reaction factors and prognosis, the aim of this study was to conduct a systematic prognostic analysis. By comprehensively evaluating the clinical data of patients with advanced gastric cancer treated by immunotherapy, a nomogram model will be established to predict the survival status of patients more accurately. To explore the characteristics and predictors of immune-related adverse reactions (irAEs) in advanced gastric cancer patients receiving immunotherapy with programmed death protein-1 (PD-1) inhibitors and to analyze the correlation between irAEs and patient prognosis. A total of 140 patients with advanced gastric cancer who were treated with PD-1 inhibitors in our hospital from June 2021 to October 2023 were selected. Patients were divided into the irAEs group and the non-irAEs group according to whether or not irAEs occurred. Clinical features, manifestations, and prognosis of irAEs in the two groups were collected and analyzed. A multivariate logistic regression model was used to analyze the related factors affecting the occurrence of irAEs, and the prediction model of irAEs was established. The receiver operating characteristic (ROC) curve was used to evaluate the ability of different indicators to predict irAEs. A Kaplan-Meier survival curve was used to analyze the correlation between irAEs and prognosis. The Cox proportional risk model was used to analyze the related factors affecting the prognosis of patients. A total of 132 patients were followed up, of whom 63 (47.7%) developed irAEs. We looked at the two groups' clinical features and found that the two groups were statistically different in age ≥ 65 years, Ki-67 index, white blood cell count, neutrophil count, and regulatory T cell (Treg) count (all P < 0.05). Multivariate logistic regression analysis showed that Treg count was a protective factor affecting irAEs occurrence (P = 0.030). The ROC curve indicated that Treg + Ki-67 + age (≥ 65 years) combined could predict irAEs well (area under the curve = 0.753, 95% confidence interval: 0.623-0.848, P = 0.001). Results of the Kaplan-Meier survival curve showed that progression-free survival (PFS) was longer in the irAEs group than in the non-irAEs group (P = 0.001). Cox proportional hazard regression analysis suggested that the occurrence of irAEs was an independent factor for PFS (P = 0.006). The number of Treg cells is a separate factor that affects irAEs in advanced gastric cancer patients receiving PD-1 inhibitor immunotherapy. irAEs can affect the patients' PFS and result in longer PFS. Treg + Ki-67 + age (≥ 65 years old) combined can better predict the occurrence of adverse reactions.

PSAT1 enhances the efficacy of the prognosis estimation nomogram model in stage-based clear cell renal cell carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) is associated with a high prevalence of cancer-related deaths. The survival rates of patients are significantly lower in late-stage ccRCC than in early-stage ccRCC, due to the spread and metastasis of late-stage ccRCC, surgery has not reached the goal of radical cure, and the effect of traditional radiotherapy and chemotherapy is poor. Thus, it is crucial to accurately assess the prognosis and provide personalized treatment at an early stage in ccRCC. This study aims to develop an efficient nomogram model for stratifying and predicting the survival of ccRCC patients based on tumor stage.MethodsWe first analyzed the microarray expression data of ccRCC patients from the Gene Expression Omnibus (GEO) database and categorized them into two groups based on the disease stage (early and late stage). Subsequently, the GEO2R tool was applied to screen out the genes that were highly expressed in all GEO datasets. Finally, the clinicopathological data of the two patient groups were obtained from The Cancer Genome Atlas (TCGA) database, and the differences were compared between groups. Survival analysis was performed to evaluate the prognostic value of candidate genes (PSAT1, PRAME, and KDELR3) in ccRCC patients. Based on the screened gene PSAT1 and clinical parameters that were significantly associated with patient prognosis, we established a new nomogram model, which was further optimized to a single clinical variable-based model. The expression level of PSAT1 in ccRCC tissues was further verified by qRT-PCR, Western blotting, and immunohistochemical analysis.ResultsThe datasets GSE73731, GSE89563, and GSE150404 identified a total of 22, 89, and 120 over-expressed differentially expressed genes (DEGs), respectively. Among these profiles, there were three genes that appeared in all three datasets based on different stage groups. The overall survival (OS) of late-stage patients was significantly shorter than that of early-stage patients. Among the three candidate genes (PSAT1, PRAME, and KDELR3), PSAT1 was shown to be associated with the OS of patients with late-stage ccRCC. Multivariate Cox regression analysis showed that age, tumor grade, neoadjuvant therapy, and PSAT1 level were significantly associated with patient prognosis. The concordance indices were 0.758 and 0.725 for the 3-year and 5-year OS, respectively. The new model demonstrated superior discrimination and calibration compared with the single clinical variable model. The enhancer PSAT1 used in the new model was shown to be significantly overexpressed in tissues from patients with late-stage ccRCC, as demonstrated by the mRNA level, protein level, and pathological evaluation.ConclusionThe new prognostic prediction nomogram model of PSAT1 and clinicopathological variables combined was thus established, which may provide a new direction for individualized treatment for different-stage ccRCC patients.