Search Results (713)

Background: Systems developed in recent years to assess the risk of in-hospital death in patients with myocardial infarction (MI) are mainly based on angiographic, electrocardiographic, and laboratory variables. Risk systems based on contemporary angiographic data and logistic variables have not been reported. The aim of this study was to develop and validate a system to assess the risk of in-hospital death in patients across the entire clinical spectrum of MI treated with primary coronary intervention (PCI) based on modern angiographic and logistic predictors. Methods: A subgroup of patients from the observational single-centre registry of MI treated with PCIs (from 1 February 2019 until 31 January 2020) was used to develop a multivariate logistic regression model predicting in-hospital mortality. The population (603 patients) was divided, with 60% of the sample used for model derivation and the remaining 40% used for internal model validation. Results: The main findings were as follows: (1) coronary angiography results and suboptimal flow after PCI were important predictors of in-hospital mortality; (2) the time of PCI as well as the mode of presentation of patients with MI contributed to in-hospital mortality; and (3) the discrimination (C statistic = 0.848, 95% CI: [0.765, 0.857]) and calibration (χ² = 2.78, pHL = 0.94) were good in the derivation set, while the discrimination (C statistic = 0.6438, 95% CI: [0.580, 0.703]) in the validation set was satisfactory. Conclusions: A novel clinical nomogram based on four available logistic and angiographic variables was developed and validated for in-hospital mortality after PCIs in a wide range of MIs. Full article

Stomach adenocarcinoma (STAD) has high incidence and mortality rates. Long non-coding RNAs (lncRNAs) and angiogenesis are closely related to the pathogenesis and metastasis of STAD. Recently, emerging evidence demonstrated that DNA methylation plays crucial roles in the development of STAD. This study explored the relationship between DNA methylation and the abnormal expression of angiogenesis-related lncRNAs (ARlncRNAs) in stomach adenocarcinoma, aiming to identify prognostic biomarkers. Moreover, a Cox analysis and Lasso regression were used to establish an ARlncRNA feature set related to angiogenesis. The prognostic model was evaluated by using a Kaplan–Meier (KM) analysis, ROC curves, and nomograms. Based on the identified 18 key ARlncRNAs, a prognostic predictive model was constructed. In addition, a specific ARlncRNA with abnormal methylation in the model, LINC00511, showed significant differences in expression and methylation across different subgroups. The methylation and expression of LINC00511 were analyzed by a correlation and co-expression analysis. The correlation analysis indicated that promoter methylation may improve LINC00511 expression. Further analysis found 355 mRNAs co-expressed with LINC00511 which may interact with 6 miRNAs to regulate target gene expression. The abnormal methylation of LINC00511 could significantly contribute to the progression of stomach adenocarcinoma. Full article

Introduction: Glioma Grade 4 (GG4) tumors, which include both IDH-mutated and IDH wild-type astrocytomas, are the most prevalent and aggressive form of primary brain tumor. Radiomics is gaining ground in neuro-oncology. The integration of this data into machine learning models has the potential to improve the accuracy of prognostic models for GG4 patients. Karnofsky Performance Status (KPS), an established preoperative prognostic factor for survival, is commonly used in these patients. In this study, we developed a nomogram to identify patients with improved functional performance as indicated by an increase in KPS after surgery by analyzing radiomic features from preoperative 3D MRI scans. Methods: Quantitative imaging features were extracted from the -3D T1 GRE sequence of 157 patients from a single center and were used to develop the machine learning (ML) model. To improve applicability and create a nomogram, multivariable logistic regression analysis was performed to build a model incorporating clinical characteristics and radiomics features. Results: We labeled 55 cases in which KPS was improved after surgery (35%, KPS-flag = 1). The resulting model was evaluated according to test series results. The best model was obtained by XGBoost using the features extracted by pyradiomics, with a Matthew coefficient score (MCC) of 0.339 (95% CI: 0.330–0.3483) in cross-validation. The out-of-sample evaluation on the test set yielded an MCC of 0.302. A nomogram evaluating the improvement of KPS post-surgery was built based on statistically significant variables from multivariate logistic regression including clinical and radiomics data (c-index = 0.760, test set). Conclusions: MRI radiomic analysis represents a powerful tool to predict postoperative functional outcomes, as evaluated by KPS. Full article

Background: Evidence to help avoid unnecessary prostate biopsies is being actively pursued. Our goal was to develop and internally validate a nomogram for predicting clinically significant prostate cancer (csPC) in men with low suspicion of disease (prostate specific antigen [PSA] < 10 ng/mL, normal digital rectal examination [DRE]), in whom magnetic resonance imaging (MRI) findings are positive. Methods: Patients with no prior prostate cancer diagnosis who underwent MRI–ultrasound fusion biopsy of the prostate were retrospectively analyzed. Inclusion criteria were PSA < 10 ng/mL, normal DRE, Prostate Imaging Reporting And Data System (PIRADS) category ≥ 3, and no extraprostatic extension or seminal vesicle invasion reported on MRI. Associations between csPC diagnosis and patient or lesion characteristics were analyzed, and a multivariable model was developed. Internal validation of the model with 5-fold cross-validation and bootstrapping methods was performed. Results: Among 209 patients, 67 were diagnosed with csPC. Factors incorporated into the model for predicting csPC were age, 5-alpha reductase inhibitor use, PSA, prostate volume, PIRADS > 3, and lesion location in the peripheral zone. The model’s ROC AUC was 0.86, with consistent performance at internal validation (0.84 with cross-validation, 0.82 with bootstrapping). With an empirical threshold of <10% csPC probability to omit biopsy, 72 (50.7%) unnecessary biopsies would have been avoided, at the cost of missing 2 (3.0%) csPC cases. Conclusions: Our nomogram might serve as a valuable tool in refining selection criteria in men considered for prostate biopsy. The major limitation of the study is its retrospective character. Prospective, external validation of the model is warranted. Full article

Numerous animal experiments have implicated ferroptosis in the pathogenesis of acute pancreatitis (AP). Nonetheless, due to sampling constraints, the precise role of ferroptosis in the human body during AP remains elusive. Method: Peripheral blood sequencing data of patients with acute pancreatitis (GSE194331) were obtained from the Gene Expression Omnibus (GEO) database. We analyzed differentially expressed genes whose expression increased or decreased with increasing disease severity and intersected them with the ferroptosis gene set to identify ferroptosis-related driver genes for the disease. The hub genes were selected using machine learning algorithms, and a nomogram diagnosis model was constructed. Clinical samples, animal models, and an in vitro experiment were also used for validation. The investigation unveiled 22 ferroptosis-related driver genes, and we identified three hub genes, AQP3, TRIB2, and MGST1, by employing two machine learning algorithms. AQP3 and TRIB2 exhibit robust correlations with various immune cells. The disease diagnosis model constructed utilizing these three genes demonstrated high sensitivity and specificity (AUC = 0.889). In the in vitro experiments, we discovered for the first time that ferroptosis occurs in pancreatic duct cells during acute pancreatitis, and that MGST1 is significantly upregulated in duct cells, where it plays a crucial role in negatively regulating ferroptosis via the ACSL4/GPX4 axis. In addition, overexpression of MGST1 protects ductal cells from inflammatory damage. In our investigation, we explored the mechanisms of ferroptosis in immune cells and pancreatic duct cells in patients with AP. These results highlight a potential pathway for the early diagnosis and treatment of acute pancreatitis. Full article

Glioblastoma (GBM) is the most aggressive primary brain cancer, with poor prognosis due to its aggressive behavior and high heterogeneity. This study aimed to identify cellular senescence (CS) and lipid metabolism (LM)-related prognostic genes to improve GBM prognosis and treatment. Transcriptome and scRNA-seq data, CS-associated genes (CSAGs), and LM-related genes (LMRGs) were acquired from public databases. Prognostic genes were identified by intersecting CSAGs, LMRGs, and differentially expressed genes (DEGs), followed by WGCNA and univariate Cox regression. A risk model and nomogram were constructed. Analyses covered clinicopathological features, immune microenvironment, somatic mutations, and drug sensitivity. GBM scRNA-seq data identified key cells and prognostic gene expression. SOCS1 and PHB2 were identified as prognostic markers, contributing to the construction of a robust risk model with excellent predictive ability. High-risk group (HRG) patients had poorer survival, higher immune and stromal scores, and distinct somatic mutation profiles. Drug sensitivity analysis revealed significant differences in IC50 values. In microglia differentiation, SOCS1 and PHB2 showed dynamic expression patterns. These findings provide new strategies for GBM prognosis and treatment. Full article

Objective: This study elucidates the prognostic significance of perioperative changes in laboratory indicators for aneurysmal SAH and develops a nomogram model for outcome prediction. Methods: Aneurysmal SAH patients who received clipping or coiling at our institution between January 2016 and December 2022 were included. All patients were randomly assigned to derivation and validation cohorts. Independent predictors of unfavorable outcomes were identified by multivariate analyses. Three models were conducted to evaluate whether perioperative laboratory changes improve prediction performance. A nomogram including all independent predictors was developed in the derivation cohort and verified in both cohorts. Results: Diabetes mellitus [OR (95% CI) = 2.84 (1.44–5.59)], WFNS grade 3–5 [OR: (95% CI), 9.17 (5.49–15.33)], clipping [OR (95% CI) = 1.71 (1.03–2.85)], perioperative changes in white blood cell count [OR (95% CI) = 2.15 (1.17–3.96)], and concentrations of ALT [OR (95% CI) = 1.41 (1.04–1.91)], sodium [OR (95% CI) = 5.40 (3.01–9.71)], and glucose [OR (95% CI) = 2.18 (1.05–4.53)] were independent predictors of an unfavorable outcome. The predictive nomogram incorporated the aforementioned predictors and performed well in the derivation cohort (AUC, 0.839; 95% CI: 0.810–0.866) and the validation cohort (AUC, 0.797; 95% CI: 0.734–0.850). Conclusions: Perioperative changes in laboratory indicators can be predictors of unfavorable outcomes in aneurysmal SAH patients. The nomogram based on clinical and laboratory risk factors can be used as a convenient tool to facilitate individualized decision making. Full article

Background: Colorectal cancer (CRC) ranks as the third most common cancer worldwide. Tertiary lymphoid structures (TLSs), organized immune cell aggregates in non-lymphoid tissues, are linked to chronic inflammation and tumorigenesis. However, the precise relationship between TLSs and CRC prognosis remains unclear. This study aimed to develop a TLS-associated genetic signature to predict CRC prognosis and support clinical applications. Methods: Utilizing the TCGA database, we analyzed TLS-related gene expression in CRC versus normal tissues. Prognostic models were constructed using Cox and Kaplan-Meier analyses. CRC samples were stratified into high and low TLS groups via ssGSEA, with validation in the GSE75500 dataset. We identified clinical characteristics associated with TLS scores, created prognostic nomograms, analyzed the top 50 differential genes, assessed tumor mutations, estimated immune infiltration using CIBERSORT, and examined correlations between TLS scores and immune checkpoints. Results: A 13-gene TLS-associated prognostic model for CRC was developed, emphasizing immune response genes. Survival analysis indicated significantly better outcomes for the TLS-high group. Cox regression identified stage IV and M1 as independent factors influencing TLS scores. Nomogram analysis demonstrated that combining TLS scores with clinical features enhances prognostic accuracy. TLS scores were closely associated with immune checkpoint genes, suggesting potential immunotherapy benefits for TLS-high patients. Conclusions: This study developed and validated a TLS-based prognostic model for CRC, exploring relevant immune cells. The model holds promise for predicting clinical prognosis and treatment responsiveness in CRC patients. Full article

Background: Preoperative recurrence risk stratification for colorectal liver metastases (CRLM) undergoing hepatectomy is essential when designing a treatment strategy. We developed a Beppu classification system consisting of three risk groups and found that the RAS mutation increased risk in low- and moderate-risk patients. Methods: A total of 173 patients undergoing initial hepatectomy for CRLM between 2004 and 2020 were analyzed. Disease-free survival (DFS) and overall survival (OS) were assessed. Patients in the low- and moderate-risk groups of the Beppu classification with RAS mutations were moved into the moderate- and high-risk groups, respectively, in the RAS-Beppu classification. Results: The DFS curves of the three risk groups in the Beppu and RAS-Beppu classification were significantly different. Five-year DFS rates were 57%, 31%, and 16% in the RAS-Beppu classification of low-, moderate-, and high-risk groups, respectively. With multivariate analysis, Beppu classifications (p = 0.0017) and RAS-Beppu classifications (p = 0.0002) were independent prognostic factors for DFS. The RAS-Beppu classification showed higher hazard ratios than the Beppu classification, as well as the genetic and morphological evaluation score and the modified clinical risk score, which include the RAS status. The hazard ratios in the RAS-Beppu classification were significant in all two-group comparisons (2.22 for moderate vs. low, 3.48 for high vs. low, and 1.70 for high vs. moderate). The multivariate analysis of OS showed benefits of the RAS-Beppu classification in the high- vs. low-risk and high- vs. moderate-risk comparisons. Conclusions: The RAS-Beppu classification using standard parameters is a novel suitable tool for predicting recurrence risk before liver resection. Full article

Background/Objectives: To develop a nomogram for the preoperative prediction of pathologic T3a (pT3a) upstaging in patients with clinical T1(cT1) renal cell carcinoma (RCC). Methods: A total of 169 cT1 patients with RCC with preoperative contrast-enhanced CT (CECT) and clinical data were enrolled in this study. Afterwards, the sample was split randomly into training and testing sets in a 7:3 ratio. Radiomics features were extracted and selected from the whole primary tumor on CECT images to develop radiomics signatures. The nomogram was constructed using the obtained radiomics signature and clinical risk factors. The predictive performance of different models was evaluated and visualized using receiver operator characteristic (ROC) curves. Results: In total, 26 radiomics features were selected for the radiomics signature construction. The radiomics signature yielded area under the curve (AUC) values of 0.945 and 0.873 in the training and testing sets, respectively. The nomogram integrating radiomics signature and predictive clinical factors, including tumor size and neutrophil–lymphocyte ratio (NLR), achieved higher predictive performance in the training [AUC, 0.958; 95% confidence interval (CI): 0.921, 0.995] and testing (AUC, 0.913; 95% CI: 0.814, 1.000) sets. Good calibration was achieved for the nomogram in both the training and testing sets (Brier score = 0.082 and 0.098). Decision curve analysis (DCA) demonstrated that the nomogram was clinically useful in predicting pT3a upstaging, with a corresponding net benefit of 0.378. Conclusions: The proposed nomogram can preoperatively predict pT3a upstaging in cT1 RCC and serve as a non-invasive imaging marker to guide individualized treatment. Full article

Background: The fatality rate of hemorrhagic fever with renal syndrome (HFRS), due to hantavirus transmitted by rodents, ranges from 1% to 12%. This study aims to delineate the clinical and laboratory characteristics of HFRS, identify factors associated with disease severity, and construct and validate a nomogram for prognosis prediction of HFRS in the central part of China. Methods: Out of 598 HFRS patients diagnosed via serology tests from four hospitals in Hubei Province, 551 were included. Clinical data were gathered and analyzed, followed by logistic univariate and multivariate analyses to identify independent prognostic factors. A nomogram was developed and validated to forecast the patient’s prognosis. Results: Vaccination led to a notable drop in HFRS incidence from 2018 to 2019, and seasonal trends exhibited bimodal changes with peaks from May to July and November to January. The 30-day mortality rate was 4.17% (23/551). Red blood cell count (RBC), age, two-stage overlap, qSOFA ≥ 2, aspartate aminotransferase (AST), and three-stage overlap were identified as independent prognostic factors. A predictive risk classification system using a nomogram chart was developed, and Kaplan–Meier curves indicated that the new system accurately distinguished 30-day mortality among the three risk groups. Conclusions: The risk score (EASTAR) system demonstrated good predictive performance for prognostic prediction, and it can be applied to quickly screen patients who require ICU admission. Full article

Background/Objectives: While speckle-tracking echocardiography (STE) is increasingly gaining acceptance in the medical community, establishing normal pediatric values and interpreting data derived from software provided by various vendors can pose significant challenges. This review aims to present an updated compilation of nomograms pertinent to speckle-tracking echocardiography. Methods: A review of research using three medical engine searches (National Library of Medicine, Science Direct, and Cochrane Library) for Medical Subject Headings (MeSH) and the free text terms “echocardiography”, “STE”, “normal values”, and ”children” was performed and refined by adding the keywords “nomograms”, “z-scores”, and “healthy children”. Results: A total of twenty-five studies were selected for the final analysis. Our research indicated that current nomograms provide adequate coverage of most strain parameters; however, those pertaining to the right ventricle and the atria are less numerous than those for the left ventricle. A noted trend suggests a decrease in strain values with advancing age and increasing body surface area; nevertheless, the relationships observed were weak and nonlinear. The absence of robust correlations between strain values and age and body size parameters hindered the generation of a Z-score possessing sufficient statistical power. Consequently, normal values are primarily represented as mean values accompanied by standard deviation. A comparative analysis of vendors demonstrated good agreement between different versions of the same platform for Philips (except for QLAB 5) and, similarly, between General Electric (GE) and TomTec. The limited data available regarding the comparison between GE and Philips revealed significant findings that warrant further investigation of differences. Conclusions: A comprehensive review and an updated list of current pediatric nomograms for STE measurements have been presented. This may serve as a valuable guide for accurately interpreting STE in pediatric patients with congenital and acquired heart disease. Full article

The presence of moisture in the insulation of power transformers accelerates the degradation of both paper and oil, thereby increasing the risk of unexpected failures. Due to the hygroscopic nature of cellulose, the insulation can retain up to 8% moisture after the transformer manufacturing process. Reducing this moisture content is crucial in extending the operational lifespan of transformers. The drying cycle of paper insulation is a critical step in power transformer manufacturing, directly influencing the insulation’s longevity and overall performance. This paper introduces a nomogram that combines degradation and drying models, enabling the precise optimization of the drying process based on various parameters such as the temperature, paper mass, and extraction rate. The results of this study demonstrate that for a given extraction rate threshold, the required drying time can be determined based on the mass of paper to be dried, the drying temperature, and the degree of paper degradation (degree of polymerization). These predictive tools are essential for engineers and researchers aiming to enhance transformer reliability. Full article

Hypoxia, a common feature in many malignancies, is particularly prominent in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). Investigating the mechanisms underlying hypoxia is essential for understanding the heterogeneity of CESC and developing personalized therapeutic regimens. Firstly, the CESC-specific hypoxia gene sets shared between single-cell RNA sequencing (scRNA-seq) and bulk data were identified through Weighted Gene Correlation Network Analysis (WGCNA)and FindMarkers analyses. A CESC-specific hypoxia-related score (CSHRS) risk model was constructed using the least absolute shrinkage and selection operator (LASSO)and Cox regression analyses based on these genes. The prognostic differences were analyzed in terms of immune infiltration, mutations, and drug resistance. Finally, a nomogram model was constructed by integrating clinicopathological features to facilitate precision treatment for CESC. This study constructed a CSHRS risk model that divides patients into two groups, and this model can comprehensively evaluate the tumor microenvironment characteristics of CESC, provide accurate prognostic predictions, and offer rational treatment options for patients. Full article

Background: Patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) who are ineligible for high-dose chemotherapy have limited treatment options and poor life expectancy. The purpose of this study is to identify a serum metabolomic profile that may be predictive of outcome in patients with R/R-DLBCL. Methods: This study included 69 R/R DLBCL patients from the R2-GDP-GOTEL trial (EudraCT 2014-001620-299). Serum samples were collected at baseline, and the mean length of follow-up was 41 months. Serum metabolites were analyzed by nuclear magnetic resonance (NMR). Metabolites were correlated with treatment response, progression-free survival (PFS), and overall survival (OS). Results: Serum levels of 3-hydroxybutyrate (3OHB) and acetone were significantly (p < 0.001) associated with PFS (3OHB: hazard ratio [HR] 7.7, 95% confidence interval [CI] 2.5–24.1; acetone: HR 9.32, 95% CI 2.75–31.6) and OS (3OHB: HR 9.32, 95% CI 2.75–31.6; acetone: HR 1.92, 95% CI 1.36–2.69). Serum values of 141 µM for 3OHB and 40 µM for acetone were the optimal cutoffs associated with the survival outcomes. Elevated 3OHB levels (>141 μM) were specific to the ABC subtype of DLBCL, while acetone levels were elevated in both types of DLCBL but more pronounced in ABC cases. In a multivariate survival analysis, including the International Prognostic Index (IPI) score and refractoriness status (R/R), 3OHB and acetone remained significant. To aid oncologists employing the R2-GDP regime, we constructed PFS and OS nomograms for R/R-DLBCL risk stratification, incorporating 3OHB levels or acetone levels, IPI score, and refractoriness status. The nomogram with 3OHB and refractoriness status showed a time-dependent AUC of 0.86 for 6-month PFS and 0.84 for 12-month OS. These nomograms provide a comprehensive tool for individualized risk assessment and treatment optimization. Conclusions: The ketone bodies 3OHB and acetone are potential prognostic biomarkers of poor outcome in R/R DLBCL patients treated with the R2-GDP regimen, independently of IPI score and chemorefractoriness status. Full article