Least Absolute Shrinkage And Selection Operator Regression Research Articles

Identification of PANoptosis-related genes as prognostic indicators of thyroid cancer

BackgroundThyroid cancer (THCA) has become a common malignancy in recent years, with the mortality rate steadily increasing. PANoptosis is a unique kind of programmed cell death (PCD), including pyroptosis, necroptosis, and apoptosis, and is involved in the proliferation and prognosis of numerous cancers. This paper demonstrated the connection between PANoptosis-related genes and THCA based on the analyses of Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, which have not been evaluated yet. MethodsWe identified PANoptosis-related expression genes (PRDEGs) by multi-analyzing the TCGA-THCA and GEO datasets. To identify the significant PRDEGs, a prognostic model was constructed using least absolute shrinkage and selection operator regression (LASSO). The predictive values of the significant PRDEGs for THCA outcomes were determined using Cox regression analysis and nomograms. Gene enrichment analyses were performed. Finally, immunohistochemistry was carried out using the human protein atlas. ResultsA LASSO regression model based on nine PRDEGs was constructed, and the prognostic value of key PRDEGs was explored via risk score. Univariate and multivariate Cox regression were implemented to identify further three significant PRDEGs closely related to distant metastasis, lymph node metastasis, and tumor stage. Then, a nomogram was constructed, which presented high predictive accuracy for 5 years survival of THCA patients. Gene enrichment analyses in THCA were strongly associated with PCD pathways. CASP6 presented significantly differential expression during clinical T stage, N stage, and PFI events (P<0.05 for all) and demonstrated the highest degree of diagnostic efficacy in PRDEGs (HR: 2.060, 95% CI: 1.170-3.628, P < 0.05). Immunohistochemistry showed CASP6 was more abundant in THCA tumor tissue. ConclusionA potential prognostic role for PRDEGs in THCA was identified, providing a new direction for treatment. CASP6 may be a potential therapeutic target and a novel prognostic biomarker for THCA.

Machine learning model for cardiovascular disease prediction in patients with chronic kidney disease

IntroductionCardiovascular disease (CVD) is the leading cause of death in patients with chronic kidney disease (CKD). This study aimed to develop CVD risk prediction models using machine learning to support clinical decision making and improve patient prognosis.MethodsElectronic medical records from patients with CKD at a single center from 2015 to 2020 were used to develop machine learning models for the prediction of CVD. Least absolute shrinkage and selection operator (LASSO) regression was used to select important features predicting the risk of developing CVD. Seven machine learning classification algorithms were used to build models, which were evaluated by receiver operating characteristic curves, accuracy, sensitivity, specificity, and F1-score, and Shapley Additive explanations was used to interpret the model results. CVD was defined as composite cardiovascular events including coronary heart disease (coronary artery disease, myocardial infarction, angina pectoris, and coronary artery revascularization), cerebrovascular disease (hemorrhagic stroke and ischemic stroke), deaths from all causes (cardiovascular deaths, non-cardiovascular deaths, unknown cause of death), congestive heart failure, and peripheral artery disease (aortic aneurysm, aortic or other peripheral arterial revascularization). A cardiovascular event was a composite outcome of multiple cardiovascular events, as determined by reviewing medical records.ResultsThis study included 8,894 patients with CKD, with a composite CVD event incidence of 25.9%; a total of 2,304 patients reached this outcome. LASSO regression identified eight important features for predicting the risk of CKD developing into CVD: age, history of hypertension, sex, antiplatelet drugs, high-density lipoprotein, sodium ions, 24-h urinary protein, and estimated glomerular filtration rate. The model developed using Extreme Gradient Boosting in the test set had an area under the curve of 0.89, outperforming the other models, indicating that it had the best CVD predictive performance.ConclusionThis study established a CVD risk prediction model for patients with CKD, based on routine clinical diagnostic and treatment data, with good predictive accuracy. This model is expected to provide a scientific basis for the management and treatment of patients with CKD.

A systematic screening and heart team approach contributes to unravel novel risk factors in revascularisation candidates of complex coronary artery disease: a machine learning approach.

The baseline characteristics affecting mortality following percutaneous or surgical revascularisation in patients with left main (LM) and/or three-vessel (3V) coronary artery disease (CAD) differ between real-world practice and those established in randomized control trials (RCT) due to the constraints of inclusion/exclusion criteria. This study aimed to assess whether systematic screening identifies novel and registry-specific baseline characteristics influencing long-term mortality. LASSO (Least Absolute Shrinkage and Selection Operator) regression was used to screen 42 baseline characteristics shared by the SYNTAX trial and a single-center Polish registry of 1035 consecutive patients with complex CAD, receiving revascularization and followed up for 5 years. After screening, classical Cox regression analysis was performed to examine the suitability of a Linear model for predicting 5-year mortality, which was then compared to the mortality predicted in the same cohort using the SYNTAX score 2020 (SS2020). Five-year mortality in the registry was 12.3%, with the strongest predictors of pulmonary hypertension, chronic obstructive pulmonary disease and insulin-dependent diabetes. In an internal validation, the linear model constructed after LASSO screening and combined with a classical Cox regression analysis improved the prediction of 5-year mortality compared to the SS2020 (c-index 0.92 and 0.75, respectively). Machine learning improved the detection of registry-specific risk factors in all comers patients amenable to surgical or percutaneous revascularization who were discussed in a heart team. The risk factors identified from RCT are not necessarily the same as those detected in real clinical practice when systematic screening is applied.

A Nomogram Prediction Model for Persistent Pulmonary Hypertension of the Newborn in Neonates Hospitalized for the First Time After Birth.

Persistent pulmonary hypertension of the newborn (PPHN) is one of the critical neonatal diseases associated with high morbidity and mortality. This study attempted to conduct a nomogram prediction model for performing early identification of PPHN and providing effective information for clinical practice. A total of 456 newborns who first admitted to the hospital after birth were included in the analysis, including 138 newborns with PPHN and 318 newborns without PPHN (as controls). The optimal predictive variables selection was performed based on LASSO (least absolute shrinkage and selection operator) regression and multivariate logistic regression. Using the selected variables, a nomogram prediction model was developed. To validate the model, the model was assessed using the receiver operating characteristic curve, calibration plot, and clinical impact curve. Six predictors, namely, gestational age, neonatal respiratory distress syndrome, the levels of hemoglobin and creatine kinase-MB, gestational thyroid dysfunction, and Pao2, were identified by LASSO and multivariate logistic regression analysis from the original 30 variables studied. The constructed model, using these predictors, exhibited favorable predictive ability for PPHN, with an area under the receiver operating characteristic of 0.897 (sensitivity = 0.876, specificity = 0.785) in the training set and 0.871 (sensitivity = 0.902, specificity = 0.695) in the validation set, and was well calibrated, as indicated by the PHosmer-Lemeshow test values of 0.233 and 0.876 for the training and validation sets, respectively. The model included gestational age, neonatal respiratory distress syndrome, the levels of hemoglobin and creatine kinase-MB, gestational thyroid dysfunction, and Pao2 had good prediction performance for predicting PPHN among newborns first admitted to the hospital after birth.

Establishment of a mortality risk nomogram for predicting in-hospital mortality of sepsis: cohort study from a Chinese single center.

To establish a mortality risk nomogram for predicting in-hospital mortality of sepsis patients in the Chinese population. Data were obtained from the medical records of sepsis patients enrolled at the Affiliated Huadu Hospital, Southern Medical University, between 2019 and 2021. A total of 696 sepsis patients were initially included in our research, and 582 cases were finally enrolled after screening and divided into the survival group (n = 400) and the non-survival group (n = 182) according to the incidence of mortality during hospitalization. Twenty-eight potential sepsis-related risk factors for mortality were identified. Least absolute shrinkage and selection operator (LASSO) regression was used to optimize variable selection by running cyclic coordinate descent with k-fold (tenfold in this case) cross-validation. We used binary logistic regression to build a model for predicting mortality from the variables based on LASSO regression selection. Binary logistic regression was used to establish a nomogram based on independent mortality risk factors. To validate the prediction accuracy of the nomogram, receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA) and restricted cubic spline (RCS) analysis were employed. Eventually, the Hosmer-Lemeshow test and calibration curve were used for nomogram calibration. LASSO regression identified a total of ten factors, namely, chronic heart disease (CHD), lymphocyte count (LYMP), neutrophil-lymphocyte ratio (NLR), red blood cell distribution width (RDW), C reactive protein (CRP), Procalcitonin (PCT), lactic acid, prothrombin time (PT), alanine aminotransferase (ALT), total bilirubin (Tbil), interleukin-6 (IL6), that were incorporated into the multivariable analysis. Finally, a nomogram including CHD, LYMP, NLR, RDW, lactic acid, PT, CRP, PCT, Tbil, ALT, and IL6 was established by multivariable logistic regression. The ROC curves of the nomogram in the training and validation sets were 0.9836 and 0.9502, respectively. DCA showed that the nomogram could be applied clinically if the risk threshold was between 29.52 and 99.61% in the training set and between 31.32 and 98.49% in the testing set. RCS showed that when the value of independent risk factors from the predicted model exceeded the median, the mortality hazard ratio increased sharply. The results of the Hosmer-Lemeshow test (χ2 = 0.1901, df = 2, p = 0.9091) and the calibration curves of the training and validation sets showed good agreement with the actual results, which indicated good stability of the model. Our nomogram, including CHD, LYMP, NLR, RDW, lactic acid, PT, CRP, PCT, Tbil, ALT, and IL6, exhibits good performance for predicting mortality risk in adult sepsis patients.

Molecular characterization of m6A RNA methylation regulators with features of immune dysregulation in IgA nephropathy.

The role of RNA N6-methyladenosine (m6A) modification in immunity is being elucidated. This study aimed to explore the potential association between m6A regulators and the immune microenvironment in IgA nephropathy (IgAN). The expression profiles of 24 m6A regulators in 107 IgAN patients were obtained from the Gene Expression Omnibus (GEO) database. The least absolute shrinkage and selection operator (LASSO) regression and logistic regression analysis were utilized to construct a model for distinguishing IgAN from control samples. Based on the expression levels of m6A regulators, unsupervised clustering was used to identify m6A-induced molecular clusters in IgAN. Gene set enrichment analysis (GSEA) and immunocyte infiltration among different clusters were examined. The gene modules with the highest correlation for each of the three clusters were identified by weighted gene co-expression network analysis (WGCNA). A model containing 10 m6A regulators was developed using LASSO and logistic regression analyses. Three molecular clusters were determined using consensus clustering of 24 m6A regulators. A decrease in the expression level of YTHDF2 in IgAN samples was significantly negatively correlated with an increase in resting natural killer (NK) cell infiltration and was positively correlated with the abundance of M2 macrophage infiltration. The risk scores calculated by the nomogram were significantly higher for cluster-3, and the expression levels of m6A regulators in this cluster were generally low. Immunocyte infiltration and pathway enrichment results for cluster-3 differed significantly from those for the other two clusters. Finally, the expression of YTHDF2 was significantly decreased in IgAN based on immunohistochemical staining. This study demonstrated that m6A methylation regulators play a significant role in the regulation of the immune microenvironment in IgAN. Based on m6A regulator expression patterns, IgAN can be classified into multiple subtypes, which might provide additional insights into novel therapeutic methods for IgAN.

Establishment of a Risk Prediction Model for Metabolic Syndrome in High Altitude Areas in Qinghai Province, China: A Cross-Sectional Study.

The prevalence of metabolic syndrome (MetS) is increasing worldwide, and early prediction of MetS risk is highly beneficial for health outcomes. This study aimed to develop and validate a nomogram to predict MetS risk in Qinghai Province, China, and it provides a methodological reference for MetS prevention and control in Qinghai Province, China. A total of 3073 participants living between 1900 and 3710 meters above sea level in Qinghai Province participated in this study between March 2014 and March 2016. We omitted 12 subjects who were missing diagnostic component data for MetS, ultimately resulting in 3061 research subjects, 70% of the subjects were assigned randomly to the training set, and the remaining subjects were assigned to the validation set. The least absolute shrinkage and selection operator (LASSO) regression analysis method was used for variable selection via running cyclic coordinate descent with 10-fold cross-validation. Multivariable logistic regression was then performed to develop a predictive model and nomogram. The receiver operating characteristic (ROC) curves was used for model evaluation, and calibration plot and decision curve analysis (DCA) were used for model validation. Of 24 variables studied, 6 risk predictors were identified by LASSO regression analysis: hyperlipidaemia, hyperglycemia, abdominal obesity, systolic blood pressure (SBP), diastolic blood pressure (DBP), and body mass index (BMI). A prediction model including these 6 risk factors was constructed and displayed good predictability with an area under the ROC curve of 0.914 for the training set and 0.930 for the validation set. DCA revealed that if the threshold probability of MetS is less than 82%, the application of this nomogram is more beneficial than both the treat-all or treat-none strategies. The nomogram developed in our study demonstrated strong discriminative power and clinical applicability, making it a valuable reference for meets prevention and control in the plateau areas of Qinghai Province.

Development and validation of a nomogram to predict surgical site infection after soft-tissue sarcoma resection.

Surgical site infection (SSI) after soft-tissue sarcoma (STS) resection is a serious complication. The purpose of this retrospective study was to investigate the risk factors for SSI after STS resection, and to develop a nomogram that allows patient-specific risk assessment. A total of 547 patients with STS who underwent tumour resection between 2005 and 2021 were divided into a development cohort and a validation cohort. In the development cohort of 402 patients, the least absolute shrinkage and selection operator (LASSO) regression model was used to screen possible risk factors of SSI. To select risk factors and construct the prediction nomogram, multivariate logistic regression was used. The predictive power of the nomogram was evaluated by receiver operating curve (ROC) analysis in the validation cohort of 145 patients. LASSO regression analysis selected possible risk factors for SSI, including age, diabetes, operating time, skin graft or flap, resected tumour size, smoking, and radiation therapy. Multivariate analysis revealed that age, diabetes, smoking during the previous year, operating time, and radiation therapy were independent risk factors for SSI. A nomogram was developed based on the results of multivariate logistic regression analysis. In the development cohort, the incidence of SSI was 4.5% in the low-risk group (risk score < 6.89) and 26.6% in the high-risk group (risk score ≥ 6.89; p < 0.001). In the validation cohort, the incidence of SSI was 2.0% in the low-risk group and 15.9% in the high-risk group (p = 0.004). Our nomogram will enable surgeons to assess the risk of SSI in patients with STS. In patients with high risk of SSI, frequent monitoring and aggressive interventions should be considered to prevent this.

Autophagy-related genes in mesial temporal lobe epilepsy: an integrated bioinformatics analysis

BackgroundAutophagy plays essential roles in the development and pathogenesis of mesial temporal lobe epilepsy (mTLE). In this research, we aim to identify and validate the autophagy-related genes associated with mTLE through bioinformatics analysis and experimental validations.MethodsWe obtained the dataset GSE143272 and high-throughput sequencing results of mTLE from public databases. Potential differentially expressed autophagy-related genes related to mTLE were identified using R software. Subsequently, genomes pathway enrichment analysis, protein-protein interactions (PPIs), and the gene ontology (GO) enrichment were performed for the selected autophagy-related genes. The mRNA expression profiles of hub genes were then used to establish a least absolute shrinkage and selection operator (LASSO) model. Finally, seven hub candidate autophagy-related genes were confirmed in hippocampus using the lithium-pilocarpine chronic epilepsy model.ResultsA total of 40 differential expression genes (DEGs) among the core autophagy-related genes were identified. The analysis results of PPI revealed that interactions among these DEGs. KEGG pathway and GO analysis of selected candidate autophagy-related genes indicated that those enriched terms mainly focused on macroautophagy, regulation of autophagy, cellular response to extracellular stimulus and mitochondrion disassembly. The results suggested that SQSTM1, VEGFA, BNIP and WIPI2 were consistent with the bioinformatics analysis. The expression levels of SQSTM1 and VEGFA in epilepsy model samples were significantly higher than those in normal control, while BNIP and WIPI2 expression levels were notably decreased. The final hub gene-based LASSO regression model accurately predicted the occurrence of epilepsy (AUC = 0.88).ConclusionsThrough bioinformatics analysis of public data, we identified 40 candidate autophagy-related genes associated with mTLE. SQSTM1, VEGFA, BNIP and WIPI2 may play significant roles in autophagy, influencing the onset and development of mTLE by regulating autophagy pathway. These findings deepen our understanding of mTLE, and may serve as sensitive and valuable indicators for the prognosis and diagnosis of this condition.

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.

Navigating the future of diabetes: innovative nomogram models for predicting all-cause mortality risk in diabetic nephropathy.

This study aims to establish and validate a nomogram model for the all-cause mortality rate in patients with diabetic nephropathy (DN). We analyzed data from the National Health and Nutrition Examination Survey (NHANES) spanning from 2007 to 2016. A random split of 7:3 was performed between the training and validation sets. Utilizing follow-up data until December 31, 2019, we examined the all-cause mortality rate. Cox regression models and Least Absolute Shrinkage and Selection Operator (LASSO) regression models were employed in the training cohort to develop a nomogram for predicting all-cause mortality in the studied population. Finally, various validation methods were employed to assess the predictive performance of the nomogram, and Decision Curve Analysis (DCA) was conducted to evaluate the clinical utility of the nomogram. After the results of LASSO regression models and Cox multivariate analyses, a total of 8 variables were selected, gender, age, poverty income ratio, heart failure, body mass index, albumin, blood urea nitrogen and serum uric acid. A nomogram model was built based on these predictors. The C-index values in training cohort of 3-year, 5-year, 10-year mortality rates were 0.820, 0.807, and 0.798. In the validation cohort, the C-index values of 3-year, 5-year, 10-year mortality rates were 0.773, 0.788, and 0.817, respectively. The calibration curve demonstrates satisfactory consistency between the two cohorts. The newly developed nomogram proves to be effective in predicting the all-cause mortality risk in patients with diabetic nephropathy, and it has undergone robust internal validation.

What are the stimulants on transportation carbon dioxide emissions?: A nation-level analysis

The transportation sector is the second-largest contributor to CO2 emissions globally. To develop a low-carbon country, identifying factors influencing transportation CO2 emissions on a country-by-country basis is necessary due to differences in travel behaviors and transportation infrastructures. This study identifies the CO2 emission stimulants across 145 countries. The first analysis uses stepwise and robust regressions to provide a holistic perspective. The second analysis employs the Least Absolute Shrinkage and Selection Operator (LASSO) regression after categorizing 145 countries based on their economic levels and geographical regions to obtain more detailed results. An additional LASSO regression involves grouping countries based on their OECD memberships and economic levels. The findings suggest that socio-demographics, travel characteristics, and logistics have distinct impacts on CO2 emissions at different economic levels of countries. This study reveals strong correlations between the increased share of cycling and decreased CO2 emissions, especially in all developed countries and Asian developing countries. In most countries, higher gasoline prices are associated with lower CO2 emissions. Moreover, the rise in electric vehicle usage is contributing to lower CO2 emissions in developed countries. These findings contribute to the global efforts to address transportation-related carbon emissions at the national level.

Optimizing early neurological deterioration prediction in acute ischemic stroke patients following intravenous thrombolysis: a LASSO regression model approach.

Acute ischemic stroke (AIS) remains a leading cause of disability and mortality globally among adults. Despite Intravenous Thrombolysis (IVT) with recombinant tissue plasminogen activator (rt-PA) emerging as the standard treatment for AIS, approximately 6-40% of patients undergoing IVT experience Early Neurological Deterioration (END), significantly impacting treatment efficacy and patient prognosis. This study aimed to develop and validate a predictive model for END in AIS patients post rt-PA administration using the Least Absolute Shrinkage and Selection Operator (LASSO) regression approach. In this retrospective cohort study, data from 531 AIS patients treated with intravenous alteplase across two hospitals were analyzed. LASSO regression was employed to identify significant predictors of END, leading to the construction of a multivariate predictive model. Six key predictors significantly associated with END were identified through LASSO regression analysis: previous stroke history, Body Mass Index (BMI), age, Onset to Treatment Time (OTT), lymphocyte count, and glucose levels. A predictive nomogram incorporating these factors was developed, effectively estimating the probability of END post-IVT. The model demonstrated robust predictive performance, with an Area Under the Curve (AUC) of 0.867 in the training set and 0.880 in the validation set. The LASSO regression-based predictive model accurately identifies critical risk factors leading to END in AIS patients following IVT. This model facilitates timely identification of high-risk patients by clinicians, enabling more personalized treatment strategies and optimizing patient management and outcomes.

Association of exposure to multiple perfluoroalkyl and polyfluoroalkyl substances and glucose metabolism in National Health and Nutrition Examination Survey 2017-2018.

To investigate the relationships between perfluoroalkyl and polyfluoroalkyl substances (PFASs) exposure and glucose metabolism indices. Data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018 waves were used. A total of 611 participants with information on serum PFASs (perfluorononanoic acid (PFNA); perfluorooctanoic acid (PFOA); perfluoroundecanoic acid (PFUA); perfluorohexane sulfonic acid (PFHxS); perfluorooctane sulfonates acid (PFOS); perfluorodecanoic acid (PFDeA)), glucose metabolism indices (fasting plasma glucose (FPG), homeostasis model assessment for insulin resistance (HOMA-IR) and insulin) as well as selected covariates were included. We used cluster analysis to categorize the participants into three exposure subgroups and compared glucose metabolism index levels between the subgroups. Least absolute shrinkage and selection operator (LASSO), multiple linear regression analysis and Bayesian kernel machine regression (BKMR) were used to assess the effects of single and mixed PFASs exposures and glucose metabolism. The cluster analysis results revealed overlapping exposure types among people with higher PFASs exposure. As the level of PFAS exposure increased, FPG level showed an upward linear trend (p < 0.001), whereas insulin levels demonstrated a downward linear trend (p = 0.012). LASSO and multiple linear regression analysis showed that PFNA and FPG had a positive relationship (>50 years-old group: β = 0.059, p < 0.001). PFOA, PFUA, and PFHxS (≤50 years-old group: insulin β = -0.194, p < 0.001, HOMA-IR β = -0.132, p = 0.020) showed negative correlation with HOMA-IR/insulin. PFNA (>50 years-old group: insulin β = 0.191, p = 0.018, HOMA-IR β = 0.220, p = 0.013) showed positive correlation with HOMA-IR/insulin, which was essentially the same as results that obtained for the univariate exposure-response map in the BKMR model. Association of exposure to PFASs on glucose metabolism indices showed positive interactions between PFOS and PFHxS and negative interactions between PFOA and PFNA/PFOS/PFHxS. Our study provides evidence that positive and negative correlations between PFASs and FPG and HOMA-IR/insulin levels are observed, respectively. Combined effects and interactions between PFASs. Given the higher risk of glucose metabolism associated with elevated levels of PFAS, future studies are needed to explore the potential underlying mechanisms.

Risk factors and prediction model construction for peripherally inserted central catheter-related infections

ObjectiveTo establish a predictive modeling for the risk of bloodstream infection associated with peripherally inserted central catheter (PICC). MethodsPatients receiving PICC treatment in Shenzhen People's Hospital from June 2020 to December 2020 were retrospectively enrolled and divided into the infection group and the non-infection group according to the presence and absence of PICC-related infections. Then, relevant clinical information of patients was collected and the predictors of PICC-related infection were screened by the least absolute shrinkage and selection operator regression (LASSO) model. Besides, multivariate logistic regression was used to analyze the influencing factors of PICC-related infection, A nomogram was constructed based on the results of the multivariate analysis. Ultimately, a receiver operating characteristic (ROC) curve was plotted to analyze the application value of influencing factors to predict PICC-related infections. ResultsA total of 505 patients were included, including 75 patients with PICC-related infections (14.85%). The main pathogen was gram-positive cocci. The predictors screened by LASSO included age >60 years, catheter movement, catheter maintenance cycle, insertion technique, immune function, complications, and body temperature ≥37.2 °C before PICC placement. Multivariate logistic regression analysis showed that independent risk factors of infections related to PICC included age >60 years [odds ratio (OR) = 1.722; 95% confidence interval (CI) = 1.312–3.579; P = 0.006], catheter movement (OR = 1.313; 95% CI = 1.119–3.240; P = 0.014), catheter maintenance cycle >7 days (OR = 2.199; 95% CI = 1.677–4.653; P = 0.000), direct insertion (OR = 1.036; 95% CI = 1.019–2.743; P = 0.000), poor immune function (OR = 2.322; 95% CI = 2.012–4.579; P = 0.000), complications (OR = 1.611; 95% CI = 1.133–3.454; P = 0.019), and body temperature ≥37.2 °C before PICC placement (OR = 1.713; 95% CI = 1.172–3.654; P = 0.012). Besides, the area under the ROC curve was 0.889. ConclusionPICC-related infections are associated with factors such as age >60 years, catheter movement, catheter maintenance cycle, insertion technique, immune function, complications, and body temperature ≥37.2 °C before PICC placement. Additionally, the LASSO model is moderately predictive for predicting the occurrence of PICC-related infections.

Development and validation of prognostic nomographs for patients with cervical cancer: SEER-based Asian population study

To develop and validate a nomograph to predict the long-term survival probability of cervical cancer (CC) patients in Asia, Surveillance, Epidemiology, and End Results (SEER) were used to collect information about CC patients in Asia. The patient data were randomly sampled and divided into a training group and a validation group by 7:3. Least absolute shrinkage and selection operator (LASSO) regression was used to screen key indicators, and multivariate Cox regression model was used to establish a prognostic risk prediction model for CC patients. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were adopted to comprehensively evaluate the nomogram model. LASSO regression and multivariate Cox proportional hazards model analysis showed that age, American Joint Committee on Cancer (AJCC) Stage, AJCC T, tumor size, and surgery were independent risk factors for prognosis. The ROC curve results proved that the area under curve (AUC) values of the training group in 3 and 5 years were 0.837 and 0.818, The AUC values of the validation group in 3 and 5 years were 0.796 and 0.783. DCA showed that the 3- and 5-year overall survival (OS) nomograms had good clinical potential value. The nomogram model developed in this study can effectively predict the prognosis of Asian patients with CC, and the risk stratification system based on this nomogram prediction model has some clinical value for discriminating high-risk patients.

Construction and validation of a nomogram for predicting remission of migraine patients with patent foramen ovale after closure

ObjectiveThis study aimed to construct and validate a nomogram model that predicts the remission of migraine attacks by screening factors that affect the prognosis of migraine patients with patent foramen ovale (PFO) after closure. MethodRetrospective analysis was conducted in migraine patients with PFO who underwent PFO closure in the Department of Cardiology of Jiangsu Province Hospital from September 2020 to April 2023. Based on the Migraine Disability Assessment (MIDAS) scores from the 1-year follow-up after PFO closure, all patients who met the inclusion criteria were categorized into a remission group and a non-remission group. The primary efficacy endpoint was remission of migraine headache. After collecting clinical data, transcranial doppler sonography (TCD) results and MIDAS scores, LASSO (least absolute shrinkage and selection operator) regression and multivariable logistic regression analysis were used to filter variables predictive to migraine remission and construct the nomogram model. The Nomogram's accuracy and consistency were respectively assessed through Receiver Operating Characteristic (ROC) curves and calibration curves. Additionally, an analysis of decision curves (DCA) was conducted to evaluate the clinical utility of this newly developed model. ResultA total of 241 consecutive patients were included in the study. The remission group included 21 males and 93 females, with a median age of 39 (30.25,50) years. The non-remission group included 26 males and 101 females, with a median age of 35 (25.5,47.5) years. All Patients were randomly divided into a training cohort and a validation cohort. Multivariable logistic regression analysis showed that 5 independent predictors, including MIDAS before closure (p = 0.0002), mitigating factors (p = 0.0057), number of attacks/month (p = 0.0058), TCD (p = 0.0093) and Platelet Crit (PCT) (p = 0.0351), played a significant role in the prediction of remission of migraine patients with PFO after closure. Based on these independent predictors, the predictive nomogram model of migraine remission in PFO patients was constructed. The application of the nomogram in the training cohort exhibited good discrimination (area under the ROC curve was 0.7763[95% CI 0.7108–0.8418]), which was confirmed in the validation cohort (AUC was 0.704[95% CI 0.5533–0.8547]). The calibration curve showed that the nomogram model demonstrated good calibration performance. Additionally, the decision curve analysis indicated the clinical utility of the nomogram model. ConclusionThe construction of the nomogram model had a considerable predictive accuracy for migraine remission in patients after PFO closure, which may provide constructive guidance for clinical decision making.

Development and validation of a prognostic model for predicting post-discharge mortality risk in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI)

BackgroundAccurately predicting post-discharge mortality risk in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI) remains a complex and critical challenge. The primary objective of this study was to develop and validate a robust risk prediction model to assess the 12-month and 24-month mortality risk in STEMI patients after hospital discharge.MethodsA retrospective study was conducted on 664 STEMI patients who underwent PPCI at Xiangtan Central Hospital Chest Pain Center between 2020 and 2022. The dataset was randomly divided into a training cohort (n = 464) and a validation cohort (n = 200) using a 7:3 ratio. The primary outcome was all-cause mortality following hospital discharge. The least absolute shrinkage and selection operator (LASSO) regression model was employed to identify the optimal predictive variables. Based on these variables, a regression model was constructed to determine the significant predictors of mortality. The performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).ResultsThe prognostic model was developed based on the LASSO regression results and further validated using the independent validation cohort. LASSO regression identified five important predictors: age, Killip classification, B-type natriuretic peptide precursor (NTpro-BNP), left ventricular ejection fraction (LVEF), and the usage of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitors (ACEI/ARB/ARNI). The Harrell's concordance index (C-index) for the training and validation cohorts were 0.863 (95% CI: 0.792–0.934) and 0.888 (95% CI: 0.821–0.955), respectively. The area under the curve (AUC) for the training cohort at 12 months and 24 months was 0.785 (95% CI: 0.771–0.948) and 0.812 (95% CI: 0.772–0.940), respectively, while the corresponding values for the validation cohort were 0.864 (95% CI: 0.604–0.965) and 0.845 (95% CI: 0.705–0.951). These results confirm the stability and predictive accuracy of our model, demonstrating its reliable discriminative ability for post-discharge all-cause mortality risk. DCA analysis exhibited favorable net benefit of the nomogram.ConclusionThe developed nomogram shows potential as a tool for predicting post-discharge mortality in STEMI patients undergoing PPCI. However, its full utility awaits confirmation through broader external and temporal validation.

Analysis of related factors for sarco-osteoporosis in middle-aged and elderly inpatients and development and validation of a nomogram

BackgroundSarco-osteoporosis is a skeletal muscle disease associated with aging and complex pathological factors. At present, there are few studies on the analysis of its related factors, and a nomogram to estimate the risk of sarco-osteoporosis in middle-aged and elderly patients is not available.MethodsA total of 386 patients admitted to our hospital from October 2021 to October 2022 were collected, and the general demographic data and clinical data of the patients were collected.386 subjects were enrolled in the study and randomly divided into training set and validation set at a ratio of 7:3. In the training set, the Least absolute shrinkage and selection operator(LASSO)regression technique was used to select the optimal predictive features, and multivariate logistic regression was used to screen the factors associated with sarco-osteoporosis, and a nomogram was constructed using meaningful variables from multivariate analysis. The performance of the nomograms was assessed and validated by Area Under Curve (AUC) and calibration curves.ResultsThere were no significant differences in baseline characteristic of individuals in training set and validation set, six variables with non-zero coefficients were screened based on LASSO regression in the training set. Multivariate logistic regression analysis showed that the related factors for sarco-osteoporosis in middle-aged and elderly inpatients included age (OR = 1.08, 95%CI 1.03 ∼ 1.14), regular exercise (OR = 0.29, 95%CI 0.15 ∼ 0.56), albumin (OR = 0.9, 95%CI 0.82 ∼ 0.98), height (OR = 0.93, 95%CI 0.88 ∼ 0.99) and lean mass index (OR = 0.66, 95%CI 0.52 ∼ 0.85), and a nomogram was constructed based on the above factors. AUC of nomogram were 0.868(95%CI 0.825 ∼ 0.912) in the training set and 0.737(95%CI 0.646 ∼ 0.828) in the validation set. Calibration curve analysis showed that the predicted probability of sarco-osteoporosis had high consistency with the actual probability, and the absolute error of the training set and verification set was 0.018 and 0.03, respectively.ConclusionsOur research showed that the occurrence of sarco-osteoporosis was associated with age, regular exercise, albumin, height and lean mass index, and we have developed a nomogram that can be effectively used in the preliminary and in-depth risk prediction of sarco-osteoporosis in middle-aged and elderly hospitalized patients.

CT-based radiomics combined with hematologic parameters for survival prediction in locally advanced esophageal cancer patients receiving definitive chemoradiotherapy

ObjectivesThe purpose of this study was to investigate the prognostic significance of radiomics in conjunction with hematological parameters in relation to the overall survival (OS) of individuals diagnosed with esophageal squamous cell carcinoma (ESCC) following definitive chemoradiotherapy (dCRT).MethodsIn this retrospective analysis, a total of 122 patients with locally advanced ESCC were included. These patients were randomly assigned to either the training cohort (n = 85) or the validation cohort (n = 37). In the training group, the least absolute shrinkage and selection operator (LASSO) regression was utilized to choose the best radiomic features for calculating the Rad-score. To develop a nomogram model, both univariate and multivariate analyses were conducted to identify the clinical factors and hematologic parameters that could predict the OS. The performance of the predictive model was evaluated using the C-index, while the accuracy was assessed through the calibration curve.ResultsThe Rad-score was calculated by selecting 10 radiomic features through LASSO regression. OS was predicted independently by neutrophil-to-monocyte ratio (NMR) and Rad-score according to the results of multivariate analysis. Patients who had a Rad-score > 0.47 and an NMR > 9.76 were at a significant risk of mortality. A nomogram was constructed using the findings from the multivariate analysis. In the training cohort, the nomogram had a C-index of 0.619, while in the validation cohort, it was 0.573. The model’s accuracy was demonstrated by the calibration curve, which was excellent.ConclusionA prognostic model utilizing radiomics and hematologic parameters was developed, enabling the prediction of OS in patients with ESCC following dCRT.Critical relevance statementPatients with esophageal cancer who underwent definitive chemoradiotherapy may benefit from including CT radiomics in the nomogram model.Key points• Predicting the prognosis of ESCC patients before treatment is particularly important.• Patients with a Rad-score > 0.47 and neutrophil-to-monocyte ratio > 9.76 had a high risk of mortality.• CT-based radiomics nomogram model could be used to predict the survival of patients.Graphical