Least Absolute Shrinkage And Selection Operator Logistic Regression Research Articles

UTILIZATION OF LASSO AND POISSON REGRESSION DEALING WITH COUNT VARIABLES IN NURSING HOME RESEARCH

Abstract Regression has been used for decades; thus, more empirical studies are necessary to choose appropriate regression methods, as traditional regression has many drawbacks and is limited in analysis and synthesis with large numbers of covariates. This study aims to investigate factors related to pressure ulcers in nursing homes using three regressions: least absolute shrinkage and selection operator (LASSO) Poisson, LASSO linear, and LASSO logistic regression. We represent how each model’s estimating, inferencing, and performance measures work. The datasets accrued from 51 nursing homes that operated between 2021 and 2022. A total of 35 independent variables included organizational and nurse staffing variables. The variable of interest was the number of residents in each nursing home who had an occurrence of pressure ulcers within the last 3 months. The significant factors were different; turnover of Certified Nurse Aides and government incentives of more RNs were significant in the LASSO Poison regression, no factors were significant in the LASSO Linear regression, while and the percentage of residents who are completely dependent for activities of daily living in LASSO was significant factor in the Logistic regression. Using the coefficients in the Poisson regression, we interpreted that more nursing staff turnover increased the percentage of residents with pressure ulcers by 3.02 times, which would not be possible with linear regression. The Lasso model uses a technique of shrinking coefficient estimates to zero to improve the fit of a model that includes all explanatory variables and improves model interpretability by identifying most important features.

Machine learning-based model for predicting major adverse cardiovascular and cerebrovascular events in patients aged 65 years and older undergoing noncardiac surgery

BackgroundFew evidence-based prediction models have been developed for predicting major adverse cardiovascular and cerebrovascular events (MACCE) in patients aged 65 years or older undergoing noncardiac surgery. In this study, we aimed to analyze the risk factors for perioperative MACCE in patients aged 65 years or older undergoing noncardiac surgery and construct a prediction model.MethodsIn this nested case–control study, a total of 342 Chinese patients who were aged ≥ 65 years and underwent medium- or high-risk noncardiac surgery in our hospital were included. There were 84 cases with MACCE (the MACCE group) and 258 without MACCE (the control group). Univariable logistic regression analysis was performed to identify the risk factors for MACCE. Least absolute shrinkage and selection operator (LASSO) regression was used to screen the variables. Nomogram was constructed using the selected variables. Machine learning methods, including Decision Tree, XGBoost, Support Vector Machine, K-nearest Neighbor, and Neural network, was used to establish, validate, and compare the performance of different prediction models.ResultsA prediction model based on nine variables, including age ≥ 85 years, history of ischemic chest pain, symptoms of decompensated heart failure, high-risk surgery, intraoperative minimum systolic blood pressure, postoperative systolic blood pressure, Cr levels over 2.0 mg/dL, left ventricular ejection fraction, and perioperative blood transfusion, was constructed. This LASSO logistic regression model showed good discriminatory ability to predict MACCE (area under the curve = 0.89; 95% confidence interval, 0.818 – 0.963) and fit to the test set (Hosmer–Lemeshow, χ2 = 7.4053, P = 0.4936). The decision curve analysis showed a positive net benefit of the new model. Compared with logistic regression model, the XGBoost model showed better prediction ability (area under the curve = 0.903). A preoperative prediction model based on five variables, including age ≥ 85 years, symptoms of decompensated heart failure, ischemic chest pain, high-risk type of surgery and Cr levels over 2.0 mg/dL was also constructed. This model showed good discriminatory ability to predict MACCE before surgery (area under the curve = 0.720 [95% CI, 0.591–0.848]. Both models compared with the modified RCRI score had improvement in reclassification.ConclusionBy analyzing Chinese patients aged ≥ 65 years undergoing medium- or high-risk noncardiac surgery, the risk factors for perioperative MACCE were identified. Then, simple prediction models were constructed and validated, which showed good prediction performance and may be used as a decision-making assistant tool for clinicians. These findings provide a basis for preventing and improving the perioperative management of MACCE.

Applications of Machine Learning to Diagnosis of Parkinson's Disease.

Accurate diagnosis of Parkinson's disease (PD) is challenging due to its diverse manifestations. Machine learning (ML) algorithms can improve diagnostic precision, but their generalizability across medical centers in China is underexplored. To assess the accuracy of an ML algorithm for PD diagnosis, trained and tested on data from different medical centers in China. A total of 1656 participants were included, with 1028 from Beijing (training set) and 628 from Fuzhou (external validation set). Models were trained using the least absolute shrinkage and selection operator-logistic regression (LASSO-LR), decision tree (DT), random forest (RF), eXtreme gradient boosting (XGboost), support vector machine (SVM), and k-nearest neighbor (KNN) techniques. Hyperparameters were optimized using five-fold cross-validation and grid search techniques. Model performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, accuracy, sensitivity (recall), specificity, precision, and F1 score. Variable importance was assessed for all models. SVM demonstrated the best differentiation between healthy controls (HCs) and PD patients (AUC: 0.928, 95% CI: 0.908-0.947; accuracy: 0.844, 95% CI: 0.814-0.871; sensitivity: 0.826, 95% CI: 0.786-0.866; specificity: 0.861, 95% CI: 0.820-0.898; precision: 0.849, 95% CI: 0.807-0.891; F1 score: 0.837, 95% CI: 0.803-0.868) in the validation set. Constipation, olfactory decline, and daytime somnolence significantly influenced predictability. We identified multiple pivotal variables and SVM as a precise and clinician-friendly ML algorithm for prediction of PD in Chinese patients.

Gene Expression and Metadata Based Identification of Key Genes for Hepatocellular Carcinoma Using Machine Learning and Statistical Models.

Biomarkers associated with hepatocellular carcinoma (HCC) are of great importance to better understand biological response mechanisms to internal or external intervention. The study aimed to identify key candidate genes for HCC using machine learning (ML) and statistics-based bioinformatics models. Differentially expressed genes (DEGs) were identified using limma and then selected their common genes among DEGs identified from four datasets. After that, protein-protein interaction networks were constructed using STRING and then Cytoscape was used to determine hub genes, significant modules, and their associated genes. Simultaneously, three ML-based techniques such as support vector machine (SVM), least absolute shrinkage and selection operator-logistic regression (LASSO-LR), and partial least squares-discriminant analysis (PLS-DA) were implemented to determine the discriminative genes of HCC from common DEGs. Moreover, metadata of hub genes were formed by listing all hub genes from existing studies to incorporate other findings in our analysis. Finally, seven key candidate genes (ASPM, CCNB1, CDK1, DLGAP5, KIF20 A, MT1X, and TOP2A) were identified by intersecting common genes among hub genes, significant modules genes, discriminative genes from SVM, LASSO-LR, and PLS-DA, and meta hub genes from existing studies. Another three independent test datasets were also used to validate these seven key candidate genes using AUC, computed from ROC.

Establishment of a prediction model for prehospital return of spontaneous circulation in out-of-hospital patients with cardiac arrest.

Out-of-hospital cardiac arrest (OHCA) is a leading cause of death worldwide. To explore factors influencing prehospital return of spontaneous circulation (P-ROSC) in patients with OHCA and develop a nomogram prediction model. Clinical data of patients with OHCA in Shenzhen, China, from January 2012 to December 2019 were retrospectively analyzed. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were applied to select the optimal factors predicting P-ROSC in patients with OHCA. A nomogram prediction model was established based on these influencing factors. Discrimination and calibration were assessed using receiver operating characteristic (ROC) and calibration curves. Decision curve analysis (DCA) was used to evaluate the model's clinical utility. Among the included 2685 patients with OHCA, the P-ROSC incidence was 5.8%. LASSO and multivariate logistic regression analyses showed that age, bystander cardiopulmonary resuscitation (CPR), initial rhythm, CPR duration, ventilation mode, and pathogenesis were independent factors influencing P-ROSC in these patients. The area under the ROC was 0.963. The calibration plot demonstrated that the predicted P-ROSC model was concordant with the actual P-ROSC. The good clinical usability of the prediction model was confirmed using DCA. The nomogram prediction model could effectively predict the probability of P-ROSC in patients with OHCA.

Predicting the prognosis of lung cancer patients treated with intensity-modulated radiotherapy based on radiomic features.

This study aimed to develop a method for predicting short-term outcomes of lung cancer patients treated with intensity-modulated radiotherapy (IMRT) using radiomic features detected through computed tomography images. A prediction model was developed based on a dataset of radiomic features obtained from 132 patients with lung cancer receiving IMRT. Dimension reduction was performed for the features using the maximum-relevance and minimum-redundancy (mRMR) algorithm, and the least absolute shrinkage and selection operator (LASSO) regression model was utilized to optimize feature selection for the IMRT-sensitivity prediction model. The model was constructed using binary logistic regression analysis and was evaluated using the concordance index (C-index), calibration plots, receiver operating characteristic curve, and decision curve analysis. Fifty features were selected from 1348 radiomic features using the mRMR method. Of these, three radiomic features were selected by LASSO logistic regression to construct the radiomics nomogram. The C-index of the model was 0.776 (95% confidence interval: 0.689-0.862) and 0.791 (95% confidence interval: 0.607-0.974) in the training and validation cohorts, respectively. Decision curve analysis showed that the radiomics nomogram was clinically useful. Radiomic features have the potential to be applied to predict the short-term efficacy of IMRT in patients with inoperable lung cancer.

Development and validation of radiomics models for the prediction of diagnosis of classic trigeminal neuralgia.

The study aims to develop a magnetic resonance imaging (MRI)-based radiomics model for the diagnosis of classic trigeminal neuralgia (cTN). This study involved 350 patients with cTN and 100 control participants. MRI data were collected retrospectively for all the enrolled subjects. The symptomatic side trigeminal nerve regions of patients and both sides of the trigeminal nerve regions of control participants were manually labeled on MRI images. Radiomics features of the areas labeled were extracted. Principle component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) regression were utilized as the preliminary feature reduction methods to decrease the high dimensionality of radiomics features. Machine learning methods were established, including LASSO logistic regression, support vector machine (SVM), and Adaboost methods, evaluating each model's diagnostic abilities using 10-fold cross-validation. All the models showed excellent diagnostic ability in predicting trigeminal neuralgia. A prospective study was conducted, 20 cTN patients and 20 control subjects were enrolled to validate the clinical utility of all models. Results showed that the radiomics models based on MRI can predict trigeminal neuralgia with high accuracy, which could be used as a diagnostic tool for this disorder.

Identifying potential biomarkers of idiopathic pulmonary fibrosis through machine learning analysis

Idiopathic pulmonary fibrosis (IPF) is the most common and serious type of idiopathic interstitial pneumonia, characterized by chronic, progressive, and low survival rates, while unknown disease etiology. Until recently, patients with idiopathic pulmonary fibrosis have a poor prognosis, high mortality, and limited treatment options, due to the lack of effective early diagnostic and prognostic tools. Therefore, we aimed to identify biomarkers for idiopathic pulmonary fibrosis based on multiple machine-learning approaches and to evaluate the role of immune infiltration in the disease. The gene expression profile and its corresponding clinical data of idiopathic pulmonary fibrosis patients were downloaded from Gene Expression Omnibus (GEO) database. Next, the differentially expressed genes (DEGs) with the threshold of FDR < 0.05 and |log2 foldchange (FC)| > 0.585 were analyzed via R package “DESeq2” and GO enrichment and KEGG pathways were run in R software. Then, least absolute shrinkage and selection operator (LASSO) logistic regression, support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms were combined to screen the key potential biomarkers of idiopathic pulmonary fibrosis. The diagnostic performance of these biomarkers was evaluated through receiver operating characteristic (ROC) curves. Moreover, the CIBERSORT algorithm was employed to assess the infiltration of immune cells and the relationship between the infiltrating immune cells and the biomarkers. Finally, we sought to understand the potential pathogenic role of the biomarker (SLAIN1) in idiopathic pulmonary fibrosis using a mouse model and cellular model. A total of 3658 differentially expressed genes of idiopathic pulmonary fibrosis were identified, including 2359 upregulated genes and 1299 downregulated genes. FHL2, HPCAL1, RNF182, and SLAIN1 were identified as biomarkers of idiopathic pulmonary fibrosis using LASSO logistic regression, RF, and SVM-RFE algorithms. The ROC curves confirmed the predictive accuracy of these biomarkers both in the training set and test set. Immune cell infiltration analysis suggested that patients with idiopathic pulmonary fibrosis had a higher level of B cells memory, Plasma cells, T cells CD8, T cells follicular helper, T cells regulatory (Tregs), Macrophages M0, and Mast cells resting compared with the control group. Correlation analysis demonstrated that FHL2 was significantly associated with the infiltrating immune cells. qPCR and western blotting analysis suggested that SLAIN1 might be a signature for the diagnosis of idiopathic pulmonary fibrosis. In this study, we identified four potential biomarkers (FHL2, HPCAL1, RNF182, and SLAIN1) and evaluated the potential pathogenic role of SLAIN1 in idiopathic pulmonary fibrosis. These findings may have great significance in guiding the understanding of disease mechanisms and potential therapeutic targets in idiopathic pulmonary fibrosis.

Developing a prediction model of children asthma risk using population-based family history health records.

Identifying children at high risk of developing asthma can facilitate prevention and early management strategies. We developed a prediction model of children's asthma risk using objectively collected population-based children and parental histories of comorbidities. We conducted a retrospective population-based cohort study using administrative data from Manitoba, Canada, and included children born from 1974 to 2000 with linkages to ≥1 parent. We identified asthma and prior comorbid condition diagnoses from hospital and outpatient records. We used two machine-learning models: least absolute shrinkage and selection operator (LASSO) logistic regression (LR) and random forest (RF) to identify important predictors. The predictors in the base model included children's demographics, allergic conditions, respiratory infections, and parental asthma. Subsequent models included additional multiple comorbidities for children and parents. The cohort included 195,666 children: 51.3% were males and 17.7% had asthma diagnosis. The base LR model achieved a low predictive performance with sensitivity of 0.47, 95% confidence interval (0.45-0.48), and specificity of 0.67 (0.66-0.67) using a predicted probability threshold of 0.20. Sensitivity significantly improved when children's comorbidities were included using LASSO LR: 0.71 (0.69-0.72). Predictive performance further improved by including parental comorbidities (sensitivity = 0.72 [0.70-0.73], specificity = 0.69 [0.69-0.70]). We observed similar results for the RF models. Children's menstrual disorders and mood and anxiety disorders, parental lipid metabolism disorders and asthma were among the most important variables that predicted asthma risk. Including children and parental comorbidities to children's asthma prediction models improves their accuracy.

Diagnosis of Early Bacterial Pneumonia and Sepsis After Cardiovascular Surgery: A Diagnostic Prediction Model Based on LASSO Logistic Regression.

Early postoperative bacterial pneumonia and sepsis (ePOPS), which occurs within the first 48 hours after cardiovascular surgery, is a serious life-threatening complication. Diagnosis of ePOPS is extremely challenging, and the existing diagnostic tools are insufficient. The purpose of this study was to construct a novel diagnostic prediction model for ePOPS. Least Absolute Shrinkage and Selection Operator (LASSO) with logistic regression was used to construct a model to diagnose ePOPS based on patients' comorbidities, medical history, and laboratory findings.The area under the receiver operating characteristic curve (AUC) was used to evaluate the model discrimination. A total of 1203 patients were recruited and randomly split into a training and validation set in a 7:3 ratio. By early morning on the 3rd postoperative day (POD3), 103 patients had experienced 133 episodes of bacterial pneumonia or sepsis (15 patients had both). LASSO logistic regression model showed that duration of mechanical ventilation (P=0.015), NYHA class ≥ III (P=0.001), diabetes (P<0.001), exudation on chest radiograph (P=0.011) and IL-6 on POD3 (P<0.001) were independent risk factors. Based on these factors, we created a nomogram named DICS-I with an AUC of 0.787 in the training set and 0.739 in the validation set. The DICS-I model may be used to predict the risk of ePOPS after cardiovascular surgery, and is also especially suitable for predicting the risk of IRAO. The DICS-I model could help clinicians to adjust antibiotics on the POD3.

Identification of a Non-Invasive Urinary Exosomal Biomarker for Diabetic Nephropathy Using Data-Independent Acquisition Proteomics.

Diabetic nephropathy (DN), as the one of most common complications of diabetes, is generally diagnosed based on a longstanding duration, albuminuria, and decreased kidney function. Some patients with the comorbidities of diabetes and other primary renal diseases have similar clinical features to DN, which is defined as non-diabetic renal disease (NDRD). It is necessary to distinguish between DN and NDRD, considering they differ in their pathological characteristics, treatment regimes, and prognosis. Renal biopsy provides a gold standard; however, it is difficult for this to be conducted in all patients. Therefore, it is necessary to discover non-invasive biomarkers that can distinguish between DN and NDRD. In this research, the urinary exosomes were isolated from the midstream morning urine based on ultracentrifugation combined with 0.22 μm membrane filtration. Data-independent acquisition-based quantitative proteomics were used to define the proteome profile of urinary exosomes from DN (n = 12) and NDRD (n = 15) patients diagnosed with renal biopsy and Type 2 diabetes mellitus (T2DM) patients without renal damage (n = 9), as well as healthy people (n = 12). In each sample, 3372 ± 722.1 proteins were identified on average. We isolated 371 urinary exosome proteins that were significantly and differentially expressed between DN and NDRD patients, and bioinformatic analysis revealed them to be mainly enriched in the immune and metabolic pathways. The use of least absolute shrinkage and selection operator (LASSO) logistic regression further identified phytanoyl-CoA dioxygenase domain containing 1 (PHYHD1) as the differential diagnostic biomarker, the efficacy of which was verified with another cohort including eight DN patients, five NDRD patients, seven T2DM patients, and nine healthy people. Additionally, a concentration above 1.203 μg/L was established for DN based on the ELISA method. Furthermore, of the 19 significantly different expressed urinary exosome proteins selected by using the protein-protein interaction network and LASSO logistic regression, 13 of them were significantly related to clinical indicators that could reflect the level of renal function and hyperglycemic management.

Development and validation of a predicted nomogram for mortality of COVID-19: a multicenter retrospective cohort study of 4,711 cases in multiethnic.

Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly worldwide. As it quickly spreads and can cause severe disease, early detection and treatment may reduce mortality. Therefore, the study aims to construct a risk model and a nomogram for predicting the mortality of COVID-19. The original data of this study were from the article "Neurologic Syndromes Predict Higher In-Hospital Mortality in COVID-19." The database contained 4,711 multiethnic patients. In this secondary analysis, a statistical difference test was conducted for clinical demographics, clinical characteristics, and laboratory indexes. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis were applied to determine the independent predictors for the mortality of COVID-19. A nomogram was conducted and validated according to the independent predictors. The area under the curve (AUC), the calibration curve, and the decision curve analysis (DCA) were carried out to evaluate the nomogram. The mortality of COVID-19 is 24.4%. LASSO and multivariate logistic regression analysis suggested that risk factors for age, PCT, glucose, D-dimer, CRP, troponin, BUN, LOS, MAP, AST, temperature, O2Sats, platelets, Asian, and stroke were independent predictors of CTO. Using these independent predictors, a nomogram was constructed with good discrimination (0.860 in the C index) and internal validation (0.8479 in the C index), respectively. The calibration curves and the DCA showed a high degree of reliability and precision for this clinical prediction model. An early warning model based on accessible variates from routine clinical tests to predict the mortality of COVID-19 were conducted. This nomogram can be conveniently used to facilitate identifying patients who might develop severe disease at an early stage of COVID-19. Further studies are warranted to validate the prognostic ability of the nomogram.

Development of a nomogram model for prediction of new adjacent vertebral compression fractures after vertebroplasty

BackgroundVertebroplasty is the main minimally invasive operation for osteoporotic vertebral compression fracture (OVCF), which has the advantages of rapid pain relief and shorter recovery time. However, new adjacent vertebral compression fracture (AVCF) occurs frequently after vertebroplasty. The purpose of this study was to investigate the risk factors of AVCF and establish a clinical prediction model.MethodsWe retrospectively collected the clinical data of patients who underwent vertebroplasty in our hospital from June 2018 to December 2019. The patients were divided into a non-refracture group (289 cases) and a refracture group (43 cases) according to the occurrence of AVCF. The independent predictive factors for postoperative new AVCF were determined by univariate analysis, least absolute shrinkage and selection operator (LASSO) logistic regression, and multivariable logistic regression analysis. A nomogram clinical prediction model was established based on relevant risk factors, and the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) were used to evaluate the prediction effect and clinical value of the model. After internal validation, patients who underwent vertebroplasty in our hospital from January 2020 to December 2020, including a non-refracture group (156 cases) and a refracture group (21 cases), were included as the validation cohort to evaluate the prediction model again.ResultsThree independent risk factors of low bone mass density (BMD), leakage of bone cement and “O” shaped distribution of bone cement were screened out by LASSO regression and logistic regression analysis. The area under the curve (AUC) of the model in the training cohort and the validation cohort was 0.848 (95%CI: 0.786–0.909) and 0.867 (95%CI: 0.796–0.939), respectively, showing good predictive ability. The calibration curves showed the correlation between prediction and actual status. The DCA showed that the prediction model was clinically useful within the whole threshold range.ConclusionLow BMD, leakage of bone cement and “O” shaped distribution of bone cement are independent risk factors for AVCF after vertebroplasty. The nomogram prediction model has good predictive ability and clinical benefit.

Combining robust urine biomarkers to assess chronic kidney disease progression.

Urinary biomarkers may improve the prediction of chronic kidney disease (CKD) progression. Yet, data reporting the applicability of most commercial biomarker assays to the detection of their target analyte in urine together with an evaluation of their predictive performance are scarce. 30 commercial assays (ELISA) were tested for their ability to quantify the target analyte in urine using strict (FDA-approved) validation criteria. In an exploratory analysis, LASSO (Least Absolute Shrinkage and Selection Operator) logistic regression analysis was used to identify potentially complementary biomarkers predicting fast CKD progression, determined as the 51CrEDTA clearance-based measured glomerular filtration rate (mGFR) decline (>10% per year) in a subsample of 229 CKD patients (mean age, 61 years; 66% men; baseline mGFR, 38mL/min) from the NephroTest prospective cohort. Among the 30 assays, directed against 24 candidate biomarkers, encompassing different pathophysiological mechanisms of CKD progression, 16 assays fulfilled the FDA-approved criteria. LASSO logistic regressions identified a combination of five biomarkers including CCL2, EGF, KIM1, NGAL, and TGF-α that improved the prediction of fast mGFR decline compared to the kidney failure risk equation variables alone: age, gender, mGFR, and albuminuria. Mean area under the curves (AUC) estimated from 100 re-samples was higher in the model with than without these biomarkers, 0.722 (95% confidence interval 0.652-0.795) vs. 0.682 (0.614-0.748), respectively. Fully-adjusted odds-ratios (95% confidence interval) for fast progression were 1.87 (1.22, 2.98), 1.86 (1.23, 2.89), 0.43 (0.25, 0.70), 1.10 (0.71, 1.83), 0.55 (0.33, 0.89), and 2.99 (1.89, 5.01) for albumin, CCL2, EGF, KIM1, NGAL, and TGF-α, respectively. This study provides a rigorous validation of multiple assays for relevant urinary biomarkers of CKD progression which combination may improve the prediction of CKD progression. This work was supported by Institut National de la Santé et de la Recherche Médicale, Université de Paris, Assistance Publique Hôpitaux de Paris, Agence Nationale de la Recherche, MSDAVENIR, Pharma Research and Early Development Roche Laboratories (Basel, Switzerland), and Institut Roche de Recherche et Médecine Translationnelle (Paris, France).

A LASSO-derived developing and validating risk model for chronic total occlusion in Asian population before coronary angiography.

Despite several previous studies that have explored the predictors of high morbidity in coronary artery disease (CAD) and developed nomograms for CAD patients prior to coronary angiography (CAG), there is a lack of models available to predict chronic total occlusion (CTO). The aim of this study is to develop a risk model and a nomogram for predicting the probability of CTO prior to CAG. The study included 1,105 patients with CAG-diagnosed CTO in the derivation cohort and 368 patients in the validation cohort. Clinical demographics, echocardiography results, and laboratory indexes were analyzed using statistical difference tests. Independent risk factors affecting the CTO indication were selected using least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. A nomogram was built and validated based on these independent indicators. The performance of the nomogram was evaluated using area under the curve (AUC), calibration curve, and decision curve analysis (DCA). LASSO and multivariate logistic regression analysis revealed that 6 variables, including sex (male), lymphocyte percentage (LYM%), ejection fraction (EF), myoglobin (Mb), non-high-density lipoprotein cholesterol (non-HDL), and N-terminal pro-B-type natriuretic peptide (NT-proBNP), were independent predictors of CTO. The nomogram constructed based on these variables showed good discrimination (C index of 0.744) and external validation (C index of 0.729). The calibration curves and DCA demonstrated high reliability and precision for this clinical prediction model. The nomogram based on sex (male), LYM%, EF, Mb, non-HDL, and NT-proBNP could be used to predict CTO in CAD patients, enhancing the ability to predict their prognosis in clinical practice. Further research is needed to validate the efficacy of the nomogram in other populations.

Risk factors for the development of sepsis in patients with cirrhosis in intensive care units.

Sepsis is a serious complication of liver cirrhosis. This study aimed to develop a risk prediction model for sepsis among patients with liver cirrhosis. A total of 3130 patients with liver cirrhosis were enrolled from the Medical Information Mart for Intensive Care IV database, and randomly assigned into training and validation cohorts in a 7:3 ratio. The least absolute shrinkage and selection operator (LASSO) regression was used to filter variables and select predictor variables. Multivariate logistic regression was used to establish the prediction model. Based on LASSO and multivariate logistic regression, gender, base excess, bicarbonate, white blood cells, potassium, fibrinogen, systolic blood pressure, mechanical ventilation, and vasopressor use were identified as independent risk variables, and then a nomogram was constructed and validated. The consistency index (C-index), receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) were used to measure the predictive performance of the nomogram. As a result of the nomogram, good discrimination was achieved, with C-indexes of 0.814 and 0.828 for the training and validation cohorts, respectively, and an area under the curve of 0.849 in the training cohort and 0.821 in the validation cohort. The calibration curves demonstrated good agreement between the predictions and observations. The DCA curves showed the nomogram had significant clinical value. We developed and validated a risk-prediction model for sepsis in patients with liver cirrhosis. This model can assist clinicians in the early detection and prevention of sepsis in patients with liver cirrhosis.

Correlation between the triglyceride-glucose index and the onset of atrial fibrillation in patients with non-alcoholic fatty liver disease

BackgroundNon-alcoholic fatty liver disease (NAFLD) is associated with atrial fibrillation (AF). Insulin resistance (IR) is the main cause of the high prevalence of AF in NAFLD patients. The triglyceride-glucose index (TyG) is a novel IR-related indicator implicated in the incidence and severity of NAFLD. However, the role of TyG in determining the risk for AF in patients with NAFLD remains unclear.MethodsA retrospective study was conducted on 912 patients diagnosed with NAFLD via ultrasonography. These patients were divided into two groups: (1) NAFLD+ AF and (2) NAFLD+ non-AF. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to assess the correlation between the TyG index and the high risk for AF. A receiver operating characteristic (ROC) curve was constructed to evaluate the predictive value for the TyG index for AF. Restricted cubic splines (RCS) were used to test the linear correlation between TyG and the risk for AF.ResultsA total of 204 patients with AF and 708 patients without AF were included in this study. The LASSO logistic regression analysis showed that TyG was an independent risk factor for AF (odds ratio [OR] = 4.84, 95% confidence interval [CI] 2.98–7.88, P < 0.001). The RCS showed that the risk for AF increased linearly with TyG over the entire TyG range; this risk was also evident when the patients were analyzed based on sex (P for nonlinear > 0.05). In addition, the correlation between TyG and AF was a consistent finding in subgroup analysis. Furthermore, ROC curve analysis showed that TyG levels combined with traditional risk factors improved the predictive value for atrial fibrillation.ConclusionThe TyG index is useful in assessing the risk for atrial fibrillation in patients with NAFLD. Patients with NAFLD and increased TyG indices have higher risks for atrial fibrillation. Therefore, TyG indices should be assessed when managing patients with NAFLD.

Development and validation of a nomogram for postoperative sleep disturbance in adults: a prospective survey of 640 patients undergoing spinal surgery

BackgroundPostoperative sleep disturbance (PSD) is a prevalent clinical complication that may arise due to various factors. The purpose of this investigation is to identify the risk factors for PSD in spinal surgery and establish a risk prediction nomogram.MethodsThe clinical records of individuals who underwent spinal surgery from January 2020 to January 2021 were gathered prospectively. The least absolute shrinkage and selection operator (LASSO) regression, along with multivariate logistic regression analysis, was employed to establish independent risk factors. A nomogram prediction model was devised based on these factors. The nomogram’s effectiveness was evaluated and verified via the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA).ResultsA total of 640 patients who underwent spinal surgery were analyzed in this investigation, among which 393 patients experienced PSD with an incidence rate of 61.4%. After conducting LASSO regression and logistic regression analyses using R software on the variables in training set, 8 independent risk factors associated to PSD were identified, including female, preoperative sleep disorder, high preoperative anxiety score, high intraoperative bleeding volume, high postoperative pain score, dissatisfaction with ward sleep environment, non-use of dexmedetomidine and non-use of erector spinae plane block (ESPB). The nomogram and online dynamic nomogram were constructed after incorporating these variables. In the training and validation sets, the area under the curve (AUC) in the receiver operating characteristic (ROC) curves were 0.806 (0.768–0.844) and 0.755 (0.667–0.844), respectively. The calibration plots indicated that the mean absolute error (MAE) values in both sets were respectively 1.2% and 1.7%. The decision curve analysis demonstrated the model had a substantial net benefit within the range of threshold probabilities between 20% and 90%.ConclusionsThe nomogram model proposed in this study included eight frequently observed clinical factors and exhibited favorable accuracy and calibration.Trial registrationThe study was retrospectively registered with the Chinese Clinical Trial Registry (ChiCTR2200061257, 18/06/2022).

Development and validation of risk prediction models for large for gestational age infants using logistic regression and two machine learning algorithms.

Large for gestational age (LGA) is one of the adverse outcomes during pregnancy that endangers the life and health of mothers and offspring. We aimed to establish prediction models for LGA at late pregnancy. Data were obtained from an established Chinese pregnant women cohort of 1285 pregnant women. LGA was diagnosed as >90th percentile of birth weight distribution of Chinese corresponding to gestational age of the same-sex newborns. Women with gestational diabetes mellitus (GDM) were classified into three subtypes according to the indexes of insulin sensitivity and insulin secretion. Models were established by logistic regression and decision tree/random forest algorithms, and validated by the data. A total of 139 newborns were diagnosed as LGA after birth. The area under the curve (AUC) for the training set is 0.760 (95% confidence interval [CI] 0.706-0.815), and 0.748 (95% CI 0.659-0.837) for the internal validation set of the logistic regression model, which consisted of eight commonly used clinical indicators (including lipid profile) and GDM subtypes. For the prediction models established by the two machine learning algorithms, which included all the variables, the training set and the internal validation set had AUCs of 0.813 (95% CI 0.786-0.839) and 0.779 (95% CI 0.735-0.824) for the decision tree model, and 0.854 (95% CI 0.831-0.877) and 0.808 (95% CI 0.766-0.850) for the random forest model. We established and validated three LGA risk prediction models to screen out the pregnant women with high risk of LGA at the early stage of the third trimester, which showed good prediction power and could guide early prevention strategies.

Integrated analysis of C3AR1 and CD163 associated with immune infiltration in intracranial aneurysms pathogenesis

BackgroundTo identify potential immune-related biomarkers, molecular mechanism, and therapeutic agents of intracranial aneurysms (IAs). MethodsWe identified the differentially expressed genes (DEGs) between IAs and control samples from GSE75436, GSE26969, GSE6551, and GSE13353 datasets. We used weighted gene co-expression network analysis (WGCNA) and protein–protein interaction (PPI) analysis to identify immune-related hub genes. We evaluated the expression of hub genes by using qRT-PCR analysis. Using miRNet, NetworkAnalyst, and DGIdb databases, we analyzed the regulatory networks and potential therapeutic agents targeting hub genes. Least absolute shrinkage and selection operator (LASSO) logistic regression was performed to identify optimal biomarkers among hub genes. The diagnostic value was validated by external GSE15629 dataset. ResultsWe identified 227 DEGs and 22 differentially infiltrating immune cells between IAs and control samples from GSE75436, GSE26969, GSE6551, and GSE13353 datasets. We further identified 41 differentially expressed immune-related genes (DEIRGs), which were primarily enriched in the chemokine-mediated signaling pathway, myeloid leukocyte migration, endocytic vesicle membrane, chemokine receptor binding, chemokine activity, and viral protein interactions with cytokines and their receptors. Among 41 DEIRGs, 10 hub genes including C3AR1, CD163, CCL4, CXCL8, CCL3, TLR2, TYROBP, C1QB, FCGR3A, and FCGR1A were identified with good diagnostic values (AUC >0.7). Hsa-mir-27a-3p and transcription factors, including YY1 and GATA2, were identified the primary regulators of hub genes. 92 potential therapeutic agents targeting hub genes were predicted. C3AR1 and CD163 were finally identified as the best diagnostic biomarkers using LASSO logistic regression (AUC = 0.994). The diagnostic value of C3AR1 and CD163 was validated by the external GSE15629 dataset (AUC = 0.914). ConclusionsThis study revealed the importance of C3AR1 and CD163 in immune infiltration in IAs pathogenesis. Our finding provided a valuable reference for subsequent research on the potential targets for molecular mechanisms and intervention of IAs.