Least Absolute Shrinkage And Selection Operator Logistic Regression Research Articles

The intra-tumoral microbiome as a potential biomarker of response to external beam radiation therapy in cervical cancer

BackgroundWe aimed to determine the potential predictive value of the intra-tumoral microbiome as a marker of the response to external beam radiation therapy (EBRT) in cervical cancer (CC).MethodsA prospective longitudinal trial of 36 CC patients receiving pelvic radiotherapy was designed to investigate microbial characteristic signatures and diversity (alpha and beta) of multiple sites (tumor, vaginal, gut, urethral, and oral) in the superior response (SR) and inferior response (IR) groups of CC patients by 16S rRNA sequencing. Utilized the least absolute shrinkage and selection operator (LASSO) logistic regression method to analyze clinicopathological factors that potentially influenced the efficacy of EBRT. LEfSe analysis highlighted the microbiome features that best distinguished the categorized patient samples. Selected parameters were validated with Spearman correlation analysis, receiver operating characteristic (ROC) area under the curve (AUC) analysis and Kaplan-Meier survival analysis.ResultsFirstly, in our cohort, LASSO logistic regression analysis revealed no association between clinicopathological factors and EBRT efficacy. Subsequently, we employed 16S rRNA sequencing to compare microbiome differences across multiple sites and their correlations with major clinicopathological factors. We discovered that the intra-tumoral microbiome was independent of clinicopathologic features and represented the most direct and reliable reflection of the microbial differences between the SR and IR groups. We found lower alpha diversity in the tumor microbiome of SR group and identified the most relevant microbiome taxa (Bifidobacteriaceae, Beijerinckiaceae, and Orbaceae) associated with the efficacy of the response to EBRT in CC patients. We then conducted ROC analysis, finding that specific microbial taxa had an AUC of 0.831 (95% CI, 0.667–0.995), indicating the potential of these taxa as biomarkers for predicting EBRT efficacy. Kaplan-Meier survival analysis showed a better prognosis for patients with lower alpha diversity and higher relative abundance of Bifidobacteriaceae.ConclusionsOur data suggested that intra-tumoral specific microbiome taxa and lower alpha diversity may play an important role in the CC patient sensitivity to EBRT and offer novel potential biomarkers for predicting the response to EBRT efficacy.

Constructing a predictive model based on peripheral blood signs to differentiate infectious mononucleosis from chronic active EBV infection.

To develop a prediction model based on peripheral blood signs to distinguish between infectious mononucleosis and chronic active EBV infection. Retrospective data was collected for 60 patients with IM (IM group) and 20 patients with CAEBV infection (CAEBV group) who were hospitalized and diagnosed at the General Hospital of Tianjin Medical University between December 2018 and September 2022. The analyses used were univariate and LASSO (least absolute shrinkage and selection operator) logistic regression. Univariate analyses revealed that both IM and CAEBV-infected patients displayed overlapping and intersecting clinical manifestations, such as fever, sore throat, enlarged lymph nodes, and enlargement of the liver and spleen, and that in contrast to inflammatory responses in peripheral blood, CAEBV-infected patients had more severe inflammatory responses. Nine biomarkers-HGB, lymphocyte count, percentage of lymphocytes, ALB, fibrinogen, CRP, IFN-, IL-6, and EBV-DNA load-were subsequently selected by LASSO logistic regression modeling to serve as discriminatory models. Our investigation offers a solid foundation for diagnosing IM and CAEBV infection using the LASSO logistic regression model based on the significance and availability of peripheral blood indicators. Infected patients with CAEBV require early medical attention.

Comparative Predictive Modeling for PICC Line Complications in Oncology: A Retrospective Study.

Aims/Background Peripherally inserted central catheter (PICC) are increasingly used in cancer treatment, offering significant therapeutic benefits while also posing risks for complications such as infection, thrombosis, and catheter migration. Effective prediction and management of these complications are crucial to optimizing patient outcomes and reducing healthcare costs. Methods This retrospective study analyzed PICC line insertion in 266 cancer patients implemented from January 2019 to December 2023 at a regional healthcare facility in China. Using least absolute shrinkage and selection operator (LASSO) logistic regression, we identified key factors influencing PICC line complications and developed a tailored nomogram for individual risk assessment. The efficacy of the model was compared with support vector machine (SVM), random forest, and gradient boosting machine (GBM) using receiver operating characteristic (ROC) and decision curve analysis (DCA) metrics. Results Factors such as body mass index (BMI), diabetic status, and age were found to be significant predictors of PICC line complications. The LASSO model demonstrated superior predictive capability (area under the curve, AUC = 0.79) over SVM (AUC = 0.40), random forest (AUC = 0.70), and GBM (AUC = 0.64). A tailored nomogram was developed for clinical use, enabling personalized risk evaluation. Conclusion The study underscores the utility of LASSO logistic regression in the personalized risk evaluation of PICC line complications, recommending its integration into clinical practice. The tailored nomogram provides a practical tool for clinicians to enhance care customization for patients requiring PICC lines, thereby improving treatment outcomes and patient safety.

Prediction of postoperative complications following transanal total mesorectal excision in middle and low rectal cancer: development and internal validation of a clinical prediction model

PurposeThe objective of this study is to develop a nomogram for the personalized prediction of postoperative complication risks in patients with middle and low rectal cancer who are undergoing transanal total mesorectal excision (taTME). This tool aims to assist clinicians in early identification of high-risk patients and in addressing preoperative risk factors to enhance surgical safety.MethodsIn this case–control study, 207 patients diagnosed with middle and low rectal cancer and undergoing taTME between February 2018 and November 2023 at The First Affiliated Hospital of Xiamen University were included. Independent risk factors for postoperative complications were analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression and multifactorial logistic regression models. A predictive nomogram was constructed using R Studio.ResultsAmong the 207 patients, 57 (27.5%) experienced postoperative complications. The LASSO and multifactorial logistic regression analyses identified operation time (OR = 1.010, P = 0.007), smoking history (OR = 9.693, P < 0.001), anastomotic technique (OR = 0.260, P = 0.004), and ASA score (OR = 9.077, P = 0.051) as significant predictors. These factors were integrated into the nomogram. The model's accuracy was validated through receiver operating characteristic curves, calibration curves, consistency indices, and decision curve analysis.ConclusionThe developed nomogram, incorporating operation time, smoking history, anastomotic technique, and ASA score, effectively forecasts postoperative complication risks in taTME procedures. It is a valuable tool for clinicians to identify patients at heightened risk and initiate timely interventions, ultimately improving patient outcomes.

Predicting risk of the subsequent early pregnancy loss in women with recurrent pregnancy loss based on preconception data

BackgroundFor women who have experienced recurrent pregnancy loss (RPL), it is crucial not only to treat them but also to evaluate the risk of recurrence. The study aimed to develop a risk predictive model to predict the subsequent early pregnancy loss (EPL) in women with RPL based on preconception data.MethodsA prospective, dynamic population cohort study was carried out at the Second Hospital of Lanzhou University. From September 2019 to December 2022, a total of 1050 non-pregnant women with RPL were participated. By December 2023, 605 women had subsequent pregnancy outcomes and were randomly divided into training and validation group by 3:1 ratio. In the training group, univariable screening was performed on RPL patients with subsequent EPL outcome. The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were utilized to select variables, respectively. Subsequent EPL prediction model was constructed using generalize linear model (GLM), gradient boosting machine (GBM), random forest (RF), and deep learning (DP). The variables selected by LASSO regression and multivariate logistic regression were then established and compared using the best prediction model. The AUC, calibration curve, and decision curve (DCA) were performed to assess the prediction performances of the best model. The best model was validated using the validation group. Finally, a nomogram was established based on the best predictive features.ResultsIn the training group, the GBM model achieved the best performance with the highest AUC (0.805). The AUC between the variables screened by the LASSO regression (16-variables) and logistic regression (9-variables) models showed no significant difference (AUC: 0.805 vs. 0.777, P = 0.1498). Meanwhile, the 9-variable model displayed a well discrimination performance in the validation group, with an AUC value of 0.781 (95%CI 0.702, 0.843). The DCA showed the model performed well and was feasible for making beneficial clinical decisions. Calibration curves revealed the goodness of fit between the predicted values by the model and the actual values, the Hosmer–Lemeshow test was 7.427, and P = 0.505.ConclusionsPredicting subsequent EPL in RPL patients using the GBM model has important clinical implications. Future prospective studies are needed to verify the clinical applicability.Trial registrationThis study was registered in the Chinese Clinical Trial Registry with the registration number of ChiCTR2000039414 (27/10/2020).

Prediction and analysis of albumin-bilirubin score combined with liver function index and carcinoembryonic antigen on liver metastasis of colorectal cancer.

Colorectal cancer (CRC) is a common malignant tumor, and liver metastasis is one of the main recurrence and metastasis modes that seriously affect patients' survival rate and quality of life. Indicators such as albumin bilirubin (ALBI) score, liver function index, and carcinoembryonic antigen (CEA) have shown some potential in the prediction of liver metastasis but have not been fully explored. To evaluate its predictive value for liver metastasis of CRC by conducting the combined analysis of ALBI, liver function index, and CEA, and to provide a more accurate liver metastasis risk assessment tool for clinical treatment guidance. This study retrospectively analyzed the clinical data of patients with CRC who received surgical treatment in our hospital from January 2018 to July 2023 and were followed up for 24 months. According to the follow-up results, the enrolled patients were divided into a liver metastasis group and a nonliver metastasis group and randomly divided into a modeling group and a verification group at a ratio of 2:1. The risk factors for liver metastasis in patients with CRC were analyzed, a prediction model was constructed by least absolute shrinkage and selection operator (LASSO) logistic regression, internal validation was performed by the bootstrap method, the reliability of the prediction model was evaluated by subject-work characteristic curves, calibration curves, and clinical decision curves, and a column graph was drawn to show the prediction results. Of 130 patients were enrolled in the modeling group and 65 patients were enrolled in the verification group out of the 195 patients with CRC who fulfilled the inclusion and exclusion criteria. Through LASSO regression variable screening and logistic regression analysis. The ALBI score, alanine aminotransferase (ALT), and CEA were found to be independent predictors of liver metastases in CRC patients [odds ratio (OR) = 8.062, 95% confidence interval (CI): 2.545-25.540], (OR = 1.037, 95%CI: 1.004-1.071) and (OR = 1.025, 95%CI: 1.008-1.043). The area under the receiver operating characteristic curve (AUC) for the combined prediction of CRLM in the modeling group was 0.921, with a sensitivity of 78.0% and a specificity of 95.0%. The H-index was 0.921, and the H-L fit curve had χ2 = 0.851, a P value of 0.654, and a slope of the calibration curve approaching 1. This indicates that the model is extremely accurate, and the clinical decision curve demonstrates that it can be applied effectively in the real world. We conducted internal verification of one thousand resamplings of the modeling group data using the bootstrap method. The AUC was 0.913, while the accuracy was 0.869 and the kappa consistency was 0.709. The combination prediction of liver metastasis in patients with CRC in the verification group had an AUC of 0.918, sensitivity of 85.0%, specificity of 95.6%, C-index of 0.918, and an H-L fitting curve with χ 2 = 0.586, P = 0.746. The ALBI score, ALT level, and CEA level have a certain value in predicting liver metastasis in patients with CRC. These three criteria exhibit a high level of efficacy in forecasting liver metastases in patients diagnosed with CRC. The risk prediction model developed in this work shows great potential for practical application.

Ascites-derived hsa-miR-181a-5p serves as a prognostic marker for gastric cancer-associated malignant ascites

BackgroundPeritoneal carcinomatosis was the main reason leading to gastric cancer (GC)-related death. We aimed to explore the roles of dysregulated microRNAs (miRNAs) and related immune regulation activities in GC-associated malignant ascites.MethodsGSE126399 were downloaded from GEO database. Differentially expressed miRNAs in GC ascites samples was firstly screened, and critical miRNAs were further investigated by LASSO (least absolute shrinkage and selection operator) logistic regression and random forest (RF) algorithm. Receiver operating characteristic of critical miRNAs was also constructed. Moreover, functional analysis, immune cell infiltration associated with differentially expressed mRNAs were further analyzed. After selecting key modules by weighted gene co-expression network analysis, mRNAs related with survival performance and transcription factor (TF)-miRNA-mRNA network were constructed.ResultsHsa-miR-181b-5p was confirmed as critical differentially expressed miRNAs in GC ascites. Then, the tumor samples were divided into high- and low- expression groups divided by mean expression levels of hsa-miR-181b-5p, and subjects with high hsa-miR-181b-5p levels had better survival outcomes. In total, 197 differentially expressed mRNAs associated with hsa-miR-181b-5p levels were obtained, and these mRNAs were mainly enriched in muscle activity and vascular smooth muscle contraction. Hsa-miR-181b-5 was positively related with activated CD4 T cells and negatively related with eosinophil. 17 mRNAs were selected as mRNAs significantly related with prognosis of GC, such as PDK4 and RAMP1. Finally, 75 TF-miRNA-mRNA relationships were obtained, including 15 TFs, hsa-miR-181b-5p, and five mRNAs.ConclusionOur data suggest that the differentially expressed hsa-miR-181b-5p in ascites samples of GC patients may be a valuable prognostic marker and a potential target for therapeutic intervention, which should be validated in the near future.

Predicting CD27 expression and clinical prognosis in serous ovarian cancer using CT-based radiomics

BackgroundThis study aimed to develop and evaluate radiomics models to predict CD27 expression and clinical prognosis before surgery in patients with serous ovarian cancer (SOC).MethodsWe used transcriptome sequencing data and contrast-enhanced computed tomography images of patients with SOC from The Cancer Genome Atlas (n = 339) and The Cancer Imaging Archive (n = 57) and evaluated the clinical significance and prognostic value of CD27 expression. Radiomics features were selected to create a recursive feature elimination-logistic regression (RFE-LR) model and a least absolute shrinkage and selection operator logistic regression (LASSO-LR) model for CD27 expression prediction.ResultsCD27 expression was upregulated in tumor samples, and a high expression level was determined to be an independent protective factor for survival. A set of three and six radiomics features were extracted to develop RFE-LR and LASSO-LR radiomics models, respectively. Both models demonstrated good calibration and clinical benefits, as determined by the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. The LASSO-LR model performed better than the RFE-LR model, owing to the area under the curve (AUC) values of the ROC curves (0.829 vs. 0.736). Furthermore, the AUC value of the radiomics score that predicted the overall survival of patients with SOC diagnosed after 60 months was 0.788 using the LASSO-LR model.ConclusionThe radiomics models we developed are promising noninvasive tools for predicting CD27 expression status and SOC prognosis. The LASSO-LR model is highly recommended for evaluating the preoperative risk stratification for SOCs in clinical applications.

Comprehensive analysis of predictive factors for efficacy in concurrent chemo-radiotherapy for locally advanced non-small cell lung cancer, utilizing individual patient data from the Japan lung cancer society integrated clinical trial database: Is there room for further improvement?

8043 Background: The standard treatment for unresectable, locally advanced NSCLC is concurrent chemoradiotherapy followed by durvalumab. While the effectiveness of chemoradiotherapy is pivotal, the exploration into predictive factors of the efficacy is notably limited. Methods: The Japanese Lung Cancer Society (JLCS) integrated eight randomized Phase II/III trials (JCOG9812, JCOG0301, NJLCG0601, OLCSG0007, SPECTRA, TORG1018, WJOG5008L, WJTOG0105) concerning chemoradiotherapy for locally advanced NSCLC, constructing the JLCS Integrated Clinical Trial Database (JIDB), with individual data of 1288 patients. This study analyzed 1162 patients who underwent concurrent chemoradiotherapy, focusing on factors impacting PFS. A logistic regression with the least absolute shrinkage and selection operator (LASSO) analyses for 3-yr PFS were performed, and the five most related factors were further evaluated using the Kaplan-Meier method and log-rank tests. The impact of pneumonitis and esophagitis on PFS was also explored. Results: Age [median (Q1;Q3)]; 64 (57;71), male/female; 961/201, Ad/Sq/Oth; 580/446/134, PS 0/1/2; 562/578/5, stage IIIA/IIIB; 468/694, T factor 4/3/2/1/0; 436/158/341/156/3, N factor 3/2/1/0; 300/692/44/62, PS 0/1/2; 562/578/5, primary site; upper, middle/lower lobe; 599/161, BMI; 21.8 (19.7;24.0), weight loss (≥5% within 6m) yes/no; 135/644, LDH (IU/l); 203 (175;276), CRP (mg/dl); 0.70 (0.20;2.60), irradiated dose (Gy); 60 (60;60), combined regimen; platinum/taxane 303, CDDP/mitomycin/vindesine; 254, CDDP/5-FU-based; 196, CDDP/vinorelbine 87, CDDP/pemetrexed 50, others 272. The median PFS in all patients was 9.72m (95% CI; 9.33 – 10.38), with 3-yr, 5-yr, and 10-yr PFS rates of 19.7, 14.3, 10.1%. LASSO logistic regression identified Sq, lower lobe primary, age, pack-years, and weight loss as the top negative influencers. The PFS curve was significantly shorter in patients with lower lobe primary (median of 8.15 vs. 9.46m, p = 0.023) and weight loss (median of 7.29 vs. 9.72m, p &lt; 0.0001). Pneumonitis occurred in 50.9% (grade≥3 in 6.7%) and esophagitis in 41.9% (grade≥3 in 3.9%). Grade≥2 pneumonitis shortened PFS (8.2 vs. 10.2m, p = 0.00015), while, unexpectedly, grade≥1 esophagitis was associated with longer PFS (12.9 vs. 8.6m, p &lt; 0.0001). Conclusions: The efficacy of concurrent chemoradiotherapy varies significantly based on the primary site. Weight loss is a significant predictor of efficacy, as is in metastatic disease. The impact of pneumonitis and esophagitis suggest that modifying lung and esophageal irradiation might enhance treatment efficacy. Further research in this area is still certainly warranted.

Applying LASSO logistic regression for the prediction of biliary complications after ex vivo liver resection and autotransplantation in patients with end-stage hepatic alveolar echinococcosis

BackgroundThe purpose of this study was to explore the relevant risk factors associated with biliary complications (BCs) in patients with end-stage hepatic alveolar echinococcosis (HAE) following ex vivo liver resection and autotransplantation (ELRA) and to establish and visualize a nomogram model.MethodsThis study retrospectively analysed patients with end-stage HAE who received ELRA treatment at the First Affiliated Hospital of Xinjiang Medical University between August 1, 2010 and May 10, 2023. The least absolute shrinkage and selection operator (LASSO) regression model was applied to optimize the feature variables for predicting the incidence of BCs following ELRA. Multivariate logistic regression analysis was used to develop a prognostic model by incorporating the selected feature variables from the LASSO regression model. The predictive ability, discrimination, consistency with the actual risk, and clinical utility of the candidate prediction model were evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Internal validation was performed by the bootstrapping method.ResultsThe candidate prediction nomogram included predictors such as age, hepatic bile duct dilation, portal hypertension, and regular resection based on hepatic segments. The model demonstrated good discrimination ability and a satisfactory calibration curve, with an area under the ROC curve (AUC) of 0.818 (95% CI 0.7417–0.8958). According to DCA, this prediction model can predict the risk of BCs occurrence within a probability threshold range of 9% to 85% to achieve clinical net benefit.ConclusionsA prognostic nomogram with good discriminative ability and high accuracy was developed and validated to predict BCs after ELRA in patients with end-stage HAE.

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 Pa o2 , 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 Pa o2 had good prediction performance for predicting PPHN among newborns first admitted to the hospital after birth.

Construction and Validation of a MRI‑Based Radiomic Nomogram to Predict Overall Survival in Patients with Local Advanced Cervical Cancer: A Multicenter Study

BackgroundCervical cancer is the fourth most common cancer among women. Radiomics has emerged as a new approach providing valuable information for cancer management. The aim of this study was to construct a radiomics nomogram to accurately predict survival outcomes in patients with locally advanced cervical cancer. MethodsThis retrospective study enrolled a total of 582 locally advanced cervical cancer patients from three center (training cohort: n=228; internal validation cohort: n=98; external validation cohort: n=256). Radiomic features were extracted from pretreatment MRI images. Least absolute shrinkage and selection operator logistic regression (LASSO) was applied to select radiomic features and calculated the radiomic scores. Univariate and multivariate Cox proportional hazards regression analyses were used to identify the independent prognostic clinic-radiological factors for cervical cancer, which were incorporated into the nomogram. ResultsA total of 6 radiomic features were found to be associated with overall survival of locally advanced cervical cancer patients. The AUC of radiomic scores in the training cohort was 0.634-0.708 for the training cohort, 0.725-0.762 for internal validation cohort and 0.788-0.881 for the external validation cohort. Age, parametrial invasion, and radiomic score were the independent prognostic indicators for cervical cancer patients (Age: HR=1.041, 95% CI=1.012-1.071, p=0.006; Parametrial invasion: HR=4.755, 95% CI=1.493-15.144, p=0.008; HR=2.324, 95% CI=1.050-5.143, p=0.037). The nomogram model incorporating these factors showed favorable discrimination in predicting the overall survival rates of cervical cancer patients, with the AUC values of 0.809, 0.808, and 0.862 for 1-, 2-, and 3-year predictions. The decision curve analysis (DCA) indicated that the nomogram model achieved the highest clinical net benefit across the entire range of reasonable threshold probabilities. ConclusionThe nomogram, incorporating clinicopathological factors and radiomic features derived from MRI images, showed satisfactory discrimination in predicting the overall survival rates of locally advanced cervical cancer patients.

Pretreatment mortality risk prediction model in patients with polymyositis/dermatomyositis-associated interstitial lung disease

ObjectivesRisk prediction for patients with polymyositis/dermatomyositis-associated interstitial lung disease (PM/DM-ILD) is challenging due to heterogeneity in the disease course. We aimed to develop a mortality risk prediction model for PM/DM-ILD.MethodsThis...

Dyslipidemia and its associated factors among community adults located in Shangcheng district, Zhejiang province

Dyslipidemia and its associated factors among community adults located in Shangcheng district, Zhejiang province

A nomogram for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients using hemodynamic parameters from transthoracic echocardiography.

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with a high risk of stroke. This study was designed to investigate the relationship between hemodynamic parameters and left atrial thrombus/spontaneous echo contrast (LAT/SEC) in non-valvular atrial fibrillation (NVAF) patients and establish a predictive nomogram that integrates hemodynamic parameters with clinical predictors to predict the risk of LAT/SEC. From January 2019 to September 2022, a total of 354 consecutive patients with NVAF were enrolled in this cross-sectional study at the First Affiliated Hospital of Guangxi Medical University. To identify the optimal predictive features, we employed least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was subsequently constructed, and the results were visualized with a nomogram. We evaluated the model's performance using discrimination, calibration, and the concordance index (C-index). We observed a 38.7% incidence of SEC/TH in NVAF patients. Independent influencing factors of LAT/SEC were identified through LASSO and multivariate logistic regression. Finally, four indicators were included, namely, previous stroke/transient ischaemic attack (OR = 4.25, 95% CI = 1.57-12.23, P = 0.006), left atrial volume index (LAVI) (OR = 1.04, 95% CI = 1.01-1.06, P = 0.001), S/D ratio (OR = 0.27, 95% CI = 0.11-0.59, P = 0.002), and left atrial acceleration factor (OR = 4.95, 95% CI = 2.05-12.79, P = 0.001). The nomogram, which incorporated these four influencing factors, demonstrated excellent predictive ability. The training set had a C-index of 0.878, while the validation set had a C-index of 0.872. Additionally, the calibration curve demonstrated great consistency between the predicted probabilities and the observed outcomes, and the decision curve analysis confirmed the important clinical advantage of the model for patients with NVAF. Our findings indicate that an enlarged left atrium and abnormal hemodynamic parameters in the left atrial and pulmonary veins are linked to a greater risk of LAT/SEC. Previous stroke/transient ischaemic attack, LAVI, the S/D ratio, and left atrial acceleration factor were independently associated with LAT/SEC in NVAF patients. With the incorporation of these four variables, the developed nomogram effectively predicts the risk of LAT/SEC and outperforms the CHA2DS2-VASc score.

A combined analysis of TyG index, SII index, and SIRI index: positive association with CHD risk and coronary atherosclerosis severity in patients with NAFLD.

Insulin resistance(IR) and inflammation have been regarded as common potential mechanisms in coronary heart disease (CHD) and non-alcoholic fatty liver disease (NAFLD). Triglyceride-glucose (TyG) index is a novel biomarker of insulin resistance, System immune-inflammation index(SII) and Systemic inflammation response index(SIRI) are novel biomarkers of inflammation, these biomarkers have not been studied in CHD with NAFLD patients. This study investigated the correlation between the TyG index, SII index, and SIRI index and CHD risk among NAFLD patients. This cross-sectional study included 407 patients with NAFLD in the Department of Cardiology, The Second Hospital of Shanxi Medical University. Of these, 250 patients with CHD were enrolled in the NAFLD+CHD group and 157 patients without CHD were enrolled as NAFLD control. To balance covariates between groups, 144 patients were selected from each group in a 1:1 ratio based on propensity score matching (PSM). Potential influences were screened using Lasso regression analysis. Univariate and multivariate logistic regression analyses and the Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to assess independent risk and protective factors for CHD. Construction of nomogram using independent risk factors screened by machine learning. The receiver operating characteristic(ROC) curve was used to assess the ability of these independent risk factors to predict coronary heart disease. The relationship between the Gensini score and independent risk factors was reflected using the Sankey diagram. The LASSO logistic regression analysis and Logistic regression analyses suggest that TyG index (OR, 2.193; 95% CI, 1.242-3.873; P = 0.007), SII index (OR, 1.002; 95% CI, 1.001-29 1.003; P <0.001), and SIRI index (OR,1.483;95%CI,1.058-2.079,P=0.022) are independent risk factors for CHD. At the same time, Neutrophils, TG, and LDL-C were also found to be independent risk factors in patients, HDL-C was a protective factor for CHD in patients with NAFLD. Further analysis using three machine learning algorithms found these independent risk factors to have good predictive value for disease diagnosis, SII index shows the highest predictive value. ROC curve analysis demonstrated that combining the SII index, SIRI index, and TyG index can improve the diagnostic ability of non-alcoholic liver cirrhosis patients with CHD.ROC curve analysis showed that the combined analysis of these independent risk factors improved the predictive value of CHD(AUC: 0.751; 95% CI: 0.704-0.798; P <0.001). TyG index, SII index, and SIRI index are all independent risk factors for CHD in patients with NAFLD and are strongly associated with prediction and the severity of CHD.

Establishment, Prediction, and Validation of a Nomogram for Cognitive Impairment in Elderly Patients With Diabetes.

Objective: The purpose of this study is to establish a predictive model of cognitive impairment in elderly people with diabetes. Methods: We analyzed a total of 878 elderly patients with diabetes who were part of the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2014. The data were randomly divided into training and validation cohorts at a ratio of 6:4. The least absolute shrinkage and selection operator (LASSO) logistic regression analysis to identify independent risk factors and construct a prediction nomogram for cognitive impairment. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis (DCA) was performed to evaluate the clinical utility of the nomogram. Results: LASSO logistic regression was used to screen eight variables, age, race, education, poverty income ratio (PIR), aspartate aminotransferase (AST), blood urea nitrogen (BUN), serum uric acid (SUA), and heart failure (HF). A nomogram model was built based on these predictors. The ROC analysis of our training set yielded an area under the curve (AUC) of 0.786, while the validation set showed an AUC of 0.777. The calibration curve demonstrated a good fit between the two groups. Furthermore, the DCA indicated that the model has a favorable net benefit when the risk threshold exceeds 0.2. Conclusion: The newly developed nomogram has proved to be an important tool for accurately predicting cognitive impairment in elderly patients with diabetes, providing important information for targeted prevention and intervention measures.

Development and validation of a predictive model for prolonged length of stay in elderly type 2 diabetes mellitus patients combined with cerebral infarction.

This study aimed to identify the predictive factors for prolonged length of stay (LOS) in elderly type 2 diabetes mellitus (T2DM) patients suffering from cerebral infarction (CI) and construct a predictive model to effectively utilize hospital resources. Clinical data were retrospectively collected from T2DM patients suffering from CI aged ≥65 years who were admitted to five tertiary hospitals in Southwest China. The least absolute shrinkage and selection operator (LASSO) regression model and multivariable logistic regression analysis were conducted to identify the independent predictors of prolonged LOS. A nomogram was constructed to visualize the model. The discrimination, calibration, and clinical practicality of the model were evaluated according to the area under the receiver operating characteristic curve (AUROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC). A total of 13,361 patients were included, comprising 6,023, 2,582, and 4,756 patients in the training, internal validation, and external validation sets, respectively. The results revealed that the ACCI score, OP, PI, analgesics use, antibiotics use, psychotropic drug use, insurance type, and ALB were independent predictors for prolonged LOS. The eight-predictor LASSO logistic regression displayed high prediction ability, with an AUROC of 0.725 (95% confidence interval [CI]: 0.710-0.739), a sensitivity of 0.662 (95% CI: 0.639-0.686), and a specificity of 0.675 (95% CI: 0.661-0.689). The calibration curve (bootstraps = 1,000) showed good calibration. In addition, the DCA and CIC also indicated good clinical practicality. An operation interface on a web page (https://xxmyyz.shinyapps.io/prolonged_los1/) was also established to facilitate clinical use. The developed model can predict the risk of prolonged LOS in elderly T2DM patients diagnosed with CI, enabling clinicians to optimize bed management.

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.