Lasso Logistic Regression Research Articles

Incorporating Digital Footprints into Credit-Scoring Models through Model Averaging

Digital footprints provide crucial insights into individuals’ behaviors and preferences. Their role in credit scoring is becoming increasingly significant. Therefore, it is crucial to combine digital footprint data with traditional data for personal credit scoring. This paper proposes a novel credit-scoring model. First, lasso-logistic regression is used to select key variables that significantly impact the prediction results. Then, digital footprint variables are categorized based on business understanding, and candidate models are constructed from various combinations of these groups. Finally, the optimal weight is selected by minimizing the Kullback–Leibler loss. Subsequently, the final prediction model is constructed. Empirical analysis validates the advantages and feasibility of the proposed method in variable selection, coefficient estimation, and predictive accuracy. Furthermore, the model-averaging method provides the weights for each candidate model, providing managerial implications to identify beneficial variable combinations for credit scoring.

Construction and validation of a dynamic nomogram using Lasso-logistic regression for predicting the severity of severe fever with thrombocytopenia syndrome patients at admission

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is a highly fatal infectious disease caused by the SFTS virus (SFTSV), posing a significant public health threat. This study aimed to construct a dynamic model for the early identification of SFTS patients at high risk of disease progression.MethodsAll eligible patients enrolled between April 2014 and July 2023 were divided into training and validation sets. Thirty-four clinical variables in the training set underwent analysis using least absolute shrinkage and selection operator (LASSO) logistic regression. Selected variables were then input into the multivariate logistic regression model to construct a dynamic nomogram. The model’s performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC), concordance index (C-index), calibration curve, and decision curve analysis (DCA) in both training and validation sets. Kaplan-Meier survival analysis was utilized to evaluate prognostic performance.Results299 SFTS patients entered the final investigation, with 208 patients in the training set and 90 patients in the validation set. LASSO and the multivariate logistic regression identified six significant prediction factors: age (OR, 1.060; 95% CI, 1.017–1.109; P = 0.007), CREA (OR, 1.017; 95% CI, 1.003–1.031; P = 0.019), PT (OR, 1.765; 95% CI, 1.175–2.752; P = 0.008), D-dimer (OR, 1.039; 95% CI, 1.005–1.078; P = 0.032), nervous system symptoms (OR, 8.244; 95% CI, 3.035–26.858; P < 0.001) and hemorrhage symptoms (OR, 3.414; 95% CI, 1.096–10.974; P = 0.035). The AUC-ROC, C-index, calibration plots, and DCA demonstrated the robust performance of the nomogram in predicting severity at admission, and Kaplan-Meier survival analysis indicated its utility in predicting 28-day mortality among SFTS patients. The dynamic nomogram is accessible at https://sfts.shinyapps.io/SFTS_severity_nomogram/.ConclusionThis study provided a practical and readily applicable tool for the early identification of high-risk SFTS patients, enabling the timely initiation of intensified treatments and protocol adjustments to mitigate disease progression.

Effect of Random Under sampling, Oversampling, and SMOTE on the Performance of Cardiovascular Disease Prediction Models

Cardiovascular Disease (CVD) or commonly known as Heart Disease is a leading cause of mortality globally, prompting extensive research into predictive models to assess individual risk and plan preventive measures. Machine learning approaches such as Random Forest, Support Vector Machine (SVM), and LASSO Logistic Regression have showed promise. Recent studies have indicated that traditional resampling methods like Random Oversampling, Random Undersampling, and SMOTE may not significantly improve model discrimination. This study aims to evaluate the impact of these techniques on the performance of Cardiovascular Disease (CVD) prediction models, utilizing data from the UCI Machine Learning Heart Disease database. By employing LASSO Logistic Regression, Random Forest, and Support Vector Machine (SVM) with resampling techniques, including Random Oversampling, Random Undersampling, and SMOTE. This research seeks to enhance understanding of model performance in addressing class imbalances within the dataset and contribute to refining cardiovascular disease (CVD) prediction strategies. This study demonstrates that the use of the SMOTE technique significantly enhances the performance of cardiovascular disease (CVD) prediction models. Specifically, when combined with the Random Forest algorithm, SMOTE achieves the best performance in terms of accuracy, sensitivity, and specificity. This highlights the importance of selecting appropriate resampling techniques to handle class imbalance in datasets. Consequently, this research contributes to refining CVD prediction strategies and provides new insights into improving prediction accuracy in imbalanced medical data.

Knowledge-slanted random forest method for high-dimensional data and small sample size with a feature selection application for gene expression data

The use of prior knowledge in the machine learning framework has been considered a potential tool to handle the curse of dimensionality in genetic and genomics data. Although random forest (RF) represents a flexible non-parametric approach with several advantages, it can provide poor accuracy in high-dimensional settings, mainly in scenarios with small sample sizes. We propose a knowledge-slanted RF that integrates biological networks as prior knowledge into the model to improve its performance and explainability, exemplifying its use for selecting and identifying relevant genes. knowledge-slanted RF is a combination of two stages. First, prior knowledge represented by graphs is translated by running a random walk with restart algorithm to determine the relevance of each gene based on its connection and localization on a protein-protein interaction network. Then, each relevance is used to modify the selection probability to draw a gene as a candidate split-feature in the conventional RF. Experiments in simulated datasets with very small sample sizes (n≤30)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(n \\le 30)$$\\end{document} comparing knowledge-slanted RF against conventional RF and logistic lasso regression, suggest an improved precision in outcome prediction compared to the other methods. The knowledge-slanted RF was completed with the introduction of a modified version of the Boruta feature selection algorithm. Finally, knowledge-slanted RF identified more relevant biological genes, offering a higher level of explainability for users than conventional RF. These findings were corroborated in one real case to identify relevant genes to calcific aortic valve stenosis.

Exploring the predictive values of CRP and lymphocytes in coronary artery disease based on a machine learning and Mendelian randomization

PurposeTo investigate the predictive value of leukocyte subsets and C-reactive protein (CRP) in coronary artery disease (CAD).MethodsWe conducted a Mendelian randomization analysis (MR) on leukocyte subsets, C-reactive protein (CRP) and CAD, incorporating data from 68,624 patients who underwent coronary angiography from 2010 to 2022. After initial screening, clinical data from 46,664 patients were analyzed. Techniques employed included propensity score matching (PSM), logistic regression, lasso regression, and random forest algorithms (RF). Risk factors were assessed, and the sensitivity and specificity of the models were evaluated using receiver operating characteristic (ROC) curves. Additionally, survival analysis was conducted based on a 36-month follow-up period.ResultsThe inverse variance weight (IVW) analysis showed that basophil count (OR 0.92, 95% CI: 0.84–1.00, P = 0.048), CRP levels (OR 0.87, 95% CI: 0.73–1.00, P = 0.040), and lymphocyte count (OR 1.10, 95% CI: 1.04–1.16, P = 0.001) are significant risk factors for CAD. Using LASSO regression, logistic regression, and RF analysis, both CRP and lymphocyte counts were consistently identified as risk factors for CAD, prior to and following PSM. The ROC curve analysis indicated that the combination of lymphocyte and CRP levels after PSM achieves a higher diagnostic value (0.85). Survival analysis revealed that high lymphocyte counts and low CRP levels are associated with a decreased risk of Major Adverse Cardiovascular Events (MACE) (P &amp;lt; 0.001). Conversely, a higher CRP level combined with lymphocyte counts correlates with a poorer prognosis.ConclusionThere is a causal relationship between lymphocytes, CRP and CAD. The combined assessment of CRP and lymphocytes offers diagnostic value for CAD. Furthermore, high CRP levels coupled with low lymphocyte counts are associated with a poor prognosis.

Predicting cerebellar mutism syndrome in children using lesion map combined with clinical features.

This study aimed to develop a predictive model for cerebellar mutism syndrome (CMS) in pediatric patients with posterior fossa tumors, integrating lesion-symptom mapping (LSM) data with clinical factors, and to assess the model's performance. A cohort of pediatric patients diagnosed with posterior fossa tumors and undergoing surgery at Beijing Children's Hospital from July 2013 to December 2023 was analyzed. Clinical variables gender, age at surgery, tumor characteristics, hydrocephalus, surgical route and pathology were collected. LSM was used to link tumor locations with CMS outcomes. Lasso regression and logistic regression were employed for feature selection and model construction, respectively. Model performance was assessed using area under the curve (AUC) and accuracy metrics. The study included 197 patients in total, with CMS rates consistent across training, validation, and prospective groups. Significant associations were found between CMS and gender, tumor type, hydrocephalus, paraventricular edema, surgical route, and pathology. A predictive model combining voxel location data from LSM with clinical factors achieved high predictive performance (C-index: training 0.956, validation 0.933, prospective 0.892). Gender, pathology, and voxel location were identified as key predictors for CMS. The study established an effective predictive model for CMS in pediatric posterior fossa tumor patients, leveraging LSM data and clinical factors. The model's accuracy and robustness suggest its potential utility in clinical practice for early CMS risk assessment and intervention planning.

A predictive model for stunting among children under the age of three.

In light of the global effort to eradicate stunting in childhood, the objective of this research endeavor was to assess the prevalence of stunting and associated factors, simultaneously construct and validate a risk prediction model for stunting among children under the age of three in Shenzhen, China. Using the stratified random sampling method, we selected 9,581 children under the age of three for research and analysis. The dataset underwent a random allocation into training and validation sets, adhering to a 8:2 split ratio. Within the training set, a combined approach of LASSO regression analysis and binary logistic regression analysis was implemented to identify and select the predictive variables for the model. Subsequently, model construction was conducted in the training set, encompassing model evaluation, visualization, and internal validation procedures. Finally, to assess the model's generalizability, external validation was performed using the validation set. A total of 684 (7.14%) had phenotypes of stunt. Utilizing a combined approach of LASSO regression and logistic regression, key predictors of stunting among children under three years of age were identified, including sex, age in months, mother's education, father's age, birth order, feeding patterns, delivery mode, average daily parent-child reading time, average time spent in child-parent interactions, and average daily outdoor time. These variables were subsequently employed to develop a comprehensive prediction model for childhood stunting. A nomogram model was constructed based on these factors, demonstrating excellent consistency and accuracy. Calibration curves validated the agreement between the nomogram predictions and actual observations. Furthermore, ROC and DCA analyses indicated the strong predictive performance of the nomograms. The developed model for forecasting stunt risk, which integrates a spectrum of variables. This analytical framework presents actionable intelligence to medical professionals, laying down a foundational framework and a pivot for the conception and execution of preemptive strategies and therapeutic interventions.

Nomogram based on clinical characteristics and ultrasound indicators for predicting severe postpartum hemorrhage in patients with anterior placenta previa combined with previous cesarean section: a retrospective case-control study

BackgroundPlacental accreta spectrum disorders (PAS) are a high-risk group for severe postpartum hemorrhage (SPPH), with the incidence of PAS increasing annually. Given that cesarean section and anterior placenta previa are the primary risk factors for PAS, therefore, our study aims to investigate the predictive value of clinical characteristics and ultrasound indicators for SPPH in patients with anterior placenta previa combined with previous cesarean section, providing a theoretical basis for early prediction of SPPH.MethodsA total of 450 patients with anterior placenta previa combined with previous cesarean section were retrospectively analyzed at Shengjing Hospital affiliated with China Medical University between January 2018 and March 2022. Clinical data and ultrasound indicators were collected. Patients were categorized into SPPH (blood loss >2000mL, 182 cases) and non-SPPH (blood loss ≤ 2000mL, 268 cases) groups based on the blood loss within 24 h postpartum. The population was randomly divided into training and validation cohorts at a 7:3 ratio. LASSO and multifactorial logistic regression analyses were utilized to identify independent risk factors for SPPH. Accordingly, a nomogram prediction model was constructed, the predictive performance was assessed using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA).ResultsAmong the 450 patients, 182 experienced SPPH (incidence rate, 40.44%). Preoperative systemic immune-inflammatory index, preoperative D-dimer level, preoperative placenta accreta spectrum ultrasound scoring system (PASUSS) score, and one-step-conservative surgery were identified as independent risk factors for SPPH in patients with anterior placenta previa combined with previous cesarean section. A nomogram was constructed based on these factors. The areas under the ROC curves for the training and validation cohorts were 0.844 (95%CI: 0.801–0.888) and 0.863 (95%CI: 0.803–0.923), respectively. Calibration curves and DCA indicated that this nomogram demonstrated good predictive accuracy.ConclusionsThis nomogram presents an effective and convenient prediction model for identifying SPPH in patients with anterior placenta previa combined with previous cesarean section. It can guide surgical planning and improve prognosis.

Gender-specific microbial signatures in saliva: Unveiling the association between the oral microbiome and the pathogenesis of glioma

The intricate interplay between the human oral microbiome and systemic health is increasingly being recognized, particularly in the context of central nervous system pathologies such as glioblastoma. In this study, we aimed to elucidate gender-specific differences in the salivary microbiome of glioma patients by utilizing 16S rRNA sequencing data from publicly available salivary microbiome datasets. We conducted comprehensive bioinformatics analysis, encompassing quality control, noise reduction, species classification, and microbial community composition analysis at various taxonomic levels. Machine learning algorithms were employed to identify microbial signatures associated with glioma. When compared to healthy controls, our analysis revealed distinct differences in the salivary microbiota of glioma patients. Notably, the genera Leptotrichia and Atopobium exhibited significant variations in abundance between genders. Leptotrichia was prevalent in healthy females but exhibited a reduced abundance in female glioma patients. In contrast, Atopobium was more abundant in male glioma patients. These findings suggest that hormonal influences might play a role in shaping the salivary microbiome and its association with glioma. We utilized a combination of LASSO-logistic regression and random forest models for feature selection, and identified key microbial features that differentiated glioma patients from healthy controls. We developed a diagnostic model with high predictive accuracy and area under the curve and principal component analysis metrics confirmed its robustness. The analysis of microbial markers, including Atopobium and Leptotrichia, highlighted the potential of the salivary microbiota as a non-invasive biomarker for the diagnosis and prognosis of glioma. Our findings highlight significant gender-specific disparities in the salivary microbiome of patients with glioma, offering new insights into the pathogenesis of glioma and paving the way for innovative diagnostic and therapeutic strategies. The use of saliva as a diagnostic fluid, given its ease of collection and non-invasive nature, holds immense promise for monitoring systemic health and the trajectory of disease. Future research should focus on investigating the underlying mechanisms by which the salivary microbiome influences the development of glioma and identifying potential microbiome-targeted therapies to enhance the management of glioma.

Exploration of the ubiquitination-related molecular classification and signature to predict the survival and immune microenvironment in colon cancer.

Ubiquitination, a major post-translational modification, significantly impacts tumorigenesis, progression, and prognosis. This study aims to classify colon cancer at the molecular level and create a reliable signature using ubiquitination-related genes (URGs) to assess the immune microenvironment and prognosis. We employed non-negative matrix factorization to subtype colon cancer based on ubiquitination-related gene (URG) expression patterns. Quantitative scores for 28 immune cell infiltrates and the tumor microenvironment were computed using single-sample gene set enrichment analysis (ssGSEA) and the Estimate algorithm. Subtype feature genes were selected through Lasso logistic regression and SVM-RFE algorithm. The ubiquitination-related signature was constructed using univariate Cox, Lasso, and stepwise regression methods to categorize patients into high and low-risk groups. Validation included log-rank tests, receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and external dataset validation. Immune therapy response was compared using Tumor Immune Dysfunction and Exclusion (TIDE), Immunophenoscore (IPS), and submap analyses. Clinical variables and risk scores were integrated into an enhanced nomogram. The early diagnostic value of four URGs was confirmed via quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry. The cell proliferation was assessed through colony formation, EdU staining, and xenograft tumorigenesis assays. Prognostic ubiquitination-related genes (URGs) stratified patients into subtypes, revealing differences in survival, immune cell infiltration, and pathological staging. A signature of 6 URGs (ARHGAP4, MID2, SIAH2, TRIM45, UBE2D2, WDR72) was identified from 57 subtype-related genes. The high-risk group exhibited characteristics indicative of enhanced epithelial-mesenchymal transition, immune escape, immunosuppressive myeloid-derived suppressor cells, regulatory T cell infiltration, and lower immunogenicity. In contrast, the low-risk group demonstrated the opposite trend but showed a better response to CTLA4 checkpoint inhibitors. The predictive performance of the nomogram significantly improved with the integration of risk score, stage, and age. ARHGAP4 and SIAH2 exhibit promising early diagnostic capabilities. Additionally, WDR72 knockdown significantly inhibited CRC cell proliferation both in vitro and in vivo. Our developed ubiquitination-related signature and genes serve as promising biomarkers for colon cancer prognosis, immune microenvironment, and diagnosis.

Predictive utility of emotional regulation abilities for assessing cognitive improvement in depression

ObjectiveTo construct a predictive model for the improvement of cognitive function in patients with depressive disorder treated with SNRIs, based on emotional regulation abilities, and to provide personalized treatment for depressed patients. MethodsClinical data from 170 patients with depressive disorder treated with SNRIs at Tongji Hospital, Shanghai, from December 2017 to May 2023 were collected. Based on whether the MoCA-B total score at 3–6 months post-treatment was at least 2 points higher than at baseline, patients were divided into the cognitive function improved group (n = 80) and the cognitive function not improved group (n = 90). Stepwise logistic regression and LASSO regression were used to select predictive factors, and logistic regression analysis was applied to construct predictive models solely based on emotional regulation abilities, combined with executive functions and HAMD scores. The models were further validated through Bootstrap internal validation, calibration curve plotting, and C-index calculation, and a comparison between the two models was performed. ResultsAn ER model with an area under the ROC curve of 0.817was established using four emotional regulation ability indicators: the valence of reappraised images, the arousal of negative images, the arousal of neutral images, and the success of reappraisal (arousal). Internal validation using Bootstrap showed a C index of 0.817, and clinical decision curves indicated that this model has a significant net benefit with a probability of improved cognitive function ranging from about 20 to 85%. Additionally, an EREH model including emotional regulation ability, executive function, and HAMD score as predictors was constructed using Lasso and logistic regression methods. This model reached an area under the ROC curve of 0.859and clinical decision curves showed high net benefits with probabilities of improved cognitive function ranging from 10 to 100%. The calibration curves of both models coincided well with the actual curves, with the latter having a higher AUC and significant statistical differences between the two models. ConclusionThis study suggests that emotional regulation ability may serve as a predictor for the improvement of cognitive functions in patients with depression depressive disorder treated with SNRIs. However, it is important to note that there may be other factors not covered or included in this study.The predictive model that includes executive functions and HAMD scores offers better differentiation and consistency and is more feasible in clinical practice.

Characteristics of regional lymph node metastasis in breast cancer and construction of a nomogram model based on ultrasonographic analysis: a retrospective study

ObjectiveThe ultrasonographic characteristics of lymph node metastasis in breast cancer patients were retrospectively analyzed, and a predictive nomogram model was constructed to provide an imaging basis for better clinical evaluation.MethodsB-mode ultrasound was used to retrospectively analyze the imaging characteristics of regional lymph nodes and tumors. Pathological examination confirmed the presence of lymph node metastasis in breast cancer patients. Univariable and multivariable logistic regression analyses were performed to analyze the risk factors for lymph node metastasis. LASSO regression analysis was performed to screen noninvasive indicators, and a nomogram prediction model was constructed for breast cancer patients with lymph node metastasis.ResultsA total of 187 breast cancer patients were enrolled, including 74 patients with lymph node metastasis in the positive group and 113 patients without lymph node metastasis in the negative group. Multivariate analysis revealed that pathological type (OR = 4.58, 95% CI: 1.44–14.6, p = 0.01), tumor diameter (OR = 1.37, 95% CI: 1.07–1.74, p = 0.012), spiculated margins (OR = 7.92, 95% CI: 3.03–20.67, p < 0.001), mixed echo of the breast tumor (OR = 37.09, 95% CI: 3.49–394.1, p = 0.003), and unclear lymphatic hilum structure (OR = 16.07, 95% CI: 2.41–107.02, p = 0.004) were independent risk factors for lymph node metastasis. A nomogram model was constructed for predicting breast cancer with lymph node metastasis, incorporating three significantly correlated indicators identified through LASSO regression analysis, namely, tumor spiculated margins, cortical thickness of lymph nodes, and unclear lymphatic hilum structure. The receiver operating characteristic (ROC) curve revealed that the area under the curve (AUC) was 0.717 (95% CI, 0.614–0.820) for the training set and 0.817 (95% CI, 0.738–0.890) for the validation set. The Hosmer–Lemeshow test results for the training set and the validation set were p = 0.9148 and p = 0.1648, respectively. The prediction nomogram has good diagnostic performance.ConclusionsB-mode ultrasound is helpful in the preoperative assessment of breast cancer patients with lymph node metastasis. The predictive nomogram model, which is based on logistic regression and LASSO regression analysis, is clinically safe, reliable, and highly practical.

Investigating CR1 as an indicated Gene for mild cognitive impairment in type 2 diabetes mellitus

ObjectiveType 2 diabetes mellitus (T2DM) has beenis known as an important risk factor for cognitive impairment. Meanwhile, the liver plays a central role in the development of T2DM and insulin resistance. The present study attempted to identify and validate marker genes for mild cognitive impairment (MCI) in patients with T2DM.MethodsIn this study, insulin resistance-related differentially expressed genes were identified from the liver tissues of individuals with T2DM and those with normal glucose tolerance using the Gene Expression Omnibus database and MCI-associated genes were identified using the GeneCards database. Next, enrichment analysis was performed with overlapping T2DM and MCI genes, followed by the identification of specific genes using the LASSO logistic regression and SVM-RFE algorithms. An important experiment involved the implementation of clinical and in vitro validation using real-time quantitative polymerase chain reaction (RT-qPCR). Finally, multiple linear regression, binary logistic regression, and receiver operating characteristic curve analyses were performed to investigate the relationship between the key gene and cognitive function in these patients.ResultThe present study identified 40 overlapping genes between MCI and T2DM, with subsequent enrichment analysis revealing their significant association with the roles of neuronal and glial projections. The marker gene complement receptor 1(CR1) was identified for both diseases using two regression algorithms. Based on RT-qPCR validation in 65 T2DM patients with MCI (MCI group) and 65 T2DM patients without MCI (NC group), a significant upregulation of CR1 mRNA in peripheral blood mononuclear cells was observed in the MCI group (P < 0.001). Furthermore, the CR1 gene level was significantly negatively associated with MoCA and MMSE scores, which reflect the overall cognitive function, and positively correlated with TMTB scores, which indicate the executive function. Finally, elevated CR1 mRNA levels were identified as an independent risk factor for MCI (OR = 1.481, P < 0.001).ConclusionThese findings suggest that CR1 is an important predictor of MCI in patients with T2DM. Thus, CR1 has potential clinical significance, which may offer new ideas and directions for the management and treatment of T2DM. The identification and clinical validation of dysregulated marker genes in both T2DM and MCI can offer valuable insights into the intrinsic association between these two conditions. The current study insights may inspire the development of novel strategies for addressing the complicated issues related to cognitive impairment associated with diabetes.

Automated Speech Analysis in Bipolar Disorder: The CALIBER Study Protocol and Preliminary Results.

Background: Bipolar disorder (BD) involves significant mood and energy shifts reflected in speech patterns. Detecting these patterns is crucial for diagnosis and monitoring, currently assessed subjectively. Advances in natural language processing offer opportunities to objectively analyze them. Aims: To (i) correlate speech features with manic-depressive symptom severity in BD, (ii) develop predictive models for diagnostic and treatment outcomes, and (iii) determine the most relevant speech features and tasks for these analyses. Methods: This naturalistic, observational study involved longitudinal audio recordings of BD patients at euthymia, during acute manic/depressive phases, and after-response. Patients participated in clinical evaluations, cognitive tasks, standard text readings, and storytelling. After automatic diarization and transcription, speech features, including acoustics, content, formal aspects, and emotionality, will be extracted. Statistical analyses will (i) correlate speech features with clinical scales, (ii) use lasso logistic regression to develop predictive models, and (iii) identify relevant speech features. Results: Audio recordings from 76 patients (24 manic, 21 depressed, 31 euthymic) were collected. The mean age was 46.0 ± 14.4 years, with 63.2% female. The mean YMRS score for manic patients was 22.9 ± 7.1, reducing to 5.3 ± 5.3 post-response. Depressed patients had a mean HDRS-17 score of 17.1 ± 4.4, decreasing to 3.3 ± 2.8 post-response. Euthymic patients had mean YMRS and HDRS-17 scores of 0.97 ± 1.4 and 3.9 ± 2.9, respectively. Following data pre-processing, including noise reduction and feature extraction, comprehensive statistical analyses will be conducted to explore correlations and develop predictive models. Conclusions: Automated speech analysis in BD could provide objective markers for psychopathological alterations, improving diagnosis, monitoring, and response prediction. This technology could identify subtle alterations, signaling early signs of relapse. Establishing standardized protocols is crucial for creating a global speech cohort, fostering collaboration, and advancing BD understanding.

Development and validation of a nomogram based on Lasso-Logistic regression for predicting splenomegaly secondary to acute pancreatitis

PurposeInvestigate the clinical characteristics of splenomegaly secondary to acute pancreatitis (SSAP) and construct a nomogram prediction model based on Lasso-Logistic regression.MethodsA retrospective case-control study was conducted to analyze the laboratory parameters and computed tomography (CT) imaging of acute pancreatitis (AP) patients recruited at Xuanwu Hospital from December 2014 to December 2021. Lasso regression was used to identify risk factors, and a novel nomogram was developed. The performance of the nomogram in discrimination, calibration, and clinical usefulness was evaluated through internal validation.ResultsThe prevalence of SSAP was 9.2% (88/950), with the first detection occurring 65(30, 125) days after AP onset. Compared with the control group, the SSAP group exhibited a higher frequency of persistent respiratory failure, persistent renal failure, infected pancreatic necrosis, and severe AP, along with an increased need for surgery and longer hospital stay (P < 0.05 for all). There were 185 and 79 patients in the training and internal validation cohorts, respectively. Variables screened by Lasso regression, including platelet count, white blood cell (WBC) count, local complications, and modified CT severity index (mCTSI), were incorporated into the Logistic model. Multivariate analysis showed that WBC count ≦9.71 × 109/L, platelet count ≦140 × 109/L, mCTSI ≧8, and the presence of local complications were independently associated with the occurrence of SSAP. The area under the receiver operating characteristic curve was 0.790. The Hosmer-Lemeshow test showed that the model had good fitness (P = 0.954). Additionally, the nomogram performed well in the internal validation cohorts.ConclusionsSSAP is relatively common, and patients with this condition often have a worse clinical prognosis. Patients with low WBC and platelet counts, high mCTSI, and local complications in the early stages of the illness are at a higher risk for SSAP. A simple nomogram tool can be helpful for early prediction of SSAP.

Predictive Factors for Tissue Inflammation in Patients with Benign Prostatic Hyperplasia: A Clinical Prediction Study

Introduction: Benign prostatic hyperplasia (BPH) is a common condition in older men, marked by the noncancerous enlargement of the prostate gland. Inflammation of the prostate plays a significant role in the progression of BPH and the symptoms it causes. The objective of this study was to create a predictive model for prostatic inflammation in men with BPH based on important clinical factors. Methods: A retrospective cohort study was conducted with 137 patients diagnosed with BPH. Data collected included various factors such as age, prostate volume (PV), preoperative international prostate symptom score, preoperative maximum urine flow rate (Qmax), preoperative post-void residue, weight of the excised tissue, body mass index, fasting blood glucose (FBG), cholesterol levels, prostate-specific antigen, blood calcium, blood phosphorus, blood uric acid, triglycerides, hypertension status, and presence of prostate calcifications. Multivariate logistic regression and LASSO regression analyses were performed to identify significant predictors and develop a nomogram. The model’s performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA). Results: Among the patients, 9.49% showed no signs of prostatic inflammation, while 22.63% had mild, 47.45% had moderate, and 20.44% had severe inflammation. Factors such as PV, FBG, and prostate calcification were identified as important predictors of prostatic inflammation. The predictive model developed exhibited strong discrimination and calibration, as evidenced by a high area under the curve value, indicating reliable predictive accuracy. DCA further validated the clinical usefulness of the nomogram. Conclusion: The developed nomogram, incorporating PV, FBG, and prostate calcification, effectively predicts prostatic inflammation in men with BPH. This tool can aid in early intervention and targeted treatment, potentially improving patient outcomes. Further validation in diverse populations is recommended to enhance its generalizability and clinical applicability.

Delayed Access to Medical Care and Psychological Distress among Chinese Immigrants in Canada during the Pandemic.

The psychological impact of medical care accessibility during the pandemic has been widely studied, but little attention has been given to Asian immigrants in Canada. This study aimed to fill this literature gap by using a cross-sectional survey, which aimed to evaluate the impact of the COVID-19 pandemic on Chinese immigrants in North America during the second wave of the pandemic. The study focused on Chinese immigrants aged 16 or older in Canada. Covariates included sociodemographic variables, delayed access to medical care (i.e., treatment or health assessment), and other COVID-19 related variables. We used logistic LASSO regression for model selection and multivariate logistic regression models to evaluate the association between delayed access to treatment/health assessment and psychological distress outcome, as measured by the COVID-19 Peritraumatic Distress Index (CPDI). Missing data were handled using multiple imputation. Our study included 746 respondents, with 47.18% in the normal CPDI group and 36.82% in the mild-to-severe CPDI group. Most respondents were originally from Mainland China and residing in Ontario. Over half have stayed in Canada for at least 15 years. The multivariate logistic regression models identified significant risk predictors of psychological distress status: delayed access to medical care (OR = 1.362, 95% CI: 1.078-1.720, p = 0.0095), fear of COVID-19 (OR = 1.604, 95% CI: 1.293-1.989, p < 0.0001), and social loneliness (OR = 1.408, 95%CI: 1.314-1.508, p < 0.0001). Sociodemographic variables and other COVID-19-related variates did not significantly impact the study's outcome. Our findings shed light on the importance of timely medical care access to psychological well-being among Chinese Canadians. Reliable health information, mental health support, and virtual care tailored to immigrants should be considered to mitigate this impact and promote their overall health and well-being.

Predicting the Risk of In-Hospital Mortality in Traumatic Brain Injury Patients on Invasive Mechanical Ventilation in the Intensive Care Unit: Construction and Validation of an Online Nomogram

To explore mortality risk factors and to construct an online nomogram for predicting in-hospital mortality in traumatic brain injury (TBI) patients receiving invasive mechanical ventilation (IMV) in intensive care unit (ICU). We retrospectively analyzed TBI patients on IMV in ICU from Medical Information Mart for Intensive Care IV database and 2 hospitals. Least absolute shrinkage and selection operation regression and multiple logistic regression were used to detect predictors of in-hospital mortality and to construct an online nomogram. The predictive performance of nomogram was evaluated using area under the receiver operating characteristic curves (AUC), calibration curves, decision curve analysis, and clinical impact curves. Five hundred ten from Medical Information Mart for Intensive Care IV database were enrolled for nomogram construction (80%, n= 408) and internal validation (20%, n= 102). One hundred eighty-five from 2 hospitals were enrolled for external validation. Least absolute shrinkage and selection operation-logistic regression revealed predictors of in-hospital mortality among TBI patients on IMV in ICU included Glasgow Coma Scale (GCS) after ICU admission, Acute Physiology Score III (APS III) after ICU admission, neutrophil and lymphocyte ratio after IMV, blood urea nitrogen after IMV, arterial serum lactate after IMV, and in-hospital tracheotomy. The AUC, calibration curves, decision curve analysis, and clinical impact curves indicated the nomogram had good discrimination, calibration, clinical benefit, and applicability. The multimodel comparisons revealed the nomogram had higher AUC than GCS, APS III, and Simplified Acute Physiology Score II. We constructed and validated an online nomogram based on routinely recorded factors at admission to ICU and at the beginning of IMV to target prediction of in-hospital mortality among TBI patients on IMV in ICU.

Depressive symptom as a risk factor for cirrhosis in patients with primary biliary cholangitis: Analysis based on Lasso-logistic regression and decision tree models.

Depressive symptoms are frequently observed in patients with primary biliary cholangitis (PBC). The role of depressive symptoms on cirrhosis has not been fully noticed in PBC. We aimed to establish a risk model for cirrhosis that took depressive symptoms into account. Depressive symptoms were assessed by the 17-item Hamilton Depression Rating Scale (HAMD-17). HAMD-17 score was analyzed in relation to clinical parameters. Least absolute shrinkage and selection operator (Lasso)-logistic regression and decision tree models were used to explore the effect of depressive symptoms on cirrhosis. The rate of depressive symptom in patients with PBC (n=162) was higher than in healthy controls (n=180) (52.5%vs. 16.1%; p<.001). HAMD-17 score was negatively associated with C4 levels and positively associated with levels of alkaline phosphatase (ALP), γ-glutamyl transpeptidase (GGT), total bilirubin (TB), Immunoglobulin (Ig) G, and IgM (r=-0.162, 0.197, 0.355, 0.203, 0.182, 0.314, p<.05). In Lasso-logistic regression analysis, HAMD-17 score, human leukocyte antigen (HLA)-DRB1*03:01 allele, age, ALP levels, and IgM levels (odds ratio [OR]=1.087, 7.353, 1.075, 1.009, 1.005; p<0.05) were independent risk factors for cirrhosis. Elevated HAMD-17 score was also a discriminating factor for high risk of cirrhosis in patients with PBC in decision tree model. Depressive symptoms were associated with disease severity. Elevated HAMD-17 score was a risk factor for cirrhosis in patients with PBC.

Risk prediction and prognostic analysis of post-implantation syndrome after thoracic endovascular aortic repair

This study aimed to establish a predictive model for the risk of post-thoracic endovascular aortic repair (TEVAR) post-implantation syndrome (PIS) in type B aortic dissection (TBAD) patients, assisting clinical physicians in early risk stratification and decision management for high-risk PIS patients. This study retrospectively analyzed the clinical data of 547 consecutive TBAD patients who underwent TEVAR treatment at our hospital. Feature variables were selected through LASSO regression and logistic regression analysis to construct a nomogram predictive model, and the model's performance was evaluated. The optimal cutoff value for the PIS risk nomogram score was calculated through receiver operating characteristic (ROC) curve analysis, further dividing patients into high-risk group (HRG) and low-risk group (LRG), and comparing the short to midterm postoperative outcomes between the two groups. In the end, a total of 158 cases (28.9%) experienced PIS. Through LASSO regression analysis and multivariable logistic regression analysis, variables including age, emergency surgery, operative time, contrast medium volume, and number of main prosthesis stents were selected to construct the nomogram predictive model. The model achieved an area under the curve (AUC) of 0.86 in the training set and 0.82 in the test set. Results from calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC) demonstrated that the predictive model exhibited good performance and clinical utility. Furthermore, after comparing the postoperative outcomes of HRG and LRG patients, we found that the incidence of postoperative PIS significantly increased in HRG patients. The duration of ICU stay and mechanical assistance time was prolonged, and the incidence of postoperative type II entry flow and acute kidney injury (AKI) was higher. The risk of aortic-related adverse events (ARAEs) and major adverse events (MAEs) at the first and twelfth months of follow-up also significantly increased. However, there was no significant difference in the mortality rate during hospitalization. This study established a nomogram model for predicting the risk of PIS in patients with TBAD undergoing TEVAR. It serves as a practical tool to assist clinicians in early risk stratification and decision-making management for patients.