Least Absolute Shrinkage And Selection Operator Research Articles

An inflammatory cytokine signature predicts IgA nephropathy severity and progression.

IgA nephropathy (IgAN) is the most prevalent primary glomerulonephritis, resulting in end-stage renal disease and increased mortality rates. Prognostic biomarkers reflecting molecular mechanisms for effective IgAN management are urgently needed. Analysis of kidney single-cell transcriptomic sequencing data demonstrated that IgAN expressed high-expression levels of inflammatory cytokines TNFSF10, TNFSF12, CCL2, CXCL1, and CXCL12 than healthy controls (HCs). We also measured the urine proteins in 120 IgAN (57 stable and 63 progressive) and 32 HCs using the proximity extension assay (PEA), and the multivariable and least absolute shrinkage and selection operator (LASSO) logistic regression analysis both revealed that CXCL12, MCP1 were the prognostic significant variables to predict IgAN progression severity. These two proteins exhibited negative correlation with the estimated glomerular filtration rate (eGFR) and patients with higher expression levels of these two proteins had a higher probability to have poorer renal outcome. We further developed a risk index model utilizing CXCL12, MCP1, and baseline clinical indicators, which achieved an impressive area under the curve (AUC) of 0.896 for prediction of IgAN progression severity. Our study highlights the significance of the inflammatory protein biomarkers for noninvasive prediction of IgAN severity and progression, offering valuable insights for clinical management.

An Integrated Nomogram Combining Deep Learning and Radiomics for Predicting Malignancy of Pulmonary Nodules Using CT-Derived Nodules and Adipose Tissue: AMulticenter Study.

Correctly distinguishing between benign and malignant pulmonary nodules can avoid unnecessary invasive procedures. This study aimed to construct a deep learning radiomics clinical nomogram (DLRCN) for predicting malignancy of pulmonary nodules. One thousand and ninety-eight patients with 6-30 mm pulmonary nodules who received histopathologic diagnosis at 3 centers were included and divided into a primary cohort (PC), an internal test cohort (I-T), and two external test cohorts (E-T1, E-T2). The DLRCN was built by integrating adipose tissue radiomics features, intranodular and perinodular deep learning features, and clinical characteristics for diagnosing malignancy of pulmonary nodules. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. The performance of DLRCN was assessed with respect to its calibration curve, area under the curve (AUC), and decision curve analysis (DCA). Furthermore, we compared it with three radiologists. The net reclassification improvement (NRI), integrated discrimination improvement (IDI), and subgroup analysis were also taken into account. The incorporation of adipose tissue radiomics features led to significant NRI and IDI (NRI = 1.028, p < 0.05, IDI = 0.137, p < 0.05). In the I-T, E-T1, and E-T2, the AUCs of DLRCN were 0.946 (95% CI: 0.936, 0.955), 0.948 (95% CI: 0.933, 0.963) and 0.962 (95% CI: 0.945, 0.979), The calibration curve revealed good predictive accuracy between the actual probability and predicted probability (p > 0.05). DCA showed that the DLRCN was clinically useful. Under equal specificity, the sensitivity of DLRCN increased by 8.6% compared to radiologist assessments. The subgroup analysis conducted on adipose tissue radiomics features further demonstrated their supplementary value in determining the malignancy of pulmonary nodules. The DLRCN demonstrated good performance in predicting the malignancy of pulmonary nodules, which was comparable to radiologist assessments. The adipose tissue radiomics features have notably enhanced the performance of DLRCN.

Prognostic nomogram model based on quantitative metrics of subregions surrounding residual cavity in glioblastoma patients

BackgroundThe hyperintensity area surrounding the residual cavity on postoperative fluid-attenuated inversion recovery (FLAIR) image is a potential site for glioblastoma (GBM) recurrence. This study aimed to develop a nomogram using quantitative metrics from subregions of this area, prior to chemoradiotherapy (CRT), to predict early GBM recurrence.MethodsAdult patients with GBM diagnosed between October 2018 and October 2022 were retrospectively analyzed. Quantitative metrics, including the mean, maximum, minimum, median values, and standard deviation of FLAIR signal intensity (SI) (measured using 3D-Slicer software), were extracted from the following subregions surrounding the residual cavity on post-contrast T1-weighted (CE-T1WI)-FLAIR fusion images: the enhancing region (ER), non-enhancing region (NER), and combined ER + NER. Independent prognostic factors were identified using Cox regression and least absolute shrinkage and selection operator (LASSO) analyses and were incorporated into the prediction nomogram model. The model’s performance was evaluated using the C-index, calibration curves, and decision curves.ResultsA total of 129 adult GBM patients were enrolled and randomly assigned to a training (n = 90) and a validation cohorts (n = 39) in a 7:3 ratio. Sixty-nine patients experienced postoperative recurrence. Cox regression analysis identified subventricular zone involvement, the median FLAIR intensity in the ER, the rFLAIR (relative FLAIR intensity compared to the contralateral normal region) of ER + NER, and corpus callosum involvement as independent prognostic factors. For predicting recurrence within 1 year after surgery, the nomogram model had a C-index of 0.733 in the training cohort and 0.746 in the validation cohort. Based on the nomogram score, post-operative GBM patients could be stratified into high- and low-risk for recurrence.ConclusionsNomogram models which based on quantitative metrics from FLAIR hyperintensity subregions may serve as potential markers for assessing GBM recurrence risk. This approach could enhance clinical decision-making and provide an alternative method for recurrence estimation in GBM patients.

Impact of virtual monochromatic images of different low-energy levels in dual-energy CT on radiomics models for predicting muscle invasion in bladder cancer.

The purpose of this study was to investigate the impact of different low-energy virtual monochromatic images (VMIs) in dual-energy CT on the performance of radiomics models for predicting muscle invasive status in bladder cancer (BCa). A total of 127 patients with pathologically proven muscle-invasive BCa (n = 49) and non-muscle-invasive BCa (n = 78) were randomly allocated into the training and test cohorts at a ratio of 7:3. Feature extraction was performed on the venous phase images reconstructed at 40, 50, 60 and 70-keV (single-energy analysis) or in combination (multi-energy analysis). Recursive feature elimination (RFE) and the least absolute shrinkage and selection operator(LASSO) were employed to select the most relevant features associated with BCa. Models were built using a support vector machine(SVM) classifier. Diagnostic performance was assessed through receiver operating characteristic curves, evaluating sensitivity, specificity, accuracy, precision, and the area-under-the curve (AUC) values. In the test cohort, the multi-energy model achieved the best diagnostic performance with AUC, sensitivity, specificity, accuracy, and precision of 0.917, 0.800, 0.833, 0.821, and 0.750, respectively. Conversely, the single-energy model exhibited lower AUC and sensitivity in predicting the muscle invasion status. By combining information from VMIs of various energies, the multi-energy model displays superior performance in preoperatively predicting the muscle invasion status of bladder cancer.

A Comprehensive Nomogram Integrating Phonocardiogram and Echocardiogram Features for the Diagnosis of Heart Failure With Preserved Ejection Fraction.

Heart failure with preserved ejection fraction (HFpEF) is associated with high hospitalization and mortality rates, representing a significant healthcare burden. This study aims to utilize various information including echocardiogram and phonocardiogram to construct and validate a nomogram, assisting in clinical decision-making. This study analyzed 204 patients (68 HFpEF and 136 non-HFpEF) from the First Affiliated Hospital of Chongqing Medical University. A total of 49 features were integrated and used, including phonocardiogram, echocardiogram features, and clinical parameters. The least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal matching factors, and a stepwise logistic regression was employed to determine independent risk factors and develop a nomogram. Model performance was evaluated by the area under receiver operating characteristic (ROC) curve (AUC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC). The nomogram was constructed using five significant indicators, including NT-proBNP (OR = 4.689, p = 0.015), E/e' (OR = 1.219, p = 0.032), LAVI (OR = 1.088, p < 0.01), D/S (OR = 0.014, p < 0.01), and QM1 (OR = 1.058, p < 0.01), and showed a better AUC of 0.945 (95% CI = 0.908-0.982) in the training set and 0.933 (95% CI = 0.873-0.992) in the testing set compared to conventional nomogram without phonocardiogram features. The calibration curve and Hosmer-Lemeshow test demonstrated no statistical significance in the training and testing sets (p = 0.814 and p = 0.736), indicating the nomogram was well-calibrated. The DCA and CIC results confirmed favorable clinical usefulness. The nomogram, integrating phonocardiogram and echocardiogram features, enhances HFpEF diagnostic efficiency, offering a valuable tool for clinical decision-making.

Integrated multi-omics revealed that dysregulated lipid metabolism played an important role in RA patients with metabolic diseases

ObjectivesPatients with rheumatoid arthritis (RA) commonly experience a high prevalence of multiple metabolic diseases (MD), leading to higher morbidity and premature mortality. Here, we aimed to investigate the pathogenesis of MD in RA patients (RA_MD) through an integrated multi-omics approach.MethodsFecal and blood samples were collected from a total of 181 subjects in this study for multi-omics analyses, including 16S rRNA and internally transcribed spacer (ITS) gene sequencing, metabolomics, transcriptomics, proteomics and phosphoproteomics. Spearman’s correlation and protein-protein interaction networks were used to assess the multi-omics data correlations. The Least Absolute Shrinkage and Selection Operator (LASSO) machine learning algorithm were used to identify disease-specific biomarkers for RA_MD diagnosis.ResultsOur results found that RA_MD was associated with differential abundance of gut microbiota such as Turicibacter and Neocosmospora, metabolites including decreased unsaturated fatty acid, genes related to linoleic acid metabolism and arachidonic acid metabolism, as well as downregulation of proteins and phosphoproteins involved in cholesterol metabolism. Furthermore, a multi-omics classifier differentiated RA_MD from RA with high accuracy (AUC: 0.958). Compared to gouty arthritis and systemic lupus erythematosus, dysregulation of lipid metabolism showed disease-specificity in RA_MD.ConclusionsThe integration of multi-omics data demonstrates that lipid metabolic pathways play a crucial role in RA_MD, providing the basis and direction for the prevention and early diagnosis of MD, as well as new insights to complement clinical treatment options.

Establishment and Validation of a Dynamic Nomogram for Persistent Organ Failure in Acute Biliary Pancreatitis: A Retrospective Study.

The objective of this study was to create a predictive model for the onset of persistent organ failure (POF) in individuals suffering from acute biliary pancreatitis (ABP) by utilizing indicators observed within 24hours of hospital admission. Early detection of high-risk POF patients is crucial for clinical decision-making. Clinical data and laboratory indicators within 24hours of admission from ABP patients diagnosed at The First Affiliated Hospital of Wenzhou Medical University between January 1, 2016, and January 1, 2024 were collected and retrospectively analyzed. The Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression (stepwise regression) methods were employed to identify variables for constructing the prediction model. The prediction model's performance was evaluated using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). It was compared with other scoring systems such as SIRS, BISAP, APACHE II, CTSI, and MCTSI. Additionally, a web-based calculator was created to simplify the calculation process. Out of 324 ABP patients, 25 developed POF. Initial screening identified 18 variables; through LASSO regression and multivariable logistic regression analysis, five variables including BMI, Hb, ALB, Ca, and LIP were determined as independent predictors of POF. According to these factors to build prediction model, draw the nomogram. The AUC's receiver operating characteristic curve analysis demonstrated a significantly higher value in comparison to other scoring systems. Calibration curve and DCA show that the established model to predict the accuracy of POF is higher, clinical decision of net benefit is also higher. A network calculator utilizing this predictive model was developed. A predictive model incorporating five risk indicators has been established exhibiting high discriminatory power and accuracy which aids in early identification of ABP patients at risk for developing POF. This holds significant value in guiding clinical decision-making.

In silico identification and verification of Tanshinone IIA-related prognostic genes in hepatocellular carcinoma.

Currently, adequate treatment and prognostic prediction means for Hepatocellular Carcinoma (HCC) haven't entered into medical vision. Tanshinone IIA (TanIIA) is a natural product, which can be utilized as a potential treatment of HCC due to its high anti-tumor activity. However, the effect on HCC prognosis, as well as the potential targets and molecular mechanism of TanIIA still remain ambiguous. Herein, we investigated them via network pharmacology, explored TanIIA-related prognostic genes by machine learning methods, and verified using molecular docking and cell experiments. Potential TanIIA-targeted genes and HCC-related genes were obtained from the corresponding database. The Protein-Protein Interaction (PPI) network and enrichment analyses of the intersection targets were conducted. Furthermore, a TanIIA-related prognostic model was built and verified. We attempted to explore the expression of the TanIIA-related prognostic genes and evaluate its chemotherapeutic sensitivities and the immune infiltrations. Followed by exploration of anti-tumor activity on the human HCC cells Hep3B and HepG2 cell lines in vitro (CCK-8, flow cytometry and transwell assay), the docking molecular was performed. Ultimately, the corresponding protein expressions were determined by western blotting. A total of 64 intersecting targets were collected. Similarly, GO/KEGG enrichment analysis showed that TanIIA can inhibit HCC by affecting multiple pathways, especially the MAPK signaling pathway. A five-gene signature related to TanIIA was constructed on account of Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model. Among five genes, ALB, ESR1 and SRC tend to be core genes because of probable status as potential targets for sorafenib. Molecular docking results demonstrated the potential for active interaction between the core genes relevant proteins and TanIIA. Studies in vitro had shown that TanIIA regulated the expressions of Bcl-2, Bax and MMP9 in HCC cells, inhibiting their growth, inducing apoptosis and preventing cell invasion. Additionally, we are able to detect an up-regulated trend in the expression of ALB and ESR1, while a down-regulated in the expression of SRC by TanIIA. Regulating the expression of TanIIA-related gene signatures (ALB, SRC and ESR1), and inhibiting the SRC/MAPK/ERK signaling axis might potentially contribute to the TanIIA treatment of HCC. And the three gene signatures could be identified for predicting the prognosis of HCC, which may provide novel biomarkers for HCC treatment.

The signature of SARS-CoV-2-related genes predicts the immune therapeutic response and prognosis in breast cancer

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an exceptionally contagious single-stranded RNA virus with a strong positive contagion. The COVID-19 pandemic refers to the swift worldwide dissemination of SARS-CoV-2 infection, which began in late 2019. The COVID-19 epidemic has disrupted many cancer treatments. A few reports indicate that the prevalence of SARS-CoV-2 has disrupted the treatment of breast cancer patients (BCs). However, the role of SARS-CoV-2 in the occurrence and prognosis of BC has not been elucidated. Here, we applied bioinformatics to construct a prognostic signature of SARS-CoV-2-related genes (SCRGs). Specifically, weighted gene co-expression network analysis (WGCNA) was utilized to extract co-expressed genes of differentially expressed genes (DEGs) in breast cancer and SCRGs. Then, we utilized the least absolute shrinkage and selection operator (LASSO) algorithm and univariate regression analysis to screen out three hub genes (DCTPP1, CLIP4 and ANO6) and constructed a risk score model. We further analyzed tumor immune invasion, HLA-related genes, immune checkpoint inhibitors (ICIs), and sensitivity to anticancer drugs in different SARS-CoV-2 related risk subgroups. In addition, we have developed a nomination map to expand clinical applicability. The results of our study indicate that BCs with a high-risk score are linked to negative outcomes, lower immune scores, and reduced responsiveness to anticancer medications. This suggests that the SARS-CoV-2 related signature could be used to guide prognosis assessment and treatment decisions for BCs.

A prediction nomogram for mortality in patients following wasp stings: a retrospective study

Numerous studies have shown that wasp stings can lead to serious, sometimes fatal, health outcomes. Predicting deaths associated with wasp stings remains challenging yet is of critical importance. This study was conducted to identify predictors and develop a visual model for predicting mortality following wasp stings. Clinical data from 486 patients were analyzed, dividing them into two groups: survival group (N = 435) and death group (N = 51). Various statistical methods were used to create a prognostic model, including one-way analysis, the least absolute shrinkage and selection operator (LASSO) regression, and binary logistic regression. The model’s accuracy was evaluated through ROC curves, calibration plots, and decision curve analysis (DCA). The study identified four key predictors of mortality: receiving more than 50 stings, having serum lactate dehydrogenase (LDH) levels of ≥ 2200 U/L, activated partial thromboplastin time (APTT) of ≥ 90 s, and the requirement for invasive mechanical ventilation within 24 h. These factors contributed to a model with an area under the ROC curve of 0.980 (95% CI: [0.968–0.992]), indicating high calibration and applicability. The decision curve analysis confirmed the model’s substantial net clinical benefit. Thus, the number of stings, serum LDH, APTT, and the need for early invasive mechanical ventilation are reliable, independent predictors of death among patients experiencing wasp stings. The developed predictive model exhibits high levels of accuracy, sensitivity, consistency, and practical use.

Machine Learning Analysis of Factors Influencing Pediatric Telehealth Visits During COVID-19: A State-Level Comparison Using 2021-22 National Survey of Children's Health Data.

Background/Objectives: The COVID-19 pandemic reduced in-person pediatric visits in the United States by over 50%, while telehealth visits increased significantly. The national use of telehealth for children and the factors influencing their use have been rarely studied. This study aimed to investigate the prevalence of telehealth use during the COVID-19 pandemic and explore the potential factors linked to its use at the state level. Methods: A cross-sectional study of the National Survey of Children's Health (2021-22) sponsored by the federal Maternal and Child Health Bureau was performed. We used the least absolute shrinkage and selection operator (LASSO) regression to predict telehealth use during the pandemic. A bar map showing the significant factors from the multivariable regression was created. Results: Of the 101,136 children, 15.25% reported using telehealth visits due to COVID-19, and 3.67% reported using telehealth visits due to other health reasons. The Northeast states showed the highest telehealth use due to COVID-19. In the Midwest and Southern states, children had a lower prevalence of telehealth visits due to other health reasons. The LASSO regressions demonstrated that telehealth visits were associated with age, insurance type, household income, usual source of pediatric preventive care, perceived child health, blood disorders, allergy, brain injury, seizure, ADHD, anxiety, depression, and special needs. Conclusions: This study demonstrated significant variability in the use of telehealth among states during the COVID-19 pandemic. Understanding who uses telehealth and why, as well as identifying access barriers, helps maximize telehealth potential and improve healthcare outcomes for all.

Development of a Practical Prediction Model for Adverse Neonatal Outcomes at the Start of the Second Stage of Labor.

To develop a prediction model for adverse neonatal outcomes using electronic fetal monitoring (EFM) interpretation data and other relevant clinical information known at the start of the second stage of labor. This was a retrospective cohort study of individuals who labored and delivered at two academic medical centers between July 2016 and June 2020. Individuals were included if they had a singleton gestation at term (more than 37 weeks of gestation), a vertex-presenting, nonanomalous fetus, and planned vaginal delivery and reached the start of the second stage of labor. The primary outcome was a composite of severe adverse neonatal outcomes. We developed and compared three modeling approaches to predict the primary outcome using factors related to EFM data (as interpreted and entered in structured data fields in the electronic health record by the bedside nurse), maternal comorbidities, and labor characteristics: traditional logistic regression, LASSO (least absolute shrinkage and selection operator), and extreme gradient boosting. Model discrimination and calibration were compared. Predicted probabilities were stratified into risk groups to facilitate clinical interpretation, and positive predictive values for adverse neonatal outcomes were calculated for each. A total of 22,454 patients were included: 14,820 in the training set and 7,634 in the test set. The composite adverse neonatal outcome occurred in 3.2% of deliveries. Of the three modeling methods compared, the logistic regression model had the highest discrimination (0.690, 95% CI, 0.656-0.724) and was well calibrated. When stratified into risk groups (no increased risk, higher risk, and highest risk), the rates of the composite adverse neonatal outcome were 2.6% (95% CI, 2.3-3.1%), 6.7% (95% CI, 4.6-9.6%), and 10.3% (95% CI, 7.6-13.8%), respectively. Factors with the strongest associations with the composite adverse neonatal outcome included the presence of meconium (adjusted odds ratio [aOR] 2.10, 95% CI, 1.68-2.62), fetal tachycardia within the 2 hours preceding the start of the second stage (aOR 1.94, 95% CI, 1.03-3.65), and number of prior deliveries (aOR 0.77, 95% CI, 0.60-0.99).

Exploring common biomarkers of ischemic stroke and obstructive sleep apnea through bioinformatics analysis.

Clinical observations have shown that many patients with ischemic stroke (IS) have a history of obstructive sleep apnea (OSA) both before and after the stroke's onset, suggesting potential underlying connections and shared comorbid mechanisms between the two conditions. The aim of this study is to identify the genetic characteristics of OSA patients who develop IS and to establish a reliable disease diagnostic model to assess the risk of IS in OSA patients. We selected IS and OSA datasets from the Gene Expression Omnibus (GEO) database as training sets. Core genes were identified using the Limma package, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning algorithms. Gene Set Variation Analysis (GSVA) was conducted for pathway enrichment analysis, while single-sample gene set enrichment analysis (ssGSEA) was employed for immune infiltration analysis. Finally, a diagnostic model was developed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, with its diagnostic efficacy validated using receiver operating characteristic (ROC) curves across two independent validation sets. The results revealed that differential analysis and machine learning identified two common genes, TM9SF2 and CCL8, shared between IS and OSA. Additionally, seven signaling pathways were found to be commonly upregulated in both conditions. Immune infiltration analysis demonstrated a significant decrease in monocyte levels, with TM9SF2 showing a negative correlation and CCL8 showing a positive correlation with monocytes. The diagnostic model we developed exhibited excellent predictive value in the validation set. In summary, two immune-related core genes, TM9SF2 and CCL8, were identified as common to both IS and OSA. The diagnostic model developed based on these genes may be used to predict the risk of IS in OSA patients.

Statistical Relationship between Satellite observed Land Surface Temperature and in-situ measured Surface Air Temperature over the Indian Region: An Exploratory Study

This paper presents an investigation of spatio-temporal estimation of daily minimum (Tmin) and maximum (Tmax) surface air temperature using satellite (INSAT and MODIS) derived Land surface temperature and in-situ observational data over the Indian region. Least Absolute Shrinkage and Selection Operator (LASSO) regression technique is used to identify influencing neighboring stations. To capture spatial and temporal variability of surface air temperature of a particular IMD station, Linear Regression and Auto-Regressive Integrated Moving Average, which is known as ARIMA, are used respectively. These models are statistically ensembled using stack generalization. Obtained station-by-station relationship is validated on an independent test data using Root Mean Squared Error (RMSE) to check the validity of the model under consideration. Results show ensembled model outperform (has lowest RMSE) the traditional methods for prediction of Tmin and Tmax with RMSE within a range of [1, 2] range for most of the regions and seasons.

Construction of nomogram for wound recurrence in elderly patients with venous leg ulcers.

Venous Leg Ulcers (VLUs) are one of the most serious and intractable complications of chronic venous insufficiency. This study aims to develop a nomogram based on a theoretical model to predict the probability of wound recurrence in older patients with VLUs. The elderly patients with VLUs attending the five hospitals between September 2021 and October 2022 were enrolled in this research, and randomized to the training and validation cohorts based on the corresponding ratio (7:3). Recurrent events were recorded during a six-month follow-up after the baseline data collection. The univariate analysis, the least absolute shrinkage and selection operator (LASSO) regression method were used to screen variables, and multiple logistic regression was used to establish a risk prediction model, which was presented by nomogram. Receiver operating curves (ROC), Hosmer-Lemeshow test, as well as calibration curves, were adopted to assess the effectiveness of the nomogram. The prognostic value of the nomogram was also examined. A total of 608 elderly patients with VLUs were included in the study. They were randomly divided into the training cohort (N = 421) and the validation cohort (N = 187). In the training cohort, Lasso regression and multivariate logistic regression analysis indicated that previous recurrence number, last ulcer duration, lower extremity DVT history, and frailty were independent risk factors for wound recurrence in elderly patients with VLUs, while daily exercise time and self-efficacy were protective factors. A nomogram was established with a good discrimination capacity and predictive efficiency with and the area under the curve (AUC) of 0.869 (95%CI: 0.831-0.908) in the training set and 0.890 (95%CI: 0.841-0.938) in the validation set. The p values of the Hosmer-Lemeshow test for both sets were 0.887 and 0.772, respectively, both greater than 0.05. The calibration degree charts showed that the data point connection was similar to the diagonal, indicating that the model's prediction probability of wound recurrence in elderly VLUs patients is close to the actual probability. This study constructed a new nomogram to predict the risk of wound recurrence in elderly patients with VLUs. The nomogram has excellent accuracy and reliability, which can help healthcare workers and patients actively monitor and follow up with patients to prevent the recurrence of ulcers and make clinical decisions.

The Relationship Between a Campus Food Pantry and Academic Success at a Public University

Food insecurity (FI) is associated with lower academic performance in university students. This research aimed to describe the relationship between a campus food pantry and academic performance, describe the characteristics of student pantry shoppers (PSs), and develop a model to predict academic success. Researchers obtained student pantry swipes and university data (2021–2022 academic year) to generate a dataset for grade point average (GPA) (N = 23,896) and a subset of PS sociodemographic data (N = 852). Variables (cumulative or term GPA) differed based on models. Explanatory variables were biological sex, age, frequency of pantry shopping, classification, Pell Grant eligibility, college, athlete status, citizenship, residency, ethnicity/race, honors, and first-generation status. The analysis included the two-sample t-test, logistic and multiple regression, and the least absolute shrinkage and selection operator (LASSO). There was no difference (t(921.8) = 0.518, p = 0.60) in the cumulative GPA between PSs (M = 3.001 [0.808]) and non-pantry shoppers (NPSs) (M = 3.016 [0.874]). In the fall term, PSs (M = 3.018 [1.012] earned a higher GPA (t(581.69) = −2.235, p = 0.03) than NPSs (M = 2.919 [1.123]). Pantry shoppers achieved academic success despite exhibiting risk factors for FI, including first-generation status, being of the female sex, and financial need. Targeted multicomponent campus programs are needed to provide food assistance to students at risk for FI.

Prevalence and prediction of masked uncontrolled hypertension in patients recently hospitalised for an acute coronary syndrome

Abstract Background Masked uncontrolled hypertension (MUCH) is defined as normal office blood pressure (BP) but elevated out-of-office BP in patients on antihypertensive treatment. MUCH is associated with similar cardiovascular risk as sustained hypertension and is clinically challenging since this often remains undetected without out-of-office BP measurements. Purpose To study the prevalence of MUCH following an acute coronary syndrome and to utilise machine learning methods to develop prediction models for identifying MUCH. Methods Ambulatory 24-h BP measurement (ABPM) was performed in 100 patients attending the cardiology outpatient clinic at a Swedish university hospital following an acute coronary syndrome, as part of a study which screened for co-morbidities. From the SWEDEHEART registry, 106 variables during the hospital care period and subsequent follow-up visit (after 6-10 weeks) were pre-processed including filtering on 5 or more missing values and zero or near-zero variance. Variable importance for the prediction of MUCH (office BP &amp;lt; 140/90 mm Hg at ABPM start but mean 24-h BP ≥ 130/80 mm Hg) was assessed using the Boruta and least absolute shrinkage and selection operator (LASSO) machine learning algorithms. Subsequently, logistic regression, LASSO and random forest models using different variable subsets were evaluated by receiver operating characteristic area under the curve (AUC) in repeated cross-validation. Results Age was 62.0±8.4 years, 74% male, 54% had NSTEMI and 46% STEMI. The follow-up visit and ABPM were performed at median 7 and 11 weeks, respectively, after hospital discharge. Among 90 patients with complete data and ABPM recordings, 31 had mean 24-h BP above target levels, of which 18 were identified with MUCH. Patients with MUCH had lower eGFR (68±11 vs 76±12 ml/min/1.73m², P=0.009), and more often a history of hypertension (89 vs 53%, P=0.005) and diabetes mellitus (44 vs 11%, P=0.003). In total, 65 variables where eligible for machine learning after filtering. Boruta and LASSO identified pulse pressure at the follow-up visit, serum creatinine, diabetes mellitus and history of hypertension as important predictors. Random forest, logistic regression and LASSO showed mean AUC 0.826, 0.822, and 822, respectively, in cross validation using these predictors. Conclusions In this small trial, one in five had MUCH at follow-up after an acute coronary syndrome, which highlights the importance of out-of-office BP measurements. The easily accessible measures of pulse pressure at the follow-up visit, serum creatinine, diabetes mellitus and history of hypertension were identified as important predictors of MUCH. A simple prediction model may be used as a clinical decision support tool after appropriate external validation.

Development and validation of a predicting model for risk of rehospitalization within 30 days in heart failure patients with preserved ejection fraction

Abstract Objective This study aimed to develop and validate a predicting model for risk of rehospitalization within 30 days post discharge in patients diagnosed with Heart Failure with Preserved Ejection Fraction (HFpEF). Methods Clinical data of 9555 HFpEF patients registered in the heart failure registry from 2014 to 2022 at our hospital were analyzed. A total of 8160 patients (from 2014-2021) were designated as the training set, and 1395 patients (from 2022) as the validation set. A predictive model was established based on risk factors associated with 30-day rehospitalization due to heart failure identified by the Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression analysis. The model's stability was ensured through Bootstrap internal validation and Temporal validation external validation. Results LASSO and logistic regression analyses identified eight risk factors associated with increased risk of 30-day rehospitalization, including New York Heart Association (NYHA) classification, albumin, c-reactive protein, N-terminal pro b-type natriuretic peptide (NT-proBNP) divided by 100, anemia, pleural effusion, atrial fibrillation, and pulmonary hypertension. The established predictive model demonstrated good discriminative ability in the internal validation with an Area Under the Curve (AUC) value of 0.714, and a 95% confidence interval (CI) of 0.693-0.736 after 500 Bootstrap replicates. In the Temporal validation external validation, the model exhibited robust stability with an AUC value of 0.740 and a 95% CI of 0.693-0.781. When compared to analogous models from prior studies, present model showcased superior predictive accuracy in that both Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) values were positive and within their respective 95% confidence intervals (P-values &amp;lt;0.05). Conclusion This study successfully developed and validated a predictive model for 30-day rehospitalization due to heart failure in patients diagnosed with HFpEF. This model might allow the timely identification of high-risk HFpEF patients for 30-day rehospitalization due to heart failure and optimizes the patient management accordingly.

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.

Development and validation of a predicting model for risk of mortality in heart failure patients with mildly reduced ejection fraction post discharge

Abstract Background Prognostic prediction for heart failure with mildly reduced ejection fraction (HFmrEF) patients remains challenging. We aimed to create and validate a mortality risk prediction model for HFmrEF patients at 6-month, 1-year, and 3-year post discharge. Methods Clinical data of 1691 HFmrEF patients registered in the heart failure registry from 2015 to 2020 at the Heart Center of our hospital were analyzed. Patients were assigned into a training (1183 patients) and validation cohort (508 patients) at 7:3 ratio. Predictive variables for mortality at various period post discharge were identified by the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. The predicting model was then established based on these variables. Model performance was evaluated with ROC curve and Decision Curve Analysis (DCA). Results Eight predictors were identified including age, NT-proBNP, hemoglobin levels, use of beta-blockers, ACEI/ARB, invasive ventilation, PCI procedures, and pulmonary artery systolic pressure values. The model achieved a C-index of 0.748 (training set) and 0.755 (validation set). Area Under the Curve (AUC) for training set at 6 months, 1 year, and 3 years were 0.813, 0.784, and 0.774, and AUC for validation set were 0.775, 0.744, and 0.770, respectively. The DCA analysis confirmed the favorable net benefit of the established nomogram model. Conclusion This predicting model might be used for post-discharge risk stratification and individual decision-making of post-discharge management in HFmrEF patients.