Patient Clinical Information Research Articles

Developing a decision support tool to predict delayed discharge from hospitals using machine learning

BackgroundThe growing demand for healthcare services challenges patient flow management in health systems. Alternative Level of Care (ALC) patients who no longer need acute care yet face discharge barriers contribute to prolonged stays and hospital overcrowding. Predicting these patients at admission allows for better resource planning, reducing bottlenecks, and improving flow. This study addresses three objectives: identifying likely ALC patients, key predictive features, and preparing guidelines for early ALC identification at admission.MethodsData from Nova Scotia Health (2015-2022) covering patient demographics, diagnoses, and clinical information was extracted. Data preparation involved managing outliers, feature engineering, handling missing values, transforming categorical variables, and standardizing. Data imbalance was addressed using class weights, random oversampling, and the Synthetic Minority Over-Sampling Technique (SMOTE). Three ML classifiers, Random Forest (RF), Artificial Neural Network (ANN), and eXtreme Gradient Boosting (XGB), were tested to classify patients as ALC or not. Also, to ensure accurate ALC prediction at admission, only features available at that time were used in a separate model iteration.ResultsModel performance was assessed using recall, F1-Score, and AUC metrics. The XGB model with SMOTE achieved the highest performance, with a recall of 0.95 and an AUC of 0.97, excelling in identifying ALC patients. The next best models were XGB with random oversampling and ANN with class weights. When limited to admission-only features, the XGB with SMOTE still performed well, achieving a recall of 0.91 and an AUC of 0.94, demonstrating its effectiveness in early ALC prediction. Additionally, the analysis identified diagnosis 1, patient age, and entry code as the top three predictors of ALC status.ConclusionsThe results demonstrate the potential of ML models to predict ALC status at admission. The findings support real-time decision-making to improve patient flow and reduce hospital overcrowding. The ALC guideline groups patients first by diagnosis, then by age, and finally by entry code, categorizing prediction outcomes into three probability ranges: below 30%, 30-70%, and above 70%. This framework assesses whether ALC status can be accurately predicted at admission or during the patient’s stay before discharge.

Application of Different Surgical Strategies in Helicobacter pylori-Associated Gastric Ulcers with Perforation: A Comparative Study of Short-Term and Long-Term Outcomes and Complication Risks

AIM: This study compared the short- and long-term efficacy of simple suture with omental patch repair (Graham patch) in open surgery versus laparoscopic omental patch repair (LOPR) in treating patients with Helicobacter pylori (H. pylori)-associated gastric ulcers with perforation, and analyzed the incidence of complications. METHODS: The clinical information of patients who had stomach perforation repair surgery in Deqing People's Hospital between January 2021 and January 2022 was retrospectively analyzed. The patients were divided into a control group (n = 54), whose subjects underwent the Graham patch repair, and an observation group (n = 52), whose subjects underwent laparoscopic gastric perforation repair. The general characteristics, therapeutic outcomes, intraoperative and postoperative surgical indicators, 1-year postoperative recurrence, and incidence of various postoperative complications were recorded and compared between the two groups. RESULTS: A total of 106 patients' clinical data were included in the study, of which 52 (49.1%) underwent LOPR and 54 (50.9%) were treated with Graham patch. The general characteristics of the patients in both groups were comparable. The observation group demonstrated significantly better outcomes in terms of operative time, intraoperative blood loss, and postoperative recovery time compared to the control group (p < 0.05). Moreover, the observation group had lower rates of postoperative complications and recurrence compared to the control group (p < 0.05). CONCLUSIONS: LOPR is a potential therapeutic method for patients with H. pylori-associated gastric ulcers with perforations on grounds of its superior efficacy and decreased incidence of comorbidities.

Analysis and characterization of interhypothalamic adhesions in adults: No longer only a pediatric finding.

Inter-hypothalamic adhesions (IHAs) are parenchymal tissue bridges traversing the third ventricle, previously reported only in the pediatric population. We aim to understand the prevalence of IHA in the adult population, assess their size and location, and ultimately investigate whether IHA volumes correlate with age. Patients who underwent routine high-resolution 3D T2WI MRI studies of the temporal bone/internal auditory canal at an otolaryngology clinic between 2008 and 2014 were consecutively selected. The presence of IHAs was confirmed when a parenchymal structure could be traced across the third ventricle, connecting bilateral hypothalamus, and was visible in at least two planes. They were classified based on their location within the third ventricle, considering their connection with hypothalamic nuclei. Patient clinical information were collected from electronic charts. The prevalence and volumes of IHAs were calculated. Associations between age and IHA volume, as well as between IHA type, age, volume, and gender, were analyzed. 779 patients, with a mean age of 54.7years were included. Among them, 44 IHAs were identified within 41 patients, resulting in a prevalence of 5.26% in our cohort. Type 1 IHA was the most frequently encountered type, comprising 70.5% of all cases. No significant correlation was observed between IHA volumes and age. Additionally, no associations were found between IHA type and age, volume, or gender. IHAs are not exclusive to the pediatric population; they are also present in adults, with a prevalence of 5.26% in patients undergoing temporal bone/internal auditory canal MRI.

Epidemiology and Ecology of Toscana Virus Infection and Its Global Risk Distribution

Toscana virus (TOSV), a member of the Phlebovirus genus transmitted by sandflies, is acknowledged for its capacity to cause neurological infections and is widely distributed across Mediterranean countries. The potential geographic distribution and risk to the human population remained obscure due to its neglected nature. We searched PubMed and Web of Science for articles published between 1 January 1971 and 30 June 2023 to extract data on TOSV detection in vectors, vertebrates and humans, clinical information of human patients, as well as the occurrence of two identified sandfly vectors for TOSV. We further predicted the global distribution of the two sandfly vectors, based on which the global risk of TOSV was projected, after incorporating the environmental, ecoclimatic, biological, and socioeconomic factors. A total of 1342 unique studies were retrieved, among which 389 met the selection criteria and were included for data extraction. TOSV infections were documented in 10 sandfly species and 14 species of vertebrates, as well as causing a total of 7571 human infections. The occurrence probabilities of two sandfly vectors have demonstrated the greatest contributions to the potential distribution of TOSV infection risk. This study provides a comprehensive overview of global TOSV distribution and potential risk zones. Future surveillance and intervention programs should prioritize high-risk areas based on updated quantitative analyses.

The landscape in telomere related gene prognostic signature for survival and medication treatment effectiveness prediction in hepatocellular carcinoma

ObjectiveTelomeres, made of repetitive DNA sequences and shelterin complexes, which were found at the ends of chromosomes and had been extensively studied in cancer research. However, in hepatocellular carcinoma (HCC) was still relatively scarce. In this study, we investigated the correlation between telomerase-related genes (TRGs) and the prognosis and immunotherapy of HCC patients to enhance clinical outcomes.MethodsIn this work, TRGs were gathered using TelNet, while clinical information and gene expression data for HCC patients were retrieved from the Cancer Genome Atlas (TCGA) database. A risk prediction model based on TRGs was created using COX and Lasso regression analyses, with ROC curves used to assess prognostic efficacy. Univariate and multifactorial COX regression analyses were used to determine if the risk model had an independent impact on prognosis. Nomograms were created to enhance clinical usability, and calibration curves were used to assess predictive ability at various time points. The Tumor Immune Dysfunction and Exclusion (TIDE) score was used to analyze differences in immune infiltrating cells between risk groups. The study analyzed the relationship between risk ratings and drug treatment effectiveness using data from the CellMiner database. The hub gene was identified and its relationship to prognostic markers of HCC patients was examined. The expression of hub genes in immune cell subpopulations was also investigated by single-cell data.Results2093 TRGs were identified, with 949 showing significant differences in expression between HCC and paracancerous tissues. Seven risk genes were overexpressed in tumor tissues, leading to lower survival rates in high-risk patients. Risk model could independently predict the prognosis of HCC patients. Analysis of tumor immune infiltrating cells revealed significant differences in cell abundance between risk groups, with notable variations in immune subset enrichment between subgroups. Higher risk scores correlated with increased sensitivity to sorafenib, mitoxantrone, oxaliplatin, gemcitabine, and entinostat, while sensitivity decreased for vincristine, etc. CDCA8 was identified as a key gene in the Protein Interaction Network, while high expression associated with poorer overall survival, tumor proliferation and metastasis. The results of single-cell data analysis suggest that CDCA8 may promote the development of HCC by affecting T lymphocytes.ConclusionThe TRG-based risk model could predict HCC patient prognosis and closely linked to tumor immune environment, which could offer new possibilities for clinical treatment.

Core genes and immune dysregulation in primary open-angle glaucoma: A molecular insight

Background Primary open-angle glaucoma (POAG) is a chronic, progressive and irreversible eye disease. Currently, there is no effective way to prevent optic nerve damage. Objective This study explored POAG gene markers to identify high-risk groups at an early stage and to find new effective therapeutic targets. Methods The mRNA and clinical information of POAG patients and normal samples were downloaded from the Gene Expression Omnibus (GEO) database. Through Weighted correlation network analysis (WGCNA) and generalized linear models (GLM), random forests (RF), support vector machines (SVM), and extreme gradient boosting (xGB) models, key risk genes were identified and an early diagnosis model was established. Functional enrichment analysis and CIBERSORT algorithm were used to further reveal the changes in the POAG immune environment and find emerging therapeutic targets. Results HERPUD1, IQCK, MRPL40, SRSF7 and TMEM243 were identified as risk genes, and the prediction model and nomogram constructed based on them had good early prediction efficiency. At the mechanistic level, the heterogeneity of T cell subsets seems to be a key factor affecting the progression of POAG and has potential therapeutic value. Conclusions HERPUD1, IQCK, MRPL40, SRSF7, and TMEM243 are of great significance for the early prediction and disease progression of POAG and have the potential value of becoming therapeutic targets.

Ganoderma lucidum (Curtis) P. Karst. Immunomodulatory Protein Has the Potential to Improve the Prognosis of Breast Cancer Through the Regulation of Key Prognosis-Related Genes.

Background/Objectives: Breast cancer in women is the most commonly diagnosed and most malignant tumor. Although luminal A breast cancer (LumA) has a relatively better prognosis, it still has a persistent pattern of recurrence. Ganoderma lucidum (Curtis) P. Karst. is a kind of traditional Chinese medicine and has antitumor effects. In this study, we aimed to identify the genes relevant to prognosis, find novel targets, and investigate the function of the bioactive protein from G. lucidum, called FIP-glu, in improving prognosis. Methods: Gene expression data and clinical information of LumA breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Using bioinformatics methods, a predictive risk model was constructed to predict the prognosis for each patient. The cell counting kit-8 (CCK8) and clone formation assays were used to validate gene function. The ability of FIP-glu to regulate RNA levels of risk genes was validated. Results: Six risk genes (slit-roundabout GTPase-activating protein 2 (SRGAP2), solute carrier family 35 member 2 (SLC35A2), sequence similarity 114 member A1 (FAM114A1), tumor protein P53-inducible protein 11 (TP53I11), transmembrane protein 63C (TMEM63C), and polymeric immunoglobulin receptor (PIGR)) were identified, and a prognostic model was constructed. The prognosis was worse in the high-risk group and better in the low-risk group. The receiver operating characteristic (ROC) curve confirmed the model's accuracy. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that the differentially expressed genes (DEGs) between the high- and low-risk groups were significantly enriched in the immune responses. TMEM63C could promote tumor viability, growth, and proliferation in vitro. FIP-glu significantly regulated these risk genes, and attenuated the promoting effect of TMEM63C in breast cancer cells. Conclusions: SRGAP2, SLC35A2, FAM114A1, TP53I11, TMEM63C, and PIGR were identified as the potential risk genes for predicting the prognosis of patients. TMEM63C could be a potential novel therapeutic target. Moreover, FIP-glu was a promising drug for improving the prognosis of LumA breast cancer.

Establishment of multiple machine learning prognostic model for gene differences between primary tumors and lymph nodes in luminal breast cancer.

This study aimed to explore the correlation between primary tumors (PT) and paired metastatic lymph nodes (LN) and to develop a predictive model to provide evidence for forecasting patient prognoses. We obtained single-cell and bulk transcriptome data from the Gene Expression Omnibus database. Furthermore, mRNA transcriptomic data, encompassing 112 normal tissues and 1066 breast cancer samples, along with survival, clinical, and mutation information for breast cancer patients, were acquired from The Cancer Genome Atlas (TCGA). Employing a machine learning integration framework incorporating ten distinct algorithms, we developed and validated a prognostic model. We constructed a prognostic model named Lymph Node Metastasis-Related Scores (LMRS) using 26 differentially expressed genes trained on eight TCGA datasets. Across validation sets, the model demonstrated a high C-index, signifying its stability and effectiveness, outperforming 64 models from other studies. Notably, cytolytic activity and T cell co-stimulation were downregulated in the high LMRS group, alongside a downregulation of immune cells, including B cells, CD8 + T cells, iDCs, and TILs. Similarly, most immune checkpoints exhibited a decreasing trend with high LMRS expression. Finally, we selected the hub biomarkers PGK1 and HSP90 for pathological verification. Results indicated higher expression levels in PT and LN compared to normal and benign tumors, with higher expression levels in LN than in PT. This comprehensive analysis sheds light on gene expression differences between PT and LN in breast cancer, culminating in the development of a multiple-gene prognostic model with high clinical accuracy for prognosis prediction.

Blood use and alloimmunization in myelodysplastic syndrome patients: A study of a hospital transfusion experience.

Patients with myelodysplastic syndrome (MDS) are characterized by chronic anemia and most of them require transfusion making them prone to developing transfusion dependence (TD) and inducing red blood cell (RBC) alloimmunization. Little information is available regarding the status of transfusions of MDS patients in China. Clinical history and transfusion information of MDS transfusion patients from 2004 to 2023 were collected from electronic medical and laboratory records. TD patients were divided into increased blasts (IB) and non-IB group. Blood component transfusion was evaluated and compared in different groups using Mann-Whitney and chi-square tests. A total of 893 MDS patients received a median of 13 (range, 0 - 436) RBC units and 10 (range, 0 - 216) platelets (PLT) units. Seventy-one (8%) patients formed 106 RBC specific antibodies of which Rh system was the most (82/106= 77%). Twelve patients (17%) had 14 antibodies (13%) evanescence whereas 7 patients had former antibodies reemerge. Alloimmunization was more common in female, older patients and patients with larger RBC transfusions. Among 222 TD patients, RBC transfusion and alloimmunization rate in IB group were lower than those in non-IB group whilst PLT was the opposite. Prophylactic Rh system compatible transfusion can reduce the risk of RBC alloimmunization in MDS patients' especially in TD and non-IB patients and can prevent reinduction of antibody evanescence. To our knowledge this is the first study we reveal RBC antibody induction and evanescence which may related to different disease stages and classifications of MDS patients.

Changes and correlation between serum β-amyloid 1-42 and total bilirubin in patients with Alzheimer's disease

Objective: To investigate the levels of β-amyloid 1-42 (Aβ1-42) and total bilirubin (TBIL) in serum of patients with Alzheimer 's disease (AD) and the relationship between them. Methods: A case-control study was conducted to select 73 patients with AD who were hospitalized in Beijing Huilongguan Hospital from November 2023 to February 2024 as AD group, and 70 healthy controls (HC) were selected as HC group. The basic information of all subjects and the clinical information of AD patients were collected, and the levels of Aβ1-42 and TBIL were detected and compared between the two groups. The effects of Aβ1-42 and TBIL on AD were analyzed by binary logistic regression. Correlation analysis was used to analyze the relationship between TBIL and Aβ1-42 in AD group and HC group. According to the level of Aβ1-42 in AD patients, they were divided into Aβ1-42 elevated group and Aβ1-42 normal group. The differences of clinical data and TBIL levels between the two groups were compared. According to the quartile of TBIL in AD patients, they were divided into Q1 group, Q2 group, Q3 group and Q4 group. The correlation between TBIL and the risk of Aβ1-42 elevation was analyzed by binary logistic regression. The receiver operating characteristic (ROC) curve was drawn to analyze the ability of TBIL level to predict the increase of Aβ1-42 in AD patients. Results: There was no significant difference in gender, marital status, education level, smoking and drinking between AD group and HC group (P>0.05), while the levels of Aβ1-42 and TBIL and age in AD group were significantly higher than those in HC group [101.10(71.20, 128.60) pg/ml/22.40(10.00, 39.60) pg/ml, Z=-8.714, P<0.001;(11.00±3.22/8.07±3.00) μmol/L, t=5.621, P<0.001;(77.14±8.20/68.30±10.27) years, t=5.672, P<0.001]. For AD patients, the TBIL level in the Aβ1-42 elevated group was lower than that in the Aβ1-42 normal group [(10.05±2.94/11.66±3.28) μmol/L, t=-2.148, P=0.035], while there was no significant difference in other demographic and clinical data between the two groups (P>0.05). Binary logistic regression analysis showed that higher levels of Aβ1-42 (OR=1.021, 95%CI：1.010-1.032) and TBIL (OR=1.505, 95%CI：1.249-1.814), older age (OR=1.083, 95%CI：1.020-1.150) and female (OR=4.348, 95%CI：1.253-15.094) were risk factors for AD. Correlation analysis showed that TBIL was negatively correlated with Aβ1-42 in AD patients (r=-0.322, P=0.006). After adjusting the relevant covariates, binary logistic regression showed that compared with AD patients in Q1 group, the risk of Aβ1-42 elevation in AD patients in Q4 group was lower (OR=0.052, 95%CI：0.005-0.535, P<0.05), and TBIL was negatively correlated with the risk of Aβ1-42 elevation (P trend<0.05). ROC curve showed that the area under the curve (AUC) of TBIL in predicting the increase of Aβ1-42 in AD patients was 0.642. Conclusion: The levels of Aβ1-42 and TBIL in AD patients were higher, and the level of TBIL was negatively correlated with the risk of Aβ1-42 elevation.

Integrating structured and unstructured data for predicting emergency severity: an association and predictive study using transformer-based natural language processing models

BackgroundEfficient triage in emergency departments (EDs) is critical for timely and appropriate care. Traditional triage systems primarily rely on structured data, but the increasing availability of unstructured data, such as clinical notes, presents an opportunity to enhance predictive models for assessing emergency severity and to explore associations between patient characteristics and severity outcomes. This study aimed to evaluate the effectiveness of combining structured and unstructured data to predict emergency severity more accurately.MethodsData from the 2021 National Hospital Ambulatory Medical Care Survey (NHAMCS) for adult ED patients were used. Emergency severity was categorized into urgent (scores 1–3) and non-urgent (scores 4–5) based on the Emergency Severity Index. Unstructured data, including chief complaints and reasons for visit, were processed using a Bidirectional Encoder Representations from Transformers (BERT) model. Structured data included patient demographics and clinical information. Four machine learning models—Logistic Regression, Random Forest, Gradient Boosting, and Extreme Gradient Boosting—were applied to three data configurations: structured data only, unstructured data only, and combined data. A mean probability model was also created by averaging the predicted probabilities from the structured and unstructured models.ResultsThe study included 8,716 adult patients, of whom 74.6% were classified as urgent. Association analysis revealed significant predictors of emergency severity, including older age (OR = 2.13 for patients 65 +), higher heart rate (OR = 1.56 for heart rates > 90 bpm), and specific chronic conditions such as chronic kidney disease (OR = 2.28) and coronary artery disease (OR = 2.55). Gradient Boosting with combined data demonstrated the highest performance, achieving an area under the curve (AUC) of 0.789, an accuracy of 0.726, and a precision of 0.892. The mean probability model also showed improvements over structured-only models.ConclusionsCombining structured and unstructured data improved the prediction of emergency severity in ED patients, highlighting the potential for enhanced triage systems. Integrating text data into predictive models can provide more accurate and nuanced severity assessments, improving resource allocation and patient outcomes. Further research should focus on real-time application and validation in diverse clinical settings.

Building and verifying a prediction model for deep vein thrombosis among spinal cord injury patients undergoing inpatient rehabilitation

ObjectiveTo explore the relevant variables that contribute to deep vein thrombosis (DVT) among spinal cord injury patients undergoing inpatient rehabilitation and to build and validate a nomogram model that predicts DVT risk. MethodsBy convenience sampling, 558 spinal cord injury patients who were hospitalized at a tertiary-level Grade A general hospital in Anhui Province, China, between January 2017 and March 2022 were chosen as the study subjects. They were split into two groups at random, one for training (n = 446) and the other for validation (n = 112). The ratio was 8:2. The clinical information of patients was gathered, including sociodemographic characteristics, data about disease characteristics, and examinations pertaining to laboratories. The related factors of DVT among spinal cord injury patients undergoing inpatient rehabilitation were analyzed using both univariate and multivariate logistic regression. Utilizing the variables identified by the multivariate logistic regression analysis, we constructed a predictive nomogram model with the aid of the R software. The model's predictive accuracy for assessing the risk of DVT was validated through the use of receiver operating characteristic (ROC) curves and calibration plots. ResultsProthrombin time (PT), D-dimer, age, and Caprini score were independent related factors for DVT among spinal cord injury patients undergoing inpatient rehabilitation, according to multivariate logistic regression analysis (OR > 1, P<0.05). This four variables selected by the multivariate logistic regression analysis was used to build a nomogram risk model, which was found to have strong predictive capacity for predicting the risk of DVT among spinal cord injury patients undergoing inpatient rehabilitation. The nomogram model's area under the ROC curve in the training group and validation group was 0.793 and 0.905, and the 95% confidence intervals were 0.750∼0.837 and 0.830∼0.980, separately, indicating good discrimination of the nomogram model. A good calibration of the model was shown by the calibration curve, which was well consistent between the model's predicted probability and the actual frequency of DVT in both the training and validation groups. ConclusionProthrombin time, D-dimer level, age and Caprini score are independent related factors for DVT among spinal cord injury patients undergoing inpatient rehabilitation. According to the variables mentioned previously, a nomogram model was constructed that can accurately and easily predict DVT risk among spinal cord injury patients undergoing inpatient rehabilitation. This facilitates the early identification of high-risk groups and the timely implementation of prevention, treatment, rehabilitation, and nursing strategies by clinical medical staff.

Clinical Characteristics and Treatment of Middle-Aged and Elderly Patients with IBD in Shanghai, China.

In order to better understand the incidence of IBD in China, we conducted a retrospective study to analyze the clinical information of IBD patients in Shanghai, China. From January 2014 to December 2021, patients diagnosed with IBD and hospitalized were enrolled. The demographic, clinical features, symptoms, laboratory tests and treatment data of the patients were retrospectively analyzed. This study included 454 patients with UC and 333 patients with CD. The rate of hospitalization for IBD showed an escalating trend throughout the period, the number of hospitalizations was significantly higher in CD patients than in UC patients. The male patients had more complications than the female patients (p < 0.05). Definitive diagnosis of IBD in older patients was difficult (p < 0.05), and misdiagnosis was common. The incidence of complications and extraintestinal manifestations in elderly IBD patients was lower, but the incidence of intestinal obstruction was higher (p < 0.05). There was a significant correlation between the disease activity grades of IBD and fibrinogen, hemoglobin, albumin. Elderly IBD patients presented with lower rates of immunosuppressant, biologics, surgery or enteral nutrition. This study analyzed the incidence, characteristics and treatment of IBD patients in Shanghai, and provided evidence-based evidence for doctors to more effectively diagnose and treat IBD in the future.

PCR68 Preferences in Receiving Clinical Trials Information of Asian, Native Hawaiian, and Pacific Islander Patients With Type 2 Diabetes Mellitus

PCR68 Preferences in Receiving Clinical Trials Information of Asian, Native Hawaiian, and Pacific Islander Patients With Type 2 Diabetes Mellitus

Determining new disulfidptosis-associated lncRNA signatures pertinent to breast cancer prognosis and immunological microenvironment.

Disulfidptosis is a novel form of cell death triggered by disulfide stress that may have important implications for breast cancer (BC) pathogenesis. Nevertheless, studies identifying disulfidptosis-associated long non-coding RNAs (lncRNAs) in BC have not been reported. This study aimed to investigate the prognostic potential of disulfidptosis-related lncRNAs in BC. RNA-sequencing data and clinical information of BC patients were obtained from The Cancer Genome Atlas (TCGA) database. The lncRNAs associated with disulfidptosis were identified through co-expression analysis. Subsequently, a risk signature consisting of 10 lncRNAs was constructed using univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses, and its predictive power was validated. The risk signature was found to be an independent prognostic factor for BC patients. Notably, the two subgroups defined by the risk signature exhibited different mutant gene profiles, and risk scores were significantly correlated with tumor mutation burden (TMB). Additionally, single-sample gene set enrichment analysis (ssGSEA) and immune checkpoint analyses indicated that the predicted trait was significantly associated with the immune status of BC patients. Furthermore, 55 potential anticancer drugs were identified that were associated with the signature. In this study, we successfully developed a prognostic model based on disulfidptosis-related lncRNAs, which enhances the accuracy of predicting the prognosis of BC patients. This model also offers a potential target and theoretical foundation for BC treatment, laying a robust groundwork for future research on the functional roles of disulfidptosis-associated lncRNAs in BC.

Long-term survival and prognosis after surgical treatment of patients with thymic carcinoma: a retrospective analysis.

Thymic carcinoma is a type of rare and highly malignant tumor that originates from the thymic epithelium. Treatment and prognosis of thymic carcinoma remain controversial. We retrospectively analyzed survival data from a large sample database in a single center in China to summarize the clinicopathological features of patients with thymic carcinoma and explore the factors affecting prognosis. The clinical data of 87 patients with thymic carcinoma who underwent surgical treatment between January 2010 and October 2023 were retrospectively analyzed. Of these, 74 patients had thymic squamous cell carcinoma, and 13 had other subtypes. The Kaplan-Meier method was used to calculate survival rate, and the log-rank test was employed for univariate analysis. The Cox proportional hazards regression model was used for multivariate analysis to evaluate the clinical, pathological, and therapeutic information of patients with thymic carcinoma, analyze long-term survival, and identify prognostic factors associated with postoperative thymic carcinoma. The 5- and 10-year overall survival (OS) rates were 85.6% and 69.9%, respectively, and the corresponding disease-free survival (DFS) rates were 76.4% and 58.6%, respectively. Univariate analysis revealed significant associations between the Masaoka-Koga stage, resection status, postoperative radiotherapy, and OS in patients with thymic carcinoma. Furthermore, the Masaoka-Koga stage is correlated with DFS in patients who underwent postoperative treatment for thymic carcinoma. Cox multivariate analysis confirmed that the Masaoka-Koga stage [hazard ratio (HR): 2.719, 95% confidence interval (CI): 1.032-7.163, P=0.043] independently influenced DFS in surgically treated patients with thymic carcinoma, whereas the Masaoka-Koga stage (HR: 3.690, 95% CI: 1.043-13.049, P=0.043) and postoperative radiotherapy (HR: 0.319, 95% CI: 0.102-0.999, P=0.049) emerged as critical prognostic factors affecting OS. Thymic squamous cell carcinoma is the most prevalent form of thymic carcinoma, and complete resection (R0 resection) is the preferred treatment modality. The Masaoka-Koga stage and postoperative radiotherapy are significant prognostic indicators for improved outcomes.

Construction and validation of a prognostic model based on oxidative stress-related genes in non-small cell lung cancer (NSCLC): predicting patient outcomes and therapy responses.

Non-small cell lung cancer (NSCLC) is a significant health concern. The prognostic value of oxidative stress (OS)-related genes in NSCLC remains unclear. The study aimed to explore the prognostic significance of OS-genes in NSCLC using extensive datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The research used the expression data and clinical information of NSCLC patients to develop a risk-score model. A total of 74 OS-related differentially expressed genes (DEGs) were identified by comparing NSCLC and control samples. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were employed to identify the prognostic biomarkers. A risk-score model was constructed and validated with receiver operating characteristic (ROC) curves in TCGA and GSE72094 datasets. The model's accuracy was further verified by univariate and multivariate Cox regression. The identified biomarkers, including lactate dehydrogenase A (LDHA), protein tyrosine phosphatase receptor type N (PTPRN), and transient receptor potential cation channel subfamily A (TRPA1) demonstrated prognostic significance in NSCLC. The risk-score model showed good predictive accuracy, with 1-year area under the curves (AUC) of 0.661, 3-year AUC of 0.648, and 5-year AUC of 0.634 in the TCGA dataset, and 1-year AUC of 0.643, 3-year AUC of 0.648, and 5-year AUC of 0.662 in the GSE72094 dataset. A nomogram integrating risk score and tumor node metastasis (TNM) stage was developed. The signature effectively distinguished between patient responses to immunotherapy. High-risk groups were characterized by an immunosuppressive microenvironment and an increased tumor mutational burden (TMB), marked by a higher incidence of mutations in genes such as TP53, DCP1B, ELN, and MAGI2. Organoid drug sensitivity testing revealed that NSCLC patients with a low-risk score responded better to chemotherapy. This study successfully developed a robust model for predicting patient prognosis in NSCLC, highlighting the critical prognostic value of OS-genes. These findings hold significant potential to refine treatment strategies, and enhance survival outcomes for NSCLC patients. By enabling a personalized therapeutic approach tailored to individual risk scores, this model may facilitate more precise decisions concerning immunotherapy and chemotherapy, thereby optimizing patient management and treatment efficacy.

A novel disulfidptosis-related LncRNA prognostic risk model: predicts the prognosis, tumor microenvironment and drug sensitivity in esophageal squamous cell carcinoma

BackgroundDisulfidptosis is a newly discovered type of cell death that differs from apoptosis, necrosis, ferroptosis and other death modes and is closely related to the occurrence and progression of tumors. However, the predictive potential and biological characteristics of disulfidptosis-related lncRNAs (DRGs-lncRNAs) in esophageal squamous cell carcinoma (ESCC) are unclear.MethodsRNA transcriptome data, clinical information and mutation data for ESCC patients were obtained from The Cancer Genome Atlas (TCGA) database. Pearson correlation and Cox regression analyses were used to identify the DRGs-lncRNAs associated with overall survival (OS). LASSO regression analysis was used to construct the prognostic model. A nomogram was created to predict the prognosis of patients with ESCC. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were used to identify the signaling pathways associated with the model. TIMER, CIBERSORT, ESTIMATE and other methods were used to analyze immune infiltration, immune function, immune checkpoints and drug sensitivity. The tumor mutation burden (TMB) were assessed between different risk groups. Real-time polymerase chain reaction (RT‒PCR) was used to detect the expression of DRGs-lncRNAs in ESCC cell lines.ResultsA total of 155 lncRNAs significantly associated with disulfidptosis were identified. Through univariate Cox regression analysis, LASSO regression analysis and multivariate Cox regression analysis, 9 lncRNAs with independent prognostic significance were selected, and a prognosis model was established. Survival analysis with the prognostic model revealed that there were obvious differences in survival between the high- and low-risk groups. Further analysis revealed that the immune microenvironment, immune infiltration, immune function, immune checkpoints, and drug sensitivity significantly differed between the high-risk and low-risk groups. Patients who exhibited both high risk and high tumor mutation burden (TMB) survived shorter, while those who fell into the low risk and low TMB categories survived longer. In addition, RT‒PCR analysis revealed differential expression of DRG lncRNAs between ESCC cell lines and esophageal epithelial cell lines.ConclusionsWe established a DRG-lncRNA prognostic model that can be used to predict the prognosis, tumor mutation burden, immune cell infiltration, and drug sensitivity of ECSS patients. The results of this study provide valuable insights into the understanding of ESCC and provide valuable assistance for the individualized treatment of ESCC patients.

Benefits of Clinical Decision Support Systems for the Management of Noncommunicable Chronic Diseases: Targeted Literature Review.

Clinical decision support systems (CDSSs) are designed to assist in health care delivery by supporting medical practice with clinical knowledge, patient information, and other relevant types of health information. CDSSs are integral parts of health care technologies assisting in disease management, including diagnosis, treatment, and monitoring. While electronic medical records (EMRs) serve as data repositories, CDSSs are used to assist clinicians in providing personalized, context-specific recommendations derived by comparing individual patient data to evidence-based guidelines. This targeted literature review (TLR) aimed to identify characteristics and features of both stand-alone and EMR-integrated CDSSs that influence their outcomes and benefits based on published scientific literature. A TLR was conducted using the Embase, MEDLINE, and Cochrane databases to identify data on CDSSs published in a 10-year frame (2012-2022). Studies on computerized, guideline-based CDSSs used by health care practitioners with a focus on chronic disease areas and reporting outcomes for CDSS utilization were eligible for inclusion. A total of 49 publications were included in the TLR. Studies predominantly reported on EMR-integrated CDSSs (ie, connected to an EMR database; n=32, 65%). The implementation of CDSSs varied globally, with substantial utilization in the United States and within the domain of cardio-renal-metabolic diseases. CDSSs were found to positively impact "quality assurance" (n=35, 69%) and provide "clinical benefits" (n=20, 41%), compared to usual care. Among CDSS features, treatment guidance and flagging were consistently reported as the most frequent elements for enhancing health care, followed by risk level estimation, diagnosis, education, and data export. The effectiveness of a CDSS was evaluated most frequently in primary care settings (n=34, 69%) across cardio-renal-metabolic disease areas (n=32, 65%), especially in diabetes (n=13, 26%). Studies reported CDSSs to be commonly used by a mixed group (n=27, 55%) of users including physicians, specialists, nurses or nurse practitioners, and allied health care professionals. Overall, both EMR-integrated and stand-alone CDSSs showed positive results, suggesting their benefits to health care providers and potential for successful adoption. Flagging and treatment recommendation features were commonly used in CDSSs to improve patient care; other features such as risk level estimation, diagnosis, education, and data export were tailored to specific requirements and collectively contributed to the effectiveness of health care delivery. While this TLR demonstrated that both stand-alone and EMR-integrated CDSSs were successful in achieving clinical outcomes, the heterogeneity of included studies reflects the evolving nature of this research area, underscoring the need for further longitudinal studies to elucidate aspects that may impact their adoption in real-world scenarios.

Real-world incidence of and risk factors for abemaciclib-induced interstitial lung disease in Japan: a nested case-control study of abemaciclib-induced interstitial lung disease (NOSIDE).

The exact incidence of and risk factors for interstitial lung disease (ILD), a serious side effect of abemaciclib, remain unknown in real-world settings. We examined the incidence of and risk factors for abemaciclib-induced ILD in patients with advanced breast cancer (ABC) in Japan. Retrospective clinical information was collected from charts of patients with ABC who had started abemaciclib treatment at 77 participating institutions between November 30, 2018 and December 31, 2019. The clinical information of patients who developed ILD (including suspected cases) were reviewed by an independent committee of extramural experts to adjudicate abemaciclib-induced ILD. We performed a nested case-control study for efficient identification of ILD risk factors and conducted multivariate Cox regression analysis to identify independent predictors of ILD. Among patients taking abemaciclib, the incidence of abemaciclib-induced ILD was 5.0% (n = 59/1189), and the mortality rate was 0.7% (n = 8). The timing of ILD onset varied but occurred most frequently within 180days of beginning abemaciclib treatment (64.4%). The incidence of abemaciclib-induced ILD was significantly associated with Eastern Cooperative Oncology Group performance status (ECOG PS) ≥ 2 [hazard ratio (HR) 5.03; 95% confidence interval (CI) 2.26-11.11] or a past medical history of interstitial pneumonia (IP) (HR 6.49; 95% CI 3.09-13.70). In this study, we have for the first time determined the real-world incidence of and risk factors for abemaciclib-induced ILD in Japan. Although abemaciclib-induced ILD is serious in real-world settings, careful patient selection and close monitoring of those with poor ECOG PS and/or a history of IP may minimize ILD risk. This study was registered on the UMIN registry (Date: May 11, 2020/ ID: UMIN000040357).