International Journal of General Medicine最新文献

Objective: To investigate the correlation between premature ventricular contraction (PVC) frequency and heart rate (HR) in Chinese adults, with an emphasis on sex-specific differences in clinical characteristics.

Patients and methods: This retrospective study analyzed 24-hour Holter monitoring data from 478 inpatients at the First People's Hospital of Yibin between January 2021 and December 2022. The inclusion criteria were age ≥18 years, ≥20 hours of Holter recording, frequent PVCs (≥ 500 PVCs), and complete clinical profiles. Patients were stratified into three groups on the basis of the hourly correlation between PVC counts and HR: fast heart rate-related PVC (F-HR-PVC), slow heart rate-related PVC (S-HR-PVC), and independent heart rate-related PVC (I-HR-PVC). Heart rate variability (HRV) indices were assessed to evaluate autonomic nervous system activity.

Results: Among the 478 patients, 267 were males and 211 were females with a mean age of 65.7±13.0 years. The mean PVC burden was 5.7±7.0%, and the mean left ventricular ejection fraction (LVEF) was 59.1±8.7%. In males, the F-HR-PVC group was most common (45.3%), while in females, the I-HR-PVC group was most prevalent (50.2%). Despite these observed differences, a chi-square test did not reveal statistically significant differences in the distribution of VPC profiles between sexes (P=0.167). Analysis of clinical characteristics and Holter indices across sex groups showed significant differences in males, particularly in age, maximum heart rate, and minimum heart rate (P < 0.05). In females, significant intergroup differences were observed in VPC burden (P < 0.05).

Conclusion: Although no significant sex differences were observed in the correlation between PVC frequency and HR, the study suggests a potential gender influence on VPC characteristics. These findings may inform future research and have implications for the development of sex-specific diagnostic and therapeutic strategies for PVCs.

Background: Atherosclerosis is a systemic vascular disease commonly affecting coronary and carotid arteries, particularly in diabetes mellitus (DM). This study assessed the association of DM with significant carotid artery stenosis (CAS) among the coronary artery disease (CAD) population undergoing isolated elective coronary artery bypass graft (CABG) surgery.

Methods: A prospective cross-sectional study evaluated 100 Bangladeshi CAD patients who underwent isolated elective CABG from January 2017 to September 2019. Initially, a univariate analysis curtails the risk patterns, followed by a Pearson correlation analysis of significant CAS and glycated haemoglobin (HbA1c).

Results: Although the majority of patients were male, females were higher in the diabetic than non-diabetic group (38.1% vs 15.5%; p=0.01). Overall, ~38% of diabetic patients had significantly higher ≥50% CAS. Further, 28.6% and 9.5% of the diabetic sample had 50-70% and >70% CAS, respectively, which is significantly (p =0.02) higher than the non-diabetic sample (8.6% and 1.7%, respectively). Additionally, diabetes was significantly more associated with both unilateral (31.0% vs 22.4%) and bilateral (45.2% vs 22.4%) CAS than the non-diabetic population (p=0.006). The Pearson correlation coefficient shows a significant positive association between higher glycated haemoglobin levels and >50% CAS (correlation coefficient 0.270; p = 0.007).

Conclusion: We found higher HbA1c had a significant positive correlation with >50% CAS in diabetic patients undergoing elective CABG, recommending preoperative carotid screening, especially elderly individuals.

Background: Perimenopausal period is a period of physiological changes in women with signs of ovarian failure, including menopausal transition period and 1 year after menopause. Ovarian function declines in perimenopausal women and lower estrogen levels lead to changes in the function of various organs, which may lead to cardiovascular disease. Major adverse cardiovascular events (MACE) are the combination of clinical events including heart failure, myocardial infarction and other cardiovascular diseases. Therefore, this study explores the factors influencing the occurrence of MACE in perimenopausal women and establishes a prediction model for MACE risk factors using three algorithms, comparing their predictive performance.

Patients and methods: A total of 411 perimenopausal women diagnosed with MACE at the Binzhou Medical University Hospital were randomly divided into a training set and a test set following a 7:3 ratio. According to the principle of 10 events per Variable, the training set sample size was sufficient. In the training set, Random Forest (RF) algorithm, backpropagation neural network (BPNN) and Logistic Regression (LR) were used to construct a MACE risk prediction model for perimenopausal women, and the test set was used to verify the model. The prediction performance of the model was evaluated in terms of accuracy, sensitivity, specificity, and area under the subject operating characteristic curve (AUC).

Results: A total of twenty-six candidate variables were included. The area under ROC curve of the RF model, BPNN model, and logistic regression model was 0.948, 0.921, and 0.866. Comparison of ROC curve AUC between logistic regression and RF model for predicting MACE risk showed a statistically significant difference (Z=2.278, P=0.023).

Conclusion: The RF model showed good performance in predicting the risk of MACE in perimenopausal women providing a reference for the early identification of high-risk patients and the development of targeted intervention strategies.

Objective: To explore the association between triglyceride glucose index (TyG) and hypertension under different diagnostic thresholds.

Methods: This study analyzed data from routine occupational health examinations conducted at the First Affiliated Hospital of Xi'an Jiaotong University in 2019. TyG and TyG-BMI, indicators of insulin resistance (IR), were calculated using triglyceride (TG), fasting blood glucose (FPG), and body mass index (BMI). Hypertension was defined by thresholds of 140/90 mmHg and 130/80 mmHg. Logistic regression models were employed to investigate the association between TyG and hypertension.

Results: Among 4028 subjects, after adjusting for covariates, the risk of hypertension (diagnosed by 140/90 mmHg) was 2.87 times higher (OR=2.87, 95% CI: 2.11-3.91) in the Q4 group of TyG compared to the Q1 group. Similarly, the risk was 8.03 times higher (OR=8.03, 95% CI: 5.05-12.75) in the Q4 group of TyG-BMI than in the Q1 group. Furthermore, the risk of hypertension (diagnosed by 130/80 mmHg) was 2.93 times higher (OR=2.93, 95% CI: 2.34-3.68) in the Q4 group of TyG compared to the Q1 group, and 7.12 times higher (OR=7.12, 95% CI: 5.08-9.99) in the Q4 group of TyG-BMI than in the Q1 group. The restricted cubic spline further showed a dose-response relationship between TyG/ TyG-BMI and hypertension. In the population with a BMI of 24.0-27.9 kg/m², the risk of developing hypertension (diagnosed by 140/90 mmHg) in the Q4 group of TyG was 2.79 times higher (OR=2.79, 95% CI: 1.67-4.66), while the Q4 group with TyG had a 3.07 times greater risk of hypertension (diagnosed by 130/80 mmHg) than the Q1 group (OR=3.07, 95% CI: 2.05-4.60).

Conclusion: TyG is an independent risk factor for hypertension across different thresholds, showing a clear dose-response relationship. BMI may influence this association, emphasizing the importance of managing insulin resistance early to aid hypertension prevention.

Background: The aim of this study was to use five machine learning approaches and logistic regression to design and validate the acute kidney injury (AKI) prediction model for critically ill individuals with cardiogenic shock (CS).

Methods: All patients who diagnosed with CS from the MIMIC-IV database, the eICU database, and Zhongnan hospital of Wuhan university were included in this study. Clinical information, including demographics, comorbidities, vital signs, critical illness scores and laboratory tests was retrospectively collected. Five machine learning algorithms (LightGBM, decision tree, XGBoost, random forest, and ensemble model) and one conventional logistic regression were applied for the prediction of AKI in critically ill individuals with CS. ROC curves were generated via python software to assess the overall performance of machine learning algorithms and the SHAP analysis was adopted to reveal the impact of prediction for each feature.

Results: The ensemble model exhibited the best predictive ability (AUC:0.91, 95% CI, 0.88-0.94), followed by random forest (AUC:0.90, 95% CI, 0.86-0.94) and XGBoost (AUC:0.89, 95% CI, 0.84-0.92). While the logistic regression model obtained the worst predictive performance (AUC:0.62, 95% CI, 0.56-0.68). When validated the prediction models with eICU database, the ensemble model exhibited the best predictive ability (AUC:0.92, 95% CI, 0.89-0.96), while the logistic model obtained the worst predictive performance (AUC:0.61, 95% CI, 0.56-0.67). Finally, we verified the prediction models using the data from our hospital and ensemble model still exhibited the best predictive ability (AUC:0.74, 95% CI, 0.62-0.86), while the decision tree model obtained the worst predictive performance (AUC:0.52, 95% CI 0.35-0.70).

Conclusion: Machine learning algorithms could be utilized for the AKI prediction among critically ill CS patients, and exhibit superior predictive performance compared to the conventional logistic regression analysis.

Objective: To evaluate the effectiveness and safety of traditional Chinese medicine (TCM) ion introduction therapy in the treatment of patients with acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF).

Methods: This study adopts a prospective cohort study design, with 60 AE-IPF patients as the research subjects. Divided into an exposed group and a non exposed group, with 30 cases in each group, based on the frequency of TCM ion introduction treatment as the exposure factor. Follow-up for 1 year to observe the acute exacerbation of the patient. The main indicator is the annual incidence of acute exacerbation, and the secondary indicators are hospitalization time, readmission rate, time to first acute exacerbation, mortality rate, all-cause mortality rate, inflammatory indicators, quality of life, etc.

Results: 51 patients completed a one-year clinical observation, including 27 in the exposed group and 24 in the non exposed group. Compared to the non exposed group, significant differences were observed in the annual incidence of acute exacerbation [incidence rate ratios (IRR) = 0.556, 95% CI: 0.315, 0.980; P = 0.035] and hospitalization time (P = 0.040), readmission rate (IRR = 0.533, 95% CI: 0.288, 0.988; P = 0.037), time to first acute exacerbation (P = 0.045), and quality of life (P < 0.05). However, there was no statistically significant difference in mortality rate and all-cause mortality rate between the two groups (P > 0.05).

Conclusion: Compared to the non exposed group, TCM ion introduction can reduce the annual incidence of acute exacerbation of IPF patients. Hospitalization time, readmission rate, time to first acute exacerbation, quality of life improved, but mortality rate and all-cause mortality rate did not improve.

This review aims to summarize and evaluate the latest clinical evidence and mechanistic studies regarding acupuncture for the treatment of allergic rhinitis AR. Compared with traditional medical treatment, acupuncture treatment of allergic rhinitis has fewer side effects and drug dependence, especially for those patients who do not respond well to medical treatment, acupuncture treatment has become a new hope. By analyzing results from RCTs, systematic reviews, and meta-analyses, the significant effectiveness of acupuncture in improving AR symptoms is clarified. We explore the mechanisms of acupuncture in immune regulation, neural modulation, inflammation modulation, and microbiota modulation, and assess its clinical efficacy and safety. The results indicate that acupuncture significantly alleviates AR symptoms through multiple biological regulatory mechanisms. Future research should further standardize acupuncture treatment protocols and deepen mechanistic studies to optimize its clinical application.

Purpose: To further identify the clinical impact of metformin on the prognosis of non-small cell lung cancer (NSCLC) with type 2 diabetes who received immunotherapy.

Methods: Stage IV NSCLC patients with type 2 diabetes receiving the immunotherapy from 2017 to 2021 were retrospectively enrolled and divided into the metformin group or non-metformin group according to the treatment strategy for type 2 diabetes (metformin vs other hypoglycemic medicines). The overall response rate (ORR) was primary endpoint, and overall survival (OS), progression-free survival (PFS) and disease control rate (DCR) were secondary endpoints. These outcomes were compared between two groups.

Results: A total of 34 patients were eventually enrolled, including 18 patients in the metformin group. No significant differences in the basic characteristics and incidence of adverse events were observed between two groups. In addition, there was no significant difference in ORR (44.4%, 8/18 vs 25.0%, 4/16, P = 0.236) and DCR (77.8%, 14/18 vs 75.0%, 12/16, P > 0.999) between the metformin and non-metformin groups. Kaplan-Meier survival curve (P = 0.039) and Cox regression analysis indicated that the use of metformin was an independent factor for OS (HR: 0.310, 95% CI: 0.113-0.845, P = 0.022), but not for PFS (Cox regression analysis: P = 0.145).

Conclusion: For NSCLC patients with type 2 diabetes, the combination of metformin and immunotherapy may contribute to OS benefits. However, more high-quality prospective studies with big sample sizes are needed to further clarify the effect of metformin use on the efficacy of immunotherapy in advanced NSCLC patients with diabetes.

Purpose: Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy. Although its mortality rate is low, some patients experience cancer recurrence during follow-up. In this study, we investigated the accuracy of a novel multimodal model by simultaneously analyzing numeric and time-series data to predict recurrence in patients with PTC after thyroidectomy.

Patients and methods: We analyzed patients with thyroid carcinoma who underwent thyroidectomy at the Chungbuk National University Hospital between January 2006 and December 2021. The proposed model used numerical data, including clinical information at the time of surgery, and time-series data, including postoperative thyroid function test results. For the model training with unbalanced data, we employed weighted binary cross-entropy with weights of 0.8 for the positive (recurrence) group and 0.2 for the negative (nonrecurrence) group. We performed four-fold cross-validation of the dataset to evaluate the model performance.

Results: Our dataset comprised 1613 patients who underwent thyroidectomy, including 1550 and 63 patients with nonrecurrent and recurrent PTC, respectively. Patients with recurrence had a larger tumor size, more tumor multiplicity, and a higher male-to-female ratio than those without recurrence. The proposed model achieved an average area under the curve of 0.9622, F1-score of 0.4603, sensitivity of 0.9042, and specificity of 0.9077.

Conclusion: When applying our proposed model, the experimental results showed that it could predict recurrence at least 1 year before occurrence. The multimodal model for predicting PTC recurrence after thyroidectomy showed good performance. In clinical practice, it may help with the early detection of recurrence during the follow-up of patients with PTC after thyroidectomy.

Purpose: To identify the epithelial cell centre regulatory transcription factors in the gastric cancer (GC) microenvironment and provide a new strategy for the diagnosis and treatment of GC.

Methods: The GC single-cell dataset was downloaded from the Gene Expression Omnibus (GEO) database. The regulatory mechanisms of transcription factors in both pan-cancer and GC microenvironments were analysed using the Cancer Genome Atlas (TGCA) database. Real-time quantitative PCR (RT-qPCR) was used to determine the mRNA expression levels of Prospero homeobox gene 1 (PROX1) and Endothelial PAS domain-containing protein 1 (EPAS1) in the human gastric mucosal normal epithelial cell line (GES-1) and the GC cell line (AGS). Immunohistochemistry (IHC) was used to determine the amounts of PROX1 and EPAS1 protein expression in GC and adjacent tissues. GC patients' overall survival (OS) was tracked through outpatient, Inpatient case inquiry, or phone follow-up.

Results: The single-cell data from GSE184198 was re-annotated, resulting in nine cell subsets: T cells (13364), NK cells (606), B cells (2525), Epithelial cells (2497), DC cells (1167), Fibroblast cells (372), Endothelial cells (271), Neutrophils cells (246) and Macrophage cells (420). Analysis of cell subgroup signalling pathways revealed that communication intensity between epithelial cells and smooth muscle cells was highest. Transcription factors PROX1 and EPAS1 were notably active in epithelial cells. Cell communication analysis indicated that IFNG may interact with IFNGR1/2 and LIF with IL6ST and LIFR to regulate the downstream PROX1 and EPAS1. PROX1 and EPAS1 were upregulated and negatively correlated with tumour mutation burden (TMB). They also exhibited high positive correlations with immune checkpoints CTLA4 and PDCD1LG2, as well as with chemokines CCL24 and CXCL12 and their receptors CCR3 and CCR4. Additionally, PROX1 and EPAS1 were positively correlated with immunosuppressive factors ADORA2A, CD160, IL10, TGFBR1, KDR and CSF1R, as well as with immunostimulators CD276, PVR, TNFRSF25, ULBP1, CXCL12 and ENTPD1. In GC tissues and AGS, PROX1 and EPAS1 were both substantially expressed. In the meantime, they showed a positive correlation with clinicopathological features such TNM stage and degree of differentiation. In GC patients, the up-regulated group's PROX1 and EPAS1 prognosis was noticeably poorer than the down-regulated group's.

Conclusion: PROX1 and EPAS1 are likely central regulatory transcription factors in the epithelial cells of the GC environment, regulated by IFNG and LIF. They may contribute to GC progression by modulating the tumour's immune microenvironment.