OBJECTIVE: To explore the efficacy of a Thalassemia risk assessment software for the screening of thalassemia mutation carriers and distribution of thalassemia genotypes detected by screening. METHODS: A total of 6 040 individuals were evaluated at Leshan Maternal and Child Health Care Hospital between 2022 and 2024 using the commonly used clinical thalassemia risk assessment method and the thalassemia screening software, respectively, and the performance indicators of the two methods were compared and analyzed against the result of thalassemia gene testing. This study was approved by the Ethics Committee of our hospital (Ethics No.: LfyLL[2022]005). RESULTS: The high-risk rate by the thalassemia screening software was 11.19%, with a sensitivity of 95.12%, specificity of 93.28%, positive predictive value of 43.20%, negative predictive value of 99.72%, and the area under the ROC curve (AUC) was 0.942. The thalassemia gene detection rate of the high-risk samples screened was 4.83%. The high-risk screening rate of the conventional method was 2.50%, with a sensitivity of 51.22%, specificity of 93.28%, positive predictive value of 80.79%, negative predictive value of 97.40%, and the AUC was 0.754. The thalassemia gene detection rate of the high-risk samples was 2.02%. CONCLUSION The software can effectively detect thalassemia carriers and significantly reduce the missed detection compared with conventional method, thereby significantly improve the efficacy of screening.
Humans ; Thalassemia/diagnosis* ; Software ; Female ; Genetic Testing/methods* ; Male ; Mutation ; Adult ; Genotype ; ROC Curve ; Risk Assessment

Objective:To investigate the clinical characteristics of residual dizziness（RD） after repositioning in patients with benign paroxysmal positional vertigo（BPPV）, identify its potential risk factors, and develop a predictive risk model. Methods:A total of 137 patients diagnosed with BPPV at the First Affiliated Hospital of Xi'an Jiaotong University between January 2023 and June 2023 were enrolled. Based on the presence or absence of subjective discomfort within 3 months after successful repositioning, patients were divided into the non-RD group（NRD, n=93） and the RD group（n=44）. Differences in demographic characteristics, comorbidities, and disease-related features were compared between groups. Multivariate logistic regression analysis was used to identify independent risk factors for RD, and a nomogram was constructed based on these factors. The predictive performance of the model was assessed using the area under the curve（AUC）. Results:The RD group showed significantly higher values in body mass index, prevalence of diabetes and motion sickness history, dizziness duration before repositioning, history of repositioning at external hospitals, number of treatments, and recurrence（all P<0.001）. Multivariate logistic regression revealed that diabetes（adjusted OR=8.73, P=0.039）, motion sickness history（adjusted OR=23.08, P<0.001）, dizziness duration ≥30 days before repositioning（adjusted OR=15.16, P<0.001）, and recurrence（adjusted OR=15.72, P=0.001） were independent risk factors for RD. The nomogram model based on these variables demonstrated good predictive ability, with an AUC of 0.804（95%CI 0.684-0.924）. Conclusion:Diabetes, motion sickness history, dizziness duration ≥30 days, and recurrence are independent risk factors for RD after repositioning in patients with BPPV. The nomogram model based on these variables shows good predictive performance, with recurrence having the highest predictive value. This model can aid in early identification of high-risk patients and guide individualized intervention strategies.
Humans ; Nomograms ; Benign Paroxysmal Positional Vertigo/therapy* ; Dizziness/etiology* ; Risk Factors ; Risk Assessment ; Multivariate Analysis ; Male ; Female ; Logistic Models ; Middle Aged ; Patient Positioning ; Adult

COMPERA 2.0 risk stratification has been demonstrated to be useful in patients with precapillary pulmonary hypertension (PH). However, its suitability for patients at risk for post-capillary PH or PH associated with left heart disease (PH-LHD) is unclear. To investigate the use of COMPERA 2.0 in patients with severe aortic stenosis (SAS) undergoing transcatheter aortic valve replacement (TAVR), who are at risk for post-capillary PH, a total of 327 eligible SAS patients undergoing TAVR at our institution between September 2015 and November 2020 were included in the study. Patients were classified into four strata before and after TAVR using the COMPERA 2.0 risk score. The primary endpoint was all-cause mortality. Survival analysis was performed using Kaplan-Meier curves, log-rank test, and Cox proportional hazards regression model. The study cohort had a median (interquartile range) age of 76 (70‒80) years and a pulmonary arterial systolic pressure of 33 (27‒43) mmHg (1 mmHg=0.133 kPa) before TAVR. The overall mortality was 11.9% during 26 (15‒47) months of follow-up. Before TAVR, cumulative mortality was higher with an increase in the risk stratum level (log-rank, both P<0.001); each increase in the risk stratum level resulted in an increased risk of death (hazard ratio (HR) 2.53, 95% confidential interval (CI) 1.54‒4.18, P<0.001), which was independent of age, sex, estimated glomerular filtration rate (eGFR), hemoglobin, albumin, and valve type (HR 1.76, 95% CI 1.01‒3.07, P=0.047). Similar results were observed at 30 d after TAVR. COMPERA 2.0 can serve as a useful tool for risk stratification in patients with SAS undergoing TAVR, indicating its potential application in the management of PH-LHD. Further validation is needed in patients with confirmed post-capillary PH by right heart catheterization.
Humans ; Aortic Valve Stenosis/complications* ; Aged ; Hypertension, Pulmonary/mortality* ; Male ; Female ; Transcatheter Aortic Valve Replacement ; Aged, 80 and over ; Risk Assessment/methods* ; Proportional Hazards Models ; Kaplan-Meier Estimate ; Retrospective Studies

OBJECTIVES: We propose a multi-feature fusion model based on manually extracted features and deep learning features from endoscopic images for grading rebleeding risk of peptic ulcers. METHODS: Based on the endoscopic appearance of peptic ulcers, color features were extracted to distinguish active bleeding (Forrest I) from non-bleeding ulcers (Forrest II and III). The edge and texture features were used to describe the morphology and appearance of the ulcers in different grades. By integrating deep features extracted from a deep learning network with manually extracted visual features, a multi-feature representation of endoscopic images was created to predict the risk of rebleeding of peptic ulcers. RESULTS: In a dataset consisting of 3573 images from 708 patients with Forrest classification, the proposed multi-feature fusion model achieved an accuracy of 74.94% in the 6-level rebleeding risk classification task, outperforming the experienced physicians who had a classification accuracy of 59.9% (P<0.05). The F1 scores of the model for identifying Forrest Ib, IIa, and III ulcers were 90.16%, 75.44%, and 77.13%, respectively, demonstrating particularly good performance of the model for Forrest Ib ulcers. Compared with the first model for peptic ulcer rebleeding classification, the proposed model had improved F1 scores by 5.8%. In the simplified 3-level risk (high-risk, low-risk, and non-endoscopic treatment) classification task, the model achieved F1 scores of 93.74%, 81.30%, and 73.59%, respectively. CONCLUSIONS The proposed multi-feature fusion model integrating deep features from CNNs with manually extracted visual features effectively improves the accuracy of rebleeding risk classification for peptic ulcers, thus providing an efficient diagnostic tool for clinical assessment of rebleeding risks of peptic ulcers.
Humans ; Deep Learning ; Peptic Ulcer ; Risk Assessment ; Peptic Ulcer Hemorrhage ; Recurrence

OBJECTIVES: To develop an early atrial fibrillation (AF) risk prediction model based on large-scale electrocardiogram (ECG) data from the Chinese population. METHODS: The data of multiple ECG records of 30 383 patients admitted in the Chinese PLA General Hospital between 2009 and 2023 were randomly divided into the training set and the internal testing set in a 7:3 ratio. The predictive factors were selected based on the training set using univariate analysis, LASSO regression, and the Boruta algorithm. Cox proportional hazards regression was used to establish the ECG model and the composite model incorporating age, gender, and ECG model score. The discrimination power, calibration, and clinical net benefits of the models were evaluated using the area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curves. RESULTS: The cohort included 51.1% male patients with a median age of the patients of 51 (36, 62) years and an AF incidence of 4.5% (1370/30 383). In the ECG model, the parameters related to the P wave and QRS complex were identified as significant predictors. In the testing set, the AUROC of the ECG model for predicting 5-year AF risk was 0.77 (95% CI: 0.74-0.80), which was increased to 0.81 (95% CI: 0.78-0.83) after incorporating age and gender, with a net reclassification improvement of 0.123 and an integrated discrimination improvement of 0.04 (P<0.05). The calibration curve of the model was close to the diagonal line. Decision curve analysis showed that the clinical net benefit of the composite model was higher than that of the ECG model across the majority of threshold probability. CONCLUSIONS The composite model incorporating quantitative ECG features during sinus rhythm, along with age and gender, can effectively predict AF risk in the Chinese population, thus providing a low-cost screening tool for early AF risk assessment and management.
Humans ; Atrial Fibrillation/epidemiology* ; Electrocardiography ; Middle Aged ; Male ; Female ; China/epidemiology* ; Proportional Hazards Models ; Adult ; Risk Factors ; Risk Assessment ; East Asian People

OBJECTIVES: To develop a risk prediction model for left ventricular adverse remodeling (LVAR) based on cardiac magnetic resonance (CMR) parameters in patients undergoing percutaneous coronary intervention (PCI) for acute ST-segment elevation myocardial infarction (STEMI). METHODS: A total of 329 acute STEMI patients undergoing primary PCI at 8 medical centers from January, 2018 to December, 2021 were prospectively enrolled. The parameters of CMR, performed at 7±2 days and 6 months post-PCI, were analyzed using CVI42 software. LVAR was defined as an increase >20% in left ventricular end-diastolic volume or >15% in left ventricular end-systolic volume at 6 months compared to baseline. The patients were randomized into training (n=230) and validation (n=99) sets in a 7∶3 ratio. In the training set, potential predictors were selected using LASSO regression, followed by univariate and multivariate logistic regression to construct a nomogram. Model performance was evaluated using receiver-operating characteristic (ROC) curves, area under the curve (AUC), calibration curves, and decision curve analysis. RESULTS: LVAR occurred in 100 patients (30.40%), who had a higher incidence of major adverse cardiovascular events than those without LVAR (58.00% vs 16.16%, P<0.001). Left ventricular global longitudinal strain (LVGLS; OR=0.76, 95% CI: 0.61-0.95, P=0.015) and left atrial active strain (LAAS; OR=0.78, 95% CI: 0.67-0.92, P=0.003) were protective factors for LVAR, while infarct size (IS; OR=1.05, 95% CI: 1.01-1.10, P=0.017) and microvascular obstruction (MVO; OR=1.26, 95% CI: 1.01-1.59, P=0.048) were risk factors for LVAR. The nomogram had an AUC of 0.90 (95% CI: 0.86-0.94) in the training set and an AUC of 0.88 (95% CI: 0.81-0.94) in the validation set. CONCLUSIONS LVGLS, LAAS, IS, and MVO are independent predictors of LVAR in STEMI patients following PCI. The constructed nomogram has a strong predictive ability to provide assistance for management and early intervention of LVAR.
Humans ; Percutaneous Coronary Intervention ; Prospective Studies ; ST Elevation Myocardial Infarction/diagnostic imaging* ; Ventricular Remodeling ; Magnetic Resonance Imaging ; Male ; Female ; Middle Aged ; Risk Factors ; Aged ; Risk Assessment

OBJECTIVES: To develop risk prediction models for postoperative hypothermia after transurethral holmium laser enucleation of the prostate (HoLEP) using machine learning algorithms. METHODS: We retrospectively analyzed the clinical data of 403 patients from our center (283 patients in the training set and 120in the internal validation set) and 120 patients from Weifang People's Hospital (as the external validation set). The risk prediction models were built using logistic regression, decision tree and support vector machine (SVM), and model performance was evaluated in terms of accuracy, recall, precision, F1 score and AUC. RESULTS: Operation duration, prostate weight, intraoperative irrigation volume, and being underweight were identified as the predictors of postoperative hypothermia following HoLEP. Among the 3 algorithms, SVM showed the best precision rate and accuracy in all the 3 data sets and the best area under the ROC (AUC) in the training set and validation set, followed by logistic regression, which had a similar AUC in the two data sets. SVM outperformed logistic regression and decision tree models in the validation set in precision, accuracy, recall, F1 score, and AUC, and performed well in the external validation set with better precision rate and accuracy than logistic regression and decision tree models but slightly lower recall rate, F1 index, and AUC value than the decision tree model. SVM outperformed logistic regression and decision tree models in precision, accuracy, F1 score, and AUC in the training set, but had slightly lower recall rate than the decision tree. CONCLUSIONS Among the 3 models, SVM has the best performance and generalizability for predicting post-HoLEP hypothermia risk to provide support for clinical decisions.
Humans ; Male ; Retrospective Studies ; Machine Learning ; Transurethral Resection of Prostate/adverse effects* ; Hypothermia/etiology* ; Prostatic Hyperplasia/surgery* ; Algorithms ; Lasers, Solid-State ; Risk Assessment ; Postoperative Complications ; Decision Trees ; Logistic Models ; Aged ; Middle Aged ; Support Vector Machine

Pelvic organ prolapse (POP), whose etiology is influenced by genetic and clinical risk factors, considerably impacts women's quality of life. However, the genetic underpinnings in non-European populations and comprehensive risk models integrating genetic and clinical factors remain underexplored. This study constructed the first polygenic risk score (PRS) for POP in the Chinese population by utilizing 20 disease-associated variants from the largest existing genome-wide association study. We analyzed a discovery cohort of 576 cases and 623 controls and a validation cohort of 264 cases and 200 controls. Results showed that the case group exhibited a significantly higher PRS than the control group. Moreover, the odds ratio of the top 10% risk group was 2.6 times higher than that of the bottom 10%. A high PRS was significantly correlated with POP occurrence in women older than 50 years old and in those with one or no childbirths. As far as we know, the integrated prediction model, which combined PRS and clinical risk factors, demonstrated better predictive accuracy than other existing PRS models. This combined risk assessment model serves as a robust tool for POP risk prediction and stratification, thereby offering insights into individualized preventive measures and treatment strategies in future clinical practice.
Humans ; Female ; Pelvic Organ Prolapse/epidemiology* ; Middle Aged ; Risk Assessment/methods* ; China/epidemiology* ; Multifactorial Inheritance ; Aged ; Risk Factors ; Genome-Wide Association Study ; Genetic Predisposition to Disease ; Case-Control Studies ; Adult ; Polymorphism, Single Nucleotide ; Genetic Risk Score ; East Asian People

OBJECTIVE: To analyze the clinical characteristics of elderly patients with sepsis, identify the key factors affecting their clinical outcomes, construct a death risk assessment scale for elderly patients with sepsis, and evaluate its predictive value. METHODS: A retrospective case-control study was conducted. The clinical data of sepsis patients admitted to intensive care unit (ICU) of the First Affiliated Hospital of Wenzhou Medical University from September 2021 to September 2023 were collected, including basic information, clinical characteristics, and clinical outcomes. The patients were divided into non-elderly group (age ≥ 65 years old) and elderly group (age < 65 years old) based on age. Additionally, the elderly patients were divided into survival group and death group based on their 30-day survival status. The clinical characteristics of elderly patients with sepsis were analyzed. Univariate and multivariate Logistic regression analyses were used to screen the independent risk factors for 30-day death in elderly patients with sepsis, and the regression equation was constructed. The regression equation was simplified, and the death risk assessment scale was established. The predictive value of different scores for the prognosis of elderly patients with sepsis was compared. RESULTS: (1) A total of 833 patients with sepsis were finally enrolled, including 485 in the elderly group and 348 in the non-elderly group. Compared with the non-elderly group, the elderly group showed significantly lower counts of lymphocyte, T cell, CD8⁺ T cell, and the ratio of T cells and CD8⁺ T cells [lymphocyte count (×10⁹/L): 0.71 (0.43, 1.06) vs. 0.83 (0.53, 1.26), T cell count (cells/μL): 394.0 (216.0, 648.0) vs. 490.5 (270.5, 793.0), CD8⁺ T cell count (cells/μL): 126.0 (62.0, 223.5) vs. 180.0 (101.0, 312.0), T cell ratio: 0.60 (0.48, 0.70) vs. 0.64 (0.51, 0.75), CD8⁺ T cell ratio: 0.19 (0.13, 0.28) vs. 0.24 (0.16, 0.34), all P < 0.01], higher natural killer cell (NK cell) count, acute physiology and chronic health evaluation II (APACHE II) score, ratio of invasive mechanical ventilation (IMV) during hospitalization, and 30-day mortality [NK cell count (cells/μL): 112.0 (61.0, 187.5) vs. 95.0 (53.0, 151.0), APACHE II score: 16.00 (12.00, 21.00) vs. 13.00 (8.00, 17.00), IMV ratio: 40.6% (197/485) vs. 31.9% (111/348), 30-day mortality: 28.9% (140/485) vs. 19.5% (68/348), all P < 0.05], and longer length of ICU stay [days: 5.5 (3.0, 10.0) vs. 5.0 (3.0, 8.0), P < 0.05]. There were no statistically significant differences in the levels of inflammatory markers such as C-reactive protein (CRP), procalcitonin (PCT), tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), and interleukins (IL-2, IL-4, IL-6, IL-10) between the two groups. (2) In 485 elderly patients with sepsis, 345 survived in 30 days, and 140 died with the 30-day mortality of 28.9%. Compared with the survival group, the patients in the death group were older, and had lower body mass index (BMI), white blood cell count (WBC), PCT, platelet count (PLT) and higher IL-6, IL-10, N-terminal pro-brain natriuretic peptide (NT-proBNP), total bilirubin (TBil), blood lactic acid (Lac), and ratio of in-hospital IMV and continuous renal replacement therapy (CRRT). Multivariate Logistic regression analysis indicated that BMI [odds ratio (OR) = 0.783, 95% confidence interval (95%CI) was 0.678-0.905, P = 0.001], IL-6 (OR = 1.073, 95%CI was 1.004-1.146, P = 0.036), TBil (OR = 1.009, 95%CI was 1.000-1.018, P = 0.045), Lac (OR = 1.211, 95%CI was 1.072-1.367, P = 0.002), and IMV during hospitalization (OR = 6.181, 95%CI was 2.214-17.256, P = 0.001) were independent risk factors for 30-day death in elderly patients with sepsis, and the regression equation was constructed (Logit P = 1.012-0.244×BMI+0.070×IL-6+0.009×TBil+0.190×Lac+1.822×IMV). The regression equation was simplified to construct a death risk assessment scale, namely BITLI score. Receiver operator characteristic curve (ROC curve) analysis showed that the area under the ROC curve (AUC) of BITLI score for predicting death risk was 0.852 (95%CI was 0.769-0.935), and it was higher than APACHE II score (AUC = 0.714, 95%CI was 0.623-0.805) and sequential organ failure assessment (SOFA) score (AUC = 0.685, 95%CI was 0.578-0.793). The determined cut-off value of BITLI score was 1.50, while achieving a sensitivity of 83.3% and specificity of 74.0%. CONCLUSIONS Elderly patients with sepsis often have reduced lymphocyte counts, severe conditions, and poor prognosis. BMI, IL-6, TBil, Lac, and IMV during hospitalization were independent risk factors for 30-day death in elderly patients with sepsis. The BITLI score constructed based above risk factors is more precise and reliable than traditional APACHE II and SOFA scores in predicting the outcomes of elderly patients with sepsis.
Humans ; Sepsis/mortality* ; Aged ; Retrospective Studies ; Risk Assessment ; Case-Control Studies ; Prognosis ; Male ; Female ; Intensive Care Units ; Risk Factors ; Aged, 80 and over ; Logistic Models ; Middle Aged

OBJECTIVE: To explore the early changes in various liver function indicators in critically injured trauma patients assessed by intelligent calculation method, aiming to develop more advantageous diagnostic and treatment strategies for traumatic liver injury. METHODS: A retrospective study was conducted. Critically injured trauma patients [injury severity score (ISS) ≥ 16, age > 18 years old] admitted to the Emergency Medical Center of Tianjin Fifth Central Hospital from January 1, 2022, to December 1, 2023 were enrolled. ISS score and acute physiology and chronic health evaluation II (APACHE II) assessed by intelligent calculation method were collected upon patient admission to the emergency medical center. Trends in liver function indicators in fasting venous serum were analyzed at 6, 24 and 72 hours after admission, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), γ-glutamyl transferase (GGT), lactate dehydrogenase (LDH), albumin (ALB), total bilirubin (TBil), prothrombin time (PT). Patients were grouped based on APACHE II scores into those with APACHE II < 15 and APACHE II ≤ 15, and liver function indicators within 6 hours of admission were compared between the two groups. RESULTS: A total of 112 critically injured trauma patients were included, with 83 males and 29 females, an average age of (47.78±14.84) years old. The median ISS score was 21.0 (18.0, 26.0). The most common cause of injury for critically injured trauma patients was road traffic accidents (68 cases, accounting for 60.71%), followed by falls from heights, compression injuries, heavy object injuries, knife stabs, and explosion injuries. The most common injured areas was the limbs and pelvis (97 cases, accounting for 86.61%), followed by chest injuries, surface skin and soft tissue injuries, abdominal and pelvic organ injuries, head injuries, and facial injuries. The proportion of elevated LDH, AST, and ALT within 6 hours of admission was 77.68%, 79.46%, and 52.68%, respectively, while the proportion of decreased ALB was 75.89%, the abnormal rates of ALP, GGT, TBil, and PT were all below 50%. The ALT and AST levels of patients at 24 hours and 72 hours after admission were significantly lower than those at 6 hours after admission [ALT (U/L): 37.0 (22.0, 66.0), 31.0 (21.2, 52.0) vs. 41.0 (25.0, 71.0), AST (U/L): 55.5 (30.0, 93.5), 40.0 (27.0, 63.2) vs. 69.5 (39.0, 130.8), all P < 0.05]. There was no statistically significant difference in ISS score between APACHE II > 15 group (45 cases) and APACHE II ≤ 15 group [67 cases; 21.0 (18.5, 26.5) vs. 20.0 (17.0, 22.0), P > 0.05]. Nevertheless, compared with patients with APACHE II ≤ 15, patients with APACHE II > 15 have a higher abnormality rate of ALT and AST within 6 hours of admission [ALT abnormal rate: 66.44% (29/45) vs. 44.78% (30/67), AST abnormal rate: 93.33% (42/45) vs. 70.15% (47/67), both P < 0.05], and the levels of ALT and AST were higher [ALT (U/L): 56.0 (30.0, 121.0) vs. 35.0 (21.0, 69.0), AST (U/L): 87.0 (48.0, 233.0) vs. 52.0 (31.0, 117.0), both P < 0.05]. CONCLUSIONS Severe trauma patients frequently exhibit a high incidence of reversible early liver function impairment. Based on intelligent calculation method, the utilization of both the ISS and APACHE II scores demonstrates a distinct advantage in the assessment of their early liver injury.
Humans ; Retrospective Studies ; Liver/physiopathology* ; Risk Assessment ; APACHE ; Wounds and Injuries ; Adult ; Injury Severity Score ; Male ; Middle Aged ; Female ; Liver Function Tests ; Alanine Transaminase/blood* ; Young Adult ; Aspartate Aminotransferases/blood*