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

BACKGROUND

Early-stage endometrial cancer often presents with favorable survival rates, but high-risk factors, including TP53 mutations and high-grade serous pathology, can lead to recurrence and poor prognosis. The standard primary treatment for endometrial cancer is surgical staging, and lymph node metastases significantly impact adjuvant therapy decisions. The subgroup of p53-abnormal (p53abn) indicates the worst prognosis and potential benefits from adjuvant chemotherapy. Molecular classification, while recommended, faces practical challenges due to resource constraints.

OBJECTIVES

The study aimed to assess the incidence of p53 abnormal expression in clinical stage 1 endometrial cancer cases that underwent surgery at a government tertiary hospital, and assess its relationship with clinicopathologic factors and pelvic and paraaortic lymph node metastasis (LNM).

METHODS

A cross-sectional retrospective analysis was conducted on clinical early-stage endometrial cancer cases that underwent surgical primary treatment between January 2018 and December 2022. Patient records were reviewed to gather demographics, surgical information, and pathological evaluations. Preoperative clinical staging was determined through imaging, and surgical staging involved comprehensive lymphadenectomy. Immunohistochemistry studies for p53 were carried out on formalin-fixed paraffin-embedded tissue samples.

RESULTS

A total of 233 endometrial cancer cases were included. The mean age at diagnosis was 53.7 years. Common comorbidities included hypertension (47.2%) and dyslipidemia (20.6%). Most cases were endometrioid histology (82.8%) and low-grade tumors (85.8%). Tumor grade (p=0.010), myometrial invasion (p<0.001) and lymphovascular space invasion (p<0.001) significantly correlated with lymph node metastasis. However, tumor size and p53abn were not significantly associated with lymph node metastasis. p53abn overexpression was significantly associated with non-endometrioid histology at 33% and Grade 3 tumors at 23%. Preoperative grading demonstrated moderate reliability (0.51) with postoperative grading, with an agreement of 65.4%.

CONCLUSION

Tumor grade, myometrial invasion, and LVSI were all significantly associated with lymph node involvement. While p53 immunohistochemical stains show promise in predicting metastasis and has been associated with tumor aggressiveness, this should still be correlated with clinicopathological parameters to carry out a more accurate risk stratification of early-stage patients.

Therapeutics ; Survival Rate ; Risk Factors ; Recurrence ; Prognosis ; Pathology ; Endometrial Neoplasms ; Immunohistochemistry ; Tumor Suppressor Protein P53 ; Lymph Node Excision ; Risk Assessment

Introduction: Metabolic dysfunction–associated steatotic liver disease (MASLD) is common among patients with type 2 diabetes (T2D) and increases the risk of cirrhosis, yet fibrosis often remains clinically silent until hepatic decompensation occurs. Current diabetes guidelines recommend noninvasive fibrosis risk stratification, such as the fibrosis-4 index (FIB-4), during routine diabetes care, but real-world adoption remains unclear. This study aimed to evaluate the implementation of FIB-4 as a risk stratification tool in patients with T2D. Methodology: This was a retrospective study of consecutive patients with T2D who were seen in the Diabetes Clinic of University of Malaya Medical Centre in 2023. Patients were identified as having a higher risk of future cirrhosis based on elevated FIB-4 ≥1.3. Results: The data for 1,009 patients were analyzed, median age 62 (52–71) years, 40.7% male. Elevated FIB-4 was seen in 28.8% (291/1,009). Only 12.0% (35/291) with elevated FIB-4 were referred for further hepatology evaluation, whereas 3.3% (24/718) with low FIB-4 were referred. Among the patients with elevated FIB-4, those referred were more likely known to have hepatic steatosis, had higher alanine aminotransferase, aspartate aminotransferase, gammaglutamyl transferase, and lower low-density lipoprotein cholesterol and platelet count. Over a median follow-up of 1.71 (0.99–1.95) years, totaling 1,440 person-years, two liverrelated events (0.2%) occurred (one each in the elevated and low FIB-4 group, respectively). Sixteen patients (4.0%) experienced cardiovascular events, including one patient (0.6%) in the elevated FIB-4 group and 15 (2.9%) in the low FIB-4 group. Conclusion Despite automated FIB-4 reporting, this risk stratification tool was underutilized in T2D, resulting in missed opportunities for early identification and management of more severe liver disease. Integration of FIB-4 into diabetes care workflows, alongside structured referral pathways and clinician education, may improve early detection and reduce long-term hepatic complications in T2D.
Diabetes Mellitus, Type 2 ; Risk Assessment ; Liver Diseases ; Referral and Consultation

Introduction: The 2021 IDF-DAR risk calculator had been previously evaluated in multiple studies and subsequently widely accepted and applied in clinical practice as a practical standardized tool for patient risk stratification. Recently updated, the 2026 IDF-DAR Risk calculator enables a more individualized, evidence-related evaluation of patientrelated and disease-related risk factors, incorporating modern diabetes technologies, including continuous glucose monitoring (CGM), automated insulin delivery (AID) systems, and advanced insulin formulations to enhance risk stratification. This tool allows medical professionals to tailor Ramadan practices based on overall factors toward promoting safe fasting. Methodology: This prospective multicentre observational study recruited adults with Type 1 and Type 2 diabetes attending public hospitals nationwide. People with diabetes (PwD) intending to perform Ramadan fasting were invited to participate and assessed using the 2026 IDF-DAR Risk Calculator in the 6-week pre-Ramadan period between 30th January and 19th March 2026. Results: A total of 458 PwD were evaluated and stratified into low (15.7%), moderate (41%), and high risk (43.3%) categories. Most participants had Type 2 diabetes (83.6%), with 60.3% having a disease duration exceeding 10 years and 43% exhibiting poor glycemic control (hemoglobin A1c >9%). Insulin therapy was used by 76.4% of participants, including two individuals with Type 1 diabetes using AID systems. Most participants reported no recent hypoglycemia (76.4%), 81.0% performed glucose monitoring, and 3.3% used CGM. Severe comorbidities were uncommon, with 1.1% having unstable macrovascular disease and 4.4% advanced chronic kidney disease (estimated glomerular filtration rate <30). Notably, 72.2% received structured Ramadan education. Conclusion Majority of PwD attending tertiary diabetes clinics were in the moderate- to high-risk category and intended to fast despite medical advice against fasting in some cases. Although most participants were on insulin therapy, hypoglycemia was low in the pre-Ramadan period. Integration of modern technologies, advanced insulin therapies, and structured education may support safer fasting practices.
Risk Assessment ; Diabetes Mellitus ; Hospitals ; Fasting

Introduction: Obesity is a heterogeneous metabolic disease in which individuals with similar body weight may exhibit markedly different physiological risk profiles. Conventional monitoring using weight alone may fail to capture underlying metabolic and cellular changes during lifestyle intervention. Integrating body composition and biochemical markers may improve risk stratification in clinical practice. Methodology: A prospective observational analysis was conducted among participants enrolled in a structured lifestyle programme. Baseline and follow-up assessments included biochemical markers (fasting glucose, renal function, lipid profile, and liver enzymes) alongside body composition parameters derived from bioelectrical impedance analysis, including percent body fat, visceral fat area, skeletal muscle index (SMI), and phase angle (PhA). Changes over time and associations between metabolic and body composition variables were analyzed. Results: Metabolic responses varied substantially despite similar anthropometric profiles. Renal function improved significantly, with increased estimated glomerular filtration rate (eGFR) observed over time (p = 0.01), and was inversely associated with fasting glucose (r = −0.41, p <0.01). Lipid abnormalities persisted, although reductions in lowdensity lipoprotein cholesterol were noted (p = 0.04). Total cholesterol remained elevated and correlated positively with fasting glucose (r = 0.36, p = 0.02). Improvements in aspartate aminotransferase were observed (p = 0.03), while other liver markers remained stable. Notably, body composition parameters, including PhA and SMI, demonstrated variability independent of weight change, reflecting heterogeneous physiological adaptation. Conclusion Metabolic and physiological responses to lifestyle intervention are heterogeneous and not fully captured by changes in body weight alone. The integration of bioimpedance-derived parameters with biochemical markers provides a more comprehensive approach to metabolic risk stratification and may support more personalized clinical management in obesity care.
Biomarkers ; Risk Assessment ; Body Composition

OBJECTIVE: To identify the risk factors related to the timing of patients receiving extracorporeal membrane oxygenation (ECMO) initiation and construct a risk prediction model for ECMO initiation timing. METHODS: Patients who received ECMO admitted to the Second Xiangya Hospital of Central South University from January 2020 to January 2024 were retrospectively collected. The case data mainly included physiological and biochemical indicators 1 hour before ECMO initiation. According to the outcome of the patients, they were divided into survival group and death group. Univariate and multivariate Logistic regression analysis were used to analyze the predictors of mortality risk in patients with ECMO, and a nomogram prediction model was constructed. The discrimination, calibration accuracy, and goodness of the model were evaluated by the receiver operator characteristic curve (ROC curve), calibration curve, and the Hosmer-Lemeshow test, respectively. Decision curve analysis (DCA) evaluated the clinical net benefit rate of the model. RESULTS: A total of 81 ECMO patients were included, including 59 males and 22 females; age range from 16 to 61 years old, with a median age of 56.0 (39.5, 61.5) years old; 20 patients received veno-arterial (V-A) ECMO, and 61 patients received veno-venous (V-V) ECMO; 23 patients ultimately survived and 58 patients died. Univariate analysis showed that age, blood urea nitrogen, serum creatinine, D-dimer, arterial blood carbon dioxide partial pressure, and prothrombin time of the death group were all higher than those of the survival group, while albumin was slightly lower than that of the survival group. There was a statistically significant difference in the direct cause of ECMO initiation between the two groups. Multivariate Logistic regression analysis showed that age [odds ratio (OR) = 1.069, 95% confidence interval (95%CI) was 1.015-1.125, P = 0.012], direct cause of ECMO initiation [with heart failure as the reference, return of spontaneous circulation (ROSC) after cardiopulmonary support (OR = 30.672, 95%CI was 1.265-743.638, P = 0.035), novel coronavirus infection (OR = 8.666, 95%CI was 0.818-91.761, P = 0.073), other severe pneumonia (OR = 4.997, 95%CI was 0.558-44.765, P = 0.150)], pre-ECMO serum creatinine (OR = 1.008, 95%CI was 1.000-1.016, P = 0.044), prothrombin time (OR = 1.078, 95%CI was 0.948-1.226, P = 0.252), and D-dimer (OR = 1.135, 95%CI was 1.047-1.231, P = 0.002) were entered into the final regression equation. A nomogram prediction model was developed based on these five factors. The area under the ROC curve (AUC) of the model was 0.889 (95%CI was 0.819-0.959), higher than the AUC of the sequential organ failure assessment (SOFA; AUC = 0.604, 95%CI was 0.467-0.742). The calibration curve showed good consistency between the model predictions and the observed results. The Hosmer-Lemeshow goodness-of-fit test showed that χ ² = 4.668, P = 0.792. DCA analysis showed that when the risk threshold was 0-0.8, the net benefit rate was greater than 0, which was significantly better than that of SOFA score. CONCLUSIONS The risk prediction model for the timing of ECMO initiation, constructed using five factors (age, direct cause of ECMO initiation, thrombin time, serum creatinine, and D-dimer), demonstrated good discrimination and calibration. It can serve as a pre-initiation assessment tool to identify and predict post-initiation mortality risk in ECMO patients.
Humans ; Extracorporeal Membrane Oxygenation ; Middle Aged ; Male ; Female ; Retrospective Studies ; Adult ; Risk Factors ; Adolescent ; Young Adult ; Logistic Models ; Nomograms ; ROC Curve ; Time Factors ; Risk Assessment

Extracorporeal membrane oxygenation (ECMO) is primarily used in clinical practice to provide continuous extracorporeal respiratory and circulatory support for patients with severe heart and lung failure, thereby sustaining life. It is a key technology for managing severe heart failure and respiratory failure that are difficult to control. With the accumulation of clinical experience in ECMO for circulatory and/or respiratory support, as well as advancements in biomedical engineering technology, more portable and stable ECMO devices have been introduced into clinical use, benefiting an increasing number of critically ill patients. Although ECMO technology has become relatively mature, the timing of ECMO initiation, management of sudden complications, and monitoring and early warning of physiological indicators are critical factors that greatly affect the therapeutic outcomes of ECMO. This article reviews traditional methods and artificial intelligence techniques used in risk assessment related to ECMO, including the latest achievements and research hotspots. Additionally, it discusses future trends in ECMO risk management, focusing on six key areas: multi-center and prospective studies, external validation and standardization of model performance, long-term prognosis considerations, integration of innovative technologies, enhancing model interpretability, and economic cost-effectiveness analysis. This provides a reference for future researchers to build models and explore new research directions.
Extracorporeal Membrane Oxygenation ; Humans ; Artificial Intelligence ; Risk Assessment ; Respiratory Insufficiency/therapy* ; Heart Failure/therapy*

PURPOSE: To analyze risk factors for severe trauma and establish a nomogram for early risk prediction, to improve the early identification of severe trauma. METHODS: This study was conducted on the patients treated in 81 trauma treatment institutions in Gansu province from 2020 to 2022. Patients were grouped by year, with 5364 patients from 2020 to 2021 as the training set and 1094 newly admitted patients in 2020 as the external validation set. Based on the injury severity score (ISS), patients in the training set were classified into 2 subgroups of the severe trauma group (n = 478, ISS scores ≥25) and the non-severe trauma group (n = 4886, ISS scores <25). Univariate and binary logistic regression analyses were employed to identify independent risk factors for severe trauma. Subsequently, a predictive model was developed using the R software environment. Furthermore, the model was subjected to internal and external validation via the Hosmer-Lemeshow test and receiver operating characteristic curve analysis. RESULTS: In total, 6458 trauma patients were included in this study. Initially, this study identified several independent risk factors for severe trauma, including multiple traumatic injuries (polytrauma), external hemorrhage, elevated shock index, elevated respiratory rate, decreased peripheral oxygen saturation, and decreased Glasgow coma scale score (all p < 0.05). For internal validation, the area under the receiver operating characteristic curve was 0.914, with the sensitivity and specificity of 88.4% and 87.6%, respectively; while for external validation, the area under the receiver operating characteristic curve was 0.936, with the sensitivity and specificity of 84.6% and 93.7%, respectively. In addition, a good model fitting was observed through the Hosmer-Lemeshow test and calibration curve analysis (p > 0.05). CONCLUSION This study establishes a nomogram for early risk prediction of severe trauma, which is suitable for primary healthcare institutions in underdeveloped western China. It facilitates early triage and quantitative assessment of trauma severity by clinicians prior to clinical interventions.
Humans ; Nomograms ; Male ; Female ; Wounds and Injuries/diagnosis* ; Risk Factors ; Middle Aged ; Adult ; Injury Severity Score ; Risk Assessment ; ROC Curve ; Aged ; Logistic Models ; China ; Glasgow Coma Scale

OBJECTIVES: This study aimed to construct a risk prediction model for the occurrence of the demora-lization syndrome in patients with oral cancer and provide a scientific basis for the prevention of this syndrome in patients with oral cancer and the development of personalized care programs. METHODS: A total of 486 patients with oral cancer in West China Hospital of Stomatology of Sichuan University and Sun Yat-sen Memorial Hospital of Sun Yat-sen University from 2024 March to July were selected by convenience sampling. We integrated clinical data and evidence from previous studies to identify the key variables affecting the demoralization syndrome in patients with oral cancer. The 486 patients were divided into a training set and a validation set in an 8∶2 ratio. A clinical risk prediction model was established based on the individual data of 365 patients in the development cohort. Through least absolute shrinkage and selection operator (LASSO) regression, a moderate to severe risk prediction model of demoralization syndrome in oral cancer was constructed, and a clinical machine-learning nomogram was constructed. Bootstrap resampling was used for internal validation. The data of 121 patients in the validation cohort were externally validated. RESULTS: The incidence of the demoralization syndrome in patients with oral cancer was 405 cases (83.3%), of which 279 cases (57.4%) were mild, 176 cases (36.2%) were moderate, and 31 cases (6.4%) were severe. The core model, including patient education level, disease understanding, and MDASI-HN score, was used to predict the risk of outcome. Internal validation of the model yielded C statistic of 0.783 6 (95% CI: 0.78-0.87), beta of 0.843 4, and calibration intercept of -0.040 6. Through external validation, the validation set C statistic was 0.80 (95%CI: 0.71-0.87), beta was 0.80, and calibration intercept was -0.08. CONCLUSIONS Our risk prediction mo-del of the demoralization syndrome in patients with oral cancer performed robustly in validation cohorts of different nur-sing environments. The model has good correction and good discrimination and can be used as an evaluation and prediction item at admission.
Humans ; Mouth Neoplasms/complications* ; Male ; Female ; Nomograms ; Middle Aged ; Syndrome ; Aged ; Adult ; Risk Factors ; Risk Assessment ; Machine Learning

The introduction of non-vitamin K antagonist oral anticoagulant (NOAC) into clinical use heralds a new age for anticoagulation therapy in patients with atrial fibrillation (AF).However,anticoagulation-related bleeding is currently a major challenge in the anticoagulation process.Assessing the risk of anticoagulation-related bleeding is an important part for the management of patients with AF.Clinical risk factor scores have moderate ability to predict the risk of anticoagulation-related bleeding.To improve the anticoagulation safety of NOACs,additional clinical and biological markers and genetic polymorphisms should be considered to enhance the predictive capability for anticoagulation-related bleeding.This review summarizes the challenges in the management of anticoagulation therapy,with emphases on the bleeding risk scores,biomarkers,clinical indicators,and genetic loci currently used to guide the risk assessment of anticoagulation-related bleeding in AF patients.This review is expected to provide research insights and reference frameworks for predicting and evaluating the bleeding risk associated with NOACs.
Humans ; Atrial Fibrillation/drug therapy* ; Anticoagulants/therapeutic use* ; Hemorrhage/chemically induced* ; Risk Assessment ; Administration, Oral ; Risk Factors