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 (pCONCLUSION

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

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 (pCONCLUSION

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

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

Objective To investigate the effect of health failure mode and effect analysis(HFMEA)in optimizing the management process of postoperative diabetes insipidus in children undergoing neurosurgery.Methods Based on HFMEA,a management flowchart for postoperative diabetes insipidus in children undergoing neurosurgery was created.Brainstorming was adopted to identify failure modes in the workflow,analyze risk factors,and develop improvement measures,thereby refining the management flowchart.The amelioration and prognosis of diabetes insipidus in these children before(October 2022 to November 2023)and after(January 2024 to February 2025)implementation of the management flowchart were compared.Results The HFMEA-based management process for postoperative diabetes insipidus in children undergoing neurosurgery alleviated the symptoms of diabetes insipidus regarding the number of diabetes insipidus in the pediatric intensive care unit(P=0.006),the average daily urine output in the pediatric intensive care unit(P=0.001),the proportion of electrolyte abnormalities at discharge/transfer(P=0.037),the duration of mechanical ventilation(P=0.007),and the length of stay in the intensive care unit(P=0.001).Conclusion The HFMEA-based management process for postoperative diabetes insipidus in children undergoing neurosurgery is beneficial to the optimization of the management process,the alleviation of postoperative diabetes insipidus,and the improvement of prognosis in these children.
Humans ; Diabetes Insipidus/etiology* ; Neurosurgical Procedures/adverse effects* ; Child ; Postoperative Complications/therapy* ; Healthcare Failure Mode and Effect Analysis ; Intensive Care Units, Pediatric ; Risk Factors

OBJECTIVE: To explore, identify, and develop novel blood-based indicators using machine learning algorithms for accurate preoperative assessment and effective prediction of postoperative complication risks in patients with rheumatoid arthritis (RA) undergoing total knee arthroplasty (TKA). METHODS: A retrospective cohort study was conducted including RA patients who underwent unilateral TKA between January 2019 and December 2024. Inpatient and 30-day postoperative outpatient follow-up data were collected. Six machine learning algorithms, including decision tree, random forest, logistic regression, support vector machine, extreme gradient boosting, and light gradient boosting machine, were used to construct predictive models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision, and recall. SHapley Additive exPlanations (SHAP) values were employed to interpret and rank the importance of individual variables. RESULTS: According to the inclusion criteria, a total of 1 548 patients were enrolled. Ultimately, 18 preoperative indicators were identified as effective predictive features, and 8 postoperative complications were defined as prediction labels for inclusion in the study. Within 30 days after surgery, 453 patients (29.2%) developed one or more complications. Considering overall accuracy, precision, recall, and F1-score, the random forest model [AUC=0.930, 95% CI (0.910, 0.950)] and the extreme gradient boosting model [AUC=0.909, 95% CI (0.880, 0.938)] demonstrated the best predictive performance. SHAP analysis revealed that anti-cyclic citrullinated peptide antibody, C-reactive protein, rheumatoid factor, interleukin-6, body mass index, age, and smoking status made significant contributions to the overall prediction of postoperative complications. CONCLUSION Machine learning-based models enable accurate prediction of postoperative complication risks among RA patients undergoing TKA. Inflammatory and immune-related blood biomarkers, such as anti-cyclic citrullinated peptide antibody, C-reactive protein, and rheumatoid factor, interleukin-6, play key predictive roles, highlighting their potential value in perioperative risk stratification and individualized management.
Humans ; Arthroplasty, Replacement, Knee/adverse effects* ; Arthritis, Rheumatoid/blood* ; Machine Learning ; Postoperative Complications/blood* ; Female ; Male ; Retrospective Studies ; Middle Aged ; Aged ; Risk Factors ; Preoperative Period ; C-Reactive Protein/analysis* ; Risk Assessment

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: To create a mixed valvular heart disease (MVHD)-related age-adjusted comorbidity index (MVACI) model for predicting mortality risk of patients with MVHD. METHODS: A total of 4080 patients with moderate or severe MVHD in the China-VHD study were included. The primary endpoint was 2-year all-cause mortality. A MVACI model prediction model was constructed based on the mortality risk factors identified by univariate and multivariate Cox regression analysis. Restricted cubic splines were used to assess the relationship between MVACI scores and 2-year all-cause mortality. The optimal threshold, determined by the maximum Youden index from receiver operator characteristic (ROC) curve analysis, was used to stratify patients. Kaplan-Meier method was used to calculate 2-year all-cause mortality and compared using the Log-rank test. Univariate and multivariate Cox proportional hazards models were employed to calculate hazard ratios (HR) and 95% confidence intervals (CI), evaluating the association between MVACI scores and mortality. Paired ROC curves were used to compare the discriminative ability of MVACI scores with the European System for Cardiac Operative Risk Evaluation Ⅱ(EuroSCORE Ⅱ) or the age-adjusted Charlson comorbidity index (ACCI) in predicting 2-year clinical outcomes, while calibration curves assessed the calibration of these models. Internal validation was performed using the Bootstrap method. Subgroup analyses were conducted based on etiology, treatment strategies, and disease severity. RESULTS: Multivariate analysis identified the following variables independently associated with 2-year all-cause mortality in patients: pulmonary hypertension, myocardiopathy, heart failure, low body weight (body mass index <18.5 kg/m²), anaemia, hypoalbuminemia, renal insufficiency, cancer, New York Heart Association (NYHA) class and age. The score was independently associated with the risk of all-cause mortality, and exhibited good discrimination (AUC=0.777, 95%CI: 0.755-0.799) and calibration (Brier score 0.062), with significantly better predictive performance than EuroSCORE Ⅱ or ACCI (both adjusted P<0.01). The internal validation showed that the MVACI model's predicted probability of 2-year all-cause mortality was generally consistent with the actual probability. The AUCs for predicting all-cause mortality risk were all above 0.750, and those for predicting adverse events were all above 0.630. The prognostic value of the score remained consistent in patients regardless of their etiology, therapeutic option, and disease severity. CONCLUSIONS The MVACI was constructed in this study based on age and comorbidities, and can be used for mortality risk prediction and risk stratification of MVHD patients. It is a simple algorithmic index and easy to use.
Humans ; Prognosis ; Comorbidity ; Heart Valve Diseases/epidemiology* ; Female ; Male ; Middle Aged ; Aged ; Proportional Hazards Models ; Risk Factors ; China/epidemiology* ; Age Factors ; Risk Assessment ; Adult ; ROC Curve

Complex crown-root fractures in the esthetic zone refer to a type of dental trauma occurring in the anterior region, characterized by concurrent fractures involving both the crown and the root, with associated pulp exposure and periodontal tissue injury. These injuries consistently exhibit critical anatomical features, including a fixed palatal fracture location below the alveolar crest, compromised residual tooth structure, and frequent encroachment of the biological width. To predict treatment outcomes, a risk assessment framework based on the restoration-tooth-periodontium interface was developed. Resistance risk was evaluated by assessing the type of residual dentin ferrule and the length of the root within the alveolar bone, while periodontal risk was assessed according to gingival phenotype and alveolar bone morphology. Based on these risk dimensions and the principles of aesthetics, stability, and minimally invasive treatment, a diagnostic classification system was established to categorize fractures into three types: favorable, intervention and high-risk. Type-specific management strategies were proposed: for favorable cases, crown lengthening combined with deep margin elevation to reduce periodontal risk is recommended; for intervention cases, orthodontic extrusion or surgical extrusion is applied to simultaneously address both ferrule deficiency and biological width violation; for high-risk cases, extraction followed by implant restoration is advised due to limited root preservation value. The presented classification enables clinicians to adopt a scientific and structured approach to treatment planning for these complex crown-root fractures in the aesthetic zone.
Humans ; Tooth Fractures/therapy* ; Tooth Root/injuries* ; Risk Assessment ; Tooth Crown/injuries* ; Periodontium ; Esthetics, Dental ; Dental Restoration, Permanent/methods*