Objective: To explore the risk factors for recurrent benign paroxysmal positional vertigo (BPPV) in postmenopausal women within 1 year of canalith repositioning procedure (CRP), and develops a risk model based on serum 25-hydroxyvitamin D (25(OH)D), estradiol, and calcium levels to provide early identification of high-risk groups and guide prevention and treatment strategies.Methods: Data from postmenopausal women with BPPV, diagnosed and successfully treated with CRP at five hospitals in Sichuan Province between January 2019 and January 2024, were retrospectively analyzed. Participants were divided into BPPV validation and training sets in a 3:7 ratio. Clinical data were categorized into recurrence and non-recurrence subgroups based on whether BPPV recurred after treatment. LASSO regression identified factors influencing recurrence within 1 year after CRP, and multivariate logistic regression (MLR) analysis was used to develop a risk nomogram prediction model (NPM).Results: A total of 490 patients were enrolled, with 147 in the validation set and 343 in the training sets. Among them, 151 patients (30.82%) experienced recurrenced, including 58 (30.61%) in the validation set and 106 (30.90%) in the training sets. LASSO and MLR analyses identified migraine (OR = 2.208, 95% CI = 1.278–3.817), serum calcium (OR = 0.601, 95% CI = 0.447–0.81), 25(OH)D (OR = 0.785, 95% CI = 0.713–0.864), and serum estradiol (OR = 0.820, 95% CI = 0.752–0.894) as significant factors influencing recurrence within 1 year after CRP treatment in postmenopausal women with BPPV.Conclusion: The recurrence rate of BPPV within 1 year after CRP treatment in postmenopausal women is high. Migraine, 25(OH)D, calcium, and estradiol are associated with recurrence. The risk prediction model, developed using these factors, demonstrates good discrimination and calibration. It effectively predicts the recurrence risk within 1 year after successful CRP treatment, offering practical clinical value.

Objective: To explore factors influencing the recurrence of benign paroxysmal positional vertigo (BPPV) within 1 year after treatment with canalith repositioning procedure (CRP) and to construct a risk prediction model with a nomogram. This model aims to offer a scientific basis for of high-risk groups’ early identification and to support the development of prevention strategies.Methods: Data of BPPV patients diagnosed and successfully treated with CRP of 5 hospitals in Sichuan Province from Jan 2020 to Mar 2024 were retrospectively analyzed. The patients were divided into a training set of BPPV and a set of validation with a 7:3 ratio. After organizing the clinical data, the training set’s patients were arranged into recurrent and non-recurrent subgroups based on the recurrence of BPPV within one-year post-treatment. Factors affecting BPPV recurrence were found using LASSO regression; then, multivariate logistic regression (MLR) analysis was used to build a nomogram-style risk prediction model. The “receiver operating characteristic” (ROC) curve, Hosmer-Lemeshow calibration curve, clinical “decision curve analysis” (DCA), and clinical impact curve were employed to assess the model’s discrimination, accuracy, and clinical applicability.Results: 600 patients in all were involved; 180 were in the validation set and 420 in the training set, following a 7:3 split. The overall recurrence rate of BPPV within 1 year was 39.17% (235/600 cases). The recurrence rates in the validation and training sets were 39.44% (71/180 cases) and 39.05% (164/420 cases), respectively. LASSO regression and logistic regression analyses identified hypertension, hyperglycemia, migraine, HADS-A, and 25(OH)D as significant factors for recurrence. On the basis of these factors, a nomogram prediction model for recurrence was established. The validation and training sets had an area under the ROC curve of 0.723 (95%CI: 0.645–0.801) and 0.728 (95%CI: 0.679–0.777), respectively. The “Hosmer-Lemeshow goodness-of-fit (HLGOF) test” indicated satisfactory calibration (training set: chi-square = 8.708, p = 0.368; validation set: chi-square = 13.303, p = 0.102). The analysis exhibited an excellent consistency between the model’s predicted probabilities and actual outcomes. DCA and clinical impact curve analyses indicated a positive net clinical benefit at various threshold probability levels, affirming the clinical value of the nomogram model in forecasting BPPV recurrence.Conclusion: BPPV patients treated with CRP exhibit a high recurrence rate within one-year post-treatment. Hypertension, hyperglycemia, migraine, HADS-A, and 25(OH)D levels were related with increased recurrence risk. The risk prediction model, presented as a nomogram, demonstrated good discrimination and calibration, effectively predicting the BPPV’s recurrence risk within 1 year and offering significant clinical utility.

Objective: To develop and validate a clinical prediction model for olfactory dysfunction in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS), evaluating the combined predictive value of polysomnography (PSG) parameters and clinical symptoms.Methods: We retrospectively analyzed 546 OSAHS patients, including 420 from the Affiliated Hospital of North Sichuan Medical College were [randomly divided into training (n = 294) and internal validation (n = 126) sets], and 126 from the Sixth People's Hospital of Chengdu (external validation set). All patients underwent overnight PSG for sleep parameter assessment and Sniffin' Sticks tests for olfactory evaluation. Predictors were selected using LASSO regression with subsequent logistic regression modeling, followed by nomogram construction. Model performance was assessed through ROC analysis, calibration curves and DCA curves.Results: Among 546 enrolled patients, with OSAHS were included in this study. The overall olfactory dysfunction incidence was 38.64% (211/546). Multivariable analysis identified seven independent predictors: gender, age, AHI, N3%, REM%, TS90%, and MoCA. The predictive efficacy AUC of the training set model was 0.832 (95% CI: 0.784–0.880); good calibration (slope = 0.89, Hosmer–Lemeshow P = 0.41); and clinical utility across threshold probabilities of 0.06–0.97.Conclusion: Our prediction model constructed based on gender, age, AHI, N3%, REM%, TS90%, and MoCA can effectively identify OSAHS patients at high risk for olfactory dysfunction. With robust discrimination and calibration, this tool provides a clinically useful, non-invasive method for early risk stratification and intervention planning.

This systematic review evaluates the efficacy and benefit of cartilage conduction hearing aids (CC-HAs) and that factors that influence purchasing decisions. The hearing thresholds and functional gain following CC-HA wear were synthesised. A one-way analysis of variance compared the differences in the hearing thresholds and functional gain at individual frequencies and in patients with a variety of pathological changes. The synchronised aided hearing threshold and functional gain at 2.0 kHz were significantly better than at 0.5, 1.0, and 4.0 kHz. There was no significant difference in the synchronised unaided hearing thresholds across individual frequencies between 0.5 and 4.0 kHz. The synchronised functional gain in patients with atresia was significantly greater than in patients with aural atresia or stenosis and middle ear pathologies with normal ear canals. The acceptability of CC-HAs in terms of purchase decision is influenced by the condition of the external auditory meatus and severity of hearing loss, with the highest purchase rate seen in patients with aural atresia or stenosis. CC-HAs’ fitting procedure advantages and cosmetic considerations make these devices a viable and preferred choice for individuals with microtia and aural atresia. Additional research to evaluate the benefits towards emotional well-being is crucial to gain insights into the psychological impact of CC-HA use.

Objective: This study aims to identify factors associated with diabetic macular edema (DME) presence in working-age (18–60 years) patients with type 2 diabetes mellitus (T2DM) by developing a model that integrates HbA1c, renal function (eGFR), and hemodynamic parameters (SBP). The model addresses critical gaps in current screening strategies by using routinely available biomarkers, thereby enabling non-ophthalmologists to efficiently identify high-risk individuals. Methods: This cross-sectional study prospectively collected data from 490 patients with type 2 diabetes mellitus (T2DM), aged 18–60 years, who were consecutively enrolled at a single medical center between January 2020 and March 2025. The participants were randomly allocated into two groups: a training cohort (n=343) and a validation cohort (n=147). Predictors were selected via LASSO regression with 10-fold cross-validation from an initial set of 19 variables, encompassing renal function, metabolic parameters, and hemodynamic indices. Subsequently, a multivariate logistic regression model was developed and illustrated through a nomogram. The model’s predictive accuracy was evaluated through receiver operating characteristic (ROC) curves (AUC), calibration curves, and decision curve analysis (DCA). Results: The overall prevalence of DME in the study cohort was 15.71% (77 of 490). Four predictors independently associated with DME were identified using LASSO regression, namely diabetes duration (OR = 1.460, 95% CI: 1.212–1.457), SBP (OR = 1.066, 95% CI: 1.037–1.095), eGFR (OR = 0.938, 95% CI: 0.916–0.961), and HbA1c (OR = 1.484, 95%CI: 1.189–1.852). The resulting nomogram exhibited robust discriminatory ability (training AUC = 0.905, 95% CI: 0.858–0.951; validation AUC = 0.884, 95% CI: 0.820–0.949) and strong calibration performance (Hosmer-Lemeshow test, P = 0.878). DCA further confirmed substantial clinical applicability within a threshold probability range from 2% to 100%, achieving a maximum net benefit of 0.14, thereby potentially preventing unnecessary intervention in 14 out of every 100 patients. Conclusion: This nomogram effectively integrates HbA1c, renal function, and hemodynamic parameters to identify key factors associated with diabetic macular edema (DME) risk in working-age patients with type 2 diabetes mellitus (T2DM), demonstrating high accuracy. By utilizing routine clinical measures, it facilitates implementation in primary care settings, offering the potential to reduce vision loss through timely referrals. Future multicenter studies are warranted to verify its generalizability and explore its integration with emerging biomarkers.