OBJECTIVE: The aim of this study is to investigate the independent risk factors of benign prostatic hyperplasia (BPH) complicated with bladder stones, and construct a nomogram prediction model for clinical progression of bladder stones in patients with BPH. METHODS: The clinical data of 368 BPH patients who underwent transurethral resection of the prostate in the Second Affiliated Hospital of Zhengzhou University from January 2018 to January 2021 were retrospectively analyzed. Patients with BPH were divided into group 1 (with bladder stones, n=94) and group 2 (without bladder stones, n=274). Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors of bladder stones in patients with BPH. A nomogram model was developed, and the areas under the ROC curve and calibration curve were calculated to assess the accuracy of clinical application. RESULTS: Logistic analysis showed that age (HR:1.075,95%CI:1.032 to 1.120), hypertension (HR:2.801,95%CI:1.520 to 5.161), blood uric acid (HR:1.006,95%CI:1.002 to 1.010), intravesical prostatic protrusion (HR:1.189,95%CI1.119 to 1.264）, prostatic urethral angel（HR:1.127,95%CI:1.078to 1.178）were independent risk factors for bladder stones in patients with BPH. The discrimination of the nomogram model based on independent risk factors to predict the occurrence of bladder stones in patients with BPH was 0.874. CONCLUSION The nomogram model can predict the risk of bladder stones in BPH patients with good differentiation and calibration, which is a good guide for clinical work on BPH patients with high risk of bladder stones.
Humans ; Male ; Prostatic Hyperplasia/complications* ; Nomograms ; Urinary Bladder Calculi/etiology* ; Retrospective Studies ; Risk Factors ; Aged ; Logistic Models ; Middle Aged ; ROC Curve ; Transurethral Resection of Prostate

Objective： The aim of this study is to explore the predictive value of biparametric magnetic resonance imaging(bpMRI)for pelvic lymph node metastasis in prostate cancer patients with PSA≤20 μg/L and establish a nomogram. Methods： The imaging data and clinical data of 363 patients undergoing radical prostatectomy and pelvic lymph node dissection in the First Affiliated Hospital of Nanjing Medical University from July 2018 to December 2023 were retrospectively analyzed. Univariate analysis and multivariate logistic regression were used to screen independent risk factors for pelvic lymph node metastasis in prostate cancer, and a nomogram of the clinical prediction model was established. Calibration curves were drawn to evaluate the accuracy of the model. Results： Multivariate logistic regression analysis showed extrocapusular extension (OR=8.08,95%CI=2.62－24.97, P<0.01), enlargement of pelvic lymph nodes (OR=4.45,95%CI=1.16－17.11,P=0.030), and biopsy ISUP grade(OR=1.97,95%CI=1.12－3.46, P=0.018)were independent risk factors for pelvic lymph node metastasis. The C-index of the prediction model was 0.834, which indicated that the model had a good prediction ability. The actual value of the model calibration curve and the prediction probability of the model fitted well, indicating that the model had a good accuracy. Further analysis of DCA curve showed that the model had good clinical application value when the risk threshold ranged from 0.05 to 0.70.Conclusion： For prostate cancer patients with PSA≤20 μg/L, bpMRI has a good predictive value for the pelvic lymph node metastasis of prostate cancer with extrocapusular extension, enlargement of pelvic lymph nodes and ISUP grade≥4.
Humans ; Male ; Prostatic Neoplasms/diagnostic imaging* ; Lymphatic Metastasis ; Retrospective Studies ; Nomograms ; Prostate-Specific Antigen/blood* ; Lymph Nodes/pathology* ; Pelvis ; Predictive Value of Tests ; Prostatectomy ; Lymph Node Excision ; Risk Factors ; Magnetic Resonance Imaging ; Logistic Models ; Middle Aged ; Aged

OBJECTIVE: To investigate the relationship between the weight-adjusted waist index (WWI) and acute, subacute pain or chronic pain among American adults. METHODS: There was a cross-sectional study. Data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES) concerning waist circumference, weight, pain status and covariates (age, gender, race, marital status, education level and income, physical activity, alcohol consumption, smoking status, and diabetes) were extracted for analysis. Multinomial Logistic regression was conducted across the three models to investigate the associations between WWI and acute, subacute and chronic pain. Model 1 did not involve any adjustments. Model 2 involved adjustments for age, gender, race, marital status, education level, and income. Model 3 was further adjusted for physical activity, alcohol consumption, smoking, and diabetes status. RESULTS: This study involved 12 694 participants with an average age of (50.6±18.7) years. Among all the participants, 9 614 people (75.74%) had no pain, 870 people (6.85%) experienced acute pain, 354 people (2.79%) suffered from subacute pain, and 1 856 people (14.62%) experienced chronic pain. The WWI of all the participants was (10.95±0.85) cm/$\sqrt{\mathrm{kg}}$, divided into four groups based on quartiles: Group Q1 (7.90-10.36) cm/$\sqrt{\mathrm{kg}}$, group Q2 (10.37-10.94) cm/$\sqrt{\mathrm{kg}}$, group Q3 (10.95-11.53) cm/$\sqrt{\mathrm{kg}}$ and group Q4 (11.54-15.20) cm/$\sqrt{\mathrm{kg}}$. With the increase of WWI, the analysis revealed a significant statistical difference in the participants' acute and chronic pain status (all P < 0.001). In Model 1, the prevalence of acute pain was lower in group Q2 and group Q4 compared with group Q1 (group Q2: OR=0.765, 95%CI: 0.615-0.953, P=0.017; group Q4: OR= 0.648, 95%CI: 0.503-0.835, P < 0.001). Compared with group Q1, the prevalence of chronic pain increased in group Q2, group Q3, and group Q4 (group Q2: OR =1.365, 95%CI: 1.149-1.622, P < 0.001; group Q3: OR=1.291, 95%CI: 1.082-1.541, P=0.005; group Q4: OR=1.874, 95%CI: 1.579-2.224, P < 0.001). In Model 2, compared with group Q1, an increase in chronic pain prevalence was still associated with an increase in WWI in other three groups (group Q2: OR=1.359, 95%CI: 1.137-1.624, P=0.001; group Q3: OR=1.260, 95%CI: 1.039-1.528, P=0.019; group Q4: OR=1.735, 95%CI: 1.413-2.132, P < 0.001). In Model 3, group Q4 had a 49.2% increased prevalence of chronic pain compared to group Q1 (OR = 1.492, 95%CI: 1.208-1.842, P < 0.001). However, in Models 2 and 3, no significant relationship was observed between acute pain and WWI (all P>0.05). And none of the three models identified a significant association between subacute pain and WWI (all P>0.05). CONCLUSION For American adults, there was no significant correlation between WWI and acute pain or subacute pain. However, as WWI increases, so does the prevalence of chronic pain. Further validation of this conclusion through large-scale prospective studies is warranted.
Humans ; Cross-Sectional Studies ; Middle Aged ; Male ; Female ; Adult ; Waist Circumference ; Chronic Pain/epidemiology* ; Aged ; Nutrition Surveys ; Pain/epidemiology* ; Body Weight ; Logistic Models ; Body Mass Index

OBJECTIVES: Cardiovascular disease (CVD) poses a major threat to global health. Evaluating atherosclerosis in asymptomatic individuals can help identify those at high risk of CVD. This study aims to establish an individualized nomogram prediction model to estimate the risk of vascular plaque formation in asymptomatic individuals. METHODS: A total of 5 655 participants who underwent CVD screening at the Health Management Center of The Third Xiangya Hospital, Central South University, between January 2022 and June 2024 we retrospectively enrolled. Using simple random sampling, participants were divided into a training set (n=4 524) and a validation set (n=1 131) in an 8꞉2 ratio. Demographic and clinical data were collected and compared between groups. Multivariate logistic regression analysis was used to identify independent factors associated with vascular plaques and to construct a nomogram prediction model. The predictive performance and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curves, the Hosmer-Lemeshow goodness-of-fit test, calibration plots, and decision curve analysis (DCA). RESULTS: The mean age of participants was 52 years old. There were 3 400 males (60.12%). The overall detection rate of vascular plaque in the screening population was 49.87% (2 820/5 655). No statistically significant differences were observed in clinical indicators between the training and validation sets (all P>0.05). Multivariate Logistic regression analysis identified age, systolic blood pressure, high-density lipoprotein (HDL), low-density lipoprotein (LDL), lipoprotein(a), male sex, smoking history, hypertension history, and diabetes history as independent risk factors for vascular plaque in asymptomatic individuals (all P<0.05). The area under the curve (AUC) of the nomogram model for predicting vascular plaque risk were 0.778 (95% CI 0.765 to 0.791, P<0.001) in the training set and 0.760 (95% CI 0.732 to 0.787, P<0.001) in the validation set. The Hosmer-Lemeshow goodness-of-fit test indicated good model calibration (training set: P=0.628; validation set: P=0.561). The calibration curve plotted using the Bootstrap method demonstrated good agreement between predicted probabilities and actual probabilities. DCA showed that the nomogram provided a clinical net benefit for predicting vascular plaque risk when the threshold probability ranged from 0.02 to 0.99. CONCLUSIONS The nomogram prediction model for vascular plaque risk, constructed using readily available and cost-effective physical examination indicators, exhibited good predictive performance. This model can assist in the early identification and intervention of asymptomatic individuals at high risk for cardiovascular disease.
Humans ; Male ; Middle Aged ; Female ; Nomograms ; Retrospective Studies ; Risk Factors ; Plaque, Atherosclerotic/diagnosis* ; Aged ; Adult ; Physical Examination ; Logistic Models ; Cardiovascular Diseases/epidemiology* ; ROC Curve

Objective:Inflammation has been confirmed to play an important role in the occurrence and development of sudden sensorineural hearing loss（SSNHL）, and the neutrophil-to-lymphocyte ratio（NLR） is a biomarker positively correlated with the degree of inflammation. This study aims to identify the difference in serum NLR between patients with SSNHL and normal population, and to evaluate the predictive efficacy of NLR for the occurrence and prognosis of SSNHL, thereby guiding the clinical diagnosis and treatment of SSNHL. Methods:In this study, 96 patients diagnosed with SSNHL admitted to our department from January 2023 to March 2024 and 96 patients diagnosed with vocal cord polyps admitted to our department during the same period were recruited as a control group. Multivariate Logistic regression was used to evaluate independent related factors, and a nomogram was constructed to predict the probability of SSNHL. The receiver operating characteristic（ROC） curve and calibration curve were used to evaluate the accuracy of prediction. Results:Multivariate logistic regression analysis showed that a high level NLR（OR2.215; 95%CI1.597-3.073; P<0.001） were independently associated with the presence of SSNHL. High age（OR1.036; 95%CI1.009-1.067; P=0.012）, high FIB（OR2.35; 95%CI1.176-4.960; P=0.019） were the risk factor for SSNHL. Incorporating these 3 factors, a forest plot and a nomogram were generated. The ROC curve, nomogram and calibration curve showed that the model had good clinical practicability. A low NLR（OR0.598; 95%CI0.439-0.816; P<0.001） was significantly associated with a favorable prognosis of SSNHL. Conclusion:Elevated NLR can serve as an promising biomarker for assessing the risk of SSNHL. The nomograms calculation model may be utilized as a tool to estimate the probability of SSNHL. Low level NLR is significantly associated with a good prognosis of SSNHL.
Humans ; Neutrophils ; Female ; Male ; Lymphocytes ; Hearing Loss, Sensorineural/blood* ; Hearing Loss, Sudden/diagnosis* ; Middle Aged ; Prognosis ; Nomograms ; ROC Curve ; Adult ; Logistic Models ; Biomarkers/blood* ; Lymphocyte Count ; Inflammation/blood* ; Clinical Relevance

Objective:To summarize and analyze the clinical characteristics of patients with sudden sensorineural hearing loss（SSHL） accompanying diabetes mellitus, to explore whether diabetes affects the treatment outcomes during hospitalization, and to identify the factors influencing the efficacy of SSHL patients with diabetes. Methods:A retrospective analysis was conducted on clinical data from 939 patients with SSHL. The baseline characteristics, and onset conditions of the diabetes group（79 cases） and the non-diabetes group（860 cases） were compared. Propensity score matching（PSM） was applied in a 1︰ 2 ratio to match initial hearing levels with baseline characteristics such as age, sex, and BMI, resulting in 73 diabetes cases and 144 non-diabetes cases for treatment efficacy comparison. For the analysis of prognostic factors, a logistic regression model was established based on the treatment outcomes of 217 patients with SSHL. Results:The proportion of SSHL patients accompanying diabetes was 8.40%（79/939）. Compared to non-diabetic patients, those with diabetes were older（median age of 53 years in the diabetes group and 39 years in the non-diabetes group） and had a higher proportion of hypertension（43.04% vs 12.67%）, with significant difference observed（P<0.05）. After PSM, the treatment efficacy during hospitalization was better in the diabetes group than in the non-diabetes group（58.90% vs 47.92%）, although the difference was not statistically significant（P>0.05）. The prognosis of patients with SSNHL accompanied by diabetes was analyzed using a multivariate logistic regression model that included age, HDL-C, and INR as variables; however, no statistically significant differences were found（P>0.05）. Conclusion:Patients with SSHL accompanying diabetes are generally older with a higher incidence of hypertension. The presence of diabetes does not affect the treatment outcomes during hospitalization.
Humans ; Propensity Score ; Retrospective Studies ; Hearing Loss, Sensorineural/therapy* ; Hearing Loss, Sudden/therapy* ; Middle Aged ; Diabetes Mellitus ; Male ; Female ; Prognosis ; Adult ; Logistic Models ; Diabetes Complications ; Aged ; Treatment Outcome

Objective:To explore the factors influencing adherence to voice therapy among patients with voice disorders in China. Methods:Patients with voice disorders who visited the Voice Therapy Center at Sun Yat-sen Memorial Hospital, Sun Yat-sen University, from February to May 2022 were enrolled in the study. Adherence was assessed using the URICA-Voice scale, while influencing factors were assessed through the Voice Handicap Index（VHI） scale and a general information questionnaire. Correlation analysis was conducted using univariate and multivariate logistic regression analysis. Results:A total of 247 patients were included in the study, comprising 57 males（23.08%） and 190 females（76.92%）. The results revealed that: ①Female patients demonstrated higher likelihood of being in the contemplation stage（OR=0.22） compared to males. ②Patients with a monthly family income per capita>6 000 yuan were more likely to be in the contemplation stage than those with<3 000 yuan with an OR = 13.94. ③High vocal-demand occupations increased contemplation stage probability（OR=7.70） compared to moderate-demand occupations. ④Residence within 30-minute commute predicted action/maintenance stages（OR=7.14） versus≥60-minute commute. ⑤Patients whose occupations had high voice demands were more likely to be in the action and maintenance stages than those with average voice demands, with an OR of 16.20. Conclusion:Gender, monthly family income per capita, occupational voice demands, and distance to the hospital significantly impact the URICA-Voice compliance stages of patients. Patients who are female, have higher family income, have occupations with high voice demands, and live closer to the hospital exhibit higher compliance with voice training.
Humans ; Male ; Female ; Voice Disorders/therapy* ; Patient Compliance ; Voice Training ; Surveys and Questionnaires ; China ; Middle Aged ; Adult ; Voice Quality ; Logistic Models ; Aged

Objective:To explore the influencing factors related to olfactory dysfunction secondary to different types of chronic rhinosinusitis（CRS）. Methods:A retrospective analysis was conducted on 185 CRS patients treated at the Department of Otolaryngology-Head and Neck Surgery of Shanxi Provincial People's Hospital from July 2023 to July 2024. Based on the presence or absence of nasal polyps, CRS was divided into two groups: chronic rhinosinusitis with nasal polyps（CRSwNP） and chronic rhinosinusitis without nasal polyps（CRSsNP）. Further, based on whether olfactory dysfunction was present, the CRSwNP and CRSsNP groups were divided into subgroups with olfactory dysfunction and normal olfaction. General data, laboratory tests, and modified sinus CT scores were compared between the subgroups. Logistic regression analysis was conducted to identify independent influencing factors based on the results of univariate analysis combined with clinical significance, and two nomogram models were established. The area under the curve of the receiver operating characteristic（ROC） curve, calibration curves, and decision curve analysis were used to assess the diagnostic performance, calibration, and clinical utility of the predictive model. Results:The proportion of blood eosinophils, blood urea nitrogen, and total modified CT scores of the bilateral olfactory region were identified as independent influencing factors in the CRSwNP group; the proportion of blood monocytes and modified CT scores of the bilateral posterior region were independent influencing factors in the CRSsNP group. The nomogram prediction model showed good diagnostic performance, calibration, and clinical utility in both the CRSwNP and CRSsNP groups. Conclusion:Olfactory dysfunction in CRSwNP patients is closely related to the proportion of blood eosinophils, blood urea nitrogen, and total modified CT scores of the bilateral olfactory region, while olfactory dysfunction in CRSsNP patients is closely related to the proportion of blood monocytes and modified CT scores of the bilateral posterior region. Moreover, the predictive model established in this study demonstrates good clinical performance and can be used for early identification and risk prediction of olfactory dysfunction secondary to CRS.
Humans ; Sinusitis/complications* ; Chronic Disease ; Retrospective Studies ; Olfaction Disorders/etiology* ; Nasal Polyps/complications* ; Rhinitis/complications* ; Female ; Male ; Logistic Models ; Middle Aged ; Smell ; Adult ; ROC Curve ; Nomograms ; Eosinophils ; Tomography, X-Ray Computed

Objective:To investigate the sleep characteristics and clinical features of patients with vestibular migraine（VM）, and to explore the influencing factors of sleep disorder in VM patients. Methods:A cross-sectional study method was adopted to collect VM patients from Otolaryngology department and neurology department of our hospital from June 2022 to June 2024（divided into sleep disorder group and non-sleep disorder group according to whether there is sleep disorder） as the experimental group, and recruit non-VM volunteers with clinical characteristics matching with the experimental group during the same period as the control group. The clinical data of the subjects were collected, and the sleep quality of the subjects was assessed using the Pittsburgh Sleep Quality Index（PSQI）. The influencing factors of sleep disorders in VM patients were analyzed by multivariate Logistic regression, and the correlation between sleep disorders and clinical features such as headache, vertigo and hearing in VM patients was analyzed by Spearman correlation coefficient. Results:A total of 530 individuals with VM were analyzed, including 332 with sleep disturbances（62.64%）, 198 without sleep issues（37.36%）, and 50 in the control group. The overall PSQI score and all its components were significantly higher in the VM group compared with the control group（P<0.05）. A positive correlation was observed between PSQI and VAS, DHI-T, DHI-E, DHI-F and DHI-P（r=0.797, P<0.05; r=0.834, P<0.05; r=0.794, P<0.05; r=0.771, P<0.05; r=0.877, P<0.05）, PSQI had no correlation with pure tone hearing（r=0.324, P=0.167）. Multivariate logistic regression analysis showed that female, age ≥60 years, living alone, duration of disease ≥3 months, motion sickness history, and HADS-A were independent influencing factors for comorbidification of sleep disorder in VM patients（P<0.05）. Conclusion:The prevalence of sleep disorders in patients with vestibular migraine（VM） was significantly higher compared to the control group. Moreover, the severity of sleep disorders was positively correlated with the intensity of headache and vertigo in VM patients. It is recommended that female VM patients aged 60 years or older, living alone, with a disease duration of three months or longer, a history of motion sickness, and anxiety symptoms undergo sleep assessments to determine the presence of sleep disorders. This approach provides a theoretical foundation for precise treatment and prevention strategies for VM.
Humans ; Migraine Disorders/complications* ; Sleep Wake Disorders/complications* ; Cross-Sectional Studies ; Vertigo ; Female ; Male ; Vestibular Diseases/complications* ; Sleep Quality ; Adult ; Middle Aged ; Logistic Models

Objective:To explore the influencing factors of olfactory impairment in patients with obstructive sleep apnea（OSA） and establish a nomogram prediction model. Methods:A total of 100 OSA patients were enrolled. Snap&Sniff olfactory test was used to evaluate the olfactory identification function and olfactory threshold of the patients. According to the scoring criteria, either olfactory identification scores below 14 points or olfactory threshold scores below 3 points was defined as olfactory impairment. Multivariate logistic regression analysis was used to explore the influencing factors of olfactory impairment in OSA. The nomogram model was constructed by using the R 4.4.2 software package. ROC curve, calibration curve and decision curve were used to evaluate the predictive efficacy, consistency and clinical utility of the model. Results:A total of 55 of 100 OSA patients had olfactory impairment. The results of multivariate logistic regression analysis showed that age, ESS score, MoCA score, and apnea-hypopnea index（AHI） were the influencing factors of olfactory impairment in OSA. Based on the above parameters, a nomogram model was established. The ROC curve analysis showed that the AUC was 0.897（95%CI 0.834-0.961）, indicating that the model had good predictive ability. The calibration curve showed that the predicted probability of the model fits the actual probability well. Decision curve analysis showed that when the threshold probability was in the range of 0-0.9, the model had a high clinical net benefit rate. Conclusion:Age, ESS score, MoCA score and AHI are the influencing factors of olfactory impairment in patients with OSA. The nomogram model constructed based on the above factors has good predictive value, which is conducive to the clinical multi-angle understanding of OSA and the formulation of scientific prevention and treatment measures.
Humans ; Sleep Apnea, Obstructive/physiopathology* ; Nomograms ; Olfaction Disorders/etiology* ; Logistic Models ; Middle Aged ; Male ; Female ; ROC Curve ; Adult ; Aged