Abstract Background Intratympanic (IT) therapy is a common procedure to deliver drugs into the cochlear for the treatment of inner ear diseases. Round window niche veil (RWNV) is an extra anatomical barrier potentially impending drug delivery. However, RWNV’s identification is neglected, and its effect on IT efficacy awaits investigation. Methods Patients with unilateral sudden sensorineural hearing loss (SSNHL), undergoing temporal bone HRCT examination and initiating IT therapy were enrolled and followed up for 30 days. Participants were independently assessed and identified into RWNV+ and RWNV− by an imaging-based workflow. Primary outcome was response (clinically important hearing improvement ≥10 dB) to IT therapy, whilst secondary outcomes included pure-tone average threshold (PTA) and word recognition score (WRS) gains from baseline. Frequency-specific hearing threshold, WRS at varying levels, and haring levels by severity were also reported. Logistic or linear regression models were employed to investigate the IT therapy outcomes in RWNV+ versus RWNV−. Results A total of 169 patients (44.4 ± 12.6 yr; 52.1% females) were included and stratified into RWNV+ (56.2%) and RWNV− (43.8%), with good inter-reader agreement (Kappa, 0.798–0.892). Clinical response to IT administration was achieved in 34.7% of RWNV+ versus 50.0% of RWNV− cases. RWNV+ associated with inferior response probability (OR = 0.441; 95% CI, 0.216–0.902), independent of clinical prevalent variables. RWNV+ potentially synergized with established risk factors (prolonged course, vertigo) and further diminished efficacy. RWNV+ also correlated with poorer PTA0.5–4 kHz gains (β = −5.16; 95% CI, −9.86 to −0.47) and worser WRS at 70–90 dB SPL (p < 0.05). Conclusions RWNV identified by a HRCT-based workflow is independently associates with inferior IT therapy efficacy in SSNHL. Pre-treatment imaging assessment may help to predict potential benefits from IT therapy.
Jia Guo, Shipei Zhuo, Yan Huang et al.· Annals medicus· 0 citations
Purpose This study aimed to develop and validate a nomogram for predicting short-term LVEF decline in patients with DMD. Methods This was a single-center retrospective cohort study enrolling male patients diagnosed with DMD at Sun Yat-sen Memorial Hospital, Sun Yat-sen University, between 2015 and 2025. Data collected included patient age, cardiac troponin I (cTnI) levels, history of steroid therapy, baseline echocardiographic LVEF, and CMR data (including LGE and native T1 values). The primary outcome was the decline in LVEF (ΔLVEF ≤ −10%) during follow-up within 12 months. Least absolute shrinkage and selection operator (LASSO) logistic regression analysis was employed to identify independent risk factors and construct a nomogram-based predictive model. Model performance was assessed by the area under the receiver operating characteristic (ROC) curve and internally validated using bootstrap resampling (1000 repetitions). Results A total of 102 patients were included, of whom 38 (37.3%) exhibited a decline in LVEF. Multivariable analysis identified older age (OR 1.16, 95% CI 1.04–1.30, p = 0.009), abnormal cTnI (cTnI ≥ 0.04 ng/mL) (OR 7.27, 95% CI 1.46–36.28, p = 0.016), longer steroid duration (OR 1.72, 95% CI 1.09–2.71, p = 0.020, likely reflecting disease severity), the presence of LGE (OR 5.45, 95% CI 1.27–23.43, p = 0.023), and higher native T1 values (OR 1.02, 95% CI 1.01–1.04, p = 0.001) as independent risk factors. A higher baseline LVEF was protective (OR 0.81, 95% CI 0.72–0.91, p<0.001). The predictive model demonstrated excellent discrimination, with an AUC of 0.943 (95% CI 0.901–0.985). Internal validation yielded an optimism-corrected C-index of 0.922. Conclusion This study successfully established a comprehensive prediction model incorporating clinical and imaging variables, which can accurately identify DMD patients at risk for short-term LVEF decline. The model demonstrated high discriminative ability (AUC 0.943) in this retrospective, single-center cohort; however, these results are preliminary and require external validation.
Xuezhen Chen, Ruohao Wu, Fang Zhang et al.· International Journal of Gen...· 0 citations
