OBJECTIVES: To compare the short- and long-term hearing outcomes after successful inlay cartilage tympanoplasty between patients with small (< or =25%) and large (> or =50%) eardrums perforations. METHODS: This is a retrospective case series study conducted in a tertiary referral center. Twenty-five patients who underwent 27 procedures were enrolled. Their mean age was 60.26 years (range, 42 to 76 years). The mean follow-up time was 18.86 months (range, 12.30 to 35.83 months). The preoperative, initial postoperative, and long-term hearing results in patients with total repair of the eardrum were analyzed. RESULTS: In the small size group, the average (+/-standard deviation) air-bone gap (ABG) closure was 1.08+/-7.53 dB in the short-term and 2.33+/-11.56 dB in the long-term hearing examinations. There was no difference between short- and long-term ABG closure (P=0.689). In the large size group, the average ABG closure was 9.77+/-9.40 dB in the short-term and 16.25+/-6.01 dB in the long-term hearing examinations. There was a significant difference between short- and long-term ABG closure (P=0.029). CONCLUSION: Patients with large perforations have continuous hearing improvement and ABG closure for more than one year. In contrast, the short- and long-term postoperative ABGs are almost the same in patients with small perforations. More long-term postoperative follow-up of hearing results is necessary for large perforations.
Cartilage* ; Follow-Up Studies ; Hearing* ; Humans ; Inlays* ; Retrospective Studies ; Tertiary Care Centers ; Tympanic Membrane* ; Tympanoplasty*

Chronic inflammation is thought to be the leading cause of colorectal cancer, and interleukin-10 (IL10) has been identified as a potent immunomodulatory cytokine that regulates inflammatory responses in the gastrointestinal tract. Although several single nucleotide polymorphisms (SNPs) in IL10 have been associated with the risk of colorectal cancer, their prognostic significance has not been determined. Two hundred and eighty-two colorectal cancer patients were genotyped for two candidate cancer-associated SNPs in IL10. The associations of these SNPs with distant metastasis-free survival and overall survival were evaluated by Kaplan-Meier analysis and Cox regression model. The minor homozygote GG genotype of IL10 rs3021094 was significantly associated with a 3.30-fold higher risk of death compared with the TT+TG genotypes (P=0.011). The patients with IL10 rs3021094 GG genotype also had a poorer overall survival in Kaplan-Meier analysis (log-rank P=0.007) and in multivariate Cox regression model (P=0.044) adjusting for age, gender, carcinoembryonic antigen levels, tumor differentiation, stage, lymphovascular invasion, and perineural invasion. In conclusion, our results suggest that IL10 rs3021094 might be a valuable prognostic biomarker for colorectal cancer patients.
Aged ; Alleles ; Carcinoembryonic Antigen/blood ; Cell Differentiation ; Colorectal Neoplasms/*genetics/mortality/pathology ; Female ; Genotype ; Homozygote ; Humans ; Interleukin-10/*genetics ; Kaplan-Meier Estimate ; Lymphatic Metastasis ; Male ; Middle Aged ; Neoplasm Staging ; *Polymorphism, Single Nucleotide ; Regression Analysis ; Tumor Markers, Biological/genetics

Objectives: . Type 2 diabetes mellitus (T2DM) is a risk factor for deep neck infection (DNI) and leads to complications and poor outcomes. Our study aimed to investigate the risk, prognosis, and complications of peritonsillar abscess (PTA) in patients with T2DM. Methods: . We extracted data of patients newly diagnosed as having T2DM between January 2000 and December 2011 from Taiwan’s National Health Insurance Research Database. These patients were matched with patients without T2DM, and PTA incidence was compared between both cohorts. Results: . In total, 67,852 patients with and 135,704 patients without T2DM were enrolled. PTA incidence was significantly higher in patients with T2DM (incidence rate ratio, 1.91; P<0.001); moreover, PTA incidence was higher at 1 to 5 years after T2DM diagnosis than at <1 and >5 years after T2DM diagnosis. Cox regression analysis showed that patients with T2DM had an approximately 2-fold higher PTA risk (adjusted hazard ratio [aHR]: 1.89, P<0.001). Patients with a higher adapted Diabetes Complications Severity Index (aDCSI) had higher PTA risk than those with a lower aDCSI (aHRs: 2.17 for aDCSI ≥1, P=0.006 and 1.81 for aDCSI=0, P=0.002). T2DM patients with a high aDCSI (≥1) had a nonsignificantly longer hospitalization duration and a higher rate of DNI complications than did those with a low aDCSI (=0). Conclusion . In patients with T2DM, PTA incidence was relatively high, and it increased with T2DM severity. Moreover, T2DM patients should be particularly careful about PTA within 1 to 5 years after the diagnosis, and physicians should keep in mind that the prognosis of PTA was correlated with T2DM severity.

Objectives: . Type 2 diabetes mellitus (T2DM) is a risk factor for deep neck infection (DNI) and leads to complications and poor outcomes. Our study aimed to investigate the risk, prognosis, and complications of peritonsillar abscess (PTA) in patients with T2DM. Methods: . We extracted data of patients newly diagnosed as having T2DM between January 2000 and December 2011 from Taiwan’s National Health Insurance Research Database. These patients were matched with patients without T2DM, and PTA incidence was compared between both cohorts. Results: . In total, 67,852 patients with and 135,704 patients without T2DM were enrolled. PTA incidence was significantly higher in patients with T2DM (incidence rate ratio, 1.91; P<0.001); moreover, PTA incidence was higher at 1 to 5 years after T2DM diagnosis than at <1 and >5 years after T2DM diagnosis. Cox regression analysis showed that patients with T2DM had an approximately 2-fold higher PTA risk (adjusted hazard ratio [aHR]: 1.89, P<0.001). Patients with a higher adapted Diabetes Complications Severity Index (aDCSI) had higher PTA risk than those with a lower aDCSI (aHRs: 2.17 for aDCSI ≥1, P=0.006 and 1.81 for aDCSI=0, P=0.002). T2DM patients with a high aDCSI (≥1) had a nonsignificantly longer hospitalization duration and a higher rate of DNI complications than did those with a low aDCSI (=0). Conclusion . In patients with T2DM, PTA incidence was relatively high, and it increased with T2DM severity. Moreover, T2DM patients should be particularly careful about PTA within 1 to 5 years after the diagnosis, and physicians should keep in mind that the prognosis of PTA was correlated with T2DM severity.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.