Objective The dental implant is an innovative instrument that enables the edentulous patient to chew. Many factors have a bearing on the success of dental implantation. There are also many complications after dental implantation. In this meta-analysis, we investigated which factors increase the risk of postoperative sinusitis and implant failure after dental implant for the first time. Data Sources Included data were searched through the PubMed, EMBASE, and Cochrane library databases. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and 2 authors (J.S.K., S.H.K.) independently extracted data by multiple observers. Review Methods We used a random-effects model considering the variation between and within the included studies. Results Twenty-seven studies were included in our final meta-analysis. The proportion of postoperative sinusitis, perforation of the sinus membrane, and implant failure was 0.05 (95% confidence interval [CI], 0.04-0.07), 0.17 (95% CI, 0.13-0.22), and 0.05 (95% CI, 0.04-0.07), respectively, using the single proportion test. The only factors that affected postoperative sinusitis were preoperative sinusitis and intraoperative perforation of the Schneiderian membrane (P < .01 and P < .01, respectively). The only factors that affected dental implant failure were smoking and residual bone height of the maxilla (P < .05 and P < .01, respectively). Conclusions Two factors affect postoperative sinusitis after implant surgery: preoperative sinusitis and Schneiderian membrane rupture. It should also be noted that the factors affecting implant failure are residual bone height and smoking. These findings will have a significant impact on the counseling and treatment policy of patients who receive dental implants.

Epidemiologic studies suggest that COPD is associated with an increased risk of poor outcome in patients with COVID-19, although they failed to demonstrate COPD as a risk factor for acquiring COVID-19. However, most data have come from a limited global population. In this nationwide cohort study based on the Korean national health insurance claims-based database, COPD is associated with increased risk for COVID-19 and having COPD does not seem to confer substantial risk for severe COVID-19 and mortality. These findings indicate that heterogeneity in the populations across many countries may complicate the net effects of COPD on the COVID-19-related outcomes.

Rationale:Mucormycosis is a rare fungal infection which mainly develops in compromised hosts and the associated mortality rate is high.Patient concerns:We report a case of mucormycosis in a 59-year-old woman following routine endoscopic sinus surgery. The patient had a history of diabetes mellitus (DM) and bronchial asthma.Diagnoses:On follow-up 4 weeks after the first functional endoscopic sinus surgery (FESS), she complained of a severe headache and was readmitted for a second period. Endoscopic examination revealed bony erosion and a whitish discharge on the left middle turbinate, which was confirmed as mucormycosis by endoscopic biopsy.Interventions:Endoscopic debridement of the necrotic tissue and middle turbinectomy were performed and the patient was treated with intravenous amphotericin B for 3 months (3.5 mg/kg/day).Outcomes:About 1 month into the second period of hospitalization, left Bell's palsy had occurred. The facial palsy improved naturally after 2 months of hospitalization. One year after endoscopic debridement, follow-up endoscopy showed that there was no residual lesion.Conclusion:This is the first report of mucormycosis after routine endoscopic sinus surgery. We did not miss headache symptom after FESS surgery, and diagnosed mucormycosis through early endoscopic biopsy, which played an important role in curing the patient. In addition to the importance of medical therapy such as DM control for patients, emotional support and psychiatric treatment are also important factors as these patients require hospitalization for a long period, 3 months in the case of this patient.

Objectives: To investigate the factors related to the severity and mo rtality of COVID-19 using big data-machine learning techniques. Methods: This study included 8070 patients in South Korea diagnosed with COVID-19 between January and July 2020, and whose data were available from the National-Health-Insurance-Service. Results: Machine-learning algorithms were performed to evaluate the effects of comorbidities on severity and mortality of COVID-19. The most common comorbidities of COVID-19 were pulmonary inflammation followed by hypertension. The model that best predicted severity was a neural network (AUC: 85.06%). The most important variable for predicting severity in the neural network model was a history of influenza (relative importance: 0.083). The model that best predicted mortality was the logistic regression elastic net (EN) model (AUC: 93.86%). The most important variables for mortality in the EN model were age (coefficient: 2.136) and anosmia (coefficient: –1.438). Conclusions: In COVID-19 patients, influenza was found to be a major adverse factor in addition to old age and male. In addition, anosmia was found to be a major factor associated with lower severity and mortality. Therefore, in the current situation where there is no adequate COVID-19 treatment at present, examining the patient's history of influenza vaccination and anosmia in addition to age and sex will be an important indicator for predicting the severity and mortality of COVID-19 patients.