Background: Emergence of coronavirus disease 2019 (COVID‐19) is a major healthcare threat. Apparently, the novel coronavirus (SARS‐CoV‐2) is armed by special abilities to spread and dysregulate the immune mechanisms. The likelihood of oropharyngeal candidiasis (OPC) development in COVID‐19 patients with a list of attributable risk factors for oral infections has not yet been investigated.Objectives: We here aim to investigate the prevalence, causative agents and antifungal susceptibility pattern of OPC in Iranian COVID‐19 patients.Patients and Methods: A total of 53 hospitalised COVID‐19 patients with OPC were studied. Relevant clinical data were mined. Strain identification was performed by 21‐plex PCR and sequencing of the internal transcribed spacer region (ITS1‐5.8S‐ITS2). Antifungal susceptibility testing to fluconazole, itraconazole, voriconazole, amphotericin B, caspofungin, micafungin and anidulafungin was performed according to the CLSI broth dilution method.Results: In 53 COVID‐19 patients with OPC, cardiovascular diseases (52.83%) and diabetes (37.7%) were the principal underlying conditions. The most common risk factor was lymphopaenia (71%). In total, 65 Candida isolates causing OPC were recovered. C albicans (70.7%) was the most common, followed by C glabrata (10.7%), C dubliniensis (9.2%), C parapsilosis sensu stricto (4.6%), C tropicalis (3%) and Pichia kudriavzevii (=C krusei, 1.5%). Majority of the Candida isolates were susceptible to all three classes of antifungal drugs.Conclusion: Our data clarified some concerns regarding the occurrence of OPC in Iranian COVID‐19 patients. Further studies should be conducted to design an appropriate prophylaxis programme and improve management of OPC in critically ill COVID‐19 patients.

Background: The clinical course of COVID-19 may vary significantly. The presence of comorbidities prolongs the recovery time. The recovery in patients with mild-to-moderate symptoms might take 10 days, while in those with a critical illness or immunocompromised status could take 15 days. Considering the lack of data about predictors that could affect the recovery time, we conducted this study to identify them. Methods: This cross-sectional study was implemented in the COVID-19 clinic of a teaching and referral university hospital in Tehran. Patients with the highly suggestive symptoms who had computed tomography (CT) imaging results with typical findings of COVID-19 or positive results of reverse transcriptase-polymerase chain reaction (RT-PCR) were enrolled in the study. Inpatient and outpatient COVID-19 participants were followed up by regular visits or phone calls, and the recovery period was recorded. Results: A total of 478 patients were enrolled. The mean age of patients was 54.11 ± 5.65 years, and 44.2% were female. The median time to recovery was 13.5 days (IQR: 9). Although in the bivariate analysis, multiple factors, including hypertension, fever, diabetes mellitus, gender, and admission location, significantly contributed to prolonging the recovery period, in multivariate analysis, only dyspnea had a significant association with this variable (p = 0.02, the adjusted OR of 2.05; 95% CI 1.12–3.75). Conclusion: This study supports that dyspnea is a predictor of recovery time. It seems like optimal management of the comorbidities plays the most crucial role in recovery from COVID-19.

Background: Surgical Site Infections (SSIs) are among the leading causes of the postoperative complications. This study aimed at investigating the epidemiologic characteristics of orthopedic SSIs and estimating the under-reporting of registries using the capture-recapture method. Methods: This study, which was a registry-based, cross-sectional one, was conducted in six educational hospitals in Tehran during a one-year period, from March, 2017 to March, 2018. The data were collected from two hospital registries (National Nosocomial Infection Surveillance System (NNIS) and Health Information Management database (HIM)). First, all orthopedic SSIs registered in these sources were used to perform capture-recapture (N = 503). Second, 202 samples were randomly selected to assess patients` characteristics. Results: Totally, 76.24% of SSIs were detected post-discharge. Staphylococcus aureus (11.38%) was the most frequently detected bacterium in orthopedic SSIs. The median time between the detection of a SSI and the discharge was 17 days. The results of a study done on 503 SSIs showed that the coverage of NNIS and HIM was 59.95 and 65.17%, respectively. After capture-recapture estimation, it was found that about 221 of orthopedic SSIs were not detected by two sources among six hospitals and the real number of SSIs were estimated to be 623 ± 36.58 (95% CI, 552–695) and under-reporting percentage was 63.32%. Conclusion: To recognize the trends of SSIs mortality and morbidity in national level, it is significant to have access to a registry with minimum underestimated data. Therefore, according to the weak coverage of NNIS and HIM among Iranian hospitals, a plan for promoting the national Infection Prevention and Control (IPC) programs and providing updated protocols is recommended.

Surveillance of health care-associated infections (HAIs) is an essential part of an efficient healthcare system. This study is an update on incidence and mortality rates of HAIs in Iran in 2018. Almost all hospitals across the country (940 hospitals) entered the data of HAIs and denominators to the Iranian Nosocomial Infections Surveillance (INIS) software. Statistics were derived from INIS. From 9,607,213 hospitalized patients, 127,953 suffered from HAI, 15.65% of whom died. The incidence rate of HAI was calculated as 4.2 per 1000 patient-days. Considering relative frequencies among HAIs, Pneumonia (29.1%) and UTIs (25.6%) were the most common types of infection. Ventilator-associated pneumonia (VAP) was the most frequent device-associated infection (DAI) 25.66 per 1000 ventilator-days, and had the highest mortality rate (43.08%). Incidence density of other DAIs was 5.43 for catheter-associated UTI and 2.86 for catheter-associated BSI per 1000 device-days. Medical ICUs had the highest incidence and percentage of deaths (15.35% and 37.63%, respectively). The most causative organisms were Escherichia coli, Acinetobacter baumannii, and Klebsiella pneumonia. The rate of methicillin-resistance Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), and Klebsiella pneumoniae carbapenemase (KPC)-producing bacteria was about 49%, 57%, and 58% respectively. This study provided an overview of HAIs in Iran and indicated that HAIs required special attention both in detection/reporting and in infection control measures. Future studies could be done on adherence rate of DAIs' preventive bundles, interventions via multimodal strategies, evaluating the effect of training, and effect of antibiotic stewardship programs.