Evaluation of 28 cases of mucormycosis.

Mucormycosis is a rare but invasive fungal disease with high mortality. The present study aimed to retrospectively investigate the demographic characteristics, as well as the clinical, radiological and laboratory features and the results of treatment, in the patients followed in our hospital because of mucormycosis. The present study retrospectively evaluated 28 cases, which were followed in our hospital because of mucormycosis between January 2002 and July 2013. The clinical form was rhinocerebral in 27 cases (rhinoorbital in 12, nasal in 8 and rhinoorbitocerebral in 7) and disseminated in one case. With regard to predisposing factors, diabetes mellitus (n = 20), haematological malignancy (n = 6) and chronic renal insufficiency (n = 5) were the leading concomitant diseases. Seventeen (61%) of 28 cases showed atypical clinical picture. With regard to the therapeutic outcomes; it was found that 14 (50%) cases died and six cases recovered with sequel. Today, when particularly the prevalence of immunosuppressive diseases and conditions are gradually increasing, the incidence of mucormycosis is also increased. Considering that the majority of our cases had atypical clinical involvement and complications, being familiar with the characteristics of this disease could be life-saving together with early diagnosis and treatment.

Clinical Features of SARS-CoV-2 Infection in Patients Undergoing Solid-Organ Transplant: Baskent University Experience.

OBJECTIVES: The clinical features and treatment approaches, outcomes, and mortality predictors of COVID-19 in solid-organ transplant recipients have not been well defined. This study investigated the clinical features of COVID-19 infection in solid-organ transplant recipients at our center in Turkey. MATERIALS AND METHODS: Our study included 23 solidorgan transplant recipients and 336 nontransplant individuals (143 previously healthy and 193 patients with at least 1 comorbidity) who were hospitalized due to COVID-19 disease in our hospital between March 2020 and January 2021. Demographic, clinical, and laboratory data of patients were compared. We used SPSS version 20.0 for statistical analysis. All groups were compared using chi-square and Mann-Whitney U tests. P <.05 was considered statistically significant. RESULTS: Mean age of solid-organ transplant recipients was 49.8 ± 13.7 years (78.3% men, 21.7% women). Among the 23 recipients, 17 (73.9%) were kidney and 6 (26.1%) were liver transplant recipients. Among nontransplant individuals, 88.7% (n = 298) had mild/moderate disease and 11.3% (n = 38) had severe disease. Among transplant recipients, 78.3% (n = 18) had mild/moderate disease and 21.7% (n = 5) had severe disease (P = .224). Transplant recipients had greater requirements for nasal oxygen (P = .005) and noninvasive mechanical ventilation (P = .003) and had longer length of intensive care unit stay (P = .030) than nontransplant individuals. No difference was found between the 2 groups in terms of mortality (P = .439). However, a subgroup analysis showed increased mortality in transplant recipients versus previously healthy patients with COVID-19 (P <.05). Secondary infections were major causes of mortality in transplant recipients. CONCLUSIONS: COVID-19 infection resulted in higher mortality in solid-organ transplant recipients versus that shown in healthy patients. More attention on secondary infections is needed in transplant recipients to reduce mortality.

Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests.

OBJECTIVES: The present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results. METHODS: We developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription-polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and external validation data set from Brazil. RESULTS: The accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study's data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%). CONCLUSIONS: ML models presented in this study can be used as clinical decision support tools to contribute to physicians' clinical judgment for COVID-19 diagnoses.

Risk Factors for Urinary Tract Infection After Kidney Transplant: A Retrospective Analysis.

OBJECTIVES: Urinary tract infections are the most common type of infections in kidney transplant recipients. They are also important factors for increased morbidity and mortality. The aims of this study were to evaluate the number of urinary tract infections, to identify possible donor/receiver-based risk factors, and to evaluate the impact of these infections on graft function. MATERIALS AND METHODS: Medical records of patients who had undergone kidney transplant between 2010 and 2017 were retrospectively analyzed. RESULTS: Our study included 145 patients (49 women [33.8%] and 96 men [66.2%]), with mean age of 35.2 ± 12.4 years. There were 105 episodes of urinary tract infections in 55 of 145 patients (37.9%) during the first year after transplant. Female sex (P = .001), glomerulonephritis as primary kidney disease (P = .04), pretransplant diabetes (P = .05), and presence of ureteral stent (P = .03) were significant risk factors for the development of urinary tract infections. The most frequent pathogens identified were Escherichiacoli and Klebsiella pneumoniae. Mean glomerular filtration rate t 12 months was significantly lower in patients with urinary tract infection than in patients without infection (80 ± 25 vs 68 ± 28 mL/min; P = .006). CONCLUSIONS: In kidney transplant recipients, urinary tract infections are common complications and have negative outcomes on graft function. These infections remain an important disease that requires frequent investigations and new ways of approach for prevention.