The prognostic value of toxin B and binary toxin in Clostridioides difficile infection.

To study the association between detection of the Clostridioides difficile gene encoding the binary toxin (CDT) and direct detection of toxinB (TcdB) from feces with the appearance of serious disease, complications, or recurrence in a prospective series of cases. A total of 220 confirmed cases were included, using a two-step algorithm: an initial study to detect the enzyme, glutamate dehydrogenase (GDH), followed, in cases of positivity, by detection of the tcdB. tcdB-positive patients were investigated for the presence of CDT and TcdB. Outcome variables were severe disease, the modified Illinois C. difficile infection (CDI) prognostic risk index (ZAR score), the appearance of complications (need for colectomy, CDI-related death, or toxic megacolon) and recurrence. Patients who tested positive for the presence of TcdB in feces were found to have greater disease severity than those who tested negative, with a ZAR score of 35.4% vs. 23% (p = .048), a higher recurrence rate (14.6% vs. 5.9%, p = .032), and a tendency for higher number of complications (20.7% vs. 11.5%), although without reaching statistical significance (p = .053). When presence of CDT was analyzed, higher frequencies of severe disease (39.2% vs. 21.2%, p = .005), complications and recurrence (21.6% vs. 10.9%, p = .037 and 14.9% vs. 5.8%, p = .029; respectively) were observed in patients where CDT was detected. TcdB and CDT act as prognostic markers of the appearance of serious disease, complications or recurrence in cases of CDI. Simultaneous detection of both markers, TcdB and CDT, had a greater impact on the prognosis than when they were detected separately.

Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach.

The COVID-19 outbreak has spread extensively around the world. Loss of smell and taste have emerged as main predictors for COVID-19. The objective of our study is to develop a comprehensive machine learning (ML) modelling framework to assess the predictive value of smell and taste disorders, along with other symptoms, in COVID-19 infection. A multicenter case-control study was performed, in which suspected cases for COVID-19, who were tested by real-time reverse-transcription polymerase chain reaction (RT-PCR), informed about the presence and severity of their symptoms using visual analog scales (VAS). ML algorithms were applied to the collected data to predict a COVID-19 diagnosis using a 50-fold cross-validation scheme by randomly splitting the patients in training (75%) and testing datasets (25%). A total of 777 patients were included. Loss of smell and taste were found to be the symptoms with higher odds ratios of 6.21 and 2.42 for COVID-19 positivity. The ML algorithms applied reached an average accuracy of 80%, a sensitivity of 82%, and a specificity of 78% when using VAS to predict a COVID-19 diagnosis. This study concludes that smell and taste disorders are accurate predictors, with ML algorithms constituting helpful tools for COVID-19 diagnostic prediction.