Relationship Between Pemphigus Vulgaris Severity and PCR-positive Herpes Simplex Virus.

Pemphigus vulgaris is a rare autoimmune skin disease. Although herpes simplex virus has been associated with autoimmune diseases, evidence regarding its association with pemphigus vulgaris exacerbations is scarce. This retrospective cohort study aimed to characterize the epidemiological and clinical features of patients with pemphigus vulgaris who were herpes simplex-positive, compared with those who were herpes simplex-negative, during disease onset. Of 62 patients with pemphigus vulgaris who underwent PCR testing for herpes simplex virus, 25 (40.3%) were positive, with a mean age of 56.1 ± 15.5 years; 35.5% were male. The herpes-positive group had significantly elevated levels of C-reactive protein, Pemphigus Disease Activity Index score, and shorter time to relapse. The time to remission, number of exacerbations per year, and remission status were non-significantly elevated in the herpes-positive group. Thus, routine testing lesions from patients with pemphigus for herpes simplex virus should be performed. If positive, antiviral treatment should be initiated; and preventive antiviral treatment should be considered in severe cases.

A machine learning model for predicting deterioration of COVID-19 inpatients.

The COVID-19 pandemic has been spreading worldwide since December 2019, presenting an urgent threat to global health. Due to the limited understanding of disease progression and of the risk factors for the disease, it is a clinical challenge to predict which hospitalized patients will deteriorate. Moreover, several studies suggested that taking early measures for treating patients at risk of deterioration could prevent or lessen condition worsening and the need for mechanical ventilation. We developed a predictive model for early identification of patients at risk for clinical deterioration by retrospective analysis of electronic health records of COVID-19 inpatients at the two largest medical centers in Israel. Our model employs machine learning methods and uses routine clinical features such as vital signs, lab measurements, demographics, and background disease. Deterioration was defined as a high NEWS2 score adjusted to COVID-19. In the prediction of deterioration within the next 7-30 h, the model achieved an area under the ROC curve of 0.84 and an area under the precision-recall curve of 0.74. In external validation on data from a different hospital, it achieved values of 0.76 and 0.7, respectively.