We describe the case of a 57-year-old man with poorly controlled type 2 diabetes mellitus who presented with 30 days of left-sided abdominal pain. He was found to have a left adrenal abscess and underwent adrenalectomy. Intraoperative cultures grew Nocardia beijingensis, which is an uncommonly identified Nocardia species rarely affecting immunocompetent patients. We review the published literature on cases of N. beijingensis among immunocompetent patients. This is the first report summarizing the diagnosis and management of N. beijingensis isolated from an adrenal abscess.

Background: Clinicians and travelers often have limited tools to differentiate bacterial from non-bacterial causes of travelers’ diarrhea (TD). Development of a clinical prediction rule assessing the etiology of TD may help identify episodes of bacterial diarrhea and limit inappropriate antibiotic use. We aimed to identify predictors of bacterial diarrhea among clinical, demographic, and weather variables, as well as to develop and cross-validate a parsimonious predictive model. Methods: We collected de-identified clinical data from 457 international travelers with acute diarrhea presenting to two healthcare centers in Nepal and Thailand. We used conventional microbiologic and multiplex molecular methods to identify diarrheal etiology from stool samples. We used random forest and logistic regression to determine predictors of bacterial diarrhea. Results: We identified 195 cases of bacterial etiology, 63 viral, 125 mixed pathogens, 6 protozoal/parasite, and 68 cases without a detected pathogen. Random forest regression indicated that the strongest predictors of bacterial over viral or non-detected etiologies were average location-specific environmental temperature and RBC on stool microscopy. In 5-fold cross-validation, the parsimonious model with the highest discriminative performance had an AUC of 0.73 using 3 variables with calibration intercept −0.01 (SD 0.31) and slope 0.95 (SD 0.36). Conclusions: We identified environmental temperature, a location-specific parameter, as an important predictor of bacterial TD, among traditional patient-specific parameters predictive of etiology. Future work includes further validation and the development of a clinical decision-support tool to inform appropriate use of antibiotics in TD.

We describe a case of prolonged SARS CoV-2 infection in a patient receiving ocrelizumab for multiple sclerosis. Viral RNA shedding, signs, and symptoms persisted for 69 days with resolution after administration of convalescent plasma and antiviral therapy. This case suggests risk for persistent SARS CoV-2 infection in patients treated with anti-CD-20 monoclonal antibodies and supports a role for humoral immunity in disease resolution.

Background Including Clostridioides difficile (CD) in gastrointestinal multiplex molecular panels (GIPCR) presents a diagnostic challenge. Incidental detection by PCR without consideration of pre-test probability may inadvertently delay diagnoses of other treatable causes of diarrhea and lead to prescription of unnecessary antibiotics. Methods We conducted a retrospective study to determine the frequency at which clinicians characterize pre-test probability (PTP) and disease severity in adult patients who test positive for CD by GIPCR. We organized subjects into cohorts based on the status of their CD PCR, glutamate dehydrogenase enzyme immunoassay (GDH), and toxin A/B detection, as well as by high, moderate, or low CD PTP. We used multivariable regression models to describe predictors of toxin positivity. Results We identified 483 patients with positive CD PCR targets. Only 22% were positive for both GDH and CD toxin. Among patients with a low PTP for CDI, 11% demonstrated a positive CD toxin result compared to 63% of patients with a high PTP. A low clinician pre-test probability for C. difficile infection (CDI) correlated with a negative CD toxin result compared to cases of moderate-to-high PTP for CDI (OR 0.19, CI 0.10-0.36). Up to 64% of patients with negative GDH and CD toxin received CD treatment. Only receipt of prior antibiotics, fever, and a moderate-to-high clinician PTP were statistically significant predictors of toxin positivity. Conclusions Patients with a positive CD PCR were likely to receive treatment regardless of PTP or CD toxin results. We recommend CD positivity on GIPCR be interpreted with caution, particularly in the setting of a low pre-test probability.

BackgroundClinicians and travelers often have limited tools to differentiate bacterial from non-bacterial causes of travelers’ diarrhea (TD). Development of a clinical prediction rule assessing the etiology of TD may help identify episodes of bacterial diarrhea and limit inappropriate antibiotic use. We aimed to identify predictors of bacterial diarrhea among clinical, demographic, and weather variables, as well as to develop and cross-validate a parsimonious predictive model.MethodsWe collected de-identified clinical data from 457 international travelers with acute diarrhea presenting to two healthcare centers in Nepal and Thailand. We used conventional microbiologic and multiplex molecular methods to identify diarrheal etiology from stool samples. We used random forest and logistic regression to determine predictors of bacterial diarrhea.ResultsWe identified 195 cases of bacterial etiology, 63 viral, 125 mixed pathogens, 6 protozoal/parasite, and 68 cases without a detected pathogen. Random forest regression indicated that the strongest predictors of bacterial over viral or non-detected etiologies were average location-specific environmental temperature and RBC on stool microscopy. In 5-fold cross-validation, the parsimonious model with the highest discriminative performance had an AUC of 0.73 using 3 variables with calibration intercept -0.01 (SD 0.31) and slope 0.95 (SD 0.36).ConclusionsWe identified environmental temperature, a location-specific parameter, as an important predictor of bacterial TD, among traditional patient-specific parameters predictive of etiology. Future work includes further validation and the development of a clinical decision-support tool to inform appropriate use of antibiotics in TD.

Despite knowledge on the causes and prevention strategies for travelers’ diarrhea (TD), it continues to be one of the most common illnesses experienced by U.S. international travelers. However, studies of risk factors associated with TD among U.S. travelers are limited. In this study, we aimed to determine the incidence rate of TD, the proportion of travelers who experience TD, and to identify risk factors associated with TD. In this cross-sectional study, we collected and analyzed data from anonymous posttravel questionnaires submitted by international travelers recruited during their pretravel visit at two travel clinics in Salt Lake City, Utah, from October 2016 to March 2020. Of 571 travelers who completed posttravel surveys, 484 (85%) answered the TD question, of which 111 (23%) reported TD, for an incidence rate of 1.1 episodes per 100 travel-days (95% confidence interval [CI]: 0.9–1.4). In a multivariable model, visiting Southeast Asian (odds ratio [OR]: 2.60; 95% CI: 1.45–4.72) and African (OR: 2.06; 95% CI: 1.09–3.93]) WHO regions, having 10 or more individuals in the group (OR: 3.91; 95% CI: 1.50–11.32]), longer trip duration (OR: 1.01; 95% CI: 1.00–1.02), visiting both urban and rural destinations (OR: 1.94; 95% CI: 1.01–3.90), and taking medications/supplements to prevent TD (OR: 2.74; 95% CI: 1.69–4.47) were statistically significantly associated with increased odds of reporting TD. TD continues to be common in international travelers from the United States. Our findings provide insights regarding travelers’ behaviors regarding TD in international travelers from high-income countries and shows the need for additional research into prevention strategies for travelers’ diarrhea.

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Background Extended spectrum beta-lactamase producing Enterobacterales (ESBL-PE) present a risk to public health by limiting the efficacy of multiple classes of beta-lactam antibiotics against infection. International travellers may acquire these organisms and identifying individuals at high risk of acquisition could help inform clinical treatment or prevention strategies. Methods We used data collected from a cohort of 528 international travellers enrolled in a multicenter US-based study to derive a clinical prediction rule (CPR) to identify travellers who developed ESBL-PE colonization, defined as those with new ESBL positivity in stool upon return to the United States. To select candidate features, we used data collected from pre-travel and post-travel questionnaires, alongside destination-specific data from external sources. We utilized LASSO regression for feature selection, followed by random forest or logistic regression modelling, to derive a CPR for ESBL acquisition. Results A CPR using machine learning and logistic regression on ten features has an internally cross-validated area under the receiver operating characteristic curve (cvAUC) of 0.70 (95% confidence interval 0.69–0.71). We also demonstrate that a four feature model performs similarly to the ten feature model, with a cvAUC of 0.68 (95% confidence interval 0.67–0.69). This model uses traveller’s diarrhoea, and antibiotics as treatment, destination country waste management rankings, and destination regional probabilities as predictors. Conclusions We demonstrate that by integrating traveller characteristics with destination-specific data, we could derive a CPR to identify those at highest risk of acquiring ESBL-PE during international travel.