The incidence of Mycobacterium kansasii varies widely over time and by region, but this organism remains one of the most clinically relevant isolated species of nontuberculous mycobacteria. In contrast to other common nontuberculous mycobacteria, M. kansasii is infrequently isolated from natural water sources or soil. The major reservoir appears to be tap water. Infection is likely acquired through the aerosol route, with low infectivity in regions of endemicity. Human-to-human transmission is thought not to occur. Clinical syndromes and radiological findings of M. kansasii infection are mostly indistinguishable from that of Mycobacterium tuberculosis, thus requiring microbiological confirmation. Disseminated disease is uncommon in HIV-negative patients and usually associated with severe immunosuppression. The majority of patients with M. kansasii pulmonary disease have underlying pulmonary comorbidities, such as smoking, chronic obstructive pulmonary disease, bronchiectasis, and prior or concurrent M. tuberculosis infection. Surveys in Great Britain, however, noted higher rates, with 8 to 9% of M. kansasii infections presenting with extrapulmonary disease. Common sites of extrapulmonary disease include the lymph nodes, skin, and musculoskeletal and genitourinary systems. The specificity of gamma interferon release assays (IGRAs) for M. tuberculosis may be reduced by M. kansasii infection, as M. kansasii encodes CFP-10 and ESAT-6, two antigens targeted by IGRAs. A study conducted to evaluate the therapy in rifampin-resistant disease found that patients with acquired rifampin resistance were treated with daily high-dose ethambutol, isoniazid, sulfamethoxazole, and pyridoxine combined with aminoglycoside therapy. Given the potential toxicities, particularly with aminoglycoside therapy, clarithromycin and/or moxifloxacin therapy could be considered as alternatives.

Background Hospital readmission is a common, costly problem. Little is known regarding risk factors for readmission in older adults with cancer. This study aims to identify factors associated with 30-day readmission in a cohort of older medical oncology patients. Setting/Participants Adults age 65 and over hospitalized to an Oncology Acute Care for Elders Unit at Barnes-Jewish Hospital. Measurements Standard geriatric screening tests were administered in routine clinical care. Clinical data and 30-day readmission status were obtained through medical record review. Results 677 patients met the inclusion criteria. 77% were white and 53% were male. Thoracic (32%), hematologic (20%), and gastrointestinal (18%) malignancies were most common. The 30-day unplanned readmission rate was 35.2%. Multivariable analyses identified complete dependence in feeding (odds ratio [OR], 3.70; 95% confidence interval [CI], 1.29 – 10.65), and some dependence (1.58, 1.04 – 2.41) and complete dependence (2.64, 1.70 – 4.12) in housekeeping, prior to admission, as associated with higher odds of readmission. Age < 75 (1.49, 1.04 – 2.14), African-American race (1.59, 1.06 – 2.39), potentially inappropriate medications (1.36, 0.94 – 1.99), and higher-risk reasons for index admission (1.93, 1.34 – 2.78) also increased odds of readmission. These factors were organized into a prognostic index. Conclusion Hospital readmission was common and higher than previously reported rates in general medical populations. We identified several previously unrecognized factors associated with increased risk for readmission, including some geriatric assessment parameters, and developed a practical tool that can be used by clinicians to assess risk of 30-day readmission.

Background SARS-CoV-2 is an RNA virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Viruses exist in complex microbial environments, and recent studies have revealed both synergistic and antagonistic effects of specific bacterial taxa on viral prevalence and infectivity. We set out to test whether specific bacterial communities predict SARS-CoV-2 occurrence in a hospital setting. Methods We collected 972 samples from hospitalized patients with COVID-19, their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and used these bacterial profiles to classify SARS-CoV-2 RNA detection with a random forest model. Results Sixteen percent of surfaces from COVID-19 patient rooms had detectable SARS-CoV-2 RNA, although infectivity was not assessed. The highest prevalence was in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples more closely resembled the patient microbiome compared to floor samples, SARS-CoV-2 RNA was detected less often in bed rail samples (11%). SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity in both human and surface samples and higher biomass in floor samples. 16S microbial community profiles enabled high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool, and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genus Rothia strongly predicted SARS-CoV-2 presence across sample types, with greater prevalence in positive surface and human samples, even when compared to samples from patients in other intensive care units prior to the COVID-19 pandemic. Conclusions These results contextualize the vast diversity of microbial niches where SARS-CoV-2 RNA is detected and identify specific bacterial taxa that associate with the viral RNA prevalence both in the host and hospital environment.

Synergistic effects of bacteria on viral stability and transmission are widely documented but remain unclear in the context of SARS-CoV-2. We collected 972 samples from hospitalized patients with coronavirus disease 2019 (COVID-19), their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and contextualized the massive microbial diversity in this dataset through meta-analysis of over 20,000 samples. Sixteen percent of surfaces from COVID-19 patient rooms were positive, with the highest prevalence in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples increasingly resembled the patient microbiome over time, SARS-CoV-2 was detected less there (11%). Despite viral surface contamination in almost all patient rooms, no health care workers contracted the disease, suggesting that personal protective equipment was effective in preventing transmissions. SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity across human and surface samples, and higher biomass in floor samples. 16S microbial community profiles allowed for high SARS-CoV-2 classifier accuracy in not only nares, but also forehead, stool, and floor samples. Across distinct microbial profiles, a single amplicon sequence variant from the genus Rothia was highly predictive of SARS-CoV-2 across sample types and had higher prevalence in positive surface and human samples, even compared to samples from patients in another intensive care unit prior to the COVID-19 pandemic. These results suggest that bacterial communities may contribute to viral prevalence both in the host and hospital environment.One Sentence SummaryMicrobial classifier highlights specific taxa predictive of SARS-CoV-2 prevalence across diverse microbial niches in a COVID-19 hospital unit.

Background: Every year, over 1 million people develop isoniazid (INH) resistant tuberculosis (TB). Yet, the optimal treatment regimen remains unclear. Given increasing prevalence, the clinical efficacy of regimens used by physicians is of interest. This study aims to examine treatment outcomes of INH resistant TB patients, treated under programmatic conditions in British Columbia, Canada.