O’Horo, John C.

Background: Blastomycosis can cause severe disease with progressive respiratory failure and dissemination even in immunocompetent individuals. We sought to evaluate risk factors for severe disease and mortality using clinical and laboratory data within a large health system in an endemic area. Methods: We performed a retrospective cohort study of patients diagnosed with blastomycosis at all Mayo Clinic sites from 1 January 2004 through 31 March 2020. Diagnosis was established by culture, histopathology/cytopathology, serology, antigen testing, or PCR. Disease was categorized as mild for patients treated in the outpatient setting, moderate for hospitalized patients who did not require intensive care, and severe for patients admitted to the intensive care unit. Logistic regression was used to evaluate risk factors for severe disease. A Cox proportional hazards model was constructed to evaluate mortality. Findings: We identified 210 patients diagnosed with blastomycosis. Mean age was 51 years (range, 6–84). Most subjects were male (71.0%). Extrapulmonary disease was confirmed in 24.8%. In this cohort, 40.5% of patients had mild disease, 37.6% had moderate disease, and 21.9% had severe disease. Independent risk factors for severe disease were neutrophilia (odds ratio (OR) 3.35 (95% CI 1.53–7.35), p = 0.002) and lymphopenia (OR 3.34 (95% CI 1.59–7.03), p = 0.001). Mortality at 90 days was 11.9%. Median time from diagnosis to death was 23 days (interquartile range 8–31 days). Independent risk factors for mortality were age (OR 1.04 (95% CI 1.01–1.08), p = 0.009), neutrophilia (OR 2.84 (95% CI 1.04–7.76), p = 0.041), and lymphopenia (OR 4.50 (95% CI 1.67–12.11), p = 0.003). Blastomyces immunodiffusion had an overall sensitivity of 39.6% (95% CI 30.1–49.8). Sensitivity was higher among those who were tested 4 weeks or longer after the onset of symptoms. Urine Blastomyces antigen had a significantly higher sensitivity of 80.8% (95% CI 68.1–89.2) compared to serology. There was a trend towards higher antigen concentration in patients with severe disease. The sensitivity of PCR from respiratory specimens was 67.6% (95% CI 50.1–85.5). Conclusion: In this cohort, we did not find an association between pharmacologic immunosuppression and disease severity. Lymphopenia at diagnosis was an independent risk factor for mortality. This simple marker may aid clinicians in determining disease prognosis.

Abstract Despite the challenges of the pandemic, there has been substantial progress with coronavirus disease 2019 (COVID-19) therapies. Pivotal COVID-19 trials like SOLIDARITY, RECOVERY, and ACCT-1 were rapidly conducted and data disseminated to support effective therapies. However, critical shortcomings remain on trial conduct, dissemination and interpretation of study results, and regulatory guidance in pandemic settings. The lessons that we learned have implications for both the current pandemic and future emerging infectious diseases. There is a need for establishing and standardizing clinical meaningful outcomes in therapeutic trials and for targeting defined populations and phenotypes that will most benefit from specific therapies. Standardized processes should be established for rapid and critical data review and dissemination to ensure scientific integrity. Clarity around the evidence standards needed for issuance of both emergency use authorization (EUA) and biologic license application (BLA) should be established and an infrastructure for executing rapid trials in epidemic settings maintained.

Abstract Given the urgent need for treatments during the coronavirus disease 2019 pandemic, the US Food and Drug Administration issued emergency use authorizations (EUAs) for multiple therapies. In several instances, however, these EUAs were issued before sufficient evidence of a given therapy’s efficacy and safety were available, potentially promoting ineffective or even harmful therapies and undermining the generation of definitive evidence. We describe the strengths and weaknesses of the different therapeutic EUAs issued during this pandemic. We also contrast them to the vaccine EUAs and suggest a framework and criteria for an evidence-based, trustworthy, and publicly transparent therapeutic EUA process for future pandemics.

Abstract The raging COVID-19 pandemic in India and reports of “vaccine breakthrough infections” globally have raised alarm mandating the characterization of the immuno-evasive features of SARS-CoV-2. Here, we systematically analyzed over 1.3 million SARS-CoV-2 genomes from 178 countries and performed whole-genome viral sequencing from 53 COVID-19 patients, including 20 vaccine breakthrough infections. We identified 116 Spike protein mutations that increased in prevalence during at least one surge in SARS-CoV-2 test positivity in any country over a three-month window. Deletions in the Spike protein N-terminal domain (NTD) are highly enriched for these ‘surge-associated mutations’ (Odds Ratio = 18.2, 95% CI: 7.53-48.7; p=1.465x10-18). In the recent COVID-19 surge in India, an NTD deletion (ΔF157/R158) increased over 10-fold in prevalence from February 2021 (1.1%) to April 2021 (15%). During the recent surge in Chile, an NTD deletion (Δ246-253) increased rapidly over 30-fold in prevalence from January 2021 (0.86%) to April 2021 (33%). Strikingly, these simultaneously emerging deletions associated with surges in different parts of the world both occur at an antigenic supersite that is targeted by neutralizing antibodies. Finally, we generated clinically annotated SARS-CoV-2 whole genome sequences and identified deletions within this NTD antigenic supersite in a patient with vaccine breakthrough infection (Δ156-164) and other deletions from unvaccinated severe COVID-19 patients that could represent emerging deletion-prone regions. Overall, the expanding repertoire of Spike protein deletions throughout the pandemic and their association with case surges and vaccine breakthrough infections point to antigenic minimalism as an emerging evolutionary strategy for SARS-CoV-2 to evade immune responses. This study highlights the urgent need to sequence SARS-CoV-2 genomes at a larger scale globally and to mandate a public health policy for transparent reporting of relevant clinical annotations (e.g. vaccination status) in order to aid the development of comprehensive therapeutic strategies.

Antibiotic use is often the target of interventions in health care organizations that aim to decrease healthcare-associated infections (HAI) such as Clostridioides difficile (CDI); this is particularly important for fluoroquinolones (FQ), which are frequently used in critical care settings. In this study, using a multiple case study research approach, we conduct an in-depth analysis of an intervention aimed at limiting ICU prescriber access to FQ in two ICUs of two hospitals. The data collection and analysis were guided by a human factors engineering approach based on the SEIPS (Systems Engineering Initiative for Patient Safety) model and evidence-based implementation principles. Our results show some differences in the implementation of the FQ intervention between the two ICUs, such as level and method of FQ restriction, and training and communication with physicians and pharmacists. In both ICUs, several organizational learning mechanisms helped to quickly identify problems with the intervention and ensure that changes were made in a just-in-time manner (e.g. just-in-time training, removal of FQ in order set for pneumonia). Despite their organizational differences, both sites developed strategies to successfully implement the FQ intervention.

Hospitalized patients with COVID-19 show increased serum concentrations of neurofilament light chain that correlate with worse clinical outcomes.

AbstractBackgroundBamlanivimab and casirivimab-imdevimab are authorized for emergency use treatment of mild to moderate coronavirus disease 2019 (COVID-19) in patients at high risk for developing severe disease or hospitalization. Their safety and efficacy have not been specifically evaluated in solid organ transplant recipients.MethodsWe retrospectively reviewed solid organ transplant recipients who received monoclonal antibody infusion for COVID-19 at Mayo Clinic sites through January 23, 2021. Outcomes included emergency department visit, hospitalization, mortality, and allograft rejection.ResultsSeventy-three patients were treated, most commonly with bamlanivimab (75.3%). The median age was 59 years, 63% were male, and the median Charlson comorbidity index was 5. Transplant type included 41 kidney (56.2%), 13 liver (17.8%), 11 heart (15.1%), 4 kidney-pancreas (5.5%), 2 lung (2.7%), 1 heart-liver, and 1 pancreas. Eleven (15.1%) patients had an emergency department visit within 28 days of infusion, including 9 (12.3%) who were hospitalized for a median of 4 days. One patient required intensive care unit admission for a nonrespiratory complication. No patients required mechanical ventilation, died, or experienced rejection. Ten adverse events occurred, with 1 seeking medical evaluation. Hypertension was associated with hospital admission (P < .05), while other baseline characteristics were similar. The median time from symptom onset to antibody administration was 4 days in nonhospitalized patients compared with 6 days among hospitalized patients (P < .05).ConclusionsMonoclonal antibody treatment has favorable outcomes with minimal adverse effects in solid organ transplant recipients with mild to moderate COVID-19. Earlier administration of monoclonal antibody therapy appears to be more efficacious.

Purpose The purpose of the present study was to investigate body mass index, multi-morbidity, and COVID-19 Risk Score as predictors of severe COVID-19 outcomes. Patients Patients from this study are from a well-characterized patient cohort collected at Mayo Clinic between January 1, 2020 and May 23, 2020; with confirmed COVID-19 diagnosis defined as a positive result on reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assays from nasopharyngeal swab specimens. Measures Demographic and clinical data were extracted from the electronic medical record. The data included: date of birth, gender, ethnicity, race, marital status, medications (active COVID-19 agents), weight and height (from which the Body Mass Index (BMI) was calculated, history of smoking, and comorbid conditions to calculate the Charlson Comorbidity Index (CCI) and the U.S Department of Health and Human Services (DHHS) multi-morbidity score. An additional COVID-19 Risk Score was also included. Outcomes included hospital admission, ICU admission, and death. Results Cox proportional hazards models were used to determine the impact on mortality or hospital admission. Age, sex, and race (white/Latino, white/non-Latino, other, did not disclose) were adjusted for in the model. Patients with higher COVID-19 Risk Scores had a significantly higher likelihood of being at least admitted to the hospital (HR = 1.80; 95% CI = 1.30, 2.50; P < .001), or experiencing death or inpatient admission (includes ICU admissions) (HR = 1.20; 95% CI = 1.02, 1.42; P = .028). Age was the only statistically significant demographic predictor, but obesity was not a significant predictor of any of the outcomes. Conclusion Age and COVID-19 Risk Scores were significant predictors of severe COVID-19 outcomes. Further work should examine the properties of the COVID-19 Risk Factors Scale.

Objective To describe the process and outcome of creating a patient cohort in the early stages of the COVID-19 pandemic in order to better understand the process of and predict the outcomes of COVID-19. Patients and Methods A total of 1169 adults aged 18 years of age or older who tested positive in Mayo Clinic Rochester or the Mayo Clinic Midwest Health System between January 1 and May 23 of 2020. Results Patients were on average 43.9 years of age and 50.7% were female. Most patients were white (69.0%), and Blacks (23.4%) and Asians (5.8%) were also represented in larger numbers. Hispanics represented 16.3% of the sample. Just under half of patients were married (48.4%). Common comorbid conditions included: cardiovascular diseases (25.1%), dyslipidemia (16.0%), diabetes mellitus (11.2%), chronic obstructive pulmonary disease (6.6%), asthma (7.5%), and cancer (5.1%). All other comorbid conditions were less the 5% in prevalence. Data on 3 comorbidity indices are also available including the: DHHS multi-morbidity score, Charlson Comorbidity Index, and Mayo Clinic COVID-19 Risk Factor Score. Conclusion In addition to managing the ever raging pandemic and growing death rates, it is equally important that we develop adequate resources for the investigation and understanding of COVID-19-related predictors and outcomes.