Understanding temporal dynamics of COVID-19 symptoms could provide fine-grained resolution to guide clinical decision-making. Here, we use deep neural networks over an institution-wide platform for the augmented curation of clinical notes from 77,167 patients subjected to COVID-19 PCR testing. By contrasting Electronic Health Record (EHR)-derived symptoms of COVID-19-positive (COVID_pos; n = 2,317) versus COVID-19-negative (COVID_neg; n = 74,850) patients for the week preceding the PCR testing date, we identify anosmia/dysgeusia (27.1-fold), fever/chills (2.6-fold), respiratory difficulty (2.2-fold), cough (2.2-fold), myalgia/arthralgia (2-fold), and diarrhea (1.4-fold) as significantly amplified in COVID_pos over COVID_neg patients. The combination of cough and fever/chills has 4.2-fold amplification in COVID_pos patients during the week prior to PCR testing, in addition to anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19. This study introduces an Augmented Intelligence platform for the real-time synthesis of institutional biomedical knowledge. The platform holds tremendous potential for scaling up curation throughput, thus enabling EHR-powered early disease diagnosis.

This Viewpoint charts the rise in publications describing risk prediction tools, such as the FRAX, CHADS2, and SOFA scores, and discusses the implications of us

Objective: Presenteeism is an expensive and challenging problem in the healthcare industry. In anticipation of the staffing challenges expected with the COVID-19 pandemic, we examined a decade of payroll data for a healthcare workforce. We aimed to determine the effect of seasonal influenza-like illness (ILI) on absences to support COVID-19 staffing plans.Design: Retrospective cohort study.Setting: Large academic medical center in the United States.Participants: Employees of the academic medical center who were on payroll between the years of 2009 and 2019.Methods: Biweekly institutional payroll data was evaluated for unscheduled absences as a marker for acute illness-related work absences. Linear regression models, stratified by payroll status (salaried vs hourly employees) were developed for unscheduled absences as a function of local ILI.Results: Both hours worked and unscheduled absences were significantly related to the community prevalence of influenza-like illness in our cohort. These effects were stronger in hourly employees.Conclusions: Organizations should target their messaging at encouraging salaried staff to stay home when ill.

Background.: Nocardia is an environmental pathogen with a predilection for causing opportunistic infections in immunocompromised patients, including solid organ transplant (SOT) recipients. Although risk factors have been identified for developing nocardiosis in this population, little is known regarding clinical factors resulting in poor outcomes. We evaluated a cohort of SOT recipients with nocardiosis for associations with 12-month mortality. Methods.: We performed a multicenter retrospective cohort study of adult SOT recipients diagnosed with culture-confirmed nocardiosis from 2000 to 2020. Patients were followed for 12 months after diagnosis, unless abbreviated by mortality. Multivariable Cox regression was performed to analyze associations with 12-month mortality. A subgroup analysis of patients with disseminated nocardiosis was performed to analyze treatment variables. Results.: A total of 125 SOT recipients met inclusion criteria; 12-month mortality was 16.8%. Liver transplantation (hazard ratio [HR] 3.52; 95% confidence interval [CI] 1.27-9.76) and time from symptom onset to presentation (HR 0.92/d; 95% CI 0.86-0.99) were independently associated with 12-month mortality, whereas disseminated infection was not (HR 1.23; 95% CI 0.49-3.13). No treatment-specific factors were significantly associated with mortality in 33 patients with disseminated nocardiosis, although survivors had a higher rate of linezolid use. Conclusions.: This study identified 2 independent associations with 12-month mortality, representing demographics and infection severity. Disseminated infection was not independently associated with poor outcomes, and specific sites of infection may be more important than dissemination itself. No treatment-specific factors were associated with mortality, though this analysis was likely underpowered. Further study of treatment strategies based on specific Nocardia syndromes is warranted.

Case reports of patients infected with COVID-19 and influenza virus (“flurona”) have raised questions around the prevalence and severity of coinfection. Using data from HHS Protect Public Data Hub, NCBI Virus, and CDC FluView, we analyzed trends in SARS-CoV-2 and influenza hospitalized coinfection cases and strain prevalences. We also characterized coinfection cases across the Mayo Clinic Enterprise from January 2020 to April 2022. We compared expected and observed coinfection case counts across different waves of the pandemic and assessed symptoms and outcomes of coinfection and COVID-19 monoinfection cases after propensity score matching on clinically relevant baseline characteristics. From both the Mayo Clinic and nationwide datasets, the observed coinfection rate for SARS-CoV-2 and influenza has been higher during the Omicron era (2021 December 14 to 2022 April 2) compared to previous waves, but no higher than expected assuming infection rates are independent. At the Mayo Clinic, only 120 coinfection cases were observed among 197,364 SARS-CoV-2 cases. Coinfected patients were relatively young (mean age: 26.7 years) and had fewer serious comorbidities compared to monoinfected patients. While there were no significant differences in 30-day hospitalization, ICU admission, or mortality rates between coinfected and matched COVID-19 monoinfection cases, coinfection cases reported higher rates of symptoms including congestion, cough, fever/chills, headache, myalgia/arthralgia, pharyngitis, and rhinitis. While most coinfection cases observed at the Mayo Clinic occurred among relatively healthy individuals, further observation is needed to assess outcomes among subpopulations with risk factors for severe COVID-19 such as older age, obesity, and immunocompromised status.