The ability to provide timely identification of the causative agents of lower respiratory tract infections can promote better patient outcomes and support antimicrobial stewardship efforts. Current diagnostic testing options include culture, molecular testing, and antigen detection. These methods may require collection of various specimens, involve extensive sample treatment, and can suffer from low sensitivity and long turnaround times. This study assessed the performance of the BioFire FilmArray Pneumonia Panel (PN panel) and Pneumonia Plus Panel (PNplus panel), an FDA-cleared sample-to-answer assay that enables the detection of viruses, atypical bacteria, bacteria, and antimicrobial resistance marker genes from lower respiratory tract specimens (sputum and bronchoalveolar lavage [BAL] fluid). Semiquantitative results are also provided for the bacterial targets. This paper describes selected analytical and clinical studies that were conducted to evaluate performance of the panel for regulatory clearance. Prospectively collected respiratory specimens (846 BAL and 836 sputum specimens) evaluated with the PN panel were also tested by quantitative reference culture and molecular methods for comparison. The PN panel showed a sensitivity of 100% for 15/22 etiologic targets using BAL specimens and for 10/24 using sputum specimens. All other targets had sensitivities of ≥75% or were unable to be calculated due to low prevalence in the study population. Specificity for all targets was ≥87.2%, with many false-positive results compared to culture that were confirmed by alternative molecular methods. Appropriate adoption of this test could provide actionable diagnostic information that is anticipated to impact patient care and antimicrobial stewardship decisions.

Lower respiratory tract infections, including hospital-acquired and ventilator-associated pneumonia, are common in hospitalized patient populations. Standard methods frequently fail to identify the infectious etiology due to the polymicrobial nature of respiratory specimens and the necessity of ordering specific tests to identify viral agents. The potential severity of these infections combined with a failure to clearly identify the causative pathogen results in administration of empirical antibiotic agents based on clinical presentation and other risk factors. We examined the impact of the multiplexed, semiquantitative BioFire FilmArray Pneumonia panel (PN panel) test on laboratory reporting for 259 adult inpatients submitting bronchoalveolar lavage (BAL) specimens for laboratory analysis. The PN panel demonstrated a combined 96.2% positive percent agreement (PPA) and 98.1% negative percent agreement (NPA) for the qualitative identification of 15 bacterial targets compared to routine bacterial culture. Semiquantitative values reported by the PN panel were frequently higher than values reported by culture, resulting in semiquantitative agreement (within the same log10 value) of 43.6% between the PN panel and culture; however, all bacterial targets reported as >105 CFU/ml in culture were reported as ≥105 genomic copies/ml by the PN panel. Viral targets were identified by the PN panel in 17.7% of specimens tested, of which 39.1% were detected in conjunction with a bacterial target. A review of patient medical records, including clinically prescribed antibiotics, revealed the potential for antibiotic adjustment in 70.7% of patients based on the PN panel result, including discontinuation or de-escalation in 48.2% of patients, resulting in an average savings of 6.2 antibiotic days/patient.

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Background: Several studies have explored hospitalization risk factors with the novel coronavirus disease 2019 (COVID-19) infection. Our goal was to identify clinical characteristics outside of laboratory or radiologic data associated with intubation or death within 7 days of admission. Methods: The first 436 patients admitted to the University of Colorado Hospital (Denver metropolitan area) with confirmed COVID-19 were included. Demographics, comorbidities, and select medications were collected by chart abstraction. Missing height for calculating body mass index (BMI) was imputed using the median height for patients’ sex and race/ethnicity. Adjusted odds ratios (aOR) were estimated using multivariable logistic regression and a minimax concave penalty (MCP) regularized logistic regression explored prediction. Results: Participants had a mean [standard deviation (SD)] age 55 (17), BMI 30.9 (8.2), 55% were male and 80% were ethnic/racial minorities. Increasing age [aOR: 1.24 (1.07, 1.45) per 10 years], higher BMI (aOR 1.03 (1.00, 1.06), and poorly controlled diabetes [hemoglobin A1C (HbA1c) ⩾ 8] (aOR 2.26 (1.24, 4.12) were significantly ( p < 0.05) associated with greater odds of intubation or death. Female sex [aOR: 0.63, 95% CI (0.40, 0.98); p value = 0.043] was associated with lesser odds of intubation or death. The odds of death and/or intubation increased 19% for every 1 unit increase in HbA1c value [OR: 1.19 (1.01, 1.43); p = 0.04]. Our final MCP model included indicators of A1C ⩾ 8, age > 65, sex, and minority status, but predicted intubation/death only slightly better than random chance [area under the receiver operating characteristic curve (AUC) = 0.61 (0.56, 0.67)]. Conclusion: In a hospitalized patient cohort with COVID-19, worsening control of diabetes as evidenced by higher HbA1c was associated with increased risk of intubation or death within 7 days of admission. These results complement and help clarify previous associations found between diabetes and acute disease in COVID-19. Importantly, our analysis is missing some known predictors of severity in COVID-19. Our predictive model had limited success, suggesting unmeasured factors contribute to disease severity differences.

Manifestations of disease, as perceived by physicians, can shape conceptual views and favor specific therapeutic actions. Historically, these factors appear to have an outsized influence on medical thinking in general. Disease concepts derived from empirical observations during pandemics impose a trade-off. We obtain unparalleled insight into medical thought and practice, but risk incurring the cost of unfortunate mistakes. The psychologist and Nobel Prize winner in economics Daniel Kahneman describes two mental systems that shape our judgments and decision-making in his book, Thinking, Fast and Slow: System One is intuitive, emotional, and fast, whereas System Two is deliberative and logical and has slower onset. 1 If we extrapolate these observations to clinical medicine, we often rely on either System One or System Two depending on particular situations. Errors can emerge when we default to fast and emotional responses in situations that instead require more deliberate and logical assessments. These include instances in which the desire to help-our humanitarian role as physicians, associated with an "adrenaline rush"-results from attempts to relieve human suffering. As mercenaries of misfortune, it is inevitable we engage medical interventions based on an incomplete understanding of the pathophysiology-in other words, without understanding the full risks and benefits.During the ongoing COVID-19 pandemic, members of the medical community continue to provide care with the utmost nobility, empathy, and desire for action amid uncertainty. However, as the number of cases continues to increase worldwide, we urge caution in evaluating the current state of scientific understanding, our approaches to treatment, and the safety of empiric medical interventions targeting COVID-19. We are concerned that the extensive history of unintended adverse consequences of therapies for emerging infectious diseases in the past is being ignored in the development of approaches to COVID-19 treatment. It is likely harms will emerge from current empiric therapies for COVID-19 given what can be learned from history.