A systematic review and metanalysis of diagnostic yield of BAL for detection of SARS-CoV-2.

BACKGROUND: The gold standard for diagnosing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is microbiological confirmation by reverse transcriptase-polymerase chain reaction (RT-PCR)1 most commonly done using oropharyngeal (OP) and nasopharyngeal swabs (NP). But in suspected cases, where these samples are false-negative, bronchoalveolar lavage (BAL) may prove diagnostic. OBJECTIVES: Hence, the diagnostic yield of BAL for detection of SARS-CoV-2 in cases of non-diagnostic upper respiratory tract samples is reviewed. METHODS: Databases such as MEDLINE, Scopus, and Google Scholar were searched using a systematic search strategy. The current study has been in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and has been registered with the International Prospective Registry of Systematic Reviews (CRD42020224088). RESULTS: 911 records were identified at initial database extraction, of which 317 duplicates were removed and, 596 records were screened for inclusion eligibility. We included total 19 studies in the systematic review, and 17 were included in metanalysis. The pooled estimate of SARS-CoV-2 positivity in BAL was 11% (95%CI: 0.01-0.24). A sensitivity analysis also showed that the results appear to be robust and minimal risk of bias amongst the studies. CONCLUSION: The current study demonstrates that BAL can be used to diagnose additional cases primary disease and superadded infections in patients with severe COVID-19 lower respiratory tract infection.

Bronchoscopy in Patients With Known or Suspected COVID-19: Results From the Global Pandemic SARS-CoV-2 Bronchoscopy Database (GPS-BD).

BACKGROUND: Amid the Coronavirus Disease 2019 (COVID-19) pandemic, the benefits and risks of bronchoscopy remain uncertain. This study was designed to characterize bronchoscopy-related practice patterns, diagnostic yields, and adverse events involving patients with known or suspected COVID-19. METHODS: An online survey tool retrospectively queried bronchoscopists about their experiences with patients with known or suspected COVID-19 between March 20 and August 20, 2020. Collected data comprised the Global Pandemic SARS-CoV-2 Bronchoscopy Database (GPS-BD). All bronchoscopists and patients were anonymous with no direct investigator-to-respondent contact. RESULTS: Bronchoscopy procedures involving 289 patients from 26 countries were analyzed. One-half of patients had known COVID-19. Most (82%) had at least 1 pre-existing comorbidity, 80% had at least 1 organ failure, 51% were critically ill, and 37% were intubated at the time of the procedure. Bronchoscopy was performed with diagnostic intent in 166 (57%) patients, yielding a diagnosis in 86 (52%). and management changes in 80 (48%). Bronchoscopy was performed with therapeutic intent in 71 (25%) patients, mostly for secretion clearance (87%). Complications attributed to bronchoscopy or significant clinical decline within 12 hours of the procedure occurred in 24 (8%) cases, with 1 death. CONCLUSION: Results from this international database provide a widely generalizable characterization of the benefits and risks of bronchoscopy in patients with known or suspected COVID-19. Bronchoscopy in this setting has reasonable clinical benefit, with diagnosis and/or management change resulting from about half of the diagnostic cases. However, it is not without risk, especially in patients with limited physiological reserve.

Assessing coronavirus disease 2019 (COVID-19) transmission to healthcare personnel: The global ACT-HCP case-control study.

OBJECTIVE: To characterize associations between exposures within and outside the medical workplace with healthcare personnel (HCP) SARS-CoV-2 infection, including the effect of various forms of respiratory protection. DESIGN: Case-control study. SETTING: We collected data from international participants via an online survey. PARTICIPANTS: In total, 1,130 HCP (244 cases with laboratory-confirmed COVID-19, and 886 controls healthy throughout the pandemic) from 67 countries not meeting prespecified exclusion (ie, healthy but not working, missing workplace exposure data, COVID symptoms without lab confirmation) were included in this study. METHODS: Respondents were queried regarding workplace exposures, respiratory protection, and extra-occupational activities. Odds ratios for HCP infection were calculated using multivariable logistic regression and sensitivity analyses controlling for confounders and known biases. RESULTS: HCP infection was associated with non-aerosol-generating contact with COVID-19 patients (adjusted OR, 1.4; 95% CI, 1.04-1.9; P = .03) and extra-occupational exposures including gatherings of ≥10 people, patronizing restaurants or bars, and public transportation (adjusted OR range, 3.1-16.2). Respirator use during aerosol-generating procedures (AGPs) was associated with lower odds of HCP infection (adjusted OR, 0.4; 95% CI, 0.2-0.8, P = .005), as was exposure to intensive care and dedicated COVID units, negative pressure rooms, and personal protective equipment (PPE) observers (adjusted OR range, 0.4-0.7). CONCLUSIONS: COVID-19 transmission to HCP was associated with medical exposures currently considered lower-risk and multiple extra-occupational exposures, and exposures associated with proper use of appropriate PPE were protective. Closer scrutiny of infection control measures surrounding healthcare activities and medical settings considered lower risk, and continued awareness of the risks of public congregation, may reduce the incidence of HCP infection.

Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19.

Artificial intelligence (AI) is transforming healthcare delivery. The digital revolution in medicine and healthcare information is prompting a staggering growth of data intertwined with elements from many digital sources such as genomics, medical imaging and electronic health records. Such massive growth has sparked the development of an increasing number of AI-based applications that can be deployed in clinical practice. Pulmonary specialists who are familiar with the principles of AI and its applications will be empowered and prepared to seize future practice and research opportunities. The goal of this review is to provide pulmonary specialists and other readers with information pertinent to the use of AI in pulmonary medicine. First, we describe the concept of AI and some of the requisites of machine learning and deep learning. Next, we review some of the literature relevant to the use of computer vision in medical imaging, predictive modelling with machine learning, and the use of AI for battling the novel severe acute respiratory syndrome-coronavirus-2 pandemic. We close our review with a discussion of limitations and challenges pertaining to the further incorporation of AI into clinical pulmonary practice.

Sending retirees to the frontlines?

Facing an unprecedented surge of patient volumes and acuity, institutions around the globe called for volunteer healthcare workers to aid in the effort against COVID-19. Specifically being sought out are retirees. But retired healthcare workers are taking on significant risk to themselves in answering these calls. Aside from the risks that come from being on the frontlines of the epidemic, they are also at risk due to their age and the comorbidities that often accompany age. If, for current or future COVID efforts, we as a society will be so bold as to exhort a vulnerable population to take on further risk, we must use much care and attention in how we involve them in this effort. Herein we describe the multifaceted nature of the risks that retired healthcare workers are taking by entering the COVID-19 workforce as well as suggest ways in which we might take advantage of their medical skills and altruism yet while optimizing caution and safety.