Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures.

Background: ChatGPT is a large language model-based chatbot developed by OpenAI. ChatGPT has many potential applications to health care, including enhanced diagnostic accuracy and efficiency, improved treatment planning, and better patient outcomes. However, health care professionals’ perceptions of ChatGPT and similar artificial intelligence tools are not well known. Understanding these attitudes is important to inform the best approaches to exploring their use in medicine. Objective: Our aim was to evaluate the health care professionals’ awareness and perceptions regarding potential applications of ChatGPT in the medical field, including potential benefits and challenges of adoption. Methods: We designed a 33-question online survey that was distributed among health care professionals via targeted emails and professional Twitter and LinkedIn accounts. The survey included a range of questions to define respondents’ demographic characteristics, familiarity with ChatGPT, perceptions of this tool’s usefulness and reliability, and opinions on its potential to improve patient care, research, and education efforts. Results: One hundred and fifteen health care professionals from 21 countries responded to the survey, including physicians, nurses, researchers, and educators. Of these, 101 (87.8%) had heard of ChatGPT, mainly from peers, social media, and news, and 77 (76.2%) had used ChatGPT at least once. Participants found ChatGPT to be helpful for writing manuscripts (n=31, 45.6%), emails (n=25, 36.8%), and grants (n=12, 17.6%); accessing the latest research and evidence-based guidelines (n=21, 30.9%); providing suggestions on diagnosis or treatment (n=15, 22.1%); and improving patient communication (n=12, 17.6%). Respondents also felt that the ability of ChatGPT to access and summarize research articles (n=22, 46.8%), provide quick answers to clinical questions (n=15, 31.9%), and generate patient education materials (n=10, 21.3%) was helpful. However, there are concerns regarding the use of ChatGPT, for example, the accuracy of responses (n=14, 29.8%), limited applicability in specific practices (n=18, 38.3%), and legal and ethical considerations (n=6, 12.8%), mainly related to plagiarism or copyright violations. Participants stated that safety protocols such as data encryption (n=63, 62.4%) and access control (n=52, 51.5%) could assist in ensuring patient privacy and data security. Conclusions: Our findings show that ChatGPT use is widespread among health care professionals in daily clinical, research, and educational activities. The majority of our participants found ChatGPT to be useful; however, there are concerns about patient privacy, data security, and its legal and ethical issues as well as the accuracy of its information. Further studies are required to understand the impact of ChatGPT and other large language models on clinical, educational, and research outcomes, and the concerns regarding its use must be addressed systematically and through appropriate methods.

COVID-19 escalated inappropriate antibiotic use. We determined the distribution of pathogens causing community-acquired co-infections, the rate, and factors associated with early empiric antibiotic (EEAB) treatment among hospitalized COVID-19 patients. The Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study (VIRUS) COVID-19 Registry including 68,428 patients from 28 countries enrolled between January 2020 and October 2021 were screened. After exclusions, 7830 patients were included in the analysis. Azithromycin and/or other antibiotic treatment given within the first 3 days of hospitalization was investigated. Univariate and multivariate analyses were performed to determine factors associated with EEAB use. The majority (6214, 79.4%) of patients received EEAB, with azithromycin combination being the most frequent (3146, 40.2%). As the pandemic advanced, the proportion of patients receiving EEAB regressed from 84.4% (786/931) in January-March 2020 to 65.2% (30/46) in April-June 2021 (P < 0.001). Beta-lactams, especially ceftriaxone was the most commonly used antibiotic. Staphylococcus aureus was the most commonly isolated pathogen. Multivariate analysis showed geographical location and pandemic timeline as the strongest independent predictors of EEAB use. EEAB administration decreased as pandemic advanced, which may be the result of intensified antimicrobial stewardship efforts. Our study provides worldwide goals for antimicrobial stewardship programs in the post-COVID-19 era.

We aimed to assess the prognostic role of the neutrophil/lymphocyte ratio (NLR) in community-acquired pneumonia (CAP) via a single-center retrospective cohort of hospitalized adult patients from 1/2009 to 12/2019. Patients were dichotomized into lower NLR (≤12) and higher NLR (>12). The primary outcome was mortality. ICU admission and hospital- and ICU-free days were secondary outcomes. The pneumonia severity index (PSI) and the NLR’s ability to predict outcomes was also tested. An NLR ≤12 was observed in 2513 (62.2%) patients and >12 in 1526 (37.8%). After adjusting for PSI, the NLR was not associated with hospital mortality (odds ratio [OR] 1.115; 95% confidence interval [CI] 0.774, 1.606; p = 0.559), but it was associated with a higher risk of ICU admission (OR 1.405; 95% CI 1.216, 1.624; p < 0.001). The PSI demonstrated acceptable discrimination for mortality (area under the receiver operating characteristic curve [AUC] 0.78; 95% CI 0.75, 0.82) which was not improved by adding the NLR (AUC 0.78; 95% CI 0.75, 0.82, p = 0.4476). The PSI’s performance in predicting ICU admission was also acceptable (AUC 0.75; 95% CI 0.74, 0.77) and improved by including the NLR (AUC 0.76, 95% CI 0.74, 0.77, p = 0.008), although with limited clinical significance. The NLR was not superior to the PSI for predicting mortality in hospitalized CAP patients.

Background: The rapid emergence of the COVID-19 pandemic globally collapsed health care organizations worldwide. Incomplete knowledge of best practices, progression of disease, and its impact could result in fallible care. Data on symptoms and advancement of the SARS-CoV-2 virus leading to critical care admission have not been captured or communicated well between international organizations experiencing the same impact from the virus. This led to the expedited need for establishing international communication and data collection on the critical care patients admitted with COVID-19. Objective: Developing a global registry to collect patient data in the critical care setting was imperative with the goal of analyzing and ameliorating outcomes. Methods: A prospective, observational global registry database was put together to record extensive deidentified clinical information for patients hospitalized with COVID-19. Results: Project management was crucial for prompt implementation of the registry for synchronization, improving efficiency, increasing innovation, and fostering global collaboration for valuable data collection. The Society of Critical Care Medicine Discovery VIRUS (Viral Infection and Respiratory Illness Universal Study): COVID-19 Registry would compile data for crucial longitudinal outcomes for disease, treatment, and research. The agile project management approach expedited establishing the registry in 15 days and submission of institutional review board agreement for 250 participating sites. There has been enrollment of sites every month with a total of 306 sites from 28 countries and 64,114 patients enrolled (as of June 7, 2021). Conclusions: This protocol addresses project management lessons in a time of crises which can be a precept for rapid project management for a large-scale health care data registry. We aim to discuss the approach and methodology for establishing the registry, the challenges faced, and the factors contributing to successful outcomes. Trial Registration: ClinicalTrials.gov NCT04323787; https://clinicaltrials.gov/ct2/show/NCT04323787

The coronavirus disease 2019 (COVID-19) pandemic has turned into a global healthcare challenge, causing significant morbidity and mortality. Healthcare workers (HCWs) who are on the frontline of the COVID-19 outbreak response face an increased risk of contracting the disease. Some common challenges encountered by HCWs include exposure to the pathogen, psychological distress, and long working hours. In addition, HCWs may be more prone to develop mental health issues such as anxiety, depression, suicidal thoughts, post-traumatic stress disorder (PTSD), sleep disorders, and drug addictions compared to the general population. These issues arise from increased job stress, fear of spreading the disease to loved ones, and potential discrimination or stigma associated with the disease. This study aims to review the current literature to explore the effects of COVID-19 on healthcare providers' physical and mental well-being and suggest interventional strategies to combat these issues. To that end, we performed a literature search on Google Scholar and PubMed databases using combinations of the following keywords and synonyms: "SARS-CoV-2", "Healthcare-worker", "COVID-19", "Well-being", "Wellness", "Depression", "Anxiety", and "PTSD."