Introduction: Culture-negative sepsis is a common but relatively understudied condition. The aim of this study was to compare the characteristics and outcomes of culture-negative versus culture-positive severe sepsis. Methods: This was a prospective observational cohort study of 1001 patients who were admitted to the medical intensive care unit (ICU) of a university hospital from 2004 to 2009 with severe sepsis. Patients with documented fungal, viral, and parasitic infections were excluded. Results: There were 415 culture-negative patients (41.5%) and 586 culture-positive patients (58.5%). Gram-positive bacteria were isolated in 257 patients, and gram-negative bacteria in 390 patients. Culture-negative patients were more often women and had fewer comorbidities, less tachycardia, higher blood pressure, lower procalcitonin levels, lower Acute Physiology and Chronic Health Evaluation II (median 25.0 (interquartile range 19.0 to 32.0) versus 27.0 (21.0 to 33.0), P = 0.001) and Sequential Organ Failure Assessment scores, less cardiovascular, central nervous system, and coagulation failures, and less need for vasoactive agents than culture-positive patients. The lungs were a more common site of infection, while urinary tract, soft tissue and skin infections, infective endocarditis and primary bacteremia were less common in culture-negative than in culture-positive patients. Culture-negative patients had a shorter duration of hospital stay (12 days (7.0 to 21.0) versus 15.0 (7.0 to27.0), P = 0.02) and lower ICU mortality than culture-positive patients. Hospital mortality was lower in the culture-negative group (35.9%) than in the culture-positive group (44.0%, P = 0.01), the culture-positive subgroup, which received early appropriate antibiotics (41.9%, P = 0.11), and the culture-positive subgroup, which did not (55.5%, P < 0.001). After adjusting for covariates, culture positivity was not independently associated with mortality on multivariable analysis. Conclusions: Significant differences between culture-negative and culture-positive sepsis are identified, with the former group having fewer comorbidities, milder severity of illness, shorter hospitalizations, and lower mortality.

Background: Decision support systems based on reinforcement learning (RL) have been implemented to facilitate the delivery of personalized care. This paper aimed to provide a comprehensive review of RL applications in the critical care setting. Objective: This review aimed to survey the literature on RL applications for clinical decision support in critical care and to provide insight into the challenges of applying various RL models. Methods: We performed an extensive search of the following databases: PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, Web of Science, Medical Literature Analysis and Retrieval System Online (MEDLINE), and Excerpta Medica Database (EMBASE). Studies published over the past 10 years (2010-2019) that have applied RL for critical care were included. Results: We included 21 papers and found that RL has been used to optimize the choice of medications, drug dosing, and timing of interventions and to target personalized laboratory values. We further compared and contrasted the design of the RL models and the evaluation metrics for each application. Conclusions: RL has great potential for enhancing decision making in critical care. Challenges regarding RL system design, evaluation metrics, and model choice exist. More importantly, further work is required to validate RL in authentic clinical environments.

Background: COVID-19 was first discovered in December 2019 and has since evolved into a pandemic. Objective: To address this global health crisis, artificial intelligence (AI) has been deployed at various levels of the health care system. However, AI has both potential benefits and limitations. We therefore conducted a review of AI applications for COVID-19. Methods: We performed an extensive search of the PubMed and EMBASE databases for COVID-19–related English-language studies published between December 1, 2019, and March 31, 2020. We supplemented the database search with reference list checks. A thematic analysis and narrative review of AI applications for COVID-19 was conducted. Results: In total, 11 papers were included for review. AI was applied to COVID-19 in four areas: diagnosis, public health, clinical decision making, and therapeutics. We identified several limitations including insufficient data, omission of multimodal methods of AI-based assessment, delay in realization of benefits, poor internal/external validation, inability to be used by laypersons, inability to be used in resource-poor settings, presence of ethical pitfalls, and presence of legal barriers. AI could potentially be explored in four other areas: surveillance, combination with big data, operation of other core clinical services, and management of patients with COVID-19. Conclusions: In view of the continuing increase in the number of cases, and given that multiple waves of infections may occur, there is a need for effective methods to help control the COVID-19 pandemic. Despite its shortcomings, AI holds the potential to greatly augment existing human efforts, which may otherwise be overwhelmed by high patient numbers.