Temperature control in sepsis.

Fever can be viewed as an adaptive response to infection. Temperature control in sepsis is aimed at preventing potential harms associated with high temperature (tachycardia, vasodilation, electrolyte and water loss) and therapeutic hypothermia may be aimed at slowing metabolic activities and protecting organs from inflammation. Although high fever (>39.5°C) control is usually performed in critically ill patients, available cohorts and randomized controlled trials do not support its use to improve sepsis prognosis. Finally, both spontaneous and therapeutic hypothermia are associated with poor outcomes in sepsis.

Management strategies for severe Pseudomonas aeruginosa infections.

PURPOSE OF REVIEW: This review focuses on the management of severe Pseudomonas aeruginosa infections in critically ill patients. RECENT FINDINGS: Pseudomonas aeruginosa is the most common pathogen in intensive care; the main related infections are nosocomial pneumonias, then bloodstream infections. Antimicrobial resistance is common; despite new antibiotics, it is associated with increased mortality, and can lead to a therapeutic deadlock. SUMMARY: Carbapenem resistance in difficult-to-treat P. aeruginosa (DTR-PA) strains is primarily mediated by loss or reduction of the OprD porin, overexpression of the cephalosporinase AmpC, and/or overexpression of efflux pumps. However, the role of carbapenemases, particularly metallo-ß-lactamases, has become more important. Ceftolozane-tazobactam, ceftazidime-avibactam and imipenem-relebactam are useful against DTR phenotypes (noncarbapenemase producers). Other new agents, such as aztreonam-ceftazidime-avibactam or cefiderocol, or colistin, might be effective for carbapenemase producers. Regarding nonantibiotic agents, only phages might be considered, pending further clinical trials. Combination therapy does not reduce mortality, but may be necessary for empirical treatment. Short-term treatment of severe P. aeruginosa infections should be preferred when it is expected that the clinical situation resolves rapidly.

What to Do with the New Antibiotics?

Multidrug-resistant Gram-negative bacteria-related infections have become a real public health problem and have exposed the risk of a therapeutic impasse. In recent years, many new antibiotics have been introduced to enrich the therapeutic armamentarium. Among these new molecules, some are mainly of interest for the treatment of the multidrug-resistant infections associated with Pseudomonas aeruginosa (ceftolozane/tazobactam and imipenem/relebactam); others are for carbapenem-resistant infections associated with Enterobacterales (ceftazidime/avibactam, meropenem/vaborbactam); and finally, there are others that are effective on the majority of multidrug-resistant Gram-negative bacilli (cefiderocol). Most international guidelines recommend these new antibiotics in the treatment of microbiologically documented infections. However, given the significant morbidity and mortality of these infections, particularly in the case of inadequate therapy, it is important to consider the place of these antibiotics in probabilistic treatment. Knowledge of the risk factors for multidrug-resistant Gram-negative bacilli (local ecology, prior colonization, failure of prior antibiotic therapy, and source of infection) seems necessary in order to optimize antibiotic prescriptions. In this review, we will assess these different antibiotics according to the epidemiological data.

A new machine learning algorithm to predict veno-arterial ECMO implantation after post-cardiotomy low cardiac output syndrome.

BACKGROUND: Post-cardiotomy low cardiac output syndrome (PC-LCOS) is a life-threatening complication after cardiac surgery involving a cardiopulmonary bypass (CPB). Mechanical circulatory support with veno-arterial membrane oxygenation (VA-ECMO) may be necessary in the case of refractory shock. The objective of the study was to develop a machine-learning algorithm to predict the need for VA-ECMO implantation in patients with PC-LCOS. PATIENTS AND METHODS: Patients were included in the study with moderate to severe PC-LCOS (defined by a vasoactive inotropic score (VIS) > 10 with clinical or biological markers of impaired organ perfusion or need for mechanical circulatory support after cardiac surgery) from two university hospitals in Paris, France. The Deep Super Learner, an ensemble machine learning algorithm, was trained to predict VA-ECMO implantation using features readily available at the end of a CPB. Feature importance was estimated using Shapley values. RESULTS: Between January 2016 and December 2019, 285 patients were included in the development dataset and 190 patients in the external validation dataset. The primary outcome, the need for VA-ECMO implantation, occurred respectively, in 16% (n = 46) and 10% (n = 19) in the development and the external validation datasets. The Deep Super Learner algorithm achieved a 0.863 (0.793-0.928) ROC AUC to predict the primary outcome in the external validation dataset. The most important features were the first postoperative arterial lactate value, intraoperative VIS, the absence of angiotensin-converting enzyme treatment, body mass index, and EuroSCORE II. CONCLUSIONS: We developed an explainable ensemble machine learning algorithm that could help clinicians predict the risk of deterioration and the need for VA-ECMO implantation in moderate to severe PC-LCOS patients.