Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.

INTRODUCTION: Several methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate. METHODS: We conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017. Adult patients (18+) admitted to participating units were eligible for this factorial, open-label study. Enrolled patients were assigned to a trial arm by a random allocation sequence. In the control group, only the current severe sepsis detector was used; in the experimental group, the machine learning algorithm (MLA) was also used. On receiving an alert, the care team evaluated the patient and initiated the severe sepsis bundle, if appropriate. Although participants were randomly assigned to a trial arm, group assignments were automatically revealed for any patients who received MLA alerts. RESULTS: Outcomes from 75 patients in the control and 67 patients in the experimental group were analysed. Average length of stay decreased from 13.0 days in the control to 10.3 days in the experimental group (p=0.042). In-hospital mortality decreased by 12.4 percentage points when using the MLA (p=0.018), a relative reduction of 58.0%. No adverse events were reported during this trial. CONCLUSION: The MLA was associated with improved patient outcomes. This is the first randomised controlled trial of a sepsis surveillance system to demonstrate statistically significant differences in length of stay and in-hospital mortality. TRIAL REGISTRATION: NCT03015454.

Special article: rescue therapies for acute hypoxemic respiratory failure.

The recent H1N1 epidemic has resulted in a large number of deaths, primarily from acute hypoxemic respiratory failure. We reviewed the current strategies to rescue patients with severe hypoxemia. Included in these strategies are high-frequency oscillatory ventilation, airway pressure release ventilation, inhaled vasodilators, and the use of extracorporeal life support. All of these strategies are targeted at improving oxygenation, but improved oxygenation alone has yet to be demonstrated to correlate with improved survival. The risks and benefits of these strategies, including cost-effectiveness data, are discussed.

Effects of monoclonal anti-PcrV antibody on Pseudomonas aeruginosa-induced acute lung injury in a rat model.

BACKGROUND: The effects of the murine monoclonal anti-PcrV antibody Mab166 on acute lung injury induced by Pseudomonas aeruginosa were analyzed in a rat model. METHODS: Lung injury was induced by the instillation of P. aeruginosa strain PA103 directly into the left lungs of anesthetized rats. One hour after the bacterial instillation, rabbit polyclonal anti-PcrV IgG, murine monoclonal anti-PcrV IgG Mab166 or Mab166 Fab-fragments were administered intratracheally directly into the lungs. The degree of alveolar epithelial injury, amount of lung edema, decrease in oxygenation and extent of lung inflammation by histology were evaluated as independent parameters of acute lung injury. RESULTS: These parameters improved in rats that had received intratracheal instillation of either rabbit polyclonal anti-PcrV IgG, murine monoclonal anti-PcrV IgG Mab166 or Mab166 Fab-fragments in comparison with the control group. CONCLUSION: Mab166 and its Fab fragments have potential as adjuvant therapy for acute lung injury due to P. aeruginosa pneumonia.

Injury and repair in lung and airways.

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are common causes of morbidity and mortality in the intensive care unit. ALI/ARDS occurs as a result of systemic inflammation, usually triggered by a microorganism. Activation of leukocytes and release of proinflammatory mediators from multiple cellular sources result in both local and distant tissue injury. Tumor necrosis factor-alpha and interleukin-1 beta are the best characterized of the proinflammatory cytokines contributing to ALI/ARDS and subsequent fibrosis. The ultimate clinical course of ALI/ARDS often is determined by the ability of the injured lung to repopulate the alveolar epithelium with functional cells. Death may occur when fibrosis predominates the healing response, as it results in worsening lung compliance and oxygenation. The rodent bleomycin model of lung fibrosis allows the use of molecular tools to dissect the cellular and subcellular processes leading to fibrosis. The elements of this response may provide therapeutic targets for the prevention of this devastating complication of ALI/ARDS.