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BACKGROUND: Endotracheal aspirates (ETAs) are widely used for microbiologic studies of the respiratory tract in intubated patients. However, they involve sampling through an established endotracheal tube using suction catheters, both of which can acquire biofilms that may confound results. RESEARCH QUESTION: Does standard clinical ETA in intubated patients accurately reflect the authentic lower airway bacterial microbiome? STUDY DESIGN AND METHODS: Comprehensive quantitative bacterial profiling using 16S rRNA V1-V2 gene sequencing was applied to compare bacterial populations captured by standard clinical ETA vs contemporaneous gold standard samples acquired directly from the lower airways through a freshly placed sterile tracheostomy tube. The study included 13 patients undergoing percutaneous tracheostomy following prolonged (median, 15 days) intubation. Metrics of bacterial composition, diversity, and relative quantification were applied to samples. RESULTS: Pre-tracheostomy ETAs closely resembled the gold standard immediate post-tracheostomy airway microbiomes in bacterial composition and community features of diversity and quantification. Endotracheal tube and suction catheter biofilms also resembled cognate ETA and fresh tracheostomy communities. INTERPRETATION: Unbiased molecular profiling shows that standard clinical ETA sampling has good concordance with the authentic lower airway microbiome in intubated patients.

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Prior studies have demonstrated suboptimal adherence to lung protective ventilation among patients with acute respiratory distress syndrome. A common barrier to providing this evidence-based practice is diagnostic uncertainty. We sought to test the hypothesis that patients with acute respiratory distress syndrome due to coronavirus disease 2019, in whom acute respiratory distress syndrome is easily recognized, would be more likely to receive low tidal volume ventilation than concurrently admitted acute respiratory distress syndrome patients without coronavirus disease 2019. DESIGN: Retrospective cohort study. SETTING: Five hospitals of a single health system. PATIENTS: Mechanically ventilated patients with coronavirus disease 2019 or noncoronavirus disease 2019 acute respiratory distress syndrome as identified by an automated, electronic acute respiratory distress syndrome finder in clinical use at study hospitals. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among 333 coronavirus disease 2019 patients and 234 noncoronavirus disease 2019 acute respiratory distress syndrome patients, the average initial tidal volume was 6.4 cc/kg predicted body weight and 6.8 cc/kg predicted body weight, respectively. Patients had tidal volumes less than or equal to 6.5 cc/kg predicted body weight for a mean of 70% of the first 72 hours of mechanical ventilation in the coronavirus disease 2019 cohort, compared with 52% in the noncoronavirus disease 2019 cohort (unadjusted p < 0.001). After adjusting for height, gender, admitting hospital, and whether or not the patient was admitted to a medical specialty ICU, coronavirus disease 2019 diagnosis was associated with a 21% higher percentage of time receiving tidal volumes less than or equal to 6.5 cc/kg predicted body weight within the first 72 hours of mechanical ventilation (95% CI, 14-28%; p < 0.001). CONCLUSIONS: Adherence to low tidal volume ventilation during the first 72 hours of mechanical ventilation is higher in patients with coronavirus disease 2019 than with acute respiratory distress syndrome without coronavirus disease 2019. This population may present an opportunity to understand facilitators of implementation of this life-saving evidence-based practice.

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OBJECTIVES: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes. DESIGN: Retrospective cohort for algorithm derivation and validation, pre-post impact evaluation. SETTING: Tertiary teaching hospital system in Philadelphia, PA. PATIENTS: All non-ICU admissions; algorithm derivation July 2011 to June 2014 (n = 162,212); algorithm validation October to December 2015 (n = 10,448); silent versus alert comparison January 2016 to February 2017 (silent n = 22,280; alert n = 32,184). INTERVENTIONS: A random-forest classifier, derived and validated using electronic health record data, was deployed both silently and later with an alert to notify clinical teams of sepsis prediction. MEASUREMENT AND MAIN RESULT: Patients identified for training the algorithm were required to have International Classification of Diseases, 9th Edition codes for severe sepsis or septic shock and a positive blood culture during their hospital encounter with either a lactate greater than 2.2 mmol/L or a systolic blood pressure less than 90 mm Hg. The algorithm demonstrated a sensitivity of 26% and specificity of 98%, with a positive predictive value of 29% and positive likelihood ratio of 13. The alert resulted in a small statistically significant increase in lactate testing and IV fluid administration. There was no significant difference in mortality, discharge disposition, or transfer to ICU, although there was a reduction in time-to-ICU transfer. CONCLUSIONS: Our machine learning algorithm can predict, with low sensitivity but high specificity, the impending occurrence of severe sepsis and septic shock. Algorithm-generated predictive alerts modestly impacted clinical measures. Next steps include describing clinical perception of this tool and optimizing algorithm design and delivery.

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OBJECTIVE: To assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0). DESIGN: Prospective observational study. SETTING: Tertiary teaching hospital in Philadelphia, PA. PATIENTS: Non-ICU admissions November-December 2016. INTERVENTIONS: During a 6-week study period conducted 5 months after Early Warning System 2.0 alert implementation, nurses and providers were surveyed twice about their perceptions of the alert's helpfulness and impact on care, first within 6 hours of the alert, and again 48 hours after the alert. MEASUREMENTS AND MAIN RESULTS: For the 362 alerts triggered, 180 nurses (50% response rate) and 107 providers (30% response rate) completed the first survey. Of these, 43 nurses (24% response rate) and 44 providers (41% response rate) completed the second survey. Few (24% nurses, 13% providers) identified new clinical findings after responding to the alert. Perceptions of the presence of sepsis at the time of alert were discrepant between nurses (13%) and providers (40%). The majority of clinicians reported no change in perception of the patient's risk for sepsis (55% nurses, 62% providers). A third of nurses (30%) but few providers (9%) reported the alert changed management. Almost half of nurses (42%) but less than a fifth of providers (16%) found the alert helpful at 6 hours. CONCLUSIONS: In general, clinical perceptions of Early Warning System 2.0 were poor. Nurses and providers differed in their perceptions of sepsis and alert benefits. These findings highlight the challenges of achieving acceptance of predictive and machine learning-based sepsis alerts.

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Sedation minimization and ventilator liberation protocols improve outcomes but are challenging to implement. We sought to demonstrate proof-of-concept and impact of an electronic application promoting sedation minimization and ventilator liberation. DESIGN: Multi-ICU proof-of-concept study and a single ICU before-after study. SETTING: University hospital ICUs. PATIENTS: Adult patients receiving mechanical ventilation. INTERVENTIONS: An automated application consisting of 1) a web-based dashboard with real-time data on spontaneous breathing trial readiness, sedation depth, sedative infusions, and nudges to wean sedation and ventilatory support and 2) text-message alerts once patients met criteria for a spontaneous breathing trial and spontaneous awakening trial. Pre-intervention, sedation minimization, and ventilator liberation were reviewed daily during a multidisciplinary huddle. Post-intervention, the dashboard was used during the multidisciplinary huddle, throughout the day by respiratory therapists, and text alerts were sent to bedside providers. MEASUREMENTS AND MAIN RESULTS: We enrolled 115 subjects in the proof-of-concept study. Spontaneous breathing trial alerts were accurate (98.3%), usually sent while patients were receiving mandatory ventilation (88.5%), and 61.9% of patients received concurrent spontaneous awakening trial alerts. We enrolled 457 subjects in the before-after study, 221 pre-intervention and 236 post-intervention. After implementation, patients were 28% more likely to be extubated (hazard ratio, 1.28; 95% CI, 1.01-1.63; p = 0.042) and 31% more likely to be discharged from the ICU (hazard ratio, 1.31; 95% CI, 1.03-1.67; p = 0.027) at any time point. After implementation, the median duration of mechanical ventilation was 2.20 days (95% CI, 0.09-4.31 d; p = 0.042) shorter and the median ICU length of stay was 2.65 days (95% CI, 0.13-5.16 d; p = 0.040) shorter, compared with the expected durations without the application. CONCLUSIONS: Implementation of an electronic dashboard and alert system promoting sedation minimization and ventilator liberation was associated with reductions in the duration of mechanical ventilation and ICU length of stay.

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OBJECTIVES: Sepsis is associated with high early and total in-hospital mortality. Despite recent revisions in the diagnostic criteria for sepsis that sought to improve predictive validity for mortality, it remains difficult to identify patients at greatest risk of death. We compared the utility of nine biomarkers to predict mortality in subjects with clinically suspected bacterial sepsis. DESIGN: Cohort study. SETTING: The medical and surgical ICUs at an academic medical center. SUBJECTS: We enrolled 139 subjects who met two or more systemic inflammatory response syndrome (systemic inflammatory response syndrome) criteria and received new broad-spectrum antibacterial therapy. INTERVENTIONS: We assayed nine biomarkers (α-2 macroglobulin, C-reactive protein, ferritin, fibrinogen, haptoglobin, procalcitonin, serum amyloid A, serum amyloid P, and tissue plasminogen activator) at onset of suspected sepsis and 24, 48, and 72 hours thereafter. We compared biomarkers between groups based on both 14-day and total in-hospital mortality and evaluated the predictive validity of single and paired biomarkers via area under the receiver operating characteristic curve. MEASUREMENTS AND MAIN RESULTS: Fourteen-day mortality was 12.9%, and total in-hospital mortality was 29.5%. Serum amyloid P was significantly lower (4/4 timepoints) and tissue plasminogen activator significantly higher (3/4 timepoints) in the 14-day mortality group, and the same pattern held for total in-hospital mortality (Wilcoxon p ≤ 0.046 for all timepoints). Serum amyloid P and tissue plasminogen activator demonstrated the best individual predictive performance for mortality, and combinations of biomarkers including serum amyloid P and tissue plasminogen activator achieved greater predictive performance (area under the receiver operating characteristic curve > 0.76 for 14-d and 0.74 for total mortality). CONCLUSIONS: Combined biomarkers predict risk for 14-day and total mortality among subjects with suspected sepsis. Serum amyloid P and tissue plasminogen activator demonstrated the best discriminatory ability in this cohort.

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PURPOSE: The purpose of this study was to detail the trajectory and outcomes of patients with severe sepsis admitted from the emergency department to a non-intensive care unit (ICU) setting and identify risk factors associated with adverse outcomes. MATERIAL AND METHODS: This was a single-center retrospective cohort study conducted at a tertiary, academic hospital in the United States between 2005 and 2009. The primary outcome was a composite outcome of ICU transfer within 48 hours of admission and/or 28-day mortality. RESULTS: Of 1853 patients admitted with severe sepsis, 841 (45%) were admitted to a non-ICU setting, the rate increased over time (P < .001), and 12.5% of these patients were transferred to the ICU within 48 hours and/or died within 28 days. In multivariable models, age (P < .001), an oncology diagnosis (P < .001), and illness severity as measured by Acute Physiologic and Chronic Health Evaluation II (P = .04) and high (≥4 mmol/L) initial serum lactate levels (P = .005) were associated with the primary outcome. CONCLUSIONS: Patients presenting to the emergency department with severe sepsis were frequently admitted to a non-ICU setting, and the rate increased over time. Of 8 patients admitted to the hospital ward, one was transferred to the ICU within 48 hours and/or died within 28 days of admission. Factors present at admission were identified that were associated with adverse outcomes.

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