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Rationale: Recent studies suggest that both hypo- and hyper-inflammatory ARDS phenotypes characterize severe COVID-19-related pneumonia. The role of lung SARS-CoV-2 viral load in contributing to these phenotypes remains unknown. Objectives: To redefine COVID-19 ARDS phenotypes when considering semi-quantitative SARS-CoV-2 RT-PCR in the bronchoalveolar lavage of intubated patients. To compare the relevance of deep respiratory samples vs plasma in linking the immune response and the semi-quantitative viral loads. Methods: Eligible subjects were adults diagnosed with COVID-19 ARDS who required mechanical ventilation and underwent bronchoscopy. We recorded the immune response in the bronchoalveolar lavage and plasma and semi-quantitative SARS-CoV-2 RT-PCR in the bronchoalveolar lavage. Hierarchical clustering on principal components was applied separately on the two compartments datasets. Baseline characteristics were compared between clusters. Measurements and Results: 20 subjects were enrolled between August 2020 and March 2021. Subjects underwent bronchoscopy on average 3.6 days after intubation. All subjects were treated with dexamethasone prior to bronchoscopy, 11 of 20 (55.6%) received remdesivir and 1 of 20 (5%) received tocilizumab. Adding viral load information to the classic two cluster model of ARDS revealed a new cluster characterized by hypo-inflammatory responses and high viral load in 23.1% of the cohort. Hyperinflammatory ARDS was noted in 15.4% of subjects. Bronchoalveolar lavage clusters were more stable compared to plasma. Conclusions: We identified a unique group of critically ill subjects with COVID-19 ARDS who exhibit hypo-inflammatory responses but high viral loads in the lower airways. Our approach adds the infection dimension to ARDS phenotypes described in COVID-19 pneumonia

Objective:To evaluate the attitudes of infectious diseases (ID) and critical care physicians toward antimicrobial stewardship in the intensive care unit (ICU).Design:Anonymous, cross-sectional, web-based surveys.Setting:Surveys were completed in March–November 2017, and data were analyzed from December 2017 to December 2019.Participants:ID and critical care fellows and attending physicians.Methods:We included 10 demographic and 17 newly developed, 5-point, Likert-scaled items measuring attitudes toward ICU antimicrobial stewardship and transdisciplinary collaboration. Exploratory principal components analysis (PCA) was used for data reduction. Multivariable linear regression models explored demographic and attitudinal variables.Results:Of 372 respondents, 315 physicians had complete data (72% attendings, 28% fellows; 63% ID specialists, and 37% critical care specialists). Our PCA yielded a 3-item factor measuring which specialty should assume ICU antimicrobial stewardship (Cronbach standardized α = 0.71; higher scores indicate that ID physicians should be stewards), and a 4-item factor measuring value of ICU transdisciplinary collaborations (α = 0.62; higher scores indicate higher value). In regression models, ID physicians (vs critical care physicians), placed higher value on ICU collaborations and expressed discomfort with uncertain diagnoses. These factors were independently associated with stronger agreement that ID physicians should be ICU antimicrobial stewards. The following factors were independently associated with higher value of transdisciplinary collaboration: female sex, less discomfort with uncertain diagnoses, and stronger agreement with ID physicians as ICU antimicrobial stewards.Conclusions:ID and critical care physicians endorsed their own group for antimicrobial stewardship, but both groups placed high value on ICU transdisciplinary collaborations. Physicians who were more uncomfortable with uncertain diagnoses reported preference for ID physicians to coordinate ICU antimicrobial stewardship; however, physicians who were less uncomfortable with uncertain diagnoses placed greater value on ICU collaborations.

Objectives To determine whether race is a major determinant of sepsis outcomes when controlling for socioeconomic factors. Design Retrospective cohort study. Setting Barnes-Jewish Hospital a 1,350 bed academic medical center. Patients Eleven-thousand four-hundred thirty-two patients hospitalized between January 2010 and April 2017 with sepsis and septic shock. Interventions Multilevel random effects modeling was employed whereby patients were nested within ZIP codes. Individual patient characteristics and socioeconomic variables aggregated at the ZIP code level (education, employment status, income, poverty level, access to healthcare) were included in the model. Measurements and main results In hospital mortality, length of stay, need for vasopressors, and mechanical ventilation were the main endpoints. Black patients had more comorbidities than White patients except for cirrhosis and malignancy. In unadjusted comparisons, White individuals were more likely to require mechanical ventilation and had higher mortality rates and longer hospital stays for both low- and high-income groups. When nesting within ZIP codes and accounting for socioeconomic variables, race did not have a significant effect on mortality. Non-White races had lower odds ratio for mechanical ventilation. Conclusions Our study demonstrates that race is not an independent risk factor for sepsis mortality, as well as sepsis-related length of stay. We should expand our inquiry into determinants of sepsis outcomes by including socioeconomic variables.

It is well established that Intensive Care Units (ICUs) are a focal point in antimicrobial consumption with a major influence on the ecological consequences of antibiotic use. With the high prevalence and mortality of infections in critically ill patients, and the clinical challenges of treating patients with septic shock, the impact of real life clinical decisions made by intensivists becomes more significant. Both under- and over-treatment with unnecessarily broad spectrum antibiotics can lead to detrimental outcomes. Even though substantial progress has been made in developing rapid diagnostic tests that can help guide antibiotic use, there is still a time window when clinicians must decide the empiric antibiotic treatment with insufficient clinical data. The continuous streams of data available in the ICU environment make antimicrobial optimization an ongoing challenge for clinicians but at the same time can serve as the input for sophisticated models. In this review, we summarize the evidence to help guide antibiotic decision-making in the ICU. We focus on 1) deciding if to start antibiotics, 2) choosing the spectrum of the empiric agents to use, and 3) de-escalating the chosen empiric antibiotics. We provide a perspective on the role of machine learning and artificial intelligence models for clinical decision support systems that can be incorporated seamlessly into clinical practice in order to improve the antibiotic selection process and, more importantly, current and future patients' outcomes.

Infection caused by carbapenem-resistant (CR) organisms is a rising problem in the United States. While the risk factors for antibiotic resistance are well known, there remains a large need for the early identification of antibiotic-resistant infections. Using machine learning (ML), we sought to develop a prediction model for carbapenem resistance. All patients >18 years of age admitted to a tertiary-care academic medical center between 1 January 2012 and 10 October 2017 with ≥1 bacterial culture were eligible for inclusion. All demographic, medication, vital sign, procedure, laboratory, and culture/sensitivity data were extracted from the electronic health record. Organisms were considered CR if a single isolate was reported as intermediate or resistant. Patients with CR and non-CR organisms were temporally matched to maintain the positive/negative case ratio. Extreme gradient boosting was used for model development. In total, 68,472 patients met inclusion criteria, with 1,088 patients identified as having CR organisms. Sixty-seven features were used for predictive modeling. The most important features were number of prior antibiotic days, recent central venous catheter placement, and inpatient surgery. After model training, the area under the receiver operating characteristic curve was 0.846. The sensitivity of the model was 30%, with a positive predictive value (PPV) of 30% and a negative predictive value of 99%. Using readily available clinical data, we were able to create a ML model capable of predicting CR infections at the time of culture collection with a high PPV.

Antibiotic resistance is recognized as a key determinant of outcome in patients with serious infections influencing empiric antibiotic practices especially for critically ill patients. Within the intensive care unit (ICU), nosocomial infections and increasingly community-onset infections are caused by multidrug-resistant bacteria. Escalating rates of antibiotic resistance adds substantially to the morbidity, mortality, and cost related to infections treated in the ICU. Both gram-positive organisms, such as methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci, and gram-negative bacteria, including Pseudomonas aeruginosa, Acinetobacter species, carbapenem-resistant Enterobacteriaceae, and extended spectrum β-lactamase producing organisms, are urgent threats. The rising rates of antimicrobial resistance have resulted in routine empiric administration of broad-spectrum antibiotics by clinicians to critically ill patients even when bacterial infection is microbiologically absent. Moreover, new broad-spectrum antibiotics are a challenge to use effectively while avoiding emergence of further resistance. Use of rapid diagnostic technologies (RDTs) will likely provide an important methodology for achieving this important balance. There is an urgent need for integrating the administration of new and existing antibiotics with RDTs in a way that is safe, cost-effective, applicable in all countries, and sustainable.

Objectives:. To describe the infectious complications and interleukin-6 trajectories in mechanically ventilated patients with coronavirus disease 2019. Design:. Retrospective cohort study. Setting:. ICUs at Washington University-Barnes Jewish Hospital in St. Louis, MO. Participants:. All consecutive patients admitted to the medical ICU and requiring mechanical ventilation from March 12, 2020, to April 21, 2020, were included. Interventions:. Tocilizumab, an interleukin-6 receptor blocker, was prescribed at the discretion of the treating physicians to patients with a clinical picture compatible with cytokine release syndrome. Measurements:. All the patients were followed to death or hospital discharge. Demographic and laboratory data were collected retrospectively from the electronic medical record. Interleukin-6 levels were measured at days 0, 3, 7, 14, and 21. Infections were divided into culture positive and culture negative (clinically suspected and treated). The main outcomes were infectious complications and interleukin-6 levels at different points in time. Results:. Forty-three patients with respiratory failure secondary to coronavirus disease 2019 were on mechanical ventilation during the study period. Twenty-seven (68%) were male, and 31 (72.1%) were African-American. Median Charlson score was 2 (interquartile range, 0–4). Median Pao2/Fio2 was 171.5 (122–221) on the day of mechanical ventilation initiation, and 13 patients (30.2%) required vasopressors. C-reactive protein was 142.7 (97.7–213.7), d-dimer 1,621 (559–13,434), and Acute Physiology and Chronic Health Evaluation-II 11 (9–15). Interleukin-6 levels at admission were 61 pg/mL (interquartile range, 28.6–439 pg/mL). Patients treated with tocilizumab had higher levels of interleukin-6 at each measurement (days 0, 3, 7, 14, and 21) compared with patients receiving standard of care. Both groups reached peak interleukin-6 levels at day 7. Administration of tocilizumab was associated with a trend toward increased risk of infection. Conclusions:. Interleukin-6 levels peak at day 7 in patients with severe coronavirus disease 2019 pneumonia requiring mechanical ventilation and follows a similar trajectory in patients with coronavirus disease 2019 pneumonia requiring mechanical ventilation irrespective of treatment with interleukin-6R blockers. Interleukin-6 levels continued to rise in nonsurvivors, in comparison with survivors, where the rise in interleukin-6 levels was followed by a decline.

ImportanceCritical care resources like ventilators, used to manage the current COVID-19 pandemic, are potentially inadequate. Established triage standards and guidelines may not contain the most appropriate severity assessment and outcome prediction models.ObjectivesDevelop a draft pandemic specific triage assessment score for the current COVID-19 pandemic. Design a website where initial Toward a COVID-19 Scores (TACS) can be quickly calculated and used to compare various treatment strategies. Create a TACS Registry where data and outcomes for suspected and confirmed COVID-19 patients can be recorded. Use the TACS Registry to develop an influenza epidemic specific database and score for use in future respiratory based epidemics.Design, Setting, ParticipantsRetrospective analysis of 3,301 ICU admissions with respiratory failure admitted to 41 U.S. Intensive Care Units from 2015-19. Independent external validation on 1,175 similar ICU Admissions using identical entry criteria from Barnes Jewish Hospital (BJH), Washington University from 2016-2019.Main OutcomesTACS was created with 16 readily available predictive variables for risk assessment of hospital mortality 24 hours after ICU Admission and the need for prolonged assisted mechanical ventilation (PAMV) (> 96 hours) at 24- and 48-hours post ICU admission.ResultsTACS achieved an Area Under the Curve (AUC) for hospital mortality after 24 hours of 0.80 in the development dataset; 0.81 in the internal validation dataset. At a probability of 50% hospital mortality, positive predictive value (PPV) was 0.55, negative predictive value (NPV) 0.89; sensitivity 22%, specificity 97%.For PAMV after 24 hours, the AUC was 0.84 in the development dataset, 0.81 in the validation dataset. For PAMV after 48 hours, the AUC was 0.82 in the development dataset, 0.78 in the validation dataset.In the external validation the AUC for TACS was 0.76 +/- 0.024. We launched a website that is scaled for mobile device use (https://covid19score.azurewebsites.net/) that provides open access to a user-friendly TACS Calculator for all predictions. We also designed a voluntary TACS Registry for collection of data and outcomes on ICU Admissions with COVID-19.Conclusions and RelevanceToward a COVID-19 score is a starting point for an epidemic specific triage assessment that could be used to evaluate various approaches to treatment. The TACS Registry provides the ability to establish a respiratory specific outcomes database that can be used to create a triage approach for future such pandemics.Key PointsQuestionCan a rapid epidemic specific risk assessment severity score and data and outcome repository be constructed in the midst of a pandemic.FindingsUsing development and validation datasets with ICU admissions similar to those developing COVID-19, developed an initial Toward a COVID-19 Score that could be used to compare various treatment approaches. Also launched an online facilitated data collection and outcome assessment registry for collection of a pandemic specific database so a new triage score could be created for use in the next pandemic.MeaningIn the midst of a pandemic rapid development of an epidemic specific triage score and a data registry for the creation of a new score for use in future pandemics appears feasible.

Background Hospital-acquired (HAP) and ventilator-associated pneumonia (VAP) represent a significant source of morbidity and mortality in hospitalized patients. Numerous studies demonstrate mortality benefit with appropriate empiric therapy. Choosing the right empiric coverage is paramount; however, this decision becomes more challenging as rates of antibiotic resistance rise. Most recent HAP/VAP guidelines use an arbitrary population resistance rate of 20% to recommended methicillin-resistant Staphylococcus aureus (MRSA) coverage and double-coverage of resistant gram negative bacilli (GNB). Using this threshold has led to overuse of broad spectrum antimicrobials. The goal of this study is to mathematically explore the impact of this threshold on patient outcomes and link population resistance rates to individual mortality risk. Methods We used the concept of excess morality risk (EMR) to develop a theoretical simulation model based for HAP/VAP caused by GNB and MRSA empirically treated with piperacillin-tazobactam and vancomycin. EMR is the product of the proportion of HAP/VAP caused by GNB/MRSA, the rate of antibiotic (piperacillin-tazobactam/ methicillin) resistance in GNB and Staphylococcus aureus isolates and the difference in mortality between discordant and appropriate antibiotic therapy. Model parameters were obtained from large surveillance networks and published clinical trials. Results At the HAP/VAP guideline threshold of 20% methicillin resistance in SA isolates, the EMR was 0.3%; when the model included only culture positive patients, EMR was 0.6%. At a threshold of 20% resistance to piperacillin-tazobactam in GNB isolates, EMR was 1.9% and 3.1% when culture-negative patients were excluded. EMR increased as baseline risk of failure with discordant therapy increased (e.g. critically ill patients, ventilated HAP). Risk Difference of Death for Staphylococcus aureus Isolates Risk Difference of Death for Gram Negative Bacilli Conclusion This model offers a mathematical exploration of the individual excess risk for death in patients with HAP/VAP caused by GNB/MRSA because of discordant therapy. The objectivity of the model would better allow clinicians, guideline authors, and health policy makers to weigh excess risk versus possible harms of broad-spectrum therapy when developing population resistance thresholds cutoffs for empiric therapy recommendations. Disclosures All Authors: No reported disclosures

BackgroundLooking only at the index infection, studies have described risk factors for infections caused by resistant bacteria. We hypothesized that septic patients with bloodstream infections may transition across states characterized by different microbiology and that their trajectory is not uniform. We also hypothesized that baseline risk factors may influence subsequent blood culture results.MethodsAll adult septic patients with positive blood cultures over a 7-year period were included in the study. Baseline risk factors were recorded. We followed all survivors longitudinally and recorded subsequent blood culture results. We separated states into bacteremia caused by gram-positive cocci, susceptible gram-negative bacilli (sGNB), resistant GNB (rGNB), and Candida spp. Detrimental transitions were considered when transitioning to a culture with a higher mortality risk (rGNB and Candida spp.). A multistate Markov-like model was used to determine risk factors associated with detrimental transitions.ResultsA total of 990 patients survived and experienced at least 1 transition, with a total of 4282 transitions. Inappropriate antibiotics, previous antibiotic exposure, and index bloodstream infection caused by either rGNB or Candida spp. were associated with detrimental transitions. Double antibiotic therapy (beta-lactam plus either an aminoglycoside or a fluoroquinolone) protected against detrimental transitions.ConclusionBaseline characteristics that include prescribed antibiotics can identify patients at risk for subsequent bloodstream infections caused by resistant bacteria. By altering the initial treatment, we could potentially influence future bacteremic states.

Previous studies have separately emphasized the importance of host, pathogen, and treatment characteristics in determining short-term or in-hospital mortality rates for patients with methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections. Less is known about the relative importance of these factors and their interactions in determining short-, medium-, and long-term mortality rates. This is an observational cohort study in which data for all patients admitted to the University of New Mexico (UNM) Health Sciences Center (HSC) between July 2002 and August 2013 with MRSA-positive blood cultures were recorded. We collected patients' demographics and treatment data, as well as data on genetic markers of the MRSA isolates. Outcomes of interest were determinants of short-term (within 30 days), medium-term (30 to 90 days), and long-term (>90 days) mortality rates. This study included 273 patients with MRSA bacteremia. Short-, medium-, and long-term mortality rates were 18.7%, 26.4%, and 48%, respectively. Thirty-day mortality rates were influenced by host variables and host-pathogen interaction characteristics. Pitt bacteremia scores, malignancy, and health care exposure contributed to 30- to 90-day mortality rates, while treatment duration of >4 weeks had a protective effect. Age remained a significant risk factor for death at >90 days, while admission leukocytosis was protective. Infection represented the most frequent cause of death for all three time frames; rates varied from 72.6% in the first 30 days and 60% for 30 to 90 days to 35.7% for >90 days ( P = 0.003). Host characteristics affect short-, medium-, and long-term mortality rates for MRSA bloodstream infections more than do pathogen genetic markers and treatment factors.

Background Predicting antimicrobial resistance in gram-negative bacteria (GNB) could balance the need for administering appropriate empiric antibiotics while also minimizing the use of clinically unwarranted broad-spectrum agents. Our objective was to develop a practical prediction rule able to identify patients with GNB infection at low risk for resistance to piperacillin-tazobactam (PT), cefepime (CE), and meropenem (ME). Methods The study included adult patients with sepsis or septic shock due to bloodstream infections caused by GNB admitted between 2008 and 2015 from Barnes-Jewish Hospital. We used multivariable logistic regression analyses to describe risk factors associated with resistance to the antibiotics of interest (PT, CE, and ME). Clinical decision trees were developed using the recursive partitioning algorithm CHAID (χ2 Automatic Interaction Detection). Results The study included 1618 consecutive patients. Prevalence rates for resistance to PT, CE, and ME were 28.6%, 21.8%, and 8.5%, respectively. Prior antibiotic use, nursing home residence, and transfer from an outside hospital were associated with resistance to all 3 antibiotics. Resistance to ME was specifically linked with infection attributed to Pseudomonas or Acinetobacter spp. Discrimination was similar for the multivariable logistic regression and CHAID tree models, with both being better for ME than for PT and CE. Recursive partitioning algorithms separated out 2 clusters with a low probability of ME resistance and 4 with a high probability of PT, CE, and ME resistance. Conclusions With simple variables, clinical decision trees can be used to distinguish patients at low, intermediate, or high risk of resistance to PT, CE, and ME.

Background One aim of antimicrobial stewardship programs is to combat climbing rates of antibiotic resistance. Typically coordinated by Infectious Diseases (ID) physicians, such programs have decreased costs, resistance rates and secondary infections. A point of contention is whether ID or Critical Care (CC) physicians should manage the antibiotics prescribed to critically ill patients in the Intensive Care Unit (ICU). We surveyed ID and CC physicians regarding their perspectives on ICU antimicrobial stewardship and collaborations. Methods In 2017, CC and ID fellows and attendings completed an online survey that included 17 Likert-scaled items (1 = strongly disagree to 5 = strongly agree) measuring views on ICU antibiotic stewardship. Principal components analysis (PCA) was used for data reduction. Multivariable linear regression models explored variables associated with outcomes measuring physicians’ views on which specialty should guide antibiotic stewardship and the value of clinical collaborations in the ICU. Results Of 334 physicians, 71% were attendings (vs. fellows) and 61% were ID (vs. CC) specialists. From the PCA, 3 factors emerged measuring views about: (1) the specialty that should serve as ICU antibiotic stewards (Cronbach’s α = 0.71; higher scores indicate ID physicians should be stewards); (2) ICU clinical collaborations (α = 0.60; higher scores indicate greater value of collaboration); and (3) ICU decision-making insecurity (α = 0.45; higher scores indicate greater insecurity). In the regression models (n = 309), CC physicians and those placing lower value on ICU collaborations reported greater agreement that ID physicians should be the primary ICU stewards; women and physicians reporting greater ICU decision-making insecurity and less agreement that ID physicians should be ICU antibiotic stewards reported greater value of clinical collaborations. Conclusion CC physicians favor ID specialists to assume ICU antibiotic stewardship. Disclosures All authors: No reported disclosures.