Quantitative SARS-CoV-2 RT-PCR and Bronchoalveolar Cytokine Concentrations Redefine the COVID-19 Phenotypes in Critically Ill Patients.

RATIONALE: Recent studies suggest that both hypo- and hyperinflammatory acute respiratory distress syndrome (ARDS) phenotypes characterize severe COVID-19-related pneumonia. The role of lung Severe Acute Respiratory Syndrome - Coronavirus 2 (SARS-CoV-2) viral load in contributing to these phenotypes remains unknown. OBJECTIVES: To redefine COVID-19 ARDS phenotypes when considering quantitative SARS-CoV-2 RT-PCR in the bronchoalveolar lavage of intubated patients. To compare the relevance of deep respiratory samples versus plasma in linking the immune response and the 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 the quantitative SARS-CoV-2 RT-PCR in the bronchoalveolar lavage. Hierarchical clustering on principal components was applied separately on the 2 compartments' datasets. Baseline characteristics were compared between clusters. MEASUREMENTS AND RESULTS: Twenty 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 2-cluster model of ARDS revealed a new cluster characterized by hypoinflammatory 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 hypoinflammatory responses but high viral loads in the lower airways. These clusters may warrant different treatment approaches to improve clinical outcomes.

Leveraging Bluetooth low-energy technology to improve contact tracing among healthcare personnel in hospital setting during the coronavirus disease 2019 (COVID-19) pandemic.

To improve contact tracing for healthcare workers, we built and configured a Bluetooth low-energy system. We predicted close contacts with great accuracy and provided an additional contact yield of 14.8%. This system would decrease the effective reproduction number by 56% and would unnecessarily quarantine 0.74% of employees weekly.

Antimicrobial stewardship for sepsis in the intensive care unit: Survey of critical care and infectious diseases physicians.

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.

Race Does Not Impact Sepsis Outcomes When Considering Socioeconomic Factors in Multilevel Modeling.

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.

A Pragmatic Machine Learning Model To Predict Carbapenem Resistance.

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.

Interleukin-6 Trajectory and Secondary Infections in Mechanically Ventilated Patients With Coronavirus Disease 2019 Acute Respiratory Distress Syndrome Treated With Interleukin-6 Receptor Blocker.

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.

Predicting Resistance to Piperacillin-Tazobactam, Cefepime and Meropenem in Septic Patients With Bloodstream Infection Due to Gram-Negative Bacteria.

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.