Faculty Bibliography

BACKGROUND/PURPOSE/OBJECTIVE:Discharge decisions in Intensive Care Unit (ICU) patients are frequently taken under pressure to free up ICU beds. In the absence of established guidelines, the evaluation of discharge readiness commonly underlies subjective judgements. The challenge is to come to the right decision at the right time for the right patient. A premature care transition puts patients at risk of readmission to the ICU. Delayed discharge is a waste of resources and may result in over-treatment and suboptimal patient flow. More objective decision support is required to assess the individual patient's discharge readiness but also the current care capabilities of the receiving unit. METHODS:In a modified online Delphi process, an international panel of 27 intensive care experts reached consensus on a set of 28 intensive care discharge criteria. An initial evidence-based proposal was developed further through the panelists' edits, adding, comments and voting over a course of 5 rounds. Consensus was defined as achieved when ≥ 90% of the experts voted for a given option on the Likert scale or in a multiple-choice survey. Round 1 to 3 focused on inclusion and exclusion of the criteria based on the consensus threshold, where round 3 was a reiteration to establish stability. Round 4 and 5 focused on the exact phrasing, values, decision makers and evaluation time frames per criterion. RESULTS:Consensus was reached on a standard set of 28 ICU discharge criteria for adult ICU patients, that reflect the patient's organ systems ((respiratory (7), cardiovascular (9), central nervous (1), and urogenital system (2)), pain (1), fluid loss and drainages (1), medication and nutrition (1), patient diagnosis, prognosis and preferences (2) and institution-specific criteria (4). All criteria have been specified in a binary decision metric (fit for ICU discharge vs. needs further intensive therapy/monitoring), with consented value calculation methods where applicable and a criterion importance rank with "mandatory to be met" flags and applicable exceptions. CONCLUSION/CONCLUSIONS:For a timely identification of stable intensive care patients and safe and efficient care transitions, a standardized discharge readiness evaluation should be based on patient factors as well as organizational boundary conditions and involve multiple stakeholders.

During the current COVID-19 pandemic, health-care workers and uninfected patients in intensive care units (ICUs) are at risk of being infected with SARS-CoV-2 as a result of transmission from infected patients and health-care workers. In the absence of high-quality evidence on the transmission of SARS-CoV-2, clinical practice of infection control and prevention in ICUs varies widely. Using a Delphi process, international experts in intensive care, infectious diseases, and infection control developed consensus statements on infection control for SARS-CoV-2 in an ICU. Consensus was achieved for 31 (94%) of 33 statements, from which 25 clinical practice statements were issued. These statements include guidance on ICU design and engineering, health-care worker safety, visiting policy, personal protective equipment, patients and procedures, disinfection, and sterilisation. Consensus was not reached on optimal return to work criteria for health-care workers who were infected with SARS-CoV-2 or the acceptable disinfection strategy for heat-sensitive instruments used for airway management of patients with SARS-CoV-2 infection. Well designed studies are needed to assess the effects of these practice statements and address the remaining uncertainties.

Mean systemic filling pressure (Pms) is a promising parameter in determining intravascular fluid status. Pms derived from venous return curves during inspiratory holds with incremental airway pressures (Pms-Insp) estimates Pms reliably but is labor-intensive. A computerized algorithm to calculate Pms (Pmsa) at the bedside has been proposed. In previous studies Pmsa and Pms-Insp correlated well but with considerable bias. This observational study was performed to validate Pmsa with Pms-Insp in cardiac surgery patients. Cardiac output, right atrial pressure and mean arterial pressure were prospectively recorded to calculate Pmsa using a bedside monitor. Pms-Insp was calculated offline after performing inspiratory holds. Intraclass-correlation coefficient (ICC) and assessment of agreement were used to compare Pmsa with Pms-Insp. Bias, coefficient of variance (COV), precision and limits of agreement (LOA) were calculated. Proportional bias was assessed with linear regression. A high degree of inter-method reliability was found between Pmsa and Pms-Insp (ICC 0.89; 95%CI 0.72-0.96, p = 0.01) in 18 patients. Pmsa and Pms-Insp differed not significantly (11.9 mmHg, IQR 9.8-13.4 vs. 12.7 mmHg, IQR 10.5-14.4, p = 0.38). Bias was -0.502 ± 1.90 mmHg (p = 0.277). COV was 4% with LOA -4.22 - 3.22 mmHg without proportional bias. Conversion coefficient Pmsa ➔ Pms-Insp was 0.94. This assessment of agreement demonstrates that the measures Pms-Insp and the computerized Pmsa-algorithm are interchangeable (bias -0.502 ± 1.90 mmHg with conversion coefficient 0.94). The choice of Pmsa is straightforward, it is non-interventional and available continuously at the bedside in contrast to Pms-Insp which is interventional and calculated off-line. Further studies should be performed to determine the place of Pmsa in the circulatory management of critically ill patients. ( www.clinicaltrials.gov ; TRN NCT04202432, release date 16-12-2019; retrospectively registered).Clinical Trial Registration www.ClinicalTrials.gov , TRN: NCT04202432, initial release date 16-12-2019 (retrospectively registered).

BACKGROUND:Transfusion is very common in the intensive care unit (ICU), but practice is highly variable, as has recently been shown in non-bleeding critically ill patients practices survey. Bleeding patients in ICU require different blood products across a range of specific patient categories. We hypothesize that a large variety in transfusion practice exists in bleeding patients. STUDY DESIGN AND METHODS/METHODS:An international online survey was performed among physicians working in the ICU. Transfusion practice in massively and non-massively bleeding patients was examined, including transfusion ratios, thresholds, and the presence of transfusion guidelines. RESULTS:/L was applied. DISCUSSION/CONCLUSIONS:Half of the centers had no massive transfusion protocol available. Transfusion practice in massively bleeding critically ill patients is highly variable and driven by the presence of an MTP. In the general non-massively bleeding ICU population restrictive transfusion triggers were chosen.

INTRODUCTION: In patients with septic shock, the diastolic shock index (DSI), defined as the ratio of heart rate to diastolic blood pressure, has been shown to correlate with mortality. This is thought to be due to the underlying vasodilation and compensatory increases in heart rate. Although infection with COVID-19 frequently presents with sepsis-like symptoms and changes in blood pressure, the role of the DSI in these patients has not been studied. Our study sought to explore if the DSI may be similarly used in patients with COVID-19 to identify individuals with an elevated mortality risk.
METHOD(S): This was an IRB approved retrospective cohort study at a large academic hospital in New York City (NYC). Data was extracted from the electronic medical record by a trained analyst. Inclusion criteria were age 18 or older, admitted from the emergency department (ED) to the intensive care unit (ICU) between 01/01/2020 and 06/30/2020 with a positive test for COVID-19. We excluded individuals who were transferred from the floor or an outside hospital to the ICU and those with incomplete data. Our final cohort included 360 patients from NYC. The heart rate and diastolic blood pressure used to calculate the DSI were based on the first recorded vitals upon presentation to the ED. This was done in conjunction with a study at a University (ICESI) hospital in Cali, Colombia, with a combined cohort of 655 patients.
RESULT(S): The 28-day mortality rate for the combined study population was 24.9%. Descriptive statistics demonstrated a DSI of >= 1.6 was correlated with elevated 28 day mortality. Cox regression controlling for age, body mass index, respiratory rate, and systolic blood pressure, demonstrated that a DSI of >= 1.6 had a hazard ratio of 1.98 (p-value < 0.01, 95% CI 1.40-2.81).
CONCLUSION(S): In our study population, the DSI that correlated with an elevated risk of mortality was considerably lower than was seen in patients with septic shock, underscoring the physiologic differences between patients with septic shock and COVID-19. Further analysis of the data will be aimed at revealing the etiology of these differences

INTRODUCTION/BACKGROUND:Hypotension in the ICU is common, yet management is challenging and variable. Insight in management by ICU physicians and nurses may improve patient care and guide future hypotension treatment trials and guidelines. METHODS:We conducted an international survey among ICU personnel to provide insight in monitoring, management, and perceived consequences of hypotension. RESULTS:Out of 1464 respondents, 1197 (81.7%) were included (928 physicians (77.5%) and 269 nurses (22.5%)). The majority indicated that hypotension is underdiagnosed (55.4%) and largely preventable (58.8%). Nurses are primarily in charge of monitoring changes in blood pressure, physicians are in charge of hypotension treatment. Balanced crystalloids, dobutamine, norepinephrine, and Trendelenburg position were the most frequently reported fluid, inotrope, vasopressor, and positional maneuver used to treat hypotension. Reported complications believed to be related to hypotension were AKI and myocardial injury. Most ICUs do not have a specific hypotension treatment guideline or protocol (70.6%), but the majority would like to have one in the future (58.1%). CONCLUSIONS:Both physicians and nurses report that hypotension in ICU patients is underdiagnosed, preventable, and believe that hypotension influences morbidity. Hypotension management is generally not protocolized, but the majority of respondents would like to have a specific hypotension management protocol.

Respiratory failure is associated with increased mortality in COVID-19 patients. There are no validated lower airway biomarkers to predict clinical outcome. We investigated whether bacterial respiratory infections were associated with poor clinical outcome of COVID-19 in a prospective, observational cohort of 589 critically ill adults, all of whom required mechanical ventilation. For a subset of 142 patients who underwent bronchoscopy, we quantified SARS-CoV-2 viral load, analysed the lower respiratory tract microbiome using metagenomics and metatranscriptomics and profiled the host immune response. Acquisition of a hospital-acquired respiratory pathogen was not associated with fatal outcome. Poor clinical outcome was associated with lower airway enrichment with an oral commensal (Mycoplasma salivarium). Increased SARS-CoV-2 abundance, low anti-SARS-CoV-2 antibody response and a distinct host transcriptome profile of the lower airways were most predictive of mortality. Our data provide evidence that secondary respiratory infections do not drive mortality in COVID-19 and clinical management strategies should prioritize reducing viral replication and maximizing host responses to SARS-CoV-2.

Starting point and problem: The aim of our research was to identify structures and new forms of collaboration which had become established during the COVID-19 pandemic and possible continuation of which beyond the pandemic might provide additional benefit to everyday clinical practice. Methods: A three-stage survey of 22 experts (senior physicians, hospital hygienists, nurse managers and representatives of hospital management) from six municipal hospitals situated in a hotspot region was used to identify measures pertaining to management of intensive care capacity and patient flows, as well as to the flow of information and data. Those measures were rated using a Likert scale. Results were subsequently structured in the setting of a round table. Results: Over the course of three evaluation stages - which saw high response rates of 82 - 91 % - the 43 initially identified measures were reduced to the 14 which received the highest levels of approval. At the round table, 10 of those 14 most relevant measures were categorised in one of three fields of action: 1) interdisciplinary management of patient flows, 2) flexible human resource (HR) concepts and 3) establishing new communication and information structures. Practical examples were formulated for each of the three fields of action and have in the meantime proven their value in everyday clinical practice. Conclusions: Using a structured decision process combined with holistic reflection on the organisational structure of hospitals, strategies were identified which had proven themselves under duress and which should be implemented in everyday clinical practice outside the setting of the pandemic. The flow of patients, resource management and communication structures can be influenced beneficially by sustained closer interdisciplinary and intersectoral collaboration within a "new clinical routine". The practical examples and recommendations put forward for each of the fields of action could provide an impulse for other healthcare providers to examine their HR concepts and communication strategies as well as their management of care capacities and patient flows, adjusting to the challenges of new everyday realities with interdisciplinary and intersectoral approaches.