ICU without borders.

Critical illness is a continuum, but patient care is often fragmented. Value-based critical care focuses on the overall health of the patient, not on an episode of care. The "ICU without borders" model incorporates a concept where members of the critical care team are involved in the management of patients from the onset of critical illness until recovery and beyond. In this paper, we summarise the potential benefits and challenges to patients, families, staff and the wider healthcare system and list some essential requirements, including a tight governance framework, advanced technologies, investment and trust. We also argue that "ICU without borders" should be viewed as a bi-directional model, allowing extended visiting hours, giving patients and families direct access to experienced critical care staff and offering mutual aid when needed.

Ten areas for ICU clinicians to be aware of to help retain nurses in the ICU.

Shortage of nurses on the ICU is not a new phenomenon, but has been exacerbated by the COVID-19 pandemic. The underlying reasons are relatively well-recognized, and include excessive workload, moral distress, and perception of inappropriate care, leading to burnout and increased intent to leave, setting up a vicious circle whereby fewer nurses result in increased pressure and stress on those remaining. Nursing shortages impact patient care and quality-of-work life for all ICU staff and efforts should be made by management, nurse leaders, and ICU clinicians to understand and ameliorate the factors that lead nurses to leave. Here, we highlight 10 broad areas that ICU clinicians should be aware of that may improve quality of work-life and thus potentially help with critical care nurse retention.

Pharmacological Studies in Hospitalized COVID-19 Patients in Belgium: We Could Do Better.

Belgium has actively participated in clinical research on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) since the beginning of the pandemic to help identify effective and safe treatments for COVID-19. The objective of this review is to provide a picture of the clinical studies carried out in hospitalized patients with COVID-19 in Belgium. We collected data on all randomized, interventional trials in patients with COVID-19 that were registered on two recognized clinical trial registers, started enrollment before 31 December 2021, and included at least one patient in a Belgian center. Data were collected concerning the therapies investigated and the nature of the trials performed. Thirty-three hospitals (32% of all Belgian hospitals) participated in at least one of 28 trials (13 sponsored by the industry and 15 by academic centers) on therapeutics for COVID-19 in hospitalized patients: 7 (25%) evaluated antivirals, 17 (61%) immunomodulators, 2 (7%) anti-coagulants, and 1 (3%) nitric oxide to improve respiratory function. Nineteen (68%) were phase II trials. Only three (11%) of the trials were international platform trials. Despite numerous trials, less than 3% of all Belgian patients hospitalized with COVID-19 participated in a clinical trial on therapeutics. As in many other countries, more efforts could have been made to avoid running small, under-powered, mono- or bicenter trials, to create better collaboration between the different Belgian hospitals, and to participate in more international clinical trials, and more specifically in adaptive, platform trials.

Ethical Lessons from an Intensivist's Perspective.

Intensive care units (ICUs) around the world have been hugely impacted by the SARS-CoV-2 pandemic and the vast numbers of patients admitted with COVID-19, requiring respiratory support and prolonged stays. This pressure, with resulting shortages of ICU beds, equipment, and staff has raised ethical dilemmas as physicians have had to determine how best to allocate the sparse resources. Here, we reflect on some of the major ethical aspects of the COVID-19 pandemic, including resource allocation and rationing, end-of-life decision-making, and communication and staff support. Importantly, these issues are regularly faced in non-pandemic ICU patient management and useful lessons can be learned from the discussions that have occurred as a result of the COVID-19 situation.

Prevention and management of thrombosis in hospitalised patients with COVID-19 pneumonia.

A proportion of people infected with SARS-CoV-2 develop moderate or severe COVID-19, with an increased risk of thromboembolic complications. The inflammatory response to SARS-CoV-2 infection can cause an acute-phase response and endothelial dysfunction, which contribute to COVID-19-associated coagulopathy, the clinical and laboratory features of which differ in some respects from those of classic disseminated intravascular coagulation. Understanding of the pathophysiology of thrombosis in COVID-19 is needed to develop approaches to management and prevention, with implications for short-term and long-term health outcomes. Evidence is emerging to support treatment decisions in patients with COVID-19, but many questions remain about the optimum approach to management. In this Viewpoint, we provide a summary of the pathophysiology of thrombosis and associated laboratory and clinical findings, and highlight key considerations in the management of coagulopathy in hospitalised patients with severe COVID-19, including coagulation assessment, identification of thromboembolic complications, and use of antithrombotic prophylaxis and therapeutic anticoagulation. We await the results of trials that are underway to establish the safety and benefits of prolonged thromboprophylaxis after hospital discharge.

Infection control in the intensive care unit: expert consensus statements for SARS-CoV-2 using a Delphi method.

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.

Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids.

PURPOSE: Coronavirus disease-2019 (COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids or remdesivir in the treatment of COVID-19. However, uncertainty remains about which patients are most likely to benefit from treatment with either drug; such knowledge is crucial for avoiding preventable adverse effects, minimizing costs, and effectively allocating resources. This study presents a machine-learning system with the capacity to identify patients in whom treatment with a corticosteroid or remdesivir is associated with improved survival time. METHODS: Gradient-boosted decision-tree models used for predicting treatment benefit were trained and tested on data from electronic health records dated between December 18, 2019, and October 18, 2020, from adult patients (age ≥18 years) with COVID-19 in 10 US hospitals. Models were evaluated for performance in identifying patients with longer survival times when treated with a corticosteroid versus remdesivir. Fine and Gray proportional-hazards models were used for identifying significant findings in treated and nontreated patients, in a subset of patients who received supplemental oxygen, and in patients identified by the algorithm. Inverse probability-of-treatment weights were used to adjust for confounding. Models were trained and tested separately for each treatment. FINDINGS: Data from 2364 patients were included, with men comprising slightly more than 50% of the sample; 893 patients were treated with remdesivir, and 1471 were treated with a corticosteroid. After adjustment for confounding, neither corticosteroids nor remdesivir use was associated with increased survival time in the overall population or in the subpopulation that received supplemental oxygen. However, in the populations identified by the algorithms, both corticosteroids and remdesivir were significantly associated with an increase in survival time, with hazard ratios of 0.56 and 0.40, respectively (both, P = 0.04). IMPLICATIONS: Machine-learning methods have the capacity to identify hospitalized patients with COVID-19 in whom treatment with a corticosteroid or remdesivir is associated with an increase in survival time. These methods may help to improve patient outcomes and allocate resources during the COVID-19 crisis.

How the COVID-19 pandemic will change the future of critical care.

Coronavirus disease 19 (COVID-19) has posed unprecedented healthcare system challenges, some of which will lead to transformative change. It is obvious to healthcare workers and policymakers alike that an effective critical care surge response must be nested within the overall care delivery model. The COVID-19 pandemic has highlighted key elements of emergency preparedness. These include having national or regional strategic reserves of personal protective equipment, intensive care unit (ICU) devices, consumables and pharmaceuticals, as well as effective supply chains and efficient utilization protocols. ICUs must also be prepared to accommodate surges of patients and ICU staffing models should allow for fluctuations in demand. Pre-existing ICU triage and end-of-life care principles should be established, implemented and updated. Daily workflow processes should be restructured to include remote connection with multidisciplinary healthcare workers and frequent communication with relatives. The pandemic has also demonstrated the benefits of digital transformation and the value of remote monitoring technologies, such as wireless monitoring. Finally, the pandemic has highlighted the value of pre-existing epidemiological registries and agile randomized controlled platform trials in generating fast, reliable data. The COVID-19 pandemic is a reminder that besides our duty to care, we are committed to improve. By meeting these challenges today, we will be able to provide better care to future patients.

COVID-19: What we've done well and what we could or should have done better-the 4 Ps.

The current coronavirus pandemic has impacted heavily on ICUs worldwide. Although many hospitals and healthcare systems had plans in place to manage multiple casualties as a result of major natural disasters or accidents, there was insufficient preparation for the sudden, massive influx of severely ill patients with COVID-19. As a result, systems and staff were placed under immense pressure as everyone tried to optimize patient management. As the pandemic continues, we must apply what we have learned about our response, both good and bad, to improve organization and thus patient care in the future.

Eosinopenia in COVID-19 Patients: A Retrospective Analysis.

Objectives: The aim of this study was to assess the diagnostic role of eosinophils count in COVID-19 patients. Methods: Retrospective analysis of patients admitted to our hospital with suspicion of COVID-19. Demographic, clinical and laboratory data were collected on admission. Eosinopenia was defined as eosinophils < 100 cells/mm3. The outcomes of this study were the association between eosinophils count on admission and positive real-time reverse transcription polymerase chain reaction (rRT-PCR) test and with suggestive chest computerized tomography (CT) of COVID-19 pneumonia. Results: A total of 174 patients was studied. Of those, 54% had positive rRT-PCR for SARS-CoV-2. A chest CT-scan was performed in 145 patients; 71% showed suggestive findings of COVID-19. Eosinophils on admission had a high predictive accuracy for positive rRT-PCR and suggestive chest CT-scan (area under the receiver operating characteristic-ROC curve, 0.84 (95% CIs 0.78-0.90) and 0.84 (95% CIs 0.77-0.91), respectively). Eosinopenia and high LDH were independent predictors of positive rRT-PCR, whereas eosinopenia, high body mass index and hypertension were predictors for suggestive CT-scan findings. Conclusions: Eosinopenia on admission could predict positive rRT-PCR test or suggestive chest CT-scan for COVID-19. This laboratory finding could help to identify patients at high-risk of COVID-19 in the setting where gold standard diagnostic methods are not available.

Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?-The IDENTIFY Trial.

Therapeutic agents for the novel coronavirus disease 2019 (COVID-19) have been proposed, but evidence supporting their use is limited. A machine learning algorithm was developed in order to identify a subpopulation of COVID-19 patients for whom hydroxychloroquine was associated with improved survival; this population might be relevant for study in a clinical trial. A pragmatic trial was conducted at six United States hospitals. We enrolled COVID-19 patients that were admitted between 10 March and 4 June 2020. Treatment was not randomized. The study endpoint was mortality; discharge was a competing event. Hazard ratios were obtained on the entire population, and on the subpopulation indicated by the algorithm as suitable for treatment. A total of 290 patients were enrolled. In the subpopulation that was identified by the algorithm, hydroxychloroquine was associated with a statistically significant (p = 0.011) increase in survival (adjusted hazard ratio 0.29, 95% confidence interval (CI) 0.11-0.75). Adjusted survival among the algorithm indicated patients was 82.6% in the treated arm and 51.2% in the arm not treated. No association between treatment and mortality was observed in the general population. A 31% increase in survival at the end of the study was observed in a population of COVID-19 patients that were identified by a machine learning algorithm as having a better outcome with hydroxychloroquine treatment. Precision medicine approaches may be useful in identifying a subpopulation of COVID-19 patients more likely to be proven to benefit from hydroxychloroquine treatment in a clinical trial.

The Acquisition of Multidrug-Resistant Bacteria in Patients Admitted to COVID-19 Intensive Care Units: A Monocentric Retrospective Case Control Study.

Whether the risk of multidrug-resistant bacteria (MDRB) acquisition in the intensive care unit (ICU) is modified by the COVID-19 crisis is unknown. In this single center case control study, we measured the rate of MDRB acquisition in patients admitted in COVID-19 ICU and compared it with patients admitted in the same ICU for subarachnoid hemorrhage (controls) matched 1:1 on length of ICU stay and mechanical ventilation. All patients were systematically and repeatedly screened for MDRB carriage. We compared the rate of MDRB acquisition in COVID-19 patients and in control using a competing risk analysis. Of note, although we tried to match COVID-19 patients with septic shock patients, we were unable due to the longer stay of COVID-19 patients. Among 72 patients admitted to the COVID-19 ICUs, 33% acquired 31 MDRB during ICU stay. The incidence density of MDRB acquisition was 30/1000 patient days. Antimicrobial therapy and exposure time were associated with higher rate of MDRB acquisition. Among the 72 SAH patients, 21% acquired MDRB, with an incidence density was 18/1000 patient days. The septic patients had more comorbidities and a greater number of previous hospitalizations than the COVID-19 patients. The incidence density of MDRB acquisition was 30/1000 patient days. The association between COVID-19 and MDRB acquisition (compared to control) risk did not reach statistical significance in the multivariable competing risk analysis (sHR 1.71 (CI 95% 0.93-3.21)). Thus, we conclude that, despite strong physical isolation, acquisition rate of MDRB in ICU patients was at least similar during the COVID-19 first wave compared to previous period.

Pathophysiology of COVID-19-associated acute respiratory distress syndrome: a multicentre prospective observational study.

BACKGROUND: Patients with COVID-19 can develop acute respiratory distress syndrome (ARDS), which is associated with high mortality. The aim of this study was to examine the functional and morphological features of COVID-19-associated ARDS and to compare these with the characteristics of ARDS unrelated to COVID-19. METHODS: This prospective observational study was done at seven hospitals in Italy. We enrolled consecutive, mechanically ventilated patients with laboratory-confirmed COVID-19 and who met Berlin criteria for ARDS, who were admitted to the intensive care unit (ICU) between March 9 and March 22, 2020. All patients were sedated, paralysed, and ventilated in volume-control mode with standard ICU ventilators. Static respiratory system compliance, the ratio of partial pressure of arterial oxygen to fractional concentration of oxygen in inspired air, ventilatory ratio (a surrogate of dead space), and D-dimer concentrations were measured within 24 h of ICU admission. Lung CT scans and CT angiograms were done when clinically indicated. A dataset for ARDS unrelated to COVID-19 was created from previous ARDS studies. Survival to day 28 was assessed. FINDINGS: Between March 9 and March 22, 2020, 301 patients with COVID-19 met the Berlin criteria for ARDS at participating hospitals. Median static compliance was 41 mL/cm H2O (33-52), which was 28% higher than in the cohort of patients with ARDS unrelated to COVID-19 (32 mL/cm H2O [25-43]; p<0·0001). 17 (6%) of 297 patients with COVID-19-associated ARDS had compliances greater than the 95th percentile of the classical ARDS cohort. Total lung weight did not differ between the two cohorts. CT pulmonary angiograms (obtained in 23 [8%] patients with COVID-19-related ARDS) showed that 15 (94%) of 16 patients with D-dimer concentrations greater than the median had bilateral areas of hypoperfusion, consistent with thromboembolic disease. Patients with D-dimer concentrations equal to or less than the median had ventilatory ratios lower than those of patients with D-dimer concentrations greater than the median (1·66 [1·32-1·95] vs 1·90 [1·50-2·33]; p=0·0001). Patients with static compliance equal to or less than the median and D-dimer concentrations greater than the median had markedly increased 28-day mortality compared with other patient subgroups (40 [56%] of 71 with high D-dimers and low compliance vs 18 [27%] of 67 with low D-dimers and high compliance, 13 [22%] of 60 with low D-dimers and low compliance, and 22 [35%] of 63 with high D-dimers and high compliance, all p=0·0001). INTERPRETATION: Patients with COVID-19-associated ARDS have a form of injury that, in many aspects, is similar to that of those with ARDS unrelated to COVID-19. Notably, patients with COVID-19-related ARDS who have a reduction in respiratory system compliance together with increased D-dimer concentrations have high mortality rates. FUNDING: None.

Unspecific post-mortem findings despite multiorgan viral spread in COVID-19 patients.

BACKGROUND: Post-mortem studies can provide important information for understanding new diseases and small autopsy case series have already reported different findings in COVID-19 patients. METHODS: We evaluated whether some specific post-mortem features are observed in these patients and if these changes are related to the presence of the virus in different organs. Complete macroscopic and microscopic autopsies were performed on different organs in 17 COVID-19 non-survivors. Presence of SARS-CoV-2 was evaluated with immunohistochemistry (IHC) in lung samples and with real-time reverse-transcription polymerase chain reaction (RT-PCR) test in the lung and other organs. RESULTS: Pulmonary findings revealed early-stage diffuse alveolar damage (DAD) in 15 out of 17 patients and microthrombi in small lung arteries in 11 patients. Late-stage DAD, atypical pneumocytes, and/or acute pneumonia were also observed. Four lung infarcts, two acute myocardial infarctions, and one ischemic enteritis were observed. There was no evidence of myocarditis, hepatitis, or encephalitis. Kidney evaluation revealed the presence of hemosiderin in tubules or pigmented casts in most patients. Spongiosis and vascular congestion were the most frequently encountered brain lesions. No specific SARS-CoV-2 lesions were observed in any organ. IHC revealed positive cells with a heterogeneous distribution in the lungs of 11 of the 17 (65%) patients; RT-PCR yielded a wide distribution of SARS-CoV-2 in different tissues, with 8 patients showing viral presence in all tested organs (i.e., lung, heart, spleen, liver, colon, kidney, and brain). CONCLUSIONS: In conclusion, autopsies revealed a great heterogeneity of COVID-19-associated organ injury and the remarkable absence of any specific viral lesions, even when RT-PCR identified the presence of the virus in many organs.

Higher Intensity Thromboprophylaxis Regimens and Pulmonary Embolism in Critically Ill Coronavirus Disease 2019 Patients.

OBJECTIVES: To assess the role of thromboprophylaxis regimens on the occurrence of pulmonary embolism in coronavirus disease 2019 patients. DESIGN: Retrospective analysis of prospectively collected data on coronavirus disease 2019 patients, included between March 10, and April 30, 2020. SETTING: ICU of an University Hospital in Belgium. PATIENTS AND INTERVENTIONS: Critically ill adult mechanically ventilated coronavirus disease 2019 patients were eligible if they underwent a CT pulmonary angiography, as part of the routine management in case of persistent hypoxemia or respiratory deterioration. The primary endpoint of this study was the occurrence of pulmonary embolism according to the use of standard thromboprophylaxis (i.e. subcutaneous enoxaparin 4,000 international units once daily) or high regimen thromboprophylaxis (i.e. subcutaneous enoxaparin 4,000 international units bid or therapeutic unfractioned heparin). MEASUREMENTS AND MAIN RESULTS: Of 49 mechanically ventilated coronavirus disease 2019, 40 underwent CT pulmonary angiography after a median of 7 days (4-8 d) since ICU admission and 12 days (9-16 d) days since the onset of symptoms. Thirteen patients (33%) were diagnosed of pulmonary embolism, which was bilateral in six patients and localized in the right lung in seven patients. D-dimers on the day of CT pulmonary angiography had a predictive accuracy of 0.90 (95% CIs: 0.78-1.00) for pulmonary embolism. The use of high-regimen thromboprophylaxis was associated with a lower occurrence of pulmonary embolism (2/18; 11%) than standard regimen (11/22, 50%-odds ratio 0.13 [0.02-0.69]; p = 0.02); this difference remained significant even after adjustment for confounders. Six patients with pulmonary embolism (46%) and 14 patients without pulmonary embolism (52%) died at ICU discharge (odds ratio 0.79 [0.24-3.26]; p = 0.99). CONCLUSIONS: In this study, one third of coronavirus disease 2019 mechanically ventilated patients have a pulmonary embolism visible on CT pulmonary angiography. High regimen thromboprophylaxis may decrease the occurrence of such complication.

Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial.

BACKGROUND: Currently, physicians are limited in their ability to provide an accurate prognosis for COVID-19 positive patients. Existing scoring systems have been ineffective for identifying patient decompensation. Machine learning (ML) may offer an alternative strategy. A prospectively validated method to predict the need for ventilation in COVID-19 patients is essential to help triage patients, allocate resources, and prevent emergency intubations and their associated risks. METHODS: In a multicenter clinical trial, we evaluated the performance of a machine learning algorithm for prediction of invasive mechanical ventilation of COVID-19 patients within 24 h of an initial encounter. We enrolled patients with a COVID-19 diagnosis who were admitted to five United States health systems between March 24 and May 4, 2020. RESULTS: 197 patients were enrolled in the REspirAtory Decompensation and model for the triage of covid-19 patients: a prospective studY (READY) clinical trial. The algorithm had a higher diagnostic odds ratio (DOR, 12.58) for predicting ventilation than a comparator early warning system, the Modified Early Warning Score (MEWS). The algorithm also achieved significantly higher sensitivity (0.90) than MEWS, which achieved a sensitivity of 0.78, while maintaining a higher specificity (p < 0.05). CONCLUSIONS: In the first clinical trial of a machine learning algorithm for ventilation needs among COVID-19 patients, the algorithm demonstrated accurate prediction of the need for mechanical ventilation within 24 h. This algorithm may help care teams effectively triage patients and allocate resources. Further, the algorithm is capable of accurately identifying 16% more patients than a widely used scoring system while minimizing false positive results.