Intensive Care Unit Beds Research Articles

Trends in COVID-19 Inpatient Cases and Hospital Capacities during the Emergence of the Omicron Variant in the United States

Introduction: The purpose of this study was to analyze hospital capacities in terms of hospital beds and staffing in the United States and explore the trends of hospital capacities during the Omicron variant emergence. Methods: Using data collected by the United States Department of Health and Human Services, this study examined hospitalization rates from 15 December 2021 to 13 February 2022. Graphical trends were plotted for visualization of hospital bed usage and coronavirus disease 2019 (COVID-19) inpatient cases. A Pearson correlation test was used to explore the relationship between critical staffing shortage and COVID-19 death cases. Descriptive statistics of all hospital data were calculated. Results: At the peak of the Omicron variant surge, approximately one-third of the adult intensive care unit beds were used for COVID-19 patients in the United States. The total number of COVID-19 death cases increased as the number of hospitals with critical staffing shortages increased (p < 0.05). Discussion: This study can inform hospital administrators and public health policymakers on how to modify the use of existing hospital and human resources as the continually evolving pandemic taxes hospital capacity. Future studies need to be conducted to determine the long-term trend and how the COVID-19 pandemic continues to change rapidly.

Increasing Supportive Care for Patients With COVID-19-Related Respiratory Deterioration in Non-ICU Settings.

Prepandemic hospital guidelines were unable to support an acute influx of patients with respiratory deterioration. New processes for general care practice were needed to facilitate patient care. To develop and evaluate guidelines to safely treat patients with COVID-19 respiratory deterioration in the general care setting. A quality improvement project with 2 PDSA (Plan-Do-Study-Act) cycles was used to develop guidelines for high-flow oxygen and prone positioning, along with frequent monitoring and collaboration with virtual critical care support. Over 6 months, 126 patients with COVID-19 were cared for on general care units. Zero intubations occurred on the general care units, with 211 patient hospital days spent in general care that previously would have required an intensive care unit bed. Patients in the general care setting with respiratory decline can safely be managed with appropriate monitoring criteria, oxygen device settings, and nursing support unitizing technology.

How should ICU beds be allocated during a crisis? Evidence from the COVID-19 pandemic

BackgroundThe first wave of the COVID-19 pandemic overwhelmed healthcare systems in many countries, and the rapid spread of the virus and the acute course of the disease resulted in a shortage of intensive care unit (ICU) beds. We studied preferences of the public in the Netherlands regarding the allocation of ICU beds during a health crisis.MethodsWe distributed a cross-sectional online survey at the end of March 2020 to a representative sample of the adult population in the Netherlands. We collected preferences regarding the allocation of ICU beds, both in terms of who should be involved in the decision-making and which rationing criteria should be considered. We conducted Probit regression analyses to investigate associations between these preferences and several characteristics and opinions of the respondents.ResultsA total of 1,019 respondents returned a completed survey. The majority favored having physicians (55%) and/or expert committees (51%) play a role in the allocation of ICU beds and approximately one-fifth did not favor any of the proposed decision-makers. Respondents preferred to assign higher priority to vulnerable patients and patients who have the best prospect of full recovery. They also preferred that personal characteristics, including age, play no role.Conclusion“Our findings show that current guidelines for allocating ICU beds that include age as an independent criterion may not be consistent with societal preferences. Age may only play a role indirectly, in relation to the vulnerability of patients and their prospect of full recovery. Allocation of ICU beds during a health crisis requires a multivalue ethical framework.”

Developing a novel prediction model in opioid overdose using machine learning; a pilot analytical study.

Background and AimsThe opioid epidemic has extended to many countries. Data regarding the accuracy of conventional prediction models including the Simplified Acute Physiologic Score (SAPS) II and acute physiology and chronic health evaluation (APACHE) II are scarce in opioid overdose cases. We evaluate the efficacy of adding quantitative electroencephalogram (qEEG) data to clinical and paraclinical data in the prediction of opioid overdose mortality using machine learning.MethodsIn a prospective study, we collected clinical/paraclinical, and qEEG data of 32 opioid‐poisoned patients. After preprocessing and Fast Fourier Transform analysis, absolute power was computed. Also, SAPS II was calculated. Eventually, data analysis was performed using SAPS II as a benchmark at three levels to predict the patient's course in comparison with SAPS II. First, the qEEG data set was used alone, secondly, the combination of the clinical/paraclinical, SAPS II, qEEG datasets, and the SAPS II‐based model was included in the pool of classifier models.ResultsSeven out of 32 (22%) died. SAPS II (cut‐off of 50.5) had a sensitivity/specificity/positive/negative predictive values of 85.7%, 84.0%, 60.0%, and 95.5% in predicting mortality, respectively. Adding majority voting on random forest with qEEG and clinical data, improved the model sensitivity, specificity, and positive and negative predictive values to 71.4%, 96%, 83.3%, and 92.3% (not significant). The model fusion level has 40% less prediction error.ConclusionConsidering the higher specificity and negative predictive value in our proposed model, it could predict survival much better than mortality. The model would constitute an indicator for better care of opioid poisoned patients in low resources settings, where intensive care unit beds are limited.

PREPARING THE INTENSIVE CARE UNIT (ICU) FOR THE COVID-19 PANDEMIC: WITH PRACTICAL AND STRATEGIC CONSIDERATIONS

Coronavirus Disease 2019 (COVID-19) has developed into a global pandemic. The coronavirus disease outbreak of 2019 (COVID-19) has posed significant challenges for healthcare systems worldwide. The establishment of an intensive care unit (ICU) is a critical component of any pandemic response. We discuss the critical principles and strategies for ICU readiness in this review. Additionally, we describe the early stages of our outbreak and discuss some of the difficulties encountered. To ensure the sustainability of intensive care unit services, we propose the following: 1) develop and implement rapid identification and isolation protocols, as well as increase ICU bed capacity; (2) provide a sustainable workforce with an infection control focus; (3) ensure adequate supplies to equip ICUs and protect health workers; and (4) maintain clinical quality of management and effective communication.

A Case Series of Trauma Resuscitation in the Intensive Care Unit Bypassing the Emergency Room During the Conversion to a COVID-19 Only Hospital

When a patient with severe trauma is admitted to the emergency room (ER), they are evaluated before transfer to either the intensive care unit (ICU) or operating room. To minimize the time until a definitive treatment can be provided, direct operating room resuscitation can be performed. In this hospital the ER was closed during the hospital’s transition to a coronavirus disease 2019-dedicated hospital, and direct ICU resuscitation for patients with trauma was performed for a short period. To perform effective trauma resuscitation, all ICU beds were reorganized to achieve a modified, experienced nurse: patient ratio (1:2-3) and 2 beds were assigned for trauma ICU resuscitation alone. The equipment for initial resuscitation was installed and ICU nurses received training. Consultations with the hospital administration, nursing, and pharmaceutical departments were completed in advance to avoid formal problems. Conversion of the ICU for direct resuscitation procedures was performed in 4 patients.

The association of strained ICU capacity with hospital patient racial and ethnic composition and federal relief during the COVID-19 pandemic.

ObjectiveTo identify the association between strained intensive care unit (ICU) capacity during the COVID‐19 pandemic and hospital racial and ethnic patient composition, federal pandemic relief, and other hospital characteristics.Data SourcesWe used government data on hospital capacity during the pandemic and Provider Relief Fund (PRF) allocations, Medicare claims and enrollment data, hospital cost reports, and Social Vulnerability Index data.Study DesignWe conducted cross‐sectional bivariate analyses relating strained capacity and PRF award per hospital bed with hospital patient composition and other characteristics, with and without adjustment for hospital referral region (HRR).Data CollectionWe linked PRF data to CMS Certification Numbers based on hospital name and location. We used measures of racial and ethnic composition generated from Medicare claims and enrollment data. Our sample period includes the weeks of September 18, 2020 through November 5, 2021, and we restricted our analysis to short‐term, general hospitals with at least one intensive care unit (ICU) bed. We defined “ICU strain share” as the proportion of ICU days occurring while a given hospital had an ICU occupancy rate ≥ 90%.Principal FindingsAfter adjusting for HRR, hospitals in the top tercile of Black patient shares had higher ICU strain shares than did hospitals in the bottom tercile (30% vs. 22%, p < 0.05) and received greater PRF amounts per bed ($118,864 vs. $92,407, p < 0.05). Having high versus low ICU occupancy relative to pre‐pandemic capacity was associated with a modest increase in PRF amounts per bed after adjusting for HRR ($107,319 vs. $96,627, p < 0.05), but there were no statistically significant differences when comparing hospitals with high versus low ICU occupancy relative to contemporaneous capacity.ConclusionsHospitals with large Black patient shares experienced greater strain during the pandemic. Although these hospitals received more federal relief, funding was not targeted overall toward hospitals with high ICU occupancy rates.

Examining the impact of ICU population interaction structure on modeled colonization dynamics of Staphylococcus aureus.

Complex transmission models of healthcare-associated infections provide insight for hospital epidemiology and infection control efforts, but they are difficult to implement and come at high computational costs. Structuring more simplified models to incorporate the heterogeneity of the intensive care unit (ICU) patient-provider interactions, we explore how methicillin-resistant Staphylococcus aureus (MRSA) dynamics and acquisitions may be better represented and approximated. Using a stochastic compartmental model of an 18-bed ICU, we compared the rates of MRSA acquisition across three ICU population interaction structures: a model with nurses and physicians as a single staff type (SST), a model with separate staff types for nurses and physicians (Nurse-MD model), and a Metapopulation model where each nurse was assigned a group of patients. The proportion of time spent with the assigned patient group (γ) within the Metapopulation model was also varied. The SST, Nurse-MD, and Metapopulation models had a mean of 40.6, 32.2 and 19.6 annual MRSA acquisitions respectively. All models were sensitive to the same parameters in the same direction, although the Metapopulation model was less sensitive. The number of acquisitions varied non-linearly by values of γ, with values below 0.40 resembling the Nurse-MD model, while values above that converged toward the Metapopulation structure. Inclusion of complex population interactions within a modeled hospital ICU has considerable impact on model results, with the SST model having more than double the acquisition rate of the more structured metapopulation model. While the direction of parameter sensitivity remained the same, the magnitude of these differences varied, producing different colonization rates across relatively similar populations. The non-linearity of the model's response to differing values of a parameter gamma (γ) suggests simple model approximations are appropriate in only a narrow space of relatively dispersed nursing assignments. Simplifying assumptions around how a hospital population is modeled, especially assuming random mixing, may overestimate infection rates and the impact of interventions. In many, if not most, cases more complex models that represent population mixing with higher granularity are justified.

Patient participation in critical care research, service design, and care delivery

Hospitals with better nursing resources report more favourable patient outcomes with almost no difference in cost as compared to those with worse nursing resources. The aim of this study was to assess the association between nursing cost per intensive care unit bed and patient outcomes (mortality, readmission, and length of stay).This was a retrospective cohort study using data collected from the intensive care units of 17 Belgian hospitals from January 01 to December 31, 2018. Hospitals were dichotomized using median annual nursing cost per bed. A total of 18,235 intensive care unit stays were included in the study with 5,664 stays in the low-cost nursing group and 12,571 in the high-cost nursing group.The rate of high length of stay outliers in the intensive care unit was significantly lower in the high-cost nursing group (9.2% vs 14.4%) compared to the low-cost nursing group. Intensive care unit readmission was not significantly different in the two groups. Mortality was lower in the high-cost nursing group for intensive care unit (9.9% vs 11.3%) and hospital (13.1% vs 14.6%) mortality. The nursing cost per intensive care bed was different in the two groups, with a median [IQR] cost of 159,387€ [140,307–166,690] for the low-cost nursing group and 214,032€ [198,094–230,058] for the high-cost group.In multivariate analysis, intensive care unit mortality (OR = 0.80, 95% CI: 0.69–0.92, p < 0.0001), in-hospital mortality (OR = 0.82, 95% CI: 0.72–0.93, p < 0.0001), and high length of stay outliers (OR = 0.48, 95% CI: 0.42–0.55, p < 0.0001) were lower in the high-cost nursing group. However, there was no significant effect on intensive care readmission between the two groups (OR = 1.24, 95% CI: 0.97–1.51, p > 0.05).This study found that higher-cost nursing per bed was associated with significantly lower intensive care unit and in-hospital mortality rates, as well as fewer high length of stay outliers, but had no significant effect on readmission to the intensive care unit..

Association between nursing cost and patient outcomes in intensive care units: A retrospective cohort study of Belgian hospitals

IntroductionHospitals with better nursing resources report more favourable patient outcomes with almost no difference in cost as compared to those with worse nursing resources. The aim of this study was to assess the association between nursing cost per intensive care unit bed and patient outcomes (mortality, readmission, and length of stay). MethodologyThis was a retrospective cohort study using data collected from the intensive care units of 17 Belgian hospitals from January 01 to December 31, 2018. Hospitals were dichotomized using median annual nursing cost per bed. A total of 18,235 intensive care unit stays were included in the study with 5,664 stays in the low-cost nursing group and 12,571 in the high-cost nursing group. ResultsThe rate of high length of stay outliers in the intensive care unit was significantly lower in the high-cost nursing group (9.2% vs 14.4%) compared to the low-cost nursing group. Intensive care unit readmission was not significantly different in the two groups. Mortality was lower in the high-cost nursing group for intensive care unit (9.9% vs 11.3%) and hospital (13.1% vs 14.6%) mortality. The nursing cost per intensive care bed was different in the two groups, with a median [IQR] cost of 159,387€ [140,307–166,690] for the low-cost nursing group and 214,032€ [198,094–230,058] for the high-cost group.In multivariate analysis, intensive care unit mortality (OR = 0.80, 95% CI: 0.69–0.92, p < 0.0001), in-hospital mortality (OR = 0.82, 95% CI: 0.72–0.93, p < 0.0001), and high length of stay outliers (OR = 0.48, 95% CI: 0.42–0.55, p < 0.0001) were lower in the high-cost nursing group. However, there was no significant effect on intensive care readmission between the two groups (OR = 1.24, 95% CI: 0.97–1.51, p > 0.05). ConclusionsThis study found that higher-cost nursing per bed was associated with significantly lower intensive care unit and in-hospital mortality rates, as well as fewer high length of stay outliers, but had no significant effect on readmission to the intensive care unit..

Noninvasive ventilation in COVID-19 patients aged ≥ 70 years—a prospective multicentre cohort study

BackgroundNoninvasive ventilation (NIV) is a promising alternative to invasive mechanical ventilation (IMV) with a particular importance amidst the shortage of intensive care unit (ICU) beds during the COVID-19 pandemic. We aimed to evaluate the use of NIV in Europe and factors associated with outcomes of patients treated with NIV.MethodsThis is a substudy of COVIP study—an international prospective observational study enrolling patients aged ≥ 70 years with confirmed COVID-19 treated in ICU. We enrolled patients in 156 ICUs across 15 European countries between March 2020 and April 2021.The primary endpoint was 30-day mortality.ResultsCohort included 3074 patients, most of whom were male (2197/3074, 71.4%) at the mean age of 75.7 years (SD 4.6). NIV frequency was 25.7% and varied from 1.1 to 62.0% between participating countries. Primary NIV failure, defined as need for endotracheal intubation or death within 30 days since ICU admission, occurred in 470/629 (74.7%) of patients. Factors associated with increased NIV failure risk were higher Sequential Organ Failure Assessment (SOFA) score (OR 3.73, 95% CI 2.36–5.90) and Clinical Frailty Scale (CFS) on admission (OR 1.46, 95% CI 1.06–2.00). Patients initially treated with NIV (n = 630) lived for 1.36 fewer days (95% CI − 2.27 to − 0.46 days) compared to primary IMV group (n = 1876).ConclusionsFrequency of NIV use varies across European countries. Higher severity of illness and more severe frailty were associated with a risk of NIV failure among critically ill older adults with COVID-19. Primary IMV was associated with better outcomes than primary NIV.Clinical Trial RegistrationNCT04321265, registered 19 March 2020, https://clinicaltrials.gov.

Providing Neurocritical Care in Resource-Limited Settings: Challenges and Opportunities.

Acute neurologic illnesses (ANI) contribute significantly to the global burden of disease and cause disproportionate death and disability in low-income and middle-income countries (LMICs) where neurocritical care resources and expertise are limited. Shifting epidemiologic trends in recent decades have increased the worldwide burden of noncommunicable diseases, including cerebrovascular disease and traumatic brain injury, which coexist in many LMICs with a persistently high burden of central nervous system infections such as tuberculosis, neurocysticercosis, and HIV-related opportunistic infections and complications. In the face of this heavy disease burden, many resource-limited countries lack the infrastructure to provide adequate care for patients with ANI. Major gaps exist between wealthy and poor countries in access to essential resources such as intensive care unit beds, neuroimaging, clinical laboratories, neurosurgical capacity, and medications for managing complex neurologic emergencies. Moreover, many resource-limited countries face critical shortages in health care workers trained to manage neurologic emergencies, with subspecialized neurocritical care expertise largely absent outside of high-income countries. Numerous opportunities exist to overcome these challenges through capacity-building efforts that improve outcomes for patients with ANI in resource-limited countries. These include research on needs and best practices for ANI management in LMICs, developing systems for effective triage, education and training to expand the neurology workforce, and supporting increased collaboration and data sharing among LMIC health care workers and systems. The success of these efforts in curbing the disproportionate and rising impact of ANI in LMICs will depend on the coordinated engagement of the global neurocritical care community.

When Outcomes Diverge: Age and Cardiovascular Risk as Determinants of Mortality and ICU Admission in COVID-19.

Background: Hospital mortality and admission to the Intensive Care Unit (ICU) are markers of disease severity in COVID-19 patients. Cardiovascular co-morbidities are one of the main determinants of negative outcomes. In this study we investigated the impact of cardiovascular co-morbidities on mortality and admission to the ICU in first-wave COVID-19 patients. Methods: A multicenter, retrospective, cohort study. A total of 1077 patients were analyzed for mortality and ICU admission. Cardiovascular risk factors were explored as determinants of the outcomes after correction for other confounders. Results: In the multivariable model, after correction for age, only a history of heart failure remained independently associated (p = 0.0013) with mortality (hazard ratio 2.22, 95% confidence interval 1.37 to 3.62). Age showed a mortality risk increase of 8% per year (hazard ratio 1.08, 95% confidence interval 1.05 to 1.10, p = 0.001). The transition from ward to the ICU had, as a single determinant, the age, but in a reversed fashion (hazard ratio 0.96, 95% confidence interval 0.94 to 0.98, p = 0.0002). Conclusions: Once adjusted for the main determinant of mortality (age) heart failure only remained independently associated with mortality. Admission to the ICU was less likely for elderly patients. This may reflect the catastrophic impact of the first wave of COVID-19 pandemic in terms of ICU bed availability in Lombardy, leading to a selection process for ICU admission.

An Examination of the Stochastic Distribution of Spatial Accessibility to Intensive Care Unit Beds during the COVID-19 Pandemic: A Case Study of the Greater Houston Area of Texas.

Sufficient and reliable health care access is necessary for people to be able to maintain good health. Hence, investigating the uncertainty embedded in the temporal changes of inputs would be beneficial for understanding their impact on spatial accessibility. However, previous studies are limited to implementing only the uncertainty of mobility, while health care resource availability is a significant concern during the coronavirus disease (COVID‐19) pandemic. Our study examined the stochastic distribution of spatial accessibility under the uncertainties underlying the availability of intensive care unit (ICU) beds and ease of mobility in the Greater Houston area of Texas. Based on the randomized supply and mobility from their historical changes, we employed Monte Carlo simulation to measure ICU bed accessibility with an enhanced two‐step floating catchment area (E2SFCA) method. We then conducted hierarchical clustering to classify regions of adequate (sufficient and reliable) accessibility and inadequate (insufficient and unreliable) accessibility. Lastly, we investigated the relationship between the accessibility measures and the case fatality ratio of COVID‐19. As result, locations of sufficient access also had reliable accessibility; downtown and outer counties, respectively, had adequate and inadequate accessibility. We also raised the possibility that inadequate health care accessibility may cause higher COVID‐19 fatality ratios.

North American COVID-19 Myocardial Infarction (NACMI) Risk Score for Prediction of In-Hospital Mortality.

BackgroundIn-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI) is higher in those with COVID-19 than in those without COVID-19. The factors that predispose to this mortality rate and their relative contribution are poorly understood. This study developed a risk score inclusive of clinical variables to predict in-hospital mortality in patients with COVID-19 and STEMI.MethodsBaseline demographic, clinical, and procedural data from patients in the North American COVID-19 Myocardial Infarction registry were extracted. Univariable logistic regression was performed using candidate predictor variables, and multivariable logistic regression was performed using backward stepwise selection to identify independent predictors of in-hospital mortality. Independent predictors were assigned a weighted integer, with the sum of the integers yielding the total risk score for each patient.ResultsIn-hospital mortality occurred in 118 of 425 (28%) patients. Eight variables present at the time of STEMI diagnosis (respiratory rate of >35 breaths/min, cardiogenic shock, oxygen saturation of <93%, age of >55 ​years, infiltrates on chest x-ray, kidney disease, diabetes, and dyspnea) were assigned a weighted integer. In-hospital mortality increased exponentially with increasing integer risk score (Cochran-Armitage χ2, P ​< ​.001), and the model demonstrated good discriminative power (c-statistic ​= ​0.81) and calibration (Hosmer-Lemeshow, P ​= ​.40). The increasing risk score was strongly associated with in-hospital mortality (3.6%-60% mortality for low-risk and very high–risk score categories, respectively).ConclusionsThe risk of in-hospital mortality in patients with COVID-19 and STEMI can be accurately predicted and discriminated using readily available clinical information.

Legality of Explicit Racial Discrimination in the Distribution of Lifesaving COVID-19 Treatments

In 2021, the Federal Drug Administration released a statement advocating for race and ethnicity to be used in rationing lifesaving COVID-19 treatments. By January 2022, three states had implemented policies explicitly prioritizing treatments based on race, which resulted in multiple legal challenges. This Article analyzes the uphill battle such policies would face in an equal protection challenge. It also rebuts the attempt to analogize these policies to the legally acceptable practice of racial preferences in college admissions. Finally, nonlegal, pragmatic consequences are considered, such as how the policy risks disproportionately favoring the wealthy reduces trust in future government pronouncements regarding COVID-19, perpetuates harmful stereotypes about racial inferiority, breeds racial resentment, and causes unnecessary delays in treatment.&#x0D; The racially disparate outcomes from the COVID-19 pandemic illuminate numerous background factors that disadvantage minority groups. However, the implementation of racial preferences in lifesaving treatments is not the answer. As demonstrated in this Article, such policies spectacularly fail judicial scrutiny. Furthermore, the nonlegal, pragmatic considerations establish that such a policy does far more harm than good. These considerations are of paramount importance not only for the current COVID-19 crisis but also for future pandemics and the rationing of other limited medical resources, such as organ transplants and intensive care unit beds.

How a Developing Country Faces COVID-19 Rehabilitation: The Chilean Experience.

The coronavirus 19 (COVID-19) pandemic has been one of the most significant challenges to public health in recent decades. The heterogeneity of government responses and the varying preparedness of health systems has determined that the pandemic's impact differs from country to country. Chile is no stranger to the challenges posed by rehabilitation in a developing country. We aimed to describe the approach to rehabilitation during the pandemic in Chile in the public health system since rehabilitation is considered a relevant health strategy from the prevention to management of complications, mitigation of sequelae, or new complications associated with COVID-19. For this, a descriptive study was conducted on the rehabilitation strategies implemented by Chile to respond to the COVID-19 pandemic. The analysis includes the context of the Chilean health system and the matrix of access to rehabilitation services in COVID-19. The Health Ministry (MINSAL) rehabilitation strategy includes five central axes: approaches, specific lines, transversal lines, intervention, and funding. Additionally, the policies were based and supported by the WHO recommendations. Intensive care unit beds were increased approximately 68%, and the primary care response was the reconversion of function depending on the epidemiological context. During the 2021–2022 period, the estimated number of people diagnosed with a post-COVID-19 condition was 80,528. With this, we can conclude that a developing country has managed to coordinate a rehabilitation policy for people with COVID-19 by generating a structure of the different health system levels. However, the effectiveness of this policy will need to be evaluated in the future.

Critical care resources, disaster preparedness, and sepsis management: Survey results from the Asia Pacific region

There is paucity of data on critical care resources, disaster preparedness, and sepsis management in countries within the Asia Pacific region. An online survey was conducted from 15 April to 17 July 2020. Snowball sampling through the Asia Pacific Sepsis Alliance and network contacts was used to recruit respondents. Countries were grouped according to the World Bank Country Income 2019 classification into lower-middle income (LMIC), upper-middle income (UMIC), and high-income (HIC). Survey questions addressed to hospital characteristics, critical care resources, disaster preparedness, and sepsis management. In total, 59 hospitals from 15 countries responded (33 LMICs, 8 UMICs, 18 HICs) with most responses from the Philippines (10; 16.9%). Median [Inter-quartile range (IQR)] hospital and Intensive Care Unit (ICU) bed capacity was 798 (500–1,001) and 37 (19–59), respectively. Median (IQR) doctor-to-patient and nurse-to-patient day ratios were 1:5 (1:3–1:8) and 1:2 (1:1–1:2), respectively. Availability of 24/7 physiotherapy services, 24/7 Medical resonance Imaging (MRI), point-of-care lactate, and “reserve” antibiotics was limited. Most ICUs had a disaster management plan (88%) and access to Personal Protective Equipment (96%). The most commonly adopted sepsis guideline was the Surviving Sepsis Campaign guidelines (77%). LMIC/UMIC ICUs had lower nurse-to patient ratio and surge capacity along with limited access to 24/7 physiotherapy and MRI services, and interventions like Extra Corporeal Membrane Oxygenation, and Continuous Renal Replacement Therapy. Self-reported adoption and adherence to sepsis guidelines was higher in LMICs/UMICs than HICs. In the Asia Pacific region, critical care resources, disaster preparedness and management of sepsis vary considerably between countries across different income categories. In particular, low surge and isolation capacity in LMICs highlights the need for better health service planning and preparation.

Survey of critical care practices for alcohol withdrawal syndrome in the intensive care unit

AbstractIntroductionAlcohol use disorder affects up to 60% of intensive care unit (ICU) patients. Alcohol withdrawal syndrome (AWS) is associated with increased length of stay, mechanical ventilation, and mortality. The lack of clinical practice guidelines specific to ICU patients leads to ambiguity about the standard management of AWS in this population.ObjectivesThe aim of this study was to describe current practice patterns employed for AWS management in ICU settings across the United States.MethodsThis was a cross‐sectional survey of practicing critical care pharmacists identified via a pharmacy professional organization critical care listserv. Emails were sent four times to the recipient list from May 14, 2021 to June 25, 2021. Retired pharmacists and trainees on the listserv were excluded. The survey was conducted by REDCap and descriptive statistics were performed.ResultsThere were a total of 144/1534 (9.4%) responses. Predominant characteristics included university hospital settings (n = 71/143, 49.7%) with greater than 75 ICU beds (n = 54/144, 37.5%), and medical (n = 53/144, 36.8%) or mixed (n = 52/144, 36.1%) ICU populations. The majority reported having an institutional AWS guideline (n = 96/140, 68.6%). A total of 139/143 (97.2%) preferred benzodiazepines, with lorazepam (n = 136/143, 97.8%) as the primary benzodiazepine agent. Most preferred lorazepam as needed, rather than preemptively (n = 137/138, 99.3%). Phenobarbital use was reported by 82.6% (n = 119/144) of respondents, primarily for treatment of benzodiazepine‐refractory withdrawal (n = 96/117, 82.1%). Similarly, dexmedetomidine use was reported by 86.1% (n = 124/144) of respondents, primarily for refractory withdrawal (n = 48/121, 39.7%). Propofol was primarily preferred for patients on mechanical ventilation (n = 56/98, 57.1%) or refractory withdrawal (n = 51/98, 52.0%). Clonidine was preferred by 45.8% (n = 66/144) and atypical antipsychotics by 33.6% (n = 48/143) of respondents.ConclusionsMost ICU pharmacists prefer symptom‐triggered benzodiazepines first line, followed by propofol in intubated patients and dexmedetomidine and/or phenobarbital for refractory cases. Clinical practice guidelines may help provide guidance for the management of these patients.

Discussion Guide for the Brogan Article.

Discussion Guide for the Brogan Article.