Hospital-Level Variation in Death for Critically Ill Patients with COVID-19

Comparison of outcomes and characteristics of patients admitted to the ICU with COVID-19 and other community-acquired pneumonia based on propensity score matching

OBJECTIVE

To compare the similarities and differences between patients with Coronavirus Disease 2019 (COVID-19) and those with other community-acquired pneumonia (CAP) admitted to the intensive care unit (ICU), utilizing propensity score matching (PSM), regarding hospitalization expenses, treatment options, and prognostic outcomes, aiming to inform the diagnosis and treatment of COVID-19.

METHODS

Patients admitted to the ICU of the Third People's Hospital of Datong City, diagnosed with COVID-19 from December 2022 to February 2023, constituted the observation group, while those with other CAP admitted from January to November 2022 formed the control group. Basic information, clinical data at admission, and time from symptom onset to admission were matched using PSM.

RESULTS

A total of 70 patients were included in the COVID-19 group and 119 in the CAP group. The patients were matched by the propensity matching method, and 37 patients were included in each of the last two groups. After matching, COVID-19 had a higher failure rate than CAP, but the difference was not statistically significant (73% vs. 51%, p = 0.055). The utilization rate of antiviral drugs (40% vs. 11%, p = 0.003), γ-globulin (19% vs. 0%, p = 0.011) and prone position ventilation (PPV) (27% vs. 0%, p < 0.001) in patients with COVID-19 were higher than those in the CAP, and the differences were statistically significant. The total hospitalization cost of COVID-19 patients was lower than that of CAP patients, and the difference was statistically significant (27889.5 vs. 50175.9, p = 0.007). The hospital stay for COVID-19 patients was shorter than for CAP patients, but the difference was not statistically significant (10.9 vs. 16.6, p = 0.071).

CONCLUSION

Our findings suggest that limited medical resources influenced patient outcomes during the COVID-19 pandemic. Addressing substantial demands for ICU capacity and medications during this period could have potentially reduced the mortality rate among COVID-19 patients.

The Association Between Prepandemic ICU Performance and Mortality Variation in COVID-19: A Multicenter Cohort Study of 35,619 Critically Ill Patients

BACKGROUND

During the COVID-19 pandemic, ICUs remained under stress and observed elevated mortality rates and high variations of outcomes. A knowledge gap exists regarding whether an ICU performing best during nonpandemic times would still perform better when under high pressure compared with the least performing ICUs.

RESEARCH QUESTION

Does prepandemic ICU performance explain the risk-adjusted mortality variability for critically ill patients with COVID-19?

STUDY DESIGN AND METHODS

This study examined a cohort of adults with real-time polymerase chain reaction-confirmed COVID-19 admitted to 156 ICUs in 35 hospitals from February 16, 2020, through December 31, 2021, in Brazil. We evaluated crude and adjusted in-hospital mortality variability of patients with COVID-19 in the ICU during the pandemic. Association of baseline (prepandemic) ICU performance and in-hospital mortality was examined using a variable life-adjusted display (VLAD) during the pandemic and a multivariable mixed regression model adjusted by clinical characteristics, interaction of performance with the year of admission, and mechanical ventilation at admission.

RESULTS

Thirty-five thousand six hundred nineteen patients with confirmed COVID-19 were evaluated. The median age was 52 years, median Simplified Acute Physiology Score 3 was 42, and 18% underwent invasive mechanical ventilation. In-hospital mortality was 13% and 54% for those receiving invasive mechanical ventilation. Adjusted in-hospital mortality ranged from 3.6% to 63.2%. VLAD in the most efficient ICUs was higher than the overall median in 18% of weeks, whereas VLAD was 62% and 84% in the underachieving and least efficient groups, respectively. The least efficient baseline ICU performance group was associated independently with increased mortality (OR, 2.30; 95% CI, 1.45-3.62) after adjusting for patient characteristics, disease severity, and pandemic surge.

INTERPRETATION

ICUs caring for patients with COVID-19 presented substantial variation in risk-adjusted mortality. ICUs with better baseline (prepandemic) performance showed reduced mortality and less variability. Our findings suggest that achieving ICU efficiency by targeting improvement in organizational aspects of ICUs may impact outcomes, and therefore should be a part of the preparedness for future pandemics.

Development and validation of a machine learning model to predict the use of renal replacement therapy in 14,374 patients with COVID-19

PURPOSE

To develop a model to predict the use of renal replacement therapy (RRT) in COVID-19 patients.

MATERIALS AND METHODS

Retrospective analysis of multicenter cohort of intensive care unit (ICU) admissions of Brazil involving COVID-19 critically adult patients, requiring ventilatory support, admitted to 126 Brazilian ICUs, from February 2020 to December 2021 (development) and January to May 2022 (validation). No interventions were performed.

RESULTS

Eight machine learning models' classifications were evaluated. Models were developed using an 80/20 testing/train split ratio and cross-validation. Thirteen candidate predictors were selected using the Recursive Feature Elimination (RFE) algorithm. Discrimination and calibration were assessed. Temporal validation was performed using data from 2022. Of 14,374 COVID-19 patients with initial respiratory support, 1924 (13%) required RRT. RRT patients were older (65 [53-75] vs. 55 [42-68]), had more comorbidities (Charlson's Comorbidity Index 1.0 [0.00-2.00] vs 0.0 [0.00-1.00]), had higher severity (SAPS-3 median: 61 [51-74] vs 48 [41-58]), and had higher in-hospital mortality (71% vs 22%) compared to non-RRT. Risk factors for RRT, such as Creatinine, Glasgow Coma Scale, Urea, Invasive Mechanical Ventilation, Age, Chronic Kidney Disease, Platelets count, Vasopressors, Noninvasive Ventilation, Hypertension, Diabetes, modified frailty index (mFI) and Gender, were identified. The best discrimination and calibration were found in the Random Forest (AUC [95%CI]: 0.78 [0.75-0.81] and Brier's Score: 0.09 [95%CI: 0.08-0.10]). The final model (Random Forest) showed comparable performance in the temporal validation (AUC [95%CI]: 0.79 [0.75-0.84] and Brier's Score, 0.08 [95%CI: 0.08-0.1]).

CONCLUSIONS

An early ML model using easily available clinical and laboratory data accurately predicted the use of RRT in critically ill patients with COVID-19. Our study demonstrates that using ML techniques is feasible to provide early prediction of use of RRT in COVID-19 patients.

Pre-COVID-19 hospital quality and hospital response to COVID-19: examining associations between risk-adjusted mortality for patients hospitalised with COVID-19 and pre-COVID-19 hospital quality

OBJECTIVES

The extent to which care quality influenced outcomes for patients hospitalised with COVID-19 is unknown. Our objective was to determine if prepandemic hospital quality is associated with mortality among Medicare patients hospitalised with COVID-19.

DESIGN

This is a retrospective observational study. We calculated hospital-level risk-standardised in-hospital and 30-day mortality rates (risk-standardised mortality rates, RSMRs) for patients hospitalised with COVID-19, and correlation coefficients between RSMRs and pre-COVID-19 hospital quality, overall and stratified by hospital characteristics.

SETTING

Short-term acute care hospitals and critical access hospitals in the USA.

PARTICIPANTS

Hospitalised Medicare beneficiaries (Fee-For-Service and Medicare Advantage) age 65 and older hospitalised with COVID-19, discharged between 1 April 2020 and 30 September 2021.

INTERVENTION/EXPOSURE

Pre-COVID-19 hospital quality.

OUTCOMES

Risk-standardised COVID-19 in-hospital and 30-day mortality rates (RSMRs).

RESULTS

In-hospital (n=4256) RSMRs for Medicare patients hospitalised with COVID-19 (April 2020-September 2021) ranged from 4.5% to 59.9% (median 18.2%; IQR 14.7%-23.7%); 30-day RSMRs ranged from 12.9% to 56.2% (IQR 24.6%-30.6%). COVID-19 RSMRs were negatively correlated with star rating summary scores (in-hospital correlation coefficient -0.41, p<0.0001; 30 days -0.38, p<0.0001). Correlations with in-hospital RSMRs were strongest for patient experience (-0.39, p<0.0001) and timely and effective care (-0.30, p<0.0001) group scores; 30-day RSMRs were strongest for patient experience (-0.34, p<0.0001) and mortality (-0.33, p<0.0001) groups. Patients admitted to 1-star hospitals had higher odds of mortality (in-hospital OR 1.87, 95% CI 1.83 to 1.91; 30-day OR 1.46, 95% CI 1.43 to 1.48) compared with 5-star hospitals. If all hospitals performed like an average 5-star hospital, we estimate 38 000 fewer COVID-19-related deaths would have occurred between April 2020 and September 2021.

CONCLUSIONS

Hospitals with better prepandemic quality may have care structures and processes that allowed for better care delivery and outcomes during the COVID-19 pandemic. Understanding the relationship between pre-COVID-19 hospital quality and COVID-19 outcomes will allow policy-makers and hospitals better prepare for future public health emergencies.

Measuring the Impact of ICU Strain on Mortality, After-Hours Discharge, Discharge Delay, Interhospital Transfer, and Readmission in Australia With the Activity Index

OBJECTIVES

ICU resource strain leads to adverse patient outcomes. Simple, well-validated measures of ICU strain are lacking. Our objective was to assess whether the "Activity index," an indicator developed during the COVID-19 pandemic, was a valid measure of ICU strain.

DESIGN

Retrospective national registry-based cohort study.

SETTING

One hundred seventy-five public and private hospitals in Australia (June 2020 through March 2022).

SUBJECTS

Two hundred seventy-seven thousand seven hundred thirty-seven adult ICU patients.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Data from the Australian and New Zealand Intensive Care Society Adult Patient Database were matched to the Critical Health Resources Information System. The mean daily Activity index of each ICU (census total of "patients with 1:1 nursing" + "invasive ventilation" + "renal replacement" + "extracorporeal membrane oxygenation" + "active COVID-19," divided by total staffed ICU beds) during the patient's stay in the ICU was calculated. Patients were categorized as being in the ICU during very quiet (Activity index < 0.1), quiet (0.1 to < 0.6), intermediate (0.6 to < 1.1), busy (1.1 to < 1.6), or very busy time-periods (≥ 1.6). The primary outcome was in-hospital mortality. Secondary outcomes included after-hours discharge from the ICU, readmission to the ICU, interhospital transfer to another ICU, and delay in discharge from the ICU. Median Activity index was 0.87 (interquartile range, 0.40-1.24). Nineteen thousand one hundred seventy-seven patients died (6.9%). In-hospital mortality ranged from 2.4% during very quiet to 10.9% during very busy time-periods. After adjusting for confounders, being in an ICU during time-periods with higher Activity indices, was associated with an increased risk of in-hospital mortality (odds ratio [OR], 1.49; 99% CI, 1.38-1.60), after-hours discharge (OR, 1.27; 99% CI, 1.21-1.34), readmission (OR, 1.18; 99% CI, 1.09-1.28), interhospital transfer (OR, 1.92; 99% CI, 1.72-2.15), and less delay in ICU discharge (OR, 0.58; 99% CI, 0.55-0.62): findings consistent with ICU strain.

CONCLUSIONS

The Activity index is a simple and valid measure that identifies ICUs in which increasing strain leads to progressively worse patient outcomes.

Factors related to COVID-19 mortality among three Swedish intensive care units-A retrospective study

BACKGROUND

Mortality due to acute hypoxemic respiratory failure (AHRF) in patients with coronavirus disease-19 (COVID-19) differs across units, regions, and countries. These variations may be attributed to several factors, including comorbidities, acute physiological derangement, disease severity, treatment, ethnicity, healthcare system strain, and socioeconomic status. This study aimed to explore the features of patient characteristics, clinical management, and staffing that may be related to mortality among three intensive care units (ICUs) within the same hospital system in South Sweden.

METHODS

We retrospectively analyzed ICU patients with COVID-19 and AHRF in Region Jönköping County, Sweden. The primary outcome was the 90-day mortality rate. We used univariate and multivariable logistic regression analyses to investigate the relationship of predictors with outcomes.

RESULTS

Between March 15, 2020, and May 31, 2021, 331 patients with AHRF and COVID-19 were admitted to the three ICUs. There were differences in disease severity, treatments, process-related factors, and socioeconomic factors between the units. These factors were related to 90-day mortality. After multivariable adjustment, age, severity of acute respiratory distress syndrome, and the number of nurses per ICU-bed independently predicted 90-day mortality.

CONCLUSION

Age, disease severity, and nurse staffing, but not treatment or socioeconomic status, were independently associated with 90-day mortality among critically ill patients with AHRF due to COVID-19. We also identified variations in care related processes, which may be a modifiable risk factor and warrants future investigation.

Implementation of a Virtual Interprofessional ICU Learning Collaborative: Successes, Challenges, and Initial Reactions From the Structured Team-Based Optimal Patient-Centered Care for Virus COVID-19 Collaborators

Initial Society of Critical Care Medicine Discovery Viral Infection and Respiratory illness Universal Study (VIRUS) Registry analysis suggested that improvements in critical care processes offered the greatest modifiable opportunity to improve critically ill COVID-19 patient outcomes.

OBJECTIVES

The Structured Team-based Optimal Patient-Centered Care for Virus COVID-19 ICU Collaborative was created to identify and speed implementation of best evidence based COVID-19 practices.

DESIGN SETTING AND PARTICIPANTS

This 6-month project included volunteer interprofessional teams from VIRUS Registry sites, who received online training on the Checklist for Early Recognition and Treatment of Acute Illness and iNjury approach, a structured and systematic method for delivering evidence based critical care. Collaborators participated in weekly 1-hour videoconference sessions on high impact topics, monthly quality improvement (QI) coaching sessions, and received extensive additional resources for asynchronous learning.

MAIN OUTCOMES AND MEASURES

Outcomes included learner engagement, satisfaction, and number of QI projects initiated by participating teams.

RESULTS

Eleven of 13 initial sites participated in the Collaborative from March 2, 2021, to September 29, 2021. A total of 67 learners participated in the Collaborative, including 23 nurses, 22 physicians, 10 pharmacists, nine respiratory therapists, and three nonclinicians. Site attendance among the 11 sites in the 25 videoconference sessions ranged between 82% and 100%, with three sites providing at least one team member for 100% of sessions. The majority reported that topics matched their scope of practice (69%) and would highly recommend the program to colleagues (77%). A total of nine QI projects were initiated across three clinical domains and focused on improving adherence to established critical care practice bundles, reducing nosocomial complications, and strengthening patient- and family-centered care in the ICU. Major factors impacting successful Collaborative engagement included an engaged interprofessional team; an established culture of engagement; opportunities to benchmark performance and accelerate institutional innovation, networking, and acclaim; and ready access to data that could be leveraged for QI purposes.

CONCLUSIONS AND RELEVANCE

Use of a virtual platform to establish a learning collaborative to accelerate the identification, dissemination, and implementation of critical care best practices for COVID-19 is feasible. Our experience offers important lessons for future collaborative efforts focused on improving ICU processes of care.

Variations in COVID-19 Hospital Mortality by Patient Race/Ethnicity and Hospital Type in Illinois

BACKGROUND AND OBJECTIVES

It is controversial whether hospital care mitigated or exacerbated population level racial and ethnic disparities in COVID-19 mortality. To begin answering that question, this study analyzed variations in COVID-19 hospital mortality in Illinois by patient race and ethnicity and by hospital characteristics, while providing an estimate of hospital-level variation in COVID-19 mortality.

METHOD

This is a retrospective cohort study based on hospital administrative data for adult patients with COVID-19 discharged from acute care, non-federal Illinois hospitals from April 1, 2020 through June 30, 2021. The association of patient and hospital characteristics with the likelihood of death was analyzed using multilevel logistic regression.

RESULTS

There were 158,569 COVID-19-coded admissions to 181 general hospitals in Illinois; 14.5% resulted in death or discharge to hospice. Hospital deaths accounted for nearly 90% of all COVID-19-associated deaths over 15 months in Illinois. After adjusting for patient- and hospital-level characteristics, Hispanic patients had higher mortality risk (aOR 1.26, 95% CI: 1.20-1.33) as compared with non-Hispanic White patients, while non-Hispanic Black patients had lower mortality risk (aOR 0.75, 95% CI: 0.71-0.79). Safety net hospitals receiving disproportionate share hospital (DSH) funds had higher mortality risk (aOR 1.81, 95% CI: 1.43-2.30) compared with other hospitals.

CONCLUSION

Risk-adjusted COVID-19 hospital mortality was highest among patients of Hispanic ethnicity, while non-Hispanic Black patients had lower risk than non-Hispanic White patients. There was significant variation in hospital mortality rates, with particularly high safety net hospital mortality.

Assessment of Symptom, Disability, and Financial Trajectories in Patients Hospitalized for COVID-19 at 6 Months

Importance

Individuals who survived COVID-19 often report persistent symptoms, disabilities, and financial consequences. However, national longitudinal estimates of symptom burden remain limited.

Objective

To measure the incidence and changes over time in symptoms, disability, and financial status after COVID-19-related hospitalization.

Design, Setting, and Participants

A national US multicenter prospective cohort study with 1-, 3-, and 6-month postdischarge visits was conducted at 44 sites participating in the National Heart, Lung, and Blood Institute Prevention and Early Treatment of Acute Lung Injury Network's Biology and Longitudinal Epidemiology: COVID-19 Observational (BLUE CORAL) study. Participants included hospitalized English- or Spanish-speaking adults without severe prehospitalization disabilities or cognitive impairment. Participants were enrolled between August 24, 2020, and July 20, 2021, with follow-up occurring through March 30, 2022.

Exposure

Hospitalization for COVID-19 as identified with a positive SARS-CoV-2 molecular test.

Main Outcomes and Measures

New or worsened cardiopulmonary symptoms, financial problems, functional impairments, perceived return to baseline health, and quality of life. Logistic regression was used to identify factors associated with new cardiopulmonary symptoms or financial problems at 6 months.

Results

A total of 825 adults (444 [54.0%] were male, and 379 [46.0%] were female) met eligibility criteria and completed at least 1 follow-up survey. Median age was 56 (IQR, 43-66) years; 253 (30.7%) participants were Hispanic, 145 (17.6%) were non-Hispanic Black, and 360 (43.6%) were non-Hispanic White. Symptoms, disabilities, and financial problems remained highly prevalent among hospitalization survivors at month 6. Rates increased between months 1 and 6 for cardiopulmonary symptoms (from 67.3% to 75.4%; P = .001) and fatigue (from 40.7% to 50.8%; P < .001). Decreases were noted over the same interval for prevalent financial problems (from 66.1% to 56.4%; P < .001) and functional limitations (from 55.3% to 47.3%; P = .004). Participants not reporting problems at month 1 often reported new symptoms (60.0%), financial problems (23.7%), disabilities (23.8%), or fatigue (41.4%) at month 6.

Conclusions and Relevance

The findings of this cohort study of people discharged after COVID-19 hospitalization suggest that recovery in symptoms, functional status, and fatigue was limited at 6 months, and some participants reported new problems 6 months after hospital discharge.

Determinants of death in critically ill COVID-19 patients during the first wave of COVID-19: a multicenter study in Brazil

OBJECTIVE

To evaluate clinical outcomes and factors associated with mortality, focusing on secondary infections, in critically ill patients with COVID-19 in three Brazilian hospitals during the first pandemic wave.

METHODS

This was a retrospective observational study involving adult patients with COVID-19 admitted to one of the participating ICUs between March and August of 2020. We analyzed clinical features, comorbidities, source of SARS-CoV-2 infection, laboratory data, microbiology data, complications, and causes of death. We assessed factors associated with in-hospital mortality using logistic regression models.

RESULTS

We included 645 patients with a mean age of 61.4 years. Of those, 387 (60.0%) were male, 12.9% (83/643) had undergone solid organ transplant, and almost 10% (59/641) had nosocomial COVID-19 infection. During ICU stay, 359/644 patients (55.7%) required invasive mechanical ventilation, 225 (34.9%) needed renal replacement therapy, 337 (52.2%) received vasopressors, and 216 (33.5%) had hospital-acquired infections (HAIs), mainly caused by multidrug-resistant gram-negative bacteria. HAIs were independently associated with a higher risk of death. The major causes of death were refractory shock and multiple organ dysfunction syndrome but not ARDS, as previously reported in the literature.

CONCLUSIONS

In this study, most of our cohort required invasive mechanical ventilation and almost one third had HAIs, which were independently associated with a higher risk of death. Other factors related to death were Charlson Comorbidity Index, SOFA score at admission, and clinical complications during ICU stay. Nosocomial COVID-19 infection was not associated with death. The main immediate causes of death were refractory shock and multiple organ dysfunction syndrome.

Clinical characteristics and factors associated with ICU mortality during the first year of the SARS-Cov-2 pandemic in Romania: A prospective, cohort, multicentre study of 9000 patients

BACKGROUND

The epidemiology of critically ill patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be different worldwide. Despite similarities in medicine quality and formation, there are also significant differences concerning healthcare and ICU organisation, staffing, financial resources and population compliance and adherence. Large cohort data of critically ill patients from Central and Eastern Europe are also lacking.

OBJECTIVES

The study objectives were to describe the clinical characteristics of patients admitted to Romanian ICUs with SARS-CoV-2 infection and to identify the factors associated with ICU mortality.

DESIGN

Prospective, cohort, observational study.

SETTING

National recruitment, multicentre study, between March 2020 to March 2021.

PATIENTS

All patients with SARS-CoV-2 infection admitted to Romanian ICUs were eligible. There were no exclusion criteria.

INTERVENTION

None.

MAIN OUTCOME MEASURE

ICU mortality.

RESULTS

The statistical analysis included 9058 patients with definitive ICU outcome. The multivariable mixed effects logistic regression model found that age [odds ratio (OR) 1.27; 95% confidence interval (CI), 1.23 to 1.31], male gender (OR 1.21; 95% CI 1.05 to 1.4), medical history of neoplasia (OR 1.74; 95% CI, 1.36 to 2.22), chronic kidney disease (OR 1.54; 95% CI, 1.27 to 1.88), type II diabetes (OR 1.23; 95% CI, 1.06 to 1.43), chronic heart failure (OR 1.24; 95% CI, 1.03 to 1.49), dyspnoea (OR 1.3; 95% CI, 1.1 to 1.5), SpO2 less than 90% (OR 3; 95% CI, 2.5 to 3.5), admission SOFA score (OR 1.07; 95% CI, 1.05 to 1.09), acute respiratory distress syndrome (ARDS) on ICU admission (OR 1.35; 95% CI, 1.1 to 1.63) and the need for noninvasive (OR 1.8, 95% CI, 1.5 to 1.22) or invasive ventilation (OR 28; 95% CI, 22 to 35) and neuromuscular blockade (OR 3.5; 95% CI, 2.6 to 4.8), were associated with larger ICU mortality.Higher GCS on admission (OR 0.81; 95% CI, 0.79 to 0.83), treatment with hydroxychloroquine (OR 0.78; 95% CI, 0.64 to 0.95) and tocilizumab (OR 0.58; 95% CI, 0.48 to 0.71) were inversely associated with ICU mortality.

CONCLUSION

The SARS-CoV-2 critically ill Romanian patients share common personal and clinical characteristics with published European cohorts. Public health measures and vaccination campaign should focus on patients at risk.

Transcriptomic clustering of critically ill COVID-19 patients

BACKGROUND

Infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may cause a severe disease, termed coronavirus disease 2019 (COVID-19), with significant mortality. Host responses to this infection, mainly in terms of systemic inflammation, have emerged as key pathogenetic mechanisms and their modulation has shown a mortality benefit.

METHODS

In a cohort of 56 critically ill COVID-19 patients, peripheral blood transcriptomes were obtained at admission to an intensive care unit (ICU) and clustered using an unsupervised algorithm. Differences in gene expression, circulating microRNAs (c-miRNAs) and clinical data between clusters were assessed, and circulating cell populations estimated from sequencing data. A transcriptomic signature was defined and applied to an external cohort to validate the findings.

RESULTS

We identified two transcriptomic clusters characterised by expression of either interferon-related or immune checkpoint genes, respectively. Steroids have cluster-specific effects, decreasing lymphocyte activation in the former but promoting B-cell activation in the latter. These profiles have different ICU outcomes, despite no major clinical differences at ICU admission. A transcriptomic signature was used to identify these clusters in two external validation cohorts (with 50 and 60 patients), yielding similar results.

CONCLUSIONS

These results reveal different underlying pathogenetic mechanisms and illustrate the potential of transcriptomics to identify patient endotypes in severe COVID-19 with the aim to ultimately personalise their therapies.

Ventilatory Parameters Measured After One Week of Mechanical Ventilation and Survival in COVID-19-Related ARDS

BACKGROUND

Ventilatory parameters measured soon after initiation of mechanical ventilation have limited ability to predict outcome of COVID-19-related ARDS. We hypothesized that ventilatory parameters measured after one week of mechanical ventilation might differ between survivors and non-survivors.

METHODS

One hundred twenty-seven subjects with COVID-related ARDS had gas exchange and lung mechanics assessed on the day of intubation and one week later. The main parameters of interest were PaO2 /FIO2 , ventilatory ratio (VR), respiratory system compliance (CRS), and a composite score that was calculated as (PaO2 /FIO2 /100) × CRS/VR. The primary outcome was death in the ICU.

RESULTS

Of the 127 subjects, 42 (33%) died in the ICU and 85 (67%) were successfully extubated. On the day of intubation, PaO2 /FIO2 , CRS, and composite score of survivors and non-survivors were similar, but survivors had a lower VR. At one week, as compared to survivors, non-survivors had a significantly higher VR (2.04 ± 0.76 vs 1.60 ± 0.43, P < .001), lower CRS (27.4 ± 6.4 mL/cm H2O vs 32.4 ± 9.3 mL/cm H2O, P = .002), and lower composite score (20.6 ± 11.9 vs 34.5 ± 18.6, P < .001), with no statistically significant difference in PaO2 /FIO2 (137 ± 49 vs 155 ± 48, P = .08).

CONCLUSIONS

In subjects with COVID ARDS, parameters that reflect dead space (VR), lung mechanics (CRS), and a combined score that included PaO2 /FIO2 , VR, and CRS differed between survivors and non-survivors after one week of mechanical ventilation but with considerable overlap of values between survivors and non-survivors.

Impact of ICU strain on outcomes

PURPOSE OF REVIEW

Acute surge events result in health capacity strain, which can result in deviations from normal care, activation of contingencies and decisions related to resource allocation. This review discusses the impact of health capacity strain on patient centered outcomes.

RECENT FINDINGS

This manuscript discusses the lack of validated metrics for ICU strain capacity and a need for understanding the complex interrelationships of strain with patient outcomes. Recent work through the coronavirus disease 2019 pandemic has shown that acute surge events are associated with significant increase in hospital mortality. Though causal data on the differential impact of surge actions and resource availability on patient outcomes remains limited the overall signal consistently highlights the link between ICU strain and critical care outcomes in both normal and surge conditions.

SUMMARY

An understanding of ICU strain is fundamental to the appropriate clinical care for critically ill patients. Accounting for stain on outcomes in critically ill patients allows for minimization of variation in care and an ability of a given healthcare system to provide equitable, and quality care even in surge scenarios.

Care of the Seriously Ill Patient with SARS-CoV-2

In late 2019, SARS-CoV-2 caused the greatest global health crisis in a century, impacting all aspects of society. As the COVID-19 pandemic evolved throughout 2020 and 2021, multiple variants emerged, contributing to multiple surges in cases of COVID-19 worldwide. In 2021, highly effective vaccines became available, although the pandemic continues into 2022. There has been tremendous expansion of basic, translational, and clinical knowledge about SARS-CoV-2 and COVID-19 since the pandemic's onset. Treatment options have been rapidly explored, attempting to repurpose preexisting medications in tandem with development and evaluation of novel agents. Care of the seriously ill patient is examined.

Hospital characteristics associated with COVID-19 mortality: data from the multicenter cohort Brazilian Registry

The COVID-19 pandemic caused unprecedented pressure over health care systems worldwide. Hospital-level data that may influence the prognosis in COVID-19 patients still needs to be better investigated. Therefore, this study analyzed regional socioeconomic, hospital, and intensive care units (ICU) characteristics associated with in-hospital mortality in COVID-19 patients admitted to Brazilian institutions. This multicenter retrospective cohort study is part of the Brazilian COVID-19 Registry. We enrolled patients ≥ 18 years old with laboratory-confirmed COVID-19 admitted to the participating hospitals from March to September 2020. Patients' data were obtained through hospital records. Hospitals' data were collected through forms filled in loco and through open national databases. Generalized linear mixed models with logit link function were used for pooling mortality and to assess the association between hospital characteristics and mortality estimates. We built two models, one tested general hospital characteristics while the other tested ICU characteristics. All analyses were adjusted for the proportion of high-risk patients at admission. Thirty-one hospitals were included. The mean number of beds was 320.4 ± 186.6. These hospitals had eligible 6556 COVID-19 admissions during the study period. Estimated in-hospital mortality ranged from 9.0 to 48.0%. The first model included all 31 hospitals and showed that a private source of funding (β = - 0.37; 95% CI - 0.71 to - 0.04; p = 0.029) and location in areas with a high gross domestic product (GDP) per capita (β = - 0.40; 95% CI - 0.72 to - 0.08; p = 0.014) were independently associated with a lower mortality. The second model included 23 hospitals and showed that hospitals with an ICU work shift composed of more than 50% of intensivists (β = - 0.59; 95% CI - 0.98 to - 0.20; p = 0.003) had lower mortality while hospitals with a higher proportion of less experienced medical professionals had higher mortality (β = 0.40; 95% CI 0.11-0.68; p = 0.006). The impact of those association increased according to the proportion of high-risk patients at admission. In-hospital mortality varied significantly among Brazilian hospitals. Private-funded hospitals and those located in municipalities with a high GDP had a lower mortality. When analyzing ICU-specific characteristics, hospitals with more experienced ICU teams had a reduced mortality.

Hospital trajectories and early predictors of clinical outcomes differ between SARS-CoV-2 and influenza pneumonia

BACKGROUND

A comparison of pneumonias due to SARS-CoV-2 and influenza, in terms of clinical course and predictors of outcomes, might inform prognosis and resource management. We aimed to compare clinical course and outcome predictors in SARS-CoV-2 and influenza pneumonia using multi-state modelling and supervised machine learning on clinical data among hospitalised patients.

METHODS

This multicenter retrospective cohort study of patients hospitalised with SARS-CoV-2 (March-December 2020) or influenza (Jan 2015-March 2020) pneumonia had the composite of hospital mortality and hospice discharge as the primary outcome. Multi-state models compared differences in oxygenation/ventilatory utilisation between pneumonias longitudinally throughout hospitalisation. Differences in predictors of outcome were modelled using supervised machine learning classifiers.

FINDINGS

Among 2,529 hospitalisations with SARS-CoV-2 and 2,256 with influenza pneumonia, the primary outcome occurred in 21% and 9%, respectively. Multi-state models differentiated oxygen requirement progression between viruses, with SARS-CoV-2 manifesting rapidly-escalating early hypoxemia. Highly contributory classifier variables for the primary outcome differed substantially between viruses.

INTERPRETATION

SARS-CoV-2 and influenza pneumonia differ in presentation, hospital course, and outcome predictors. These pathogen-specific differential responses in viral pneumonias suggest distinct management approaches should be investigated.

FUNDING

This project was supported by NIH/NCATS UL1 TR002345, NIH/NCATS KL2 TR002346 (PGL), the Doris Duke Charitable Foundation grant 2015215 (PGL), NIH/NHLBI R35 HL140026 (CSC), and a Big Ideas Award from the BJC HealthCare and Washington University School of Medicine Healthcare Innovation Lab and NIH/NIGMS R35 GM142992 (PS).

Variation in the risk of death due to COVID-19: An international multicenter cohort study of hospitalized adults

BACKGROUND

There is wide variation in mortality among patients hospitalized with COVID-19. Whether this is related to patient or hospital factors is unknown.

OBJECTIVE

To compare the risk of mortality for patients hospitalized with COVID-19 and to determine whether the majority of that variation was explained by differences in patient characteristics across sites.

DESIGN, SETTING, AND PARTICIPANTS

An international multicenter cohort study of hospitalized adults with laboratory-confirmed COVID-19 enrolled from 10 hospitals in Ontario, Canada and 8 hospitals in Copenhagen, Denmark between January 1, 2020 and November 11, 2020.

MAIN OUTCOMES AND MEASURES

Inpatient mortality. We used a multivariable multilevel regression model to compare the in-hospital mortality risk across hospitals and quantify the variation attributable to patient-level factors.

RESULTS

There were 1364 adults hospitalized with COVID-19 in Ontario (n = 1149) and in Denmark (n = 215). In Ontario, the absolute risk of in-hospital mortality ranged from 12.0% to 39.8% across hospitals. Ninety-eight percent of the variation in mortality in Ontario was explained by differences in the characteristics of the patients. In Denmark, the absolute risk of inpatients ranged from 13.8% to 20.6%. One hundred percent of the variation in mortality in Denmark was explained by differences in the characteristics of the inpatients.

CONCLUSION

There was wide variation in inpatient COVID-19 mortality across hospitals, which was largely explained by patient-level factors, such as age and severity of presenting illness. However, hospital-level factors that could have affected care, including resource availability and capacity, were not taken into account. These findings highlight potential limitations in comparing crude mortality rates across hospitals for the purposes of reporting on the quality of care.

Characteristics and outcomes of SARS-COV 2 critically ill patients after emergence of the variant of concern 20H/501Y.V2: A comparative cohort study

There are currently no data regarding characteristics of critically ill patients with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variant of concern (VOC) 20H/501Y.V2. We therefore aimed to describe changes of characteristics in critically ill patients with Covid-19 between the first and the second wave when viral genome sequencing indicated that VOC was largely dominant in Mayotte Island (Indian Ocean). Consecutive patients with Covid-19 and over 18 years admitted in the unique intensive care unit (ICU) of Mayotte during wave 2 were compared with an historical cohort of patients admitted during wave 1. We performed a LR comparing wave 1 and wave 2 as outcomes. To complete analysis, we built a Random Forest model (RF), that is, a machine learning classification tool- using the same variable set as that of the LR. We included 156 patients, 41 (26.3%) and 115 (73.7%) belonging to the first and second waves respectively. Univariate analysis did not find difference in demographic data or in mortality. Our multivariate LR found that patients in wave 2 had less fever (absence of fever aOR 5.23, 95% confidence interval (CI) 1.89-14.48, p = .001) and a lower simplified acute physiology score (SAPS II) (aOR 0.95, 95% CI 0.91-0.99, p = .007) at admission; at 24 hours, the need of invasive mechanical ventilation was higher (aOR 3.49, 95% CI 0.98-12.51, p = .055) and pO2/FiO2 ratio was lower (aOR 0.99, 95 % CI 0.98-0.99, p = .03). Patients in wave 2 had also an increased risk of ventilator-associated pneumonia (VAP) (aOR 4.64, 95% CI 1.54-13.93, p = .006). Occurrence of VAP was also a key variable to classify patients between wave 1 and wave 2 in the variable importance plot of the RF model. Our data suggested that VOC 20H/501Y.V2 could be associated with a higher severity of respiratory failure at admission and a higher risk for developing VAP. We hypothesized that the expected gain in survival brought by recent improvements in critical care management could have been mitigated by increased transmissibility of the new lineage leading to admission of more severe patients. The immunological role of VOC 20H/501Y.V2 in the propensity for VAP requires further investigations.

Building local decision-making competencies during COVID-19: Accelerating the transition from learning healthcare systems to learning health communities

Introduction

The persisting and evolving COVID-19 pandemic has made apparent that no singular policy of mitigation at a regional, national or global level has achieved satisfactory and universally acceptable results. In the United States, carefully planned and executed pandemic policies have been neither effective nor popular and COVID-19 risk management decisions have been relegated to individual citizens and communities. In this paper, we argue that a more effective approach is to equip and strengthen community coalitions to become local learning health communities (LLHCs) that use data over time to make adaptive decisions that can optimize the equity and well-being in their communities.

Methods

We used data from the North Carolina (NC) county and zip code levels from May to August 2020 to demonstrate how a LLHC could use statistical process control (SPC) charts and simple statistical analysis to make local decisions about how to respond to COVID-19.

Results

We found many patterns of COVID-19 progression at the local (county and zip code) levels during the same time period within the state that were completely different from the aggregate NC state level data used for policy making.

Conclusions

Systematic approaches to learning from local data to support effective decisions have promise well beyond the current pandemic. These tools can help address other complex public health issues, and advance outcomes and equity. Building this capacity requires investment in data infrastructure and the strengthening of data competencies in community coalitions to better interpret data with limited need for advanced statistical expertise. Additional incentives that build trust, support data transparency, encourage truth-telling and promote meaningful teamwork are also critical. These must be carefully designed, contextually appropriate and multifaceted to motivate citizens to create and sustain an effective learning system that works for their communities.

Modulation of the Association Between Age and Death by Risk Factor Burden in Critically Ill Patients With COVID-19

Older age is a key risk factor for adverse outcomes in critically ill patients with COVID-19. However, few studies have investigated whether preexisting comorbidities and acute physiologic ICU factors modify the association between age and death.

DESIGN

Multicenter cohort study.

SETTING

ICUs at 68 hospitals across the United States.

PATIENTS

A total of 5,037 critically ill adults with COVID-19 admitted to ICUs between March 1, 2020, and July 1, 2020.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The primary exposure was age, modeled as a continuous variable. The primary outcome was 28-day inhospital mortality. Multivariable logistic regression tested the association between age and death. Effect modification by the number of risk factors was assessed through a multiplicative interaction term in the logistic regression model. Among the 5,037 patients included (mean age, 60.9 yr [± 14.7], 3,179 [63.1%] male), 1,786 (35.4%) died within 28 days. Age had a nonlinear association with 28-day mortality (p for nonlinearity <0.001) after adjustment for covariates that included demographics, preexisting comorbidities, acute physiologic ICU factors, number of ICU beds, and treatments for COVID-19. The number of preexisting comorbidities and acute physiologic ICU factors modified the association between age and 28-day mortality (p for interaction <0.001), but this effect modification was modest as age still had an exponential relationship with death in subgroups stratified by the number of risk factors.

CONCLUSIONS

In a large population of critically ill patients with COVID-19, age had an independent exponential association with death. The number of preexisting comorbidities and acute physiologic ICU factors modified the association between age and death, but age still had an exponential association with death in subgroups according to the number of risk factors present. Additional studies are needed to identify the mechanisms underpinning why older age confers an increased risk of death in critically ill patients with COVID-19.

Factors associated with, and variations in, COVID-19 hospital death rates in England's first two waves: observational study

OBJECTIVES

To assess patient-level and hospital-level predictors of death and variation in death rates following admission for COVID-19 in England's first two waves after accounting for random variation. To quantify the correlation between hospitals' first and second wave death rates.

DESIGN

Observational study using administrative data.

SETTING

Acute non-specialist hospitals in England.

PARTICIPANTS

All patients admitted with a primary diagnosis of COVID-19.

PRIMARY AND SECONDARY OUTCOMES

In-hospital death.

RESULTS

Hospital Episode Statistics (HES) data were extracted for all acute hospitals in England for COVID-19 admissions from March 2020 to March 2021. In wave 1 (March to July 2020), there were 74 484 admissions and 21 883 deaths (crude rate 29.4%); in wave 2 (August 2020 to March 2021), there were 165 642 admissions and 36 040 deaths (21.8%). Wave 2 patients were younger, with more hypertension and obesity but lower rates of other comorbidities. Mortality improved for all ages; in wave 2, it peaked in December 2020 at 24.2% (lower than wave 1's peak) but halved by March 2021. In multiple multilevel modelling combining HES with hospital-level data from Situational Reports, wave 2 and wave 1 variables significantly associated with death were mostly the same. The median odds ratio for wave 1 was just 1.05 and for wave 2 was 1.07. At 99.8% control limits, 3% of hospitals were high and 7% were low funnel plot outliers in wave 1; these figures were 9% and 12% for wave 2. Four hospitals were (low) outliers in both waves. The correlation between hospitals' adjusted mortality rates between waves was 0.45 (p<0.0001). Length of stay was similar in each wave.

CONCLUSIONS

England's first two COVID-19 waves were similar regarding predictors and moderate interhospital variation. Despite the challenges, variation in death rates and length of stay between hospitals was modest and might be accounted for by unobserved patient factors.

First-line Vasopressor Use in Septic Shock and Route of Administration: An Epidemiologic Study

RATIONALE

Norepinephrine is a first-line agent for treatment of hypotension in septic shock. However, its frequency of use, and potential barriers to its use are unclear.

OBJECTIVES

To evaluate the frequency of use of norepinephrine in septic shock, to identify potential barriers to its use, and to evaluate trends in use of vasopressors over time.

METHODS

Retrospective population-based cohort study of patients with septic shock in Alberta, Canada between July 1, 2012 and December 31, 2018. The primary outcome was receipt of a first-line vasopressor other than norepinephrine ("non-norepinephrine vasopressor"). Predictors of receiving a non-norepinephrine vasopressor were assessed using a multivariable-adjusted, multilevel logistic regression model with intensive care unit (ICU) as a random effect.

RESULTS

Among 6343 patients with septic shock, the proportion of patients receiving non-norepinephrine vasopressors as first-line treatment decreased steadily from 11.5% in 2012 to 3.0% in 2018. Two factors most strongly associated with their receipt were having peripheral intravenous access only (adjusted odds ratio (aOR) 6.15, 95% confidence interval (CI) 4.58-8.26, p<0.001) and year of admission (aOR 0.74 per year after 2012, 95% CI 0.69-0.80, p<0.001). Other factors that had associations after adjustment included admission to a non-teaching hospital (aOR 2.19, 95% CI 1.23-3.89, p=0.007), admission to a coronary care unit (aOR 2.56, 95% CI 1.001-6.54, p=0.05), SOFA score (aOR 0.92 per unit increase, 95% CI 0.88-0.96, p<0.001) and heart rate (aOR 0.92 per 10 beat per minute increase, 95% CI 0.87-0.97, p=0.002).

CONCLUSIONS

In a large cohort of patients in Alberta, Canada, we found a steady decrease in use of first-line vasopressors other than norepinephrine in septic shock. The strongest factor associated with their use was the presence of only peripheral venous access, suggesting this may still be considered a barrier to administration of norepinephrine.

Mortality trends and lengths of stay among hospitalized COVID-19 patients in Ontario and Québec (Canada): a population-based cohort study of the first three epidemic waves

Background

Epidemics of COVID-19 strained hospital resources. We describe temporal trends in mortality risk and length of stays in hospital and intensive care units (ICUs) among patients with COVID-19 hospitalized through the first three epidemic waves in Canada.

Methods

We used population-based provincial hospitalization data from the epicenters of Canada's epidemics (Ontario and Québec). Adjusted estimates were obtained using marginal standardization of logistic regression models, accounting for patient-level and hospital-level determinants.

Results

Using all hospitalizations from Ontario (N = 26,538) and Québec (N = 23,857), we found that unadjusted in-hospital mortality risks peaked at 31% in the first wave and was lowest at the end of the third wave at 6–7%. This general trend remained after adjustments. The odds of in-hospital mortality in the highest patient load quintile were 1.2-fold (95% CI: 1.0–1.4; Ontario) and 1.6-fold (95% CI: 1.3–1.9; Québec) that of the lowest quintile. Mean hospital and ICU length of stays decreased over time but ICU stays were consistently higher in Ontario than Québec.

Conclusions

In-hospital mortality risks and length of ICU stays declined over time despite changing patient demographics. Continuous population-based monitoring of patient outcomes in an evolving epidemic is necessary for health system preparedness and response.

Mortality of Mechanically Ventilated COVID-19 Patients in Traditional versus Expanded ICUs in NY

RATIONALE

During the first wave of the coronavirus disease 2019 (COVID-19) pandemic in New York City, the number of mechanically ventilated COVID-19 patients rapidly surpassed the capacity of traditional Intensive Care Units (ICUs), resulting in health systems utilizing other areas as expanded ICUs to provide critical care.

OBJECTIVES

To evaluate the mortality of patients admitted to expanded ICUs compared with those admitted to traditional ICUs.

METHODS

Multicenter, retrospective, cohort study of mechanically ventilated patients with COVID-19 admitted to the ICUs at 11 Northwell Health hospitals in the greater New York City area between March 1, 2020 and April 30, 2020.

MEASUREMENTS

In-hospital mortality up to 28 days after intubation of COVID-19 patients.

RESULTS

Among 1,966 mechanically ventilated patients with COVID-19, 1,198 (61%) died within 28 days after intubation, 46 (2%) were transferred to other hospitals outside of the Northwell Health system, 722 (37%) survived in the hospital until 28 days or were discharged after recovery. The risk of mortality of mechanically ventilated patients admitted to expanded ICUs was not different from those admitted to traditional ICUs (HR, 1.07; 95% CI, 0.95-1.20; p = 0.28), while hospital occupancy for critically ill patients itself was associated with increased risk of mortality (HR, 1.28; 95% CI, 1.12-1.45; p < 0.001).

CONCLUSIONS

Although increased hospital occupancy for critically ill patients itself was associated with increased mortality, the risk of 28-day in-hospital mortality of mechanically ventilated patients with COVID-19 who were admitted to expanded ICUs was not different from those admitted to traditional ICUs.

Pathophysiology of coronavirus-19 disease acute lung injury

PURPOSE OF REVIEW

More than 230 million people have tested positive for severe acute respiratory syndrome-coronavirus-2 infection globally by September 2021. The infection affects primarily the function of the respiratory system, where ∼20% of infected individuals develop coronavirus-19 disease (COVID-19) pneumonia. This review provides an update on the pathophysiology of the COVID-19 acute lung injury.

RECENT FINDINGS

In patients with COVID-19 pneumonia admitted to the intensive care unit, the PaO2/FiO2 ratio is typically <26.7 kPa (200 mmHg), whereas lung volume appears relatively unchanged. This hypoxaemia is likely determined by a heterogeneous mismatch of pulmonary ventilation and perfusion, mainly associated with immunothrombosis, endothelialitis and neovascularisation. During the disease, lung weight, elastance and dead space can increase, affecting respiratory drive, effort and dyspnoea. In some severe cases, COVID-19 pneumonia may lead to irreversible pulmonary fibrosis.

SUMMARY

This review summarises the fundamental pathophysiological features of COVID-19 in the context of the respiratory system. It provides an overview of the key clinical manifestations of COVID-19 pneumonia, including gas exchange impairment, altered pulmonary mechanics and implications of abnormal chemical and mechanical stimuli. It also critically discusses the clinical implications for mechanical ventilation therapy.

Latent Class Analysis Reveals COVID-19-related Acute Respiratory Distress Syndrome Subgroups with Differential Responses to Corticosteroids

Rationale

Two distinct subphenotypes have been identified in acute respiratory distress syndrome (ARDS), but the presence of subgroups in ARDS associated with coronavirus disease (COVID-19) is unknown.

Objectives

To identify clinically relevant, novel subgroups in COVID-19–related ARDS and compare them with previously described ARDS subphenotypes.

Methods

Eligible participants were adults with COVID-19 and ARDS at Columbia University Irving Medical Center. Latent class analysis was used to identify subgroups with baseline clinical, respiratory, and laboratory data serving as partitioning variables. A previously developed machine learning model was used to classify patients as the hypoinflammatory and hyperinflammatory subphenotypes. Baseline characteristics and clinical outcomes were compared between subgroups. Heterogeneity of treatment effect for corticosteroid use in subgroups was tested.

Measurements and Main Results

From March 2, 2020, to April 30, 2020, 483 patients with COVID-19–related ARDS met study criteria. A two-class latent class analysis model best fit the population (P = 0.0075). Class 2 (23%) had higher proinflammatory markers, troponin, creatinine, and lactate, lower bicarbonate, and lower blood pressure than class 1 (77%). Ninety-day mortality was higher in class 2 versus class 1 (75% vs. 48%; P < 0.0001). Considerable overlap was observed between these subgroups and ARDS subphenotypes. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RT-PCR cycle threshold was associated with mortality in the hypoinflammatory but not the hyperinflammatory phenotype. Heterogeneity of treatment effect to corticosteroids was observed (P = 0.0295), with improved mortality in the hyperinflammatory phenotype and worse mortality in the hypoinflammatory phenotype, with the caveat that corticosteroid treatment was not randomized.

Conclusions

We identified two COVID-19–related ARDS subgroups with differential outcomes, similar to previously described ARDS subphenotypes. SARS-CoV-2 PCR cycle threshold had differential value for predicting mortality in the subphenotypes. The subphenotypes had differential treatment responses to corticosteroids.

The Impact of Chronic Kidney Disease on Outcomes of Patients with COVID-19 Admitted to the Intensive Care Unit

CONTEXT

Coronavirus disease 2019 (COVID-19) disproportionately impacts patients with chronic kidney disease (CKD), especially those with kidney failure requiring replacement therapy (KFRT). Patients with KFRT have increased risk of developing COVID-19, and though initial reports suggested that mortality of these patients in the intensive care unit (ICU) setting is prohibitively high, those studies suffered from significant limitations. Subject of Review: The Study of the Treatment and Outcomes in Critically Ill Patients With COVID-19 (STOP-COVID) is a multicenter cohort study that enrolled adults with COVID-19 admitted to ICUs in 68 medical centers across the USA. STOP-COVID investigators compared characteristics at the time of ICU admission and clinical outcomes in 143 patients with KFRT, 521 with nondialysis-dependent CKD (ND-CKD), and 3,600 patients without CKD. Patients with KFRT were less likely to have typical COVID-19 symptoms but more likely to have altered mental status at the time of ICU admission and were less likely to require mechanical ventilation during hospitalization than those without kidney disease. Approximately, 50% of patients with KFRT and ND-CKD died within 28 days of ICU admission, and in fully adjusted models, patients with KFRT and ND-CKD had 1.41- and 1.25-fold higher risk of 28-day mortality than those without CKD. Patients with KFRT and ND-CKD were also less likely to receive emerging therapies for COVID-19 than those without CKD. Second Opinion: This study provides important new data demonstrating differences in clinical presentation in patients with KFRT and ND-CKD with COVID-19. Alhough patients with severe CKD had higher mortality than those without CKD, approximately half survived after 28 days, demonstrating that patients with COVID-19 and severe CKD can benefit from ICU care. The markedly lower use of emerging COVID-19 treatments in patients with severe CKD highlights the need to include these patients in clinical trials of new COVID-19 therapies and for clinicians to ensure equal access to care in patients with severe CKD and COVID-19.

Sepsis in patients hospitalized with coronavirus disease 2019: how often and how severe?

PURPOSE OF REVIEW

To discuss why severe COVID-19 should be considered sepsis and how co-infection and secondary infection can aggravate this condition and perpetuate organ dysfunction leading to high mortality rates.

RECENT FINDINGS

In severe COVID-19, there is both direct viral toxicity and dysregulated host response to infection. Although both coinfection and/or secondary infection are present, the latest is of greater concern mainly in resource-poor settings. Patients with severe COVID-19 present a phenotype of multiorgan dysfunction that leads to death in an unacceptable high percentage of the patients, with wide variability around the world. Similarly to endemic sepsis, the mortality of COVID-19 critically ill patients is higher in low-income and middle-income countries as compared with high-income countries. Disparities, including hospital strain, resources limitations, higher incidence of healthcare-associated infections (HAI), and staffing issues could in part explain this variability.

SUMMARY

The high mortality rates of critically ill patients with severe COVID-19 disease are not only related to the severity of patient disease but also to modifiable factors, such as the ICU strain, HAI incidence, and organizational aspects. Therefore, HAI prevention and the delivery of best evidence-based care for these patients to avoid additional damage is important. Quality improvement interventions might help in improving outcomes mainly in resource-limited settings.

Distinctive features of severe SARS-CoV-2 pneumonia

The coronavirus disease 2019 (COVID-19) pandemic is among the most important public health crises of our generation. Despite the promise of prevention offered by effective vaccines, patients with severe COVID-19 will continue to populate hospitals and intensive care units for the foreseeable future. The most common clinical presentation of severe COVID-19 is hypoxemia and respiratory failure, typical of the acute respiratory distress syndrome (ARDS). Whether the clinical features and pathobiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia differ from those of pneumonia secondary to other pathogens is unclear. This uncertainty has created variability in the application of historically proven therapies for ARDS to patients with COVID-19. We review the available literature and find many similarities between patients with ARDS from pneumonia attributable to SARS-CoV-2 versus other respiratory pathogens. A notable exception is the long duration of illness among patients with COVID-19, which could result from its unique pathobiology. Available data support the use of care pathways and therapies proven effective for patients with ARDS, while pointing to unique features that might be therapeutically targeted for patients with severe SARS-CoV-2 pneumonia.

A rational approach on the use of extracorporeal membrane oxygenation in severe hypoxemia: advanced technology is not a panacea

Veno-venous extracorporeal membrane oxygenation (ECMO) is a helpful intervention in patients with severe refractory hypoxemia either because mechanical ventilation cannot ensure adequate oxygenation or because lung protective ventilation is not feasible. Since ECMO is a highly invasive procedure with several, potentially devastating complications and its implementation is complex and expensive, simpler and less invasive therapeutic options should be first exploited. Low tidal volume and driving pressure ventilation, prone position, neuromuscular blocking agents and individualized ventilation based on transpulmonary pressure measurements have been demonstrated to successfully treat the vast majority of mechanically ventilated patients with severe hypoxemia. Veno-venous ECMO has a place in the small portion of severely hypoxemic patients in whom these strategies fail. A combined analysis of recent ARDS trials revealed that ECMO was used in only 2.15% of patients (n = 145/6736). Nevertheless, ECMO use has sharply increased in the last decade, raising questions regarding its thoughtful use. Such a policy could be harmful both for patients as well as for the ECMO technique itself. This narrative review attempts to describe together the practical approaches that can be offered to the sickest patients before going to ECMO, as well as the rationale and the limitations of ECMO. The benefit and the drawbacks associated with ECMO use along with a direct comparison with less invasive therapeutic strategies will be analyzed.