Time From Infiltrate on Chest Radiograph to Venovenous Extracorporeal Membrane Oxygenation in COVID-19 Affects Mortality : ASAIO Journal

Secondary Logo

Journal Logo

Management of COVID-19 Patients

Time From Infiltrate on Chest Radiograph to Venovenous Extracorporeal Membrane Oxygenation in COVID-19 Affects Mortality

Powell, Elizabeth K.*,†; Krause, Eric†,‡; Esposito, Emily*,†; Lankford, Allison§,†; Levine, Andrea; Young, Bree Ann C.∥,#; Haase, Daniel J.†,*,**; Tabatabai, Ali†,¶; Taylor, Bradley S.; Scalea, Thomas M.†,**; Galvagno, Samuel M. Jr†,††

Author Information
ASAIO Journal 69(1):p 23-30, January 2023. | DOI: 10.1097/MAT.0000000000001789
  • Free

Abstract

The disease caused by coronavirus disease 2019 (COVID-19) (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) can be severe and cause respiratory failure and acute respiratory distress syndrome (ARDS).1 Severe acute respiratory syndrome in COVID-19 was first detected in the United States on January 20, 2020, and has increased in prevalence since March, 2020.2 Both traditional ARDS treatments and COVID-19–specific treatments have been utilized to manage patients.3,4 The case fatality rate for COVID-19 patients with ARDS is reported to range from 39% to 45%.5,6

Historically, for patients with ARDS, venovenous extracorporeal membrane oxygenation (VV ECMO) has been used successfully as a bridge to recovery or lung transplant.7,8 For patients with severe COVID-19 related ARDS, VV ECMO has also been utilized. There are varying reports on the mortality rate of COVID-19 patients on VV ECMO. Early in the pandemic, mortality for COVID-19 ARDS patients on VV ECMO was reported as high as 94.1%.9 A subsequent study, using the Extracorporeal Life Support Organization (ELSO) registry, quoted a mortality 90 days after initiation of ECMO of 37.4%.10 More recently, the ELSO registry reports COVID-19 ECMO mortality at 48%11 and 37.1% in a meta-analysis.12

Deciding which patients benefit most from VV ECMO is challenging and requires an assessment of many factors to include timing of symptoms and worsening respiratory status.13 In non-COVID cases, some studies suggest patient outcomes are worse after being placed on the ventilator greater than seven days from cannulation,7 whereas others have reported that mechanical ventilation greater than four days is the most important predictor of death in non-COVID-19 VV ECMO patients.14 The Respiratory ECMO Survival Prediction (RESP) score uses mechanical ventilation duration as a factor in calculating estimated survival for adult patients undergoing VV ECMO,15 with longer ventilator duration associated with greater mortality. It is generally recommended to initiate VV ECMO in intubated COVID-19 ARDS patients within 7 to 10 days, but there is no clear cutoff.16,17 Several COVID-19 studies have suggested that delayed intubation is a management strategy that does not adversely affect outcomes.18 Thus, unlike other ARDS processes, some COVID-19 patients are intubated later in their ARDS course.

Whereas duration of mechanical ventilation is an established factor that helps clinicians determine which patients will derive the most benefit from VV ECMO, less is known about the impact on mortality from the time from diagnosis of infiltrates on chest radiograph (x-ray) to initiation of VV ECMO in COVID-19 patients. We sought to determine if (1) time of COVID-19 pneumonia (PNA) diagnosis by infiltrate on chest x-ray to VV ECMO cannulation effects mortality and (2) if time of original COVID-19 positive test to cannulation effects mortality. We hypothesized that an increasing duration between a positive COVID-19 test or presentation with infiltrates on chest x-ray and cannulation would be associated with increased mortality.

Materials and Methods

Patient Selection

This is a single-center, retrospective study including adult (age ≥ 18 years) COVID-19 patients who required VV ECMO from January 1, 2020, to July 28, 2021. This work was deemed exempt by the institutional review board (IRB) at the University of Maryland School of Medicine (IRB no. HM-HP-00096309-1). Any patients less than 18 or placed on veno-arterial (VA) ECMO were excluded.

COVID-19 VV ECMO Candidate Selection

Our institution utilizes a multidisciplinary approach for VV ECMO candidate selection. On each referral call, an intensivist from the Critical Care Resuscitation Unit (CCRU), an intensivist from the Lung Rescue Unit (LRU), and a cardiac surgeon discuss the case and apply institutional guidelines for selection. The CCRU19 is a receiving intensive care unit (ICU) for the Shock Trauma Center and the University of Maryland Medical Center and the LRU20 is a dedicated VV ECMO ICU.

Throughout the pandemic, selection guidelines were based on the literature,21 and included: age (≤ 55 years), body mass index (BMI) ≤ 40 kg/m2), comorbidities, end-organ function, laboratory values, ventilator settings, and November 26, 2020 onward, the Prediction of Survival on ECMO Therapy (PRESET) score.22 Throughout the pandemic, time from intubation to cannulation duration of 7 days or less was used as part of our evaluation criteria. Time from infiltrate on chest x-ray to cannulation was not considered when evaluating COVID-19 patients for VV ECMO. All physicians involved in the selection process discussed the case, applied the guidelines, and made a medical determination. Care of COVID-19 patients before transfer to our institution was dependent on the resources of the individual hospital (ability to be prone, medication availability, etc.). Subsequent care of cannulated COVID-19 VV ECMO patients followed National Institutes of Health guidelines.23

Data Storage and Analysis

Study data were collected and managed using REDCap electronic data capture tools hosted at the University of Maryland.24,25 Survival was determined at hospital discharge. Time of initial COVID-19 diagnosis was based on the first confirmed positive test date reported in the electronic medical record. Diagnosis of COVID-19 PNA was determined by the initial presence of infiltrate on chest x-ray in a patient diagnosed with COVID-19. Patients were deemed to have an infiltrate on chest x-ray based on attending radiologist read in the electronic medical record. Chest x-ray and COVID-19 test data from our institution and from transfers from other hospitals were available for all patients. All patients evaluated for cannulation for VV ECMO were transferred to the CCRU or LRU for cannulation and managed in the LRU. Daily chest x-rays were performed for these patients. Scoring systems and pre-ECMO characteristics were calculated and measured precannulation.

Data were analyzed with descriptive statistics. Parametric or nonparametric statistics were used based on the nature of the data. Normality was assessed with the Shapiro-Wilk test, and examination of stem-and-leaf as well as q-q plots. The Student’s t-test was used to assess differences with parametric continuous data and the Kruskal-Wallis and Wilcoxon rank sum tests were used to analyze nonparametric data. Chi-square tests were used to analyze categorical data. All tests were 2-tailed, and a P value of < 0.05 was used to define statistical significance.

Logistic regression, with calculation of robust standard errors, was performed after variables were selected based on Akaike’s information criterion (AIC), prior data, and clinical evaluation criteria (i.e., age, as per Deatrick et al.26). Regression diagnostics were performed including a link test to assure proper model specification and the Hosmer-Lemeshow χ2 goodness-of-fit test. Model fit (P = 0.08) and specification were confirmed. Deviance residuals and Pearson residuals as well as leverage and influence were also assessed to confirm the required assumptions for the logistic regression model. Bayesian regression was performed using the Random-walk Metropolis-Hastings sampling technique and with a Markov Chain Monte Carlo (MCMC) search algorithm used to generate 10,000 samples of tree models according to their posterior probabilities. A Bayesian analysis was performed to generate a posterior probability distribution rather than a single-point estimate and to calculate more precise coefficients and credible intervals. All tests were performed in Stata version 15.1 (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC.) and GraphPad Prism 7.0 for Mac (GraphPad Software, La Jolla, CA).

Results

Demographics and Characteristics

A total of 504 ECMO cases were screened for inclusion. One hundred thirty-four were excluded for having non-VV ECMO cannulation strategies. An additional 276 cases were excluded for being non-COVID-19 VV ECMO. There were 94 COVID-19 VV ECMO cases identified. One patient was decannulated from VV ECMO but still in the hospital at the time of analysis and was excluded, leaving 93 cases for analysis. The mean age was 44.2 with 71% being male (Table 1). The mean BMI was 35.3. Approximately 71% were on vasopressors at the time of cannulation and 18% were on inotropes. The median PaO2/FiO2 (P/F) ratio indicated that patients in our study had severe ARDS (73 [59–84]). Most patients were paralyzed and placed in prone positioning before cannulation. Most also received steroids and remdesivir before cannulation. PRESET, Respiratory ECMO Survival Prediction (RESP), Murray, and Sequential Organ Failure Assessment (SOFA) scores are reported in Table 1. Overall mortality for all patients was 32.3% at the time of hospital discharge. When examining means and standard deviations of time from infiltrate on chest x-ray to cannulation, month to month throughout our study period, there was no significant difference observed (P = 0.48) (Figure 1).

Table 1. - Demographics and Characteristics of All Cases
Parameter All Patients (n = 93)
Age (mean, S.D.) 44.2 (10.5)
Sex (n, %)
 Male 66 (71)
 Female 27 (29)
Comorbidities (n, %)
 Smoker 2 (2.2)
 Asthma or COPD 10 (10.8)
 Diabetes mellitus 22 (23.7)
 Congestive heart failure 1 (1.1)
 Coronary artery disease 0
 Cancer/malignancy 0
 Liver disease 1 (1.1)
 Substance abuse 3 (3.2)
 Perinatal 4 (4.3)
 HIV 0
BMI (mean, SD) 35.3 (9.2)
Vasoactives
 On vasopressor at time of cannulation (n, %) 66 (70.9)
 On inotrope at time of cannulation (n, %) 17 (18.3)
Pre-ECMO therapies (n, %)
 Paralysis 89 (95.7)
 Prone position 63 (67.7)
 Inhaled pulmonary vasodilator 15 (16.1)
 Steroids 73 (78.5)
 Convalescent plasma 37 (39.8)
 Remdesivir 56 (60.2)
 Monoclonal antibody 22 (23.7)
P/F ratio (median, IQR) 73 (59–84)
Scoring systems
 PRESET (median, IQR) 7 (5–8)
 Murray (median, IQR) 3.5 (3.5–3.8)
 RESP (median, IQR) 4 (3–5)
 SOFA (median, IQR) 11 (11–12)
Mortality (n, %) 30 (32.3)
BMI, body mass index (g/m2); COPD, chronic obstructive pulmonary disease; extracorporeal membrane oxygenation; HIV, human immunodeficiency virus; IQR, interquartile range; P/F Ratio, PaO2/FiO2; PRESET, Prediction of Survival on ECMO Therapy; RESP, Respiratory ECMO Survival Prediction; SD, standard deviation; SOFA, Sequential Organ Failure Assessment.

F1
Figure 1.:
Analysis of means and S.D. of time from infiltrate to cannulation for patients selected for VV ECMO from January 1, 2020 to July 28, 2021. Each bar represents one month beginning with March 2020 and ending with July 2021. Bars with no standard deviation had only one patient cannulated for the month. S.D., standard deviation; VV ECMO, venovenous extracorporeal membrane oxygenation.

Characteristics and Variables Stratified by Survivors and Nonsurvivors

There were 63 survivors to discharge and 30 patients who died before discharge (Table 2). Survivors and non-survivors had similar ages (42.2–44.7, P = 0.28), and there was a greater percentage of female patients in the survivor group (25%–2%, P < 0.001). Comorbidities were similar between the 2 groups. There was no difference in total hospital length of stay or BMI between survivors and nonsurvivors. RESP and Murray scores were also not statistically significantly different. PRESET (6.5–8, P = 0.06) and SOFA (11–12, P = 0.07) scores had a trend towards being higher in the non-survivor group, however, neither was statistically significant. Nonsurvivors received a higher percentage of monoclonal antibodies than survivors.

Table 2. - Survivors and Nonsurvivors, Stratified by Key Variables
Variable Survivors (n = 63) Non-Survivors (n = 30) P value
Age (mean, SD) 42.2 (9.7) 44.7 (11.5) 0.28
Sex
 Female (n, %) 25 (26.9) 2 (2.1) <0.001
Comorbidities (n, %)
 Smoker 0 2 (2.2)
 Asthma or COPD 10 (10.8) 0
 Diabetes mellitus 15 (16.1) 7 (7.5) 0.96
 Congestive heart failure 1 (1.1) 0
 Coronary artery disease 0 0
 Cancer/malignancy 0 0
 Liver disease 1 (1.1) 0
 Substance abuse 1 (1.1) 2 (2.1) 0.20
 Perinatal 3 (3.2) 1 (1) 0.75
 HIV 0 0
BMI (mean, SD) 35.9 (8.5) 34.1 (10.6) 0.38
Total hospital length of stay (median days, IQR) 54.5 (40–83) 52.5 (32–78) 0.47
Pre-ECMO therapies (n, %)
 Paralysis 59 (63.4) 30 (32.3) 0.16
 Prone position 41 (44.1) 22 (23.7) 0.43
 Inhaled pulmonary vasodilator 9 (9.7) 6 (6.5) 0.48
 Steroids 50 (53.8) 23 (24.7) 0.77
 Convalescent plasma 22 (23.7) 15 (16.2) 0.16
 Remdesivir 38 (40.9) 18 (19.4) 0.98
 Monoclonal antibody 11 (11.8) 11 (11.8) 0.04
P/F ratio (median, IQR) 71 (59–86) 75 (59–84) 0.75
Scoring systems
 PRESET score (median, IQR) 6.5 (5–8) 8 (6–9) 0.06
 Murray score (median, IQR) 3.5 (3.5–3.8) 3.8 (3.3–4) 0.47
 RESP score (median, IQR) 4 (2–5) 4 (2–5) 0.22
 SOFA score (median, IQR) 11 (10–12) 12 (11–13) 0.07
All percentages recorded are calculated from the total number of cases (n = 93).
BMI, body mass index (g/m2); COPD, chronic obstructive pulmonary disease; ECMO, extracorporeal membrane oxygenation; HIV, human immunodeficiency virus; IQR, Interquartile range; P/F Ratio, PaO2/FiO2; PRESET, PREdiction of Survival on ECMO Therapy; RESP, Respiratory ECMO Survival Prediction; SD, Standard deviation; SOFA, Sequential Organ Failure Assessment.

Impact of Time from Infiltrate on Chest x-ray to Cannulation on Outcomes

Unadjusted associations between time from positive test, infiltrate on chest x-ray, cannulation, and outcomes were analyzed (Figure 2). Patients who died before hospital discharge had a shorter median time from positive test to infiltrates on chest x-ray (2–0, P = 0.007) (Figure 3). Although there was a trend toward nonsurvivors having a longer time from positive test to cannulation, this was not statistically significant (7.5–11.0, P = 0.06). Nonsurvivors also had a significantly longer time from infiltrate on chest x-ray to cannulation (5–9, P = 0.002) (Figure 4). Median time from intubation to cannulation and median time from cannulation to death or discharge were not statistically significant between the groups (Table 3).

Table 3. - Unadjusted Associations With Time From Diagnosis, Cannulation, and Discharge or Death
Variable Survivors (n = 63) Non-Survivors (n = 30) P value
Median time from positive test to infiltrates on chest x-ray (days, IQR) 2 (0–6) 0 (0–1) 0.007
Median time from positive test to cannulation (days, IQR) 7.5 (4–14) 11 (8–14) 0.06
Median time from abnormal chest x-ray to cannulation (days, IQR) 5 (2–7) 9 (5–13) 0.002
Median time from intubation to cannulation (days, IQR) 2 (1–5) 2 (1–4) 0.78
Median time from cannulation to death or discharge (days, IQR) 46.5 (35–75.5) 42.5 (26–64) 0.29
IQR, interquartile range.

F2
Figure 2.:
Median time (days) from positive test to infiltrate, cannulation, and discharge or death.
F3
Figure 3.:
Time from positive COVID-19 test to infiltrate on chest x-ray (days), survivors versus non-survivors. COVID-19, coronavirus disease 2019.
F4
Figure 4.:
Time from infiltrate on chest x-ray to cannulation for VV ECMO (days), survivors versus non-survivors. VV ECMO, venovenous extracorporeal membrane oxygenation.

A multivariable logistic regression analysis was performed including age, sex, BMI, scoring systems, pre-ECMO therapies, and P/F ratio. From the logistic regression analysis, increased days from infiltrate to cannulation was significant and associated with increased odds of mortality (OR: 1.58, 95% CI: 1.24–2.00, P < 0.01). Female sex was also found to be significant (OR: 0.03, 95% CI: 0.002–0.49, P = 0.013) and associated with increased odds of survival. A Bayesian logistic regression analysis was also performed. Similar results were obtained; days from infiltrate to cannulation (OR: 1.60, 95% credible interval: 1.30–2.02) and female sex (OR: 0.02, 95% credible interval: 0.0004–0.11) were both significant.

Pulmonary Function Tests in Survivors

At the time of data collection, 14 patients (15.1%) had undergone pulmonary function testing (PFT) after discharge from the hospital (Table 4). Given the small number of patients with PFT data, we present this subanalysis as descriptive data only. Overall, most of these patients were found to have varying degrees of restrictive disease patterns (11/14) with decreased diffusion lung capacity (8/12, 2 subjects missing diffusion data) after their VV ECMO support run. Two patients had severely restrictive pulmonary disease at 3 months postdischarge.

Table 4. - Pulmonary Function Test Data in 14 COVID-19 Patients Who Survived to Discharge After VV ECMO
Patient Months Postdischarge FVC FVC% FEV1 FEV1% FEV/FVC% TLC% DLCO% Interpretation
1 3 2.47 83 2.16 90.1 88 84.5 93.6 Normal
2 10 2.33 59.9 2.06 67.9 88 57.9 62.3 Moderately restrictive, mildly reduced diffusion capacity
3 5 2.56 66 2.05 67 80 58 40 Moderately restrictive, moderately reduced diffusion capacity
4 1 3.43 94.1 2.9 90.5 85 Normal, no diffusion capacity data
5 6 3.46 101 2.82 102.4 81 87.2 73.5 Normal spirometry, mildly reduced diffusion
6 3 1.68 47 1.53 50 91 62 48 Severe restrictive, moderately reduced diffusion capacity
7 3 1.93 52.1 1.68 57.3 68 48.7 53.9 moderate restrictive, moderately reduced diffusion capacity
8 5 1.92 45 1.64 46.4 70 53.2 46.1 Severely restricted, moderately reduced diffusion capacity
9 5 3.37 70 2.66 67 79 62 54 Moderately restrictive pattern, moderately reduced diffusion capacity
10 4 2.17 66.4 1.94 69 75 76.8 84.6 Moderate restrictive, normal diffusion capacity
11 2 2.71 72.9 2.09 69.5 77 79.8 Moderate restrictive, normal diffusion capacity
12 2 2.32 76.7 1.88 74.3 81.16 58 57.5 Moderately restrictive, moderately reduced diffusion capacity
13 4 2.83 69.5 2.56 80.5 90.39 77.6 Moderate restrictive, no diffusion capacity data
14 2.33 74.5 1.85 70.1 79.68 77.3 84.4 Moderate restrictive, normal diffusion capacity
COVID-19, coronavirus disease 2019; DLCO, diffusing capacity of the lungs for carbon monoxide; FEV, forced expiratory volume; FVC, forced vital capacity; TLC, total lung capacity; and VV ECMO, venovenous extracorporeal membrane oxygenation.

Discussion

Time from Infiltrate Diagnosis to Cannulation

Our data indicates that with each one day increase from appearance of infiltrate on chest x-ray to cannulation odds of death are significantly increased. Other pre-ECMO factors and scoring systems had no effect in multivariable analysis. Time from intubation to cannulation and days from diagnosis to cannulation also had no effect on mortality in our study. Although time from intubation to cannulation may be relatively short, time from infiltrate to referral may be another important consideration when assessing which patients will benefit the most from ECMO. At our institution, COVID-19 patients referred for VV ECMO need to have failed endotracheal intubation first.

Using time from infiltrate on chest x-ray as part of the VV ECMO selection screening process instead of time from intubation may be more beneficial because of the oxygenation and ventilation management strategies of COVID-19 patients. During the COVID-19 pandemic, the optimal time of intubation compared with other noninvasive oxygenation strategies is unknown.18,27 Patient management has focused on the use of high flow nasal cannula and noninvasive positive pressure ventilation before intubation.28 The use of these other modalities leads to an increased time from presentation to intubation in many cases. Specific data on which of our patients were on various oxygenation strategies was not available so there could be further difference in the level and duration of noninvasive support before intubation that we are not capturing. Although the initial date of infiltrate on chest x-ray and COVID-19–positive test are objective data points readily available on each of our patients, the time of onset of symptoms and the specific symptoms experienced was not available. Patients could have experienced respiratory symptoms before presentation which could indicate a more prolonged respiratory illness.

Interstitial and intra-alveolar fibrosis are hallmarks of the more advanced stages of ARDS.29 Many factors including age, initiating insult, genetic predisposition, comorbidities, and mechanical ventilation can all influence the progression to fibrotic disease.29 Perhaps patients with longer hospitalizations and COVID-19 pneumonia have entered the fibrotic phase of the illness and are less likely to benefit from VV ECMO which could be why we demonstrated an increased mortality rate with longer times from infiltrate diagnosis to cannulation. Our data suggest that the duration from infiltrate on chest x-ray to time to cannulation is more important than time to intubation and time from diagnosis in determining which patients will most benefit from VV ECMO. Thus, other parameters such as time from infiltrate as opposed to time from intubation to cannulation in the selection of appropriate COVID-19 VV ECMO candidates may be useful. Further research on the physiologic basis of this finding and additional prospective data is needed.

Time from COVID Diagnosis to Infiltrates

In our unadjusted analysis, patients who had a longer time from positive COVID-19 test to development of an infiltrate on chest x-ray were more likely to survive when placed on VV ECMO. The exact etiology of this finding is unclear. Perhaps the rapid development and subsequent lung injury are more irreversible.30 There may also be some limitations of this data as patients could theoretically receive a COVID-19 test without a chest x-ray so perhaps these infiltrates are not being identified until patients present to the hospital with respiratory symptoms. Yet another possibility, patients who present to a hospital with severe symptoms and receive both a COVID-19 test, and chest x-ray simultaneously may have more rapidly progressive disease with decreased survival. Further study of a larger patient population and the potential molecular basis of these findings are needed.

Female Sex and Outcomes

Female sex was independently associated with a decreased odds of death in our study, however, our sample size was small. Although our study did not examine gender-specific differences in comorbidities, one study found that men, in general, have increased prevalence of “high-risk” behaviors, such as smoking tobacco and alcohol consumption.31 In another COVID-19 survey study, men reported wearing masks less frequently.32 Perhaps a combination of factors could lead to increased severity of COVID-19 disease in some male patients; however, larger cohorts are required to specifically study this observation.

Restrictive Disease in Survivors

On limited review of PFTs in survivors, only one was found to have severe disease. This data are limited by the low number of patients with complete testing, and wide variations on the timing of testing after discharge. However, this descriptive set highlights the need for close follow-up of these patients with outpatient pulmonology to monitor their functional recovery. Furthermore, it emphasizes the need for further studies to compare pulmonary functional recovery in patients receiving ECMO support versus those supported with conventional mechanical ventilatory support in the setting of both COVID-19 pneumonia and non-COVID ARDS.

Conclusions

Our study demonstrates for every 1 day increase from COVID-19 infiltrate on chest x-ray to VV ECMO cannulation, odds of death increase significantly. When determining which COVID-19 patients may benefit from VV ECMO therapy, time from development of an abnormal chest x-ray should be considered as part of multifactorial decision-making and may be more important than time from intubation to cannulation.

Limitations

This is a single-center, retrospective study. COVID-19 therapies evolved during the study period. Further multicenter and prospective research and larger cohorts of patients are required.

References

1. Gibson PG, Qin L, Puah SH: COVID-19 acute respiratory distress syndrome (ARDS): clinical features and differences from typical pre-COVID-19 ARDS. Med J Aust. 213: 54–56.e1, 2020.
2. Prevention CfDCa. Centers for Disease Control and Prevention. https://covid.cdc.gov/covid-data-tracker/#cases_casesper100klast7days. Accessed 2/5, 2021.
3. Mittermaier M, Pickerodt P, Kurth F, et al.: Evaluation of PEEP and prone positioning in early COVID-19 ARDS. EClinicalMedicine. 28: 100579, 2020.
4. Health NIo. National Institutes of Health. https://www.covid19treatmentguidelines.nih.gov/whats-new/. Accessed 2/5, 2021.
5. Hasan SS CT, Ahmed R, et al.: Mortality in COVID-19 patients with acute respiratory distress syndrome and corticosteroids use: a systematic review and meta-analysis. Expert Rev Respir Med. 11: 1149–1163, 2020.
6. Tzotzos SJ, Fischer B, Fischer H, Zeitlinger M: Incidence of ARDS and outcomes in hospitalized patients with COVID-19: a global literature survey. Crit Care. 24: 516, 2020.
7. Peek GJ, Mugford M, Tiruvoipati R, et al.; CESAR trial collaboration: Efficacy and economic assessment of conventional ventilatory support versus extracorporeal membrane oxygenation for severe adult respiratory failure (CESAR): a multicentre randomised controlled trial. Lancet. 374: 1351–1363, 2009.
8. Biscotti M, Sonett J, Bacchetta M: ECMO as bridge to lung transplant. Thorac Surg Clin. 25: 17–25, 2015.
9. Hilder M, Herbstreit F, Adamzik M, et al.: Comparison of mortality prediction models in acute respiratory distress syndrome undergoing extracorporeal membrane oxygenation and development of a novel prediction score: the PREdiction of Survival on ECMO Therapy-Score (PRESET-Score). Crit Care. 21: 301, 2017.
10. Barbaro RP, MacLaren G, Boonstra PS, et al.; Extracorporeal Life Support Organization: Extracorporeal membrane oxygenation support in COVID-19: an international cohort study of the Extracorporeal Life Support Organization registry. Lancet. 396: 1071–1078, 2020.
11. Extracorporeal Life Support O: VV ECMO mortality 90 days after initiation. Extracorporeal Life Support Organization. https://www.elso.org/Registry/FullCOVID19RegistryDashboard.aspx. Accessed 1/27, 2022.
12. Ramanathan K, Shekar K, Ling RR, et al.: Correction to: Extracorporeal membrane oxygenation for COVID-19: a systematic review and meta-analysis. Crit Care. 25: 375, 2021.
13. Riera J, Alcántara S, Bonilla C, et al.: Risk factors for mortality in patients with COVID-19 needing extracorporeal respiratory support. Eur Respir J. 59: 2102463, 2022.
14. Cheng YT, Wu MY, Chang YS, Huang CC, Lin PJ: Developing a simple preinterventional score to predict hospital mortality in adult venovenous extracorporeal membrane oxygenation: A pilot study. Medicine (Baltimore). 95: e4380, 2016.
15. Schmidt M, Bailey M, Sheldrake J, et al.: Predicting survival after extracorporeal membrane oxygenation for severe acute respiratory failure. The Respiratory Extracorporeal Membrane Oxygenation Survival Prediction (RESP) score. Am J Respir Crit Care Med. 189: 1374–1382, 2014.
16. Bartlett RH, Ogino MT, Brodie D, et al.: Initial ELSO Guidance Document: ECMO for COVID-19 Patients with Severe Cardiopulmonary Failure. ASAIO J. 66: 472–474, 2020.
17. Augoustides JG: Cardiopulmonary Resuscitation During the Coronavirus Crisis: Important Updates for the Cardiothoracic and Vascular Anesthesia Community. J Cardiothorac Vasc Anesth. 34: 2312–2314, 2020.
18. Papoutsi E, Giannakoulis VG, Xourgia E, Routsi C, Kotanidou A, Siempos II: Effect of timing of intubation on clinical outcomes of critically ill patients with COVID-19: a systematic review and meta-analysis of non-randomized cohort studies. Crit Care. 25: 121, 2021.
19. Scalea TM, Rubinson L, Tran Q, et al.: Critical Care Resuscitation Unit: An Innovative Solution to Expedite Transfer of Patients with Time-Sensitive Critical Illness. J Am Coll Surg. 222: 614–621, 2016.
20. Menaker J, Dolly K, Rector R, et al.: The lung rescue unit-Does a dedicated intensive care unit for venovenous extracorporeal membrane oxygenation improve survival to discharge? J Trauma Acute Care Surg. 83: 438–442, 2017.
21. Badulak J, Antonini MV, Stead CM, et al.; ELSO COVID-19 Working Group Members: Extracorporeal Membrane Oxygenation for COVID-19: Updated 2021 Guidelines from the Extracorporeal Life Support Organization. ASAIO J. 67: 485–495, 2021.
22. Tabatabai A, Ghneim MH, Kaczorowski DJ, et al.: Mortality Risk Assessment in COVID-19 Venovenous Extracorporeal Membrane Oxygenation. Ann Thorac Surg. 112: 1983–1989, 2021.
23. Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults--The Evidence Report. National Institutes of Health. Obes Res. 6 Suppl 2: 51S–209S, 1998.
24. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG: Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 42: 377–381, 2009.
25. Harris PA, Taylor R, Minor BL, et al.; REDCap Consortium: The REDCap consortium: Building an international community of software platform partners. J Biomed Inform. 95: 103208, 2019.
26. Deatrick KB, Mazzeffi MA, Galvagno SM Jr, et al.: Outcomes of Venovenous Extracorporeal Membrane Oxygenation When Stratified by Age: How Old Is Too Old? ASAIO J. 66: 946–951, 2020.
27. Siempos II, Xourgia E, Ntaidou TK, et al.: Effect of Early vs. Delayed or No Intubation on Clinical Outcomes of Patients With COVID-19: An Observational Study. Front Med (Lausanne). 7: 614152, 2020.
28. Duan J, Chen B, Liu X, et al.: Use of high-flow nasal cannula and noninvasive ventilation in patients with COVID-19: A multicenter observational study. Am J Emerg Med. 46: 276–281, 2021.
29. Marshall R, Bellingan G, Laurent G: The acute respiratory distress syndrome: fibrosis in the fast lane. Thorax. 53: 815–817, 1998.
30. Bösmüller H, Traxler S, Bitzer M, et al.: The evolution of pulmonary pathology in fatal COVID-19 disease: an autopsy study with clinical correlation. Virchows Arch. 477: 349–357, 2020.
31. Galasso V, Pons V, Profeta P, Becher M, Brouard S, Foucault M: Gender differences in COVID-19 attitudes and behavior: Panel evidence from eight countries. Proc Natl Acad Sci U S A. 117: 27285–27291, 2020.
32. Hearne BN, Nino MD: Understanding How Race, Ethnicity, and Gender Shape Mask-Wearing Adherence During the COVID-19 Pandemic: Evidence from the COVID Impact Survey. J Racial Ethn Health Disparities. 9:176–183, 2021.
Keywords:

circulatory support; circulatory temporary support; extracorporeal membrane oxygenation; COVID-19

Copyright © ASAIO 2022