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. 2022 Nov 22;49(1):37–49. doi: 10.1007/s00134-022-06926-4

Impact of BMI on outcomes in respiratory ECMO: an ELSO registry study

Marijke Peetermans 1,2, Ipek Guler 3, Philippe Meersseman 1, Alexander Wilmer 1,2, Joost Wauters 1,2, Bart Meyns 4, Alexander P J Vlaar 5,6, Alain Combes 7,8, Greet Hermans 1,9,✉
PMCID: PMC9684759  PMID: 36416896

Abstract

Purpose

The impact of body mass index (BMI) on outcomes in respiratory failure necessitating extracorporeal membrane oxygenation (ECMO) has been poorly described. We aimed to assess: (i) whether adults with class II obesity or more (BMI ≥ 35 kg/m2) have worse outcomes than lean counterparts, (ii) the form of the relationship between BMI and outcomes, (iii) whether a cutoff marking futility can be identified.

Methods

A retrospective analysis of the Extracorporeal Life Support Organization (ELSO) Registry from 1/1/2010 to 31/12/2020 was conducted. Impact of BMI ≥ 35 kg/m2 was assessed with propensity-score (PS) matching, inverse propensity-score weighted (IPSW) and multivariable models (MV), adjusting for a priori identified confounders. Primary outcome was in-hospital mortality. The form of the relationship between BMI and outcomes was studied with generalized additive models. Outcomes across World Health Organisation (WHO)-defined BMI categories were compared.

Results

Among 18,529 patients, BMI ≥ 35 kg/m2 was consistently associated with reduced in-hospital mortality [PS-matched: OR: 0.878(95%CI 0.798–0.966), p = 0.008; IPSW: OR: 0.899(95%CI 0.827–0.979), p = 0.014; MV: OR: 0.900(95%CI 0.834–0.971), p = 0.007] and shorter hospital length of stays. In patients with BMI ≥ 35 kg/m2, cardiovascular (17.3% versus 15.3%), renal (37% versus 30%) and device-related complications (25.7% versus 20.6%) increased, whereas pulmonary complications decreased (7.6% versus 9.3%). These findings were independent of confounders throughout PS-matched, IPSW and MV models. The relationship between BMI and outcomes was non-linear and no cutoff for futility was identified.

Conclusion

Patients with obesity class II or more treated with ECMO for respiratory failure have lower mortality risk and shorter stays, despite increased cardiovascular, device-related, and renal complications. No upper limit of BMI indicating futility of ECMO treatment could be identified. BMI as single parameter should not be a contra-indication for respiratory ECMO.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00134-022-06926-4.

Keywords: Extracorporeal membrane oxygenation, Obesity, Body mass index, Respiratory failure

Take-home message

This large and multi-center registry study shows that patients with obesity class II or more (body mass index (BMI) ≥ 35 kg/m2) undergoing extracorporeal membrane oxygenation for respiratory failure have decreased mortality risk and decreased hospital length of stay, despite an increased risk of cardiovascular, device-related, and renal complications. The association between BMI and these outcomes is non-linear and no upper limit of BMI indicating futility was identified.
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Introduction

Obesity is prevalent in up to 20% of critically ill patients [1]. Obesity compromises long-term health and survival due to increased cardiovascular disorders, metabolic diseases, cancer, and other health problems [2]. Management of critically ill obese patients poses specific challenges [3]. Also, obese patients have an increased risk of developing acute respiratory distress syndrome (ARDS) [4] and other respiratory problems [3].

Extracorporeal membrane oxygenation (ECMO) is an established treatment for patients with severe, potentially reversible respiratory failure, not responding to conventional treatment [5]. Hence, clinicians are increasingly confronted with potential ECMO indications in obese patients. ECMO is an invasive, high resource-demanding technology associated with complications [6]. Severely obese patients may challenge the risk–benefit balance because of anticipated difficulties with cannulation and reaching sufficient blood flows.

In overweight and moderately obese critically ill patients, lower mortality was reported compared to patients with normal weight, despite increased complications. This contra-intuitive finding is labeled the ‘obesity paradox’ [1]. Data on complications and outcomes of respiratory ECMO in extreme obesity are confined to small case series with limited numbers of patients in the upper body mass index (BMI) range and variable cutoffs in analyses. Most studies did not identify obesity as risk factor for poor outcomes, though data are inconsistent [7–20]. Given the paucity of data, hesitancy to consider ECMO in daily care of these patients persists [21]. This reluctance is translated into recent ELSO guidelines, considering BMI > 40 kg/m2 as a relative contraindication for ECMO in patients affected by coronavirus disease 2019 (COVID-19) [22].

We aimed to assess whether adults with class II obesity or more (BMI ≥ 35 kg/m2) treated with ECMO for respiratory failure have worse outcomes and experience more complications than patients with BMI < 35 kg/m2. We evaluated the form of the relationship between BMI and outcomes to evaluate whether a cutoff for futility can be identified.

Methods

Data source

We queried the international ELSO registry [23], including data on primary diagnosis for ECMO and comorbidities based on the International Classification of diseases 9th and 10th (ICD9/10) revision. Patient and ECMO characteristics [24], treatments, complications, and outcomes are entered in dedicated fields. As data are de-identified, the Clinical Trial Center UZLeuven waived the need for local Ethical Committee approval.

Patients

Adults (age ≥ 18 years) receiving ECMO for respiratory failure from 1/1/2010 to 31/12/2020 were eligible. Exclusion criteria involved other than first runs, pregnancy-related runs, and runs with missing data on height or weight. Patients were dichotomized at BMI ≥ 35 kg/m2, and further stratified according to groups as defined by the World Health Organisation (WHO): underweight (BMI < 18.5 kg/m2), normal weight (BMI 18.5–25 kg/m2), overweight (BMI 25–30 kg/m2), class I (BMI 30–35 kg/m2), class II (BMI 35–40 kg/m2) or class III obesity (> 40 kg/m2); the last group was further divided into three subgroups (BMI 40–50 kg/m2, 50–60 kg/m2, > 60 kg/m2). Extreme values (BMI < 12 kg/m2 and > 70 kg/m2) were excluded [25].

Outcomes

Primary outcome was in-hospital mortality. Secondary outcomes included ECMO duration, reason for discontinuation, complications (device-related, hemorrhagic, neurological, renal, cardiovascular, pulmonary, infectious, metabolic, limb), hospital length of stay and discharge destination (Supplemental Table 1).

Confounders

Analyses were corrected for literature search-based, a priori-selected variables [26], including demographics, comorbidities, admission characteristics, and pre-ECMO variables and treatments, associated with outcomes in respiratory ECMO and recorded in the ELSO registry (Supplemental Table 2). These included gender, age, primary diagnosis, pre-ECMO variables (respiratory rate, PO2/FiO2, peak inspiratory pressure, positive end-expiratory pressure (PEEP), pH, PCO2, HCO3, lactate, mean blood pressure), pre-ECMO treatments and characteristics (renal replacement therapy, nitric oxide, prone ventilation, neuromuscular blocking agents, vasopressors/inotropes, bicarbonate, corticosteroids, cardiac arrest, bridge-to-transplantation), ECMO characteristics (cannulation type, transport on ECMO), and year of ECMO. As the number of eligible patients before 2016 was small, these were grouped. Primary diagnoses were categorized into viral pneumonia, non-viral pneumonia, trauma/burn, chemical/aspiration, asthma, pulmonary embolism/pulmonary hypertension, lung transplant complications, unspecified ARDS/acute respiratory failure and other (Supplemental Table 3) [27, 28]. Comorbidities and co-existent conditions, including associated infection, chronic respiratory and cardiac disease, and immunosuppressive condition, were identified as previously (Supplemental Table 4) [27, 28]. MP and GH independently reviewed ICD-9/10 codes and discrepancies were resolved by discussion.

Statistics

Descriptives included mean and standard deviation for continuous variables, median and interquartile range for time variables, and numbers and percentages for categorical variables. Analyses were performed using R software (4.0.3) (see online supplement). p values were considered statistically significant if two-sided p values ≤ 0.05.

Association between class II obesity or more and outcomes

To assess impact of class II obesity or more (BMI ≥ 35 kg/m2) on outcomes, we adjusted for confounders through propensity-score matching, inverse propensity-score weighted (IPSW), and multivariable models. The propensity-based models constituted the primary analyses. For ECMO duration, inverse hyperbolic sign transformation was performed because of its skewed distribution.

Propensity scores were obtained by logistic regression including the literature search-based variables. Propensity methods included: (i) IPSW calculated with the propensity-score estimations using generalized boosted models [29–31], (ii) propensity-score matching using one-to-one nearest neighbor matching without replacement with BMI ≥ 35 kg/m2 as the dependent variable within a caliper width of 0.2. Satisfactory matching was defined as an absolute value of the standardized mean difference (SMD) < 0.1 for all variables [31, 32]. For binary outcomes, matched data were analyzed using logistic mixed effects regression models. For continuous data, linear mixed effects regression models were used.

Multivariable logistic regressions were used as an alternative approach to verify the impact of BMI ≥ 35 kg/m2 on outcomes. Collinearity between confounders was checked and judged problematic in case of variation inflation factor > 5.

Exploratory analyses

Differences in complications between BMI categories were further explored to define potential drivers by comparing the incidence of the subcategories of the complications of interest in the total and propensity-matched populations.

Assessment of the shape of the relationship between BMI and outcomes and BMI subcategories

Anticipating a non-linear relationship, weighted generalized additive models (GAM) using the propensity scores were used to capture the effect of BMI as a continuous variable on outcomes. The smooth effect of BMI was calculated using smoothed splines. We explored differences across BMI categories within BMI < 35 kg/m2 and BMI ≥ 35 kg/m2 with Kruskal–Wallis rank-sum test, Chi-square, or Fisher’s exact in the propensity-matched set.

Missing data

Data were considered to be missing completely at random, except for proning. For propensity models, multivariable models and GAM analyses, multiple imputation for missing data was performed, generating five imputed datasets [33, 34]. Results were pooled by Rubin’s rule.

Sensitivity analyses

As prolonged prone ventilation was not recorded in the registry prior to December 2017, sensitivity analyses were performed limited to admissions from 2018 onward. As death is a competing risk for development of complications, sensitivity analyses for the secondary outcomes were performed on survivors only.

Results

Patient population and characteristics

The registry included 31,061 ECMO runs. We excluded 279 pregnancy-related runs, 1088 other than first runs and 11,165 runs with missing height or weight (Fig. 1). The characteristics of the 18,529 included patients are provided in Table 1. Missingness is reported in Supplemental Table 5. The mean age was 48 ± 15 years. Most patients were male (64%). The mean BMI was 31 ± 9 kg/m2, and 25% had BMI ≥ 35 kg/m2. This proportion significantly increased over time from 21% before 2016 up to 30% in 2020.

Fig. 1.

Fig. 1

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Patient flowchart

Table 1.

Demographic, clinical, pre-ECMO, and cannulation characteristics

Total population Propensity matched
Total
N = 18,529		 BMI < 35 kg/m2
N = 13,822		 BMI ≥ 35 kg/m2
N = 4707		 p valuea Total
N = 9414		 BMI < 35 kg/m2
N = 4707		 BMI ≥ 35 kg/m2
N = 4707		 SMD
Demographics
Age 48 (15) 49 (15) 47 (13)  < 0.001 47 (14) 47 (15) 47 (13) 0.016
Sex, male 11,828 (64%) 9104 (66%) 2724 (58%)  < 0.001 5453 (58%) 2722 (58%) 2731 (58%) 0.020
BMI (kg/m2) 30.7 (8.6) 26.7 (4.6) 42.4 (6.9)  < 0.001 35 (10) 27 (5) 42 (7) NA
Primary diagnosis  < 0.001 0.026
Viral pneumonia 3958 (22%) 2637 (20%) 1321 (29%) 2686 (29%) 1329 (28%) 1357 (29%)
Non-viral pneumonia 1149 (6.4%) 947 (7.1%) 202 (4.4%) 407 (4.3%) 195 (4.1%) 212 (4.5%)
Chemical/aspiration 265 (1.5%) 200 (1.5%) 65 (1.4%) 129 (1.4%) 63 (1.3%) 66 (1.4%)
Trauma/burn 595 (3.3%) 453 (3.4%) 142 (3.1%) 295 (3.1%) 151 (3.2%) 144 (3.1%)
Asthma 329 (1.8%) 278 (2.1%) 51 (1.1%) 112 (1.2%) 57 (1.2%) 55 (1.2%)
Pulmonary embolism/pulmonary hypertension 327 (1.8%) 224 (1.7%) 103 (2.2%) 213 (2.3%) 108 (2.3%) 105 (2.2%)
Lung transplant complications 476 (2.7%) 466 (3.5%) 10 (0.2%) 25 (0.3%) 15 (0.3%) 10 (0.2%)
Unspecified ARDS/ARF 7223 (40%) 5165 (39%) 2058 (45%) 4248 (45%) 2134 (45%) 2114 (45%)
Other 3627 (20%) 2996 (22%) 631 (14%) 1299 (14%) 655 (14%) 644 (14%)
Comorbidities and associated conditions
Chronic respiratory disease 2864 (15%) 2426 (18%) 438 (9.3%)  < 0.001 856 (9.1%) 418 (8.9%) 438 (9.3%) 0.004
Chronic heart disease 1452 (7.8%) 1034 (7.5%) 418 (8.9%) 0.002 850 (9%) 432 (9.2%) 418 (8.9%) 0.002
Immunocompromised 2053 (11%) 1837 (13%) 216 (4.6%)  < 0.001 439 (4.7%) 223 (4.7%) 216 (4.6%) 0.018
Associated infection 788 (4.3%) 592 (4.3%) 196 (4.2%) 0.73 394 (4.2%) 198 (4.2%) 196 (4.2%) 0.014
Pre-ECMO variables and treatments
RR (/min) 24 (7) 24 (7) 24 (7)  < 0.001 24 (7) 24 (7) 24 (7) 0.008
pO2/FiO2 0.71 (0.56, 0.99) 0.73 (0.57, 1.03) 0.67 (0.54, 0.88)  < 0.001 0.68 (0.55, 0.91) 0.69 (0.55, 0.93) 0.67 (0.54, 0.89) 0.012
PIP (cmH2O) 34 (8) 33 (8) 35 (8)  < 0.001 35 (8) 35 (9) 35 (8) 0.003
PEEP (cmH2O) 13 (5) 12 (5) 14 (5)  < 0.001 14 (5) 14 (5) 14 (5) 0.012
pH 7.25 (0.14) 7.25 (0.14) 7.25 (0.13) 0.38 7.25 (0.14) 7.25 (0.14) 7.25 (0.14)  < 0.001
pCO2 (cmH2O) 63 (22) 63 (23) 62 (20) 0.74 62 (21) 62 (21) 62 (20) 0.007
HCO3 (mmol/l) 26 (7) 26 (7) 26 (7) 0.024 26 (7) 26 (7) 26 (7) 0.008
Lactate 3.4 (3.9) 3.5 (4) 3.2 (3.6)  < 0.001 3.1 (3.5) 3.2 (3.5) 3.1 (3.5) 0.004
Mean BP (mmHg) 76 (17) 76 (17) 77 (17)  < 0.001 77 (17) 77 (17) 77 (17)  < 0.001
pre-ECMO arrest 1659 (9%) 1214 (9%) 445 (9.6%) 0.21 912 (9.7%) 462 (9.8%) 450 (9.6%) 0.002
Bridge to transplant 1178 (6.7%) 1126 (8.6%) 52 (1.2%)  < 0.001 128 (1.4%) 72 (1.5%) 56 (1.2%)  < 0.001
Intubation-to-time to ECMO (hours) 33 (8–110) 29 (8–107) 45 (12–116)  < 0.001 42 (11–119) 40 (10–120) 45 (12–117) 0.014
pre-ECMO RRT 1663 (9%) 1205 (8.7%) 458 (9.7%) 0.036 935 (9.9%) 477 (10%) 458 (9.7%) 0.004
pre-ECMO NO 2170 (12%) 1600 (12%) 570 (12%) 0.33 1137 (12%) 567 (12%) 570 (12%) 0.025
pre-ECMO prone 3888 (21%) 2696 (20%) 1192 (25%)  < 0.001 2351 (25%) 1159 (25%) 1192 (25%) 0.015
pre-ECMO NMBA 9914 (54%) 7018 (51%) 2896 (62%)  < 0.001 5757 (61%) 2861 (61%) 2896 (62%)  < 0.001
pre-ECMO vasopressors/inotropes 11,132 (60%) 8401 (61%) 2731 (58%)  < 0.001 5498 (58%) 2767 (59%) 2731 (58%)  < 0.001
pre-ECMO bicarbonate 2131 (12%) 1591 (12%) 540 (11%) 0.94 1080 (11%) 540 (11%) 540 (11%) 0.004
pre-ECMO corticosteroids 212 (1.1%) 181 (1.3%) 31 (0.7%)  < 0.001 55 (0.6%) 24 (0.5%) 31 (0.7%) 0.008
ECMO
Cannulation   < 0.001 0.020
 VV 16,575 (89%) 12,242 (89%) 4333 (92%) 8667 (92%) 4334 (92%) 4333 (92%)
 Non-VV
  VA 915 (4.9) 773 (5.6) 142 (3) 281 (3) 139 (3) 142 (3)
  VVA 146 (0.8) 118 (0.9) 28 (0.6) 55 (0.6) 27 (0.6) 28 (0.6)
  Other 892 (4.8) 688 (5) 204 (4.3) 406 (4.4) 202 (4.3) 204 (4.3)
Year  < 0.001 0.025
2010–2016 1649 (9%) 1305 (9%) 344 (7%) 716 (8%) 372 (8%) 344 (7%)
2017 2843 (15%) 2254 (16%) 589 (13%) 1190 (13%) 601 (13%) 589 (13%)
2018 3524 (19%) 2724 (20%) 800 (17%) 1597 (17%) 797 (17%) 800 (17%)
2019 4071 (22%) 3052 (22%) 1019 (22%) 2038 (22%) 1019 (22%) 1019 (22%)
2020 6442 (35%) 4487 (32%) 1955 (42%) 3873 (41%) 1918 (41%) 1955 (42%)
 Transported on ECMO 4150 (28%) 2890 (26%) 1260 (32%)  < 0.001 3016 (32%) 1509 (32%) 1507 (32%) 0.010
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Continuous variables are presented as mean (SD); time variables and PO2/FiO2 are presented as median (interquartile range); categorical variables are presented as numbers (proportions)

BMI body mass index; ECMO extracorporeal membrane oxygenation; ARDS acute respiratory distress syndrome; ARF acute respiratory failure; PIP peak inspiratory pressure, PEEP positive end-expiratory pressure; BP blood pressure; RRT renal replacement therapy; NO nitric oxide; NMBA neuromuscular blocking agent; RR respiratory rate; VV veno-venous; SMD absolute standardized mean difference

ap values are calculated with Wilcoxon signed-rank test, Chi-square, or Fisher exact as appropriate

Patients with class II obesity or more differed from those with BMI < 35 kg/m2, including being younger, more frequently female, and having different primary diagnoses. Less patients with BMI ≥ 35 kg/m2 suffered from chronic respiratory disease or immunocompromised state, yet chronic heart disease was more frequent. Patients with BMI ≥ 35 kg/m2 had lower PO2/FiO2 despite receiving higher PEEP and more rescue treatments with neuromuscular blocking agent (NMBAs), prone ventilation, and nitric oxyde (NO) prior to cannulation. Patients with BMI ≥ 35 kg/m2 were more frequently transported on ECMO, had longer delays between intubation and ECMO, and more frequently received renal replacement therapy prior to cannulation. Bridge-to-transplantation was performed less frequently. A subset of 4707 patients with BMI ≥ 35 kg/m2 were matched to 4707 patients with BMI < 35 kg/m2, rendering satisfactory confounder balance (Table 1) (Supplemental Fig. 1). The characteristics for the BMI subcategories are provided in Supplemental Table 6.

Association between class II obesity or more and hospital mortality

7394 (39.9%) patients died in hospital. Crude mortality was 37.9% in patients with BMI ≥ 35 kg/m2 and 40.6% for those with BMI < 35 kg/m2 (Table 2). Propensity-matched (odds ratio (OR) 0.878, 95% confidence interval (CI) 0.798–0.996, p = 0.008) and IPSW analyses (OR 0.899, 95%CI 0.827–0.979, p = 0.014) showed that patients with BMI ≥ 35 kg/m2 had a reduced mortality risk when adjusting for confounders. Multivariable logistic regression analysis (OR 0.900, 95%CI 0.834–0.971, p = 0.007) was consistent with these findings.

Table 2.

Primary and secondary outcomes for patients with class II obesity or more

BMI < 35 kg/m2 BMI ≥ 35 kg/m2 Analysis Effect size p value
Primary outcome
In-hospital mortality
 Total population 5608 (40.6%) 1786 (37.9%) Univariable analysis 0.8916 (0.8333–0.9540)d 0.001
 PS-matched set 1904 (40.4%) 1786 (37.9%) Multivariable analysisa 0.8999 (0.8337–0.9714)d 0.007
PS-matched analysisb 0.8782 (0.7982–0.9662)d 0.008
Inverse PS weighted analysisc 0.8994 (0.8266–0.9787)d 0.014
Secondary outcomes
Cardiovascular complications
 Total population 2117 (15.3%) 813 (17.3%) Univariable analysis 1.1493 (1.0523–1.2552)d 0.002
 PS-matched set 681 (14.5%) 813 (17.3%) Multivariable analysisa 1.2363 (1.1232–1.3609)d  < 0.001
PS-matched analysisb 1.2381 (1.0901–1.4061)d 0.001
Inverse PS weighted analysisc 1.2101 (1.0830–1.3521)d 0.001
Hemorrhagic complications
 Total population 2279 (16.5%) 749 (15.9%) Univariable analysis 0.9584 (0.8758–1.0488)d 0.356
 PS-matched set 760 (16.1%) 749 (15.9%) Multivariable analysisa 0.9908 (0.9000–1.0908)d 0.851
PS-matched analysisb 0.9696 (0.8632–1.0893)d 0.603
Inverse PS weighted analysisc 0.9463 (0.8458–1.0586)d 0.335
Limb complications
 Total population 234 (1.7%) 70 (1.5%) Univariable analysis 0.8765 (0.6697–1.1473)d 0.337
 PS-matched set 82 (1.7%) 70 (1.5%) Multivariable analysisa 0.8618 (0.6482–1.1458)d 0.306
PS-matched analysisb 0.7360 (0.3959–1.3683)d 0.318
Inverse PS weighted analysisc 0.8599 (0.6222–1.1885)d 0.361
Infectious complications
 Total population 541 (3.9%) 176 (3.7%) Univariable analysis 0.9535 (0.8017–1.1341)d 0.591
 PS-matched set 192 (4.8%) 176 (3.7%) Multivariable analysisa 1.1059 (0.9145–1.3374)d 0.299
PS-matched analysisb 1.1472 (0.8243–1.5965) d 0.403
Inverse PS weighted analysisc 1.0611 (0.8540–1.3186)d 0.592
Device-related complications
 Total population 2850 (20.6%) 1210 (25.7%) Univariable analysis 1.3320 (1.2330–1.4391) d  < 0.001
 PS-matched set 1044 (22.2%) 1210 (25.7%) Multivariable analysisa 1.2240 (1.1252–1.3314)d  < 0.001
PS-matched analysisb 1.2177 (1.0846–1.3673)d 0.001
Inverse PS weighted analysisc 1.1882 (1.0763–1.3116)d 0.001
Renal complications
 Total population 4142 (30%) 1743 (37%) Univariable analysis 1.3615 (1.2706–1.4588)d  < 0.001
 PS-matched set 1487 (31.6%) 1743 (37) Multivariable analysisa 1.3241 (1.2282–1.4274)d  < 0.001
PS-matched analysisb 1.2895 (1.1755–1.4146)d  < 0.001
Inverse PS weighted analysisc 1.2857 (1.1802–1.4008)d  < 0.001
Pulmonary complications
 Total population 1283 (9.3%) 358 (7.6%) Univariable analysis 0.9619 (0.8207–1.1275)d 0.001
 PS-matched set 513 (10.9%) 358 (7.6%) Multivariable analysisa 0.8333 (0.7039–0.9865)d  < 0.001
PS-matched analysisb 0.8231 (0.6465–0.9654)d  < 0.001
Inverse PS weighted analysisc 0.8530 (0.7046–0.9678)d  < 0.001
Metabolic complications
 Total population 1203 (8.7%) 477 (10.1%) Univariable analysis 1.1883 (1.0636–1.3277)d 0.002
 PS-matched set 422 (9%) 477 (10.1%) Multivariable analysisa 1.1594 (1.0296–1.3055)d 0.015
PS-matched analysisb 1.1361 (0.9548–1.3516)d 0.145
Inverse PS weighted analysisc 1.1353(0.9903–1.3016)d 0.069
Neurological complications
 Total population 917 (6.6%) 340 (7.2%) Univariable analysis 1.0956 (0.9629–1.2466) d 0.165
 PS-matched set 359 (7.6%) 340 (7.2%) Multivariable analysisa 0.9834 (0.8577–1.1277)d 0.812
PS-matched analysisb 0.9981 (0.7595–1.3116)d 0.989
Inverse PS weighted analysisc 0.9651 (0.8264–1.1270)d 0.654
Repair
 Total population 5091 (36.8%) 1719 (36.5%) Univariable analysis 0.9837 (0.9187–1.0532)d 0.638
 PS-matched set 1690 (35.9) 1719 (36.5) Multivariable analysisa 1.0235 (0.9515–1.1010)d 0.531
PS-matched analysisb 1.0183 (0.9353–1.1088)d 0.675
Inverse PS weighted analysisc 1.0196 (0.9355–1.1112)d 0.658
Time on ECMO (hours)
 Total population 206 (95–432) 237 (126–433) Univariable analysis 0.1451 (0.0215)e,g  < 0.001
 PS-matched set 230 (112-472) 237 (126–433) Multivariable analysisa − 0.0161 (0.0200)e,g 0.419
PS-matched analysisb − 0.0058 (0.0273)e,g 0.832
Inverse PS weighted analysisc − 0.0026 (0.0176)e,g 0.883
Reason for discontinuation
 Total population NA NA  < 0.001
 Died or poor prognosis 4591 (34%) 1503(32%)
 Expected recovery 8368 (62%) 3067 (66%)
 Transplantation 440 (3.2%) 14 (0.3%)
 Other 179 (1.3%) 60 (1.3%)
 Matched population NA NA  < 0.001f
 Died or poor prognosis 1589 (34%) 1514 (32%)
 Expected recovery 3010 (64%) 3115 (66%)
 Transplantation 48 (1%) 17 (0.4%)
 Other 60 (1.3%) 61 (1.3%)
Hospital length of stay
 Total population 27 (14–47) 25 (14–41) Univariable analysis − 4.9460 (0.6266)e  < 0.001
 PS-matched set 27 (14–44) 25 (15–41) Multivariable analysisa − 2.9107 (0.6318)e  < 0.001
PS-matched analysisb − 2.7546 (0.6856)e  < 0.001
Inverse PS weighted analysisc − 2.8285 (0.4882)e  < 0.001
Discharge destinationd
 Total population NA NA  < 0.001
  Home 3329 (37%) 874 (28%)
  Other hospital 1741 (19%) 608 (20%)
  Transfer to LTAC/rehab/hospice 2238 (25%) 1137 (37%)
  Other 1692 (19%) 487 (16%) NA NA  < 0.001f
 Matched population
  Home 1201 (26%) 985 (21%)
  Other hospital 681 (14%) 644 (14%)
  Transfer to LTAC/rehab/hospice 667 (19%) 1202 (26%)
  Other 1938 (41%) 1,876 (40%)
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BMI body mass index; LTAC long-term acute care; PS propensity score; ECMO extracorporeal membrane oxygenation

aMultivariable model for imputed datasets (pooled by Rubin’s rule)

bLogistic mixed effects model with imputed matched datasets (pooled by Rubin’s rule)

cInverse probability weighted regression model with propensity-score estimates for imputed datasets (pooled by Rubin’s rule)

dEffect size: odds ratio (95% confidence interval)

eCoefficient (standard deviation)

fMcNemar test for paired data

gTime on ECMO was transformed with inverse hyperbolic sign transformation because of skewness

Association between class II obesity or more and secondary outcomes

Incidence of complications and duration of ECMO run and hospitalization according to BMI groups are shown in Table 2. Patients with BMI ≥ 35 kg/m2 more frequently suffered from cardiovascular (17.3% versus 15.3%), renal (37% versus 30%), and device-related complications (25.7% versus 20.6%) compared to patients with BMI < 35 kg/m2. For all complications, this increased risk was consistent across both propensity score-based and multivariable models. Exploratory analyses suggested that the increased risk for cardiovascular complications is possibly driven by an increased incidence of arrhythmia and need for cardio-pulmonary resuscitation (CPR) in patients with BMI ≥ 35 kg/m2 (Supplemental Table 7). Increased incidence of renal complications was observed across all subtypes (Supplemental Table 8). Excess device-related complications appear predominantly due to oxygenator failure (Supplemental Table 9). In contrast, 7.7% of patients with BMI ≥ 35 kg/m2 compared to 9.3% of patients with BMI < 35 kg/m2 developed pulmonary complications. This observation remained present across all adjusted analyses (Table 2) and appears due to reduced incidence of pneumothorax (Supplemental Table 10). The risk of developing other complications was not different (Table 2).

In all confounder-adjusted analyses, ECMO duration was not affected, whereas duration of hospitalization (−3 days) was significantly shorter in patients with BMI ≥ 35 kg/m2 compared to patients with BMI < 35 kg/m2. Discharge destination significantly differed with more patients being transferred to long-term acute care, rehab, or hospice and less patients being discharged home in BMI ≥ 35 kg/m2 (Table 2).

Sensitivity analyses

Sensitivity analyses in patients admitted from 2018 onward yielded similar results (Supplemental Table 11).

To assess whether the higher risk of cardiovascular, renal and device-related complications in patients with BMI ≥ 35 kg/m2 could be explained by a higher survival rate and thus a longer time exposed to the potential development of these complications—the so-called ‘immortal time bias’—additional sensitivity analyses involved survivors only. This showed that the excess risk for developing these complications persisted in survivors only (Supplemental Table 11). The effect on the duration of hospitalization further increased, implying that this is a true effect and cannot be explained by deaths occurring earlier during the disease course of patients with BMI ≥ 35 kg/m2.

Assessment of the shape of the relationship between BMI and outcomes and study of BMI subcategories

GAM analysis confirmed a non-linear relationship between BMI and mortality (Fig. 2). The shape of the relationship between BMI and mortality suggests that mortality is the highest for patients with BMI 30–35 kg/m2. Subsequently, mortality decreases until BMI values around 60 kg/m2, after which mortality appears to increase up to BMI 70 kg/m2. The confidence interval between 60 and 70 kg/m2, however, is wide because of the small number of patients. At BMI < 25 kg/m2, mortality decreases, although the margin of error is high for BMI < 20 kg/m2 due to the small sample size. Cardiovascular, renal, and device-related complications appear to increase up to a BMI of 40–50 kg/m2, after which a plateau is reached. The risk of pulmonary complications appears to gradually decrease between BMI of 25 and 45 kg/m2. For duration of ECMO, an inverse U-shape relationship was observed between the BMI and duration of ECMO and with a peak around BMI 30 kg/m2. Hospital length of stay decreased across the entire range of BMI values. Outcomes for the BMI subcategories are reported in Supplemental Table 6. Consistent with GAM analyses, data do no suggest an upper limit of BMI, above which treatment appears futile although the margin of error in BMI > 60 kg/m2 is large.

Fig. 2.

Fig. 2

Open in a new tab

Relationship between BMI and outcomes. A Adjusted relationship between BMI and mortality risk (EDF: 6.848, p = 0.002); B adjusted relationship between BMI and duration of ECMO (EDF: 6.943, p < 0.001); C adjusted relationship between BMI and hospital length of stay (EDF: 1, p < 0.001); D adjusted relationship between BMI and cardiovascular complications (EDF:1.76, p = 0.004); E adjusted relationship between BMI and hemorrhagic complications (EDF: 1.004, p = 0.988); F adjusted relationship between BMI and infectious complications (EDF: 1.059, p = 0.311); G adjusted relationship between BMI and limb complications (EDF: 1.264, p = 0.086); H adjusted relationship between BMI and pulmonary complications (EDF: 3.114, p < 0.001); I adjusted relationship between BMI and renal complications (EDF: 2.331 p < 0.001); J adjusted relationship between BMI and device-related complications (EDF: 2.992, p < 0.001); K adjusted relationship between BMI and metabolic complications (EDF: 2.105, p = 0.047); L adjusted relationship between BMI and neurological complications (EDF: 2.449, p = 0.006). Y-axis (left) represents the probability for the binary variables, and for continuous variables the axis represents the scaled centered effect. Y-axis (right) represents the numbers of patients in the histogram. p values are calculated by generalized additive models, p values < 0.05 indicate non-linear relationship. The degree of freedom (EDF) represents the degree of the smooth relationship between the outcome and BMI. A value of 1 is considered a linear relationship, and larger values are considered as a more complex effect of BMI on the outcome

Discussion

In this large multicenter ELSO registry, we documented significantly lower mortality for adults with class II obesity or more requiring ECMO for respiratory failure, compared to those with BMI < 35 kg/m2, despite increased cardiovascular, device-related, and renal complications in the former. Hospital stay was also decreased in patients with BMI ≥ 35 kg/m2, not explained by a disproportionate number of early deaths. The findings were consistent throughout multiple confounder-adjusted statistical approaches. No BMI limit could be identified that would mark futility, albeit that the margin of error for BMI > 60 kg/m2 is large.

Obesity was considered a relative contraindication for ECMO [21] and during the COVID-19 pandemic, it was formally listed as such [22]. Previous studies on the impact of obesity on mortality in respiratory ECMO yielded inconsistent results. Al-Soufi reported no mortality difference according to weight quartiles in 1,334 adults in an earlier ELSO registry analysis, though a trend toward decreased mortality was noted among the highest quartile [18]. Small case series found no survival difference with BMI dichotomized at 30 kg/m2 [19], 35 kg/m2 [20], or 40 kg/m2 [11], as a continuous variable [8, 9, 35] or stratified by subcategories [9, 10, 12], although a trend toward improved survival was suggested for the highest BMI values [11, 12]. A systematic review and meta-analysis of mixed cardiac and respiratory ECMO patients found no association of BMI ≥ 30 kg/m2 with mortality [36]. Others observed lower mortality for patients with BMI ≥ 25 kg/m2 [13] or BMI ≥ 40 kg/m2 [14]. In a large United States nationwide readmission database including 23,876 ECMO patients, 25% of which were respiratory indications, BMI ≥ 30 kg/m2 was not associated with mortality [37]. Confounder adjustment in this study was limited to demographics and comorbidities. Paucity of data in COVID-related ECMO are contradictory [15–17, 38]. We consistently showed decreased mortality among patients with BMI ≥ 35 kg/m2 across multiple analyses extensively adjusted for confounders, associated with reduced hospital length of stay. This was not attributable to early deaths. No cutoff in the upper BMI range was identified, at which outcomes were clearly compromised.

These findings appear in line with the obesity paradox, referring to improved survival in obese critically ill patients. The obesity paradox was observed in general intensive care unit (ICU) populations [39], in pneumonia [40] and ARDS [41]. Obese COVID-19 patients have a higher risk for severe disease, but once admitted to the ICU, mortality is not higher than in non-obese patients [40]. Possible explanations include more adequate nutritional reserves, adipose tissue-released factors exerting favorable immune-modulatory effects, lower weight-based treatment dosing, and selection bias [1, 3]. Obese patients have altered pulmonary mechanics due to increased abdominal compression, promoting atelectasis and complete airway closure [42]. Increased metabolic demands and work of breathing may further predispose to the development of respiratory failure earlier in the disease course, when there is less severe parenchymal lung disease compared with non-obese patients [43]. Obese patients require higher PEEP and physicians may be hesitant to prone these patients. Optimal ventilation may necessitate transpulmonary pressure guidance, accepting airway pressures exceeding traditional safety guidelines. Hence, conventional ventilation may not be fully exploited prior to ECMO [3]. The lower incidence of pneumothorax in patients with BMI ≥ 35 kg/m2 in our cohort supports these findings and reduced length of stay might reflect lower illness severity.

Improved survival came with increased cardiovascular, device-related and renal complications. Previous small cohorts did not identify increased complications in obese patients undergoing respiratory ECMO. Studied complications include vasopressor use [14], renal replacement therapy [12, 19], bleeding or thrombotic events [11], including cannulation site bleeding [10] and cannulation-associated deep vein thrombosis [12], cerebrovascular accidents [11], and oxygenator clotting [8]. Percutaneous cannulation appeared feasible and safe [20]. The excess of cardiovascular complications in our study was explained by a higher incidence of arrhythmia and the need for CPR. Obesity is associated with increased cardiovascular morbidity, including hypertension, ventricular hypertrophy, ischemic heart disease, pulmonary hypertension, and atrial fibrillation, further promoted by co-existent diabetes, metabolic syndrome, and immune effects [1, 44]. Although the registry misses granularity on the nature of the cardiovascular events, it is conceivable that these comorbidities predispose to acute events in the critical setting. Increased renal complications in ECMO patients with BMI ≥ 35 kg/m2 is consistent with increased risk of acute kidney injury (AKI) associated with obesity in general ICU patients [45]. Vulnerability for AKI is likely multifactorial, including increased renal blood flow and hyperfiltration, possibly increasing vulnerability to acute damage, increased oxidative stress, altered immunometabolic state, challenging intravascular volume assessment, and increased abdominal pressure [46]. Finally, device-related complications were more likely to occur. Only oxygenator failure was consistently more frequent in patients with BMI ≥ 35 kg/m2. This could be linked with a procoagulant state and challenging anticoagulation management. No other clot-related problems occurred more frequently. Cannulation, theoretically, may be more challenging because of poor anatomical reference, poor visualization, and sharper entrance angle of the guidewire [10]. Strikingly, cannula problems requiring intervention for misplacement, dislodgement, clots/fibrin, mechanical failure, or inappropriate position were not more frequent in the matched population. In contrast to veno-arterial ECMO in obesity [10], we observed no excess canula-related bleeding or limb complications. Markedly, the increased complications in obese patients, consistent with other reports [1, 39, 47, 48], did not counterbalance the mortality benefit.

Our study has several strengths. This is the largest study on the relationship between BMI and outcomes in adult respiratory ECMO. Multiple statistical approaches to correct for confounding showed consistent results. Data from over 500 centers worldwide, represent real-life daily care practice. This study also has limitations. First, the ELSO registry gathers voluntarily entered information. Participating centers may be more experienced, possibly leading to selection bias. Second, the registry format is characterized by variable missingness of data (e.g., absence of delay between intubation and ECMO in 15%). We attempted to mitigate this with multiple imputation, reducing bias compared to complete case analyses. Whether low rates of proning (21%) and use of NMBA (54%) represent underreporting and/or suboptimal treatment remains unclear. Third, the registry likely suffers from underreporting of comorbidities, treatments and complications, imprecision in diagnosis (e.g., unspecified ARDS/acute respiratory failure in 40%), and lack of ventilation data during ECMO. Fourth, we cannot exclude residual confounding by unmeasured variables, or variables not released by ELSO (e.g., center information). Fifth, the decision to start ECMO in obese patients may have been scrutinized more compared to non-obese counterparts, resulting in additional selection bias. Sixth, the registry is limited to the hospital stay; longer-term patient-centered outcomes such as quality of life are not available. Seventh, the cutoff to dichotomize BMI may be considered somewhat arbitrary. However, dichotomization allowed matching and multiple other statistical approaches were performed to study the relation between BMI and outcomes. Finally, as this is a retrospective analysis, causality cannot be inferred, although we attempted to approximate a random design by different propensity-score methods.

Conclusion

Patients with obesity class II or more treated with respiratory ECMO have decreased mortality and shorter hospital length of stays, despite increased cardiovascular, device-related, and renal complications. The relationship between BMI and outcomes in non-linear. No upper limit of BMI indicating futility was identified. BMI as a single parameter should not be a contraindication for ECMO in respiratory failure.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 538 KB) (537.7KB, docx)

Acknowledgements

We thank Steffen Fieuws for his statistical advice during the design and execution of the statistical analyses. We thank all the ESLO centers for providing data to the registry.

Author contributions

GH conceived the study; GH and IGF conceived the analysis plan; GH, MP, IGF, AV, and AC designed the study; IGF led the data analyses, supported by GH; GH, MP, and IGF drafted the initial manuscript. All authors revised and approved the manuscript for submission. GH and IGF have directly accessed and verified the underlying data reported in the manuscript. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Funding

This research was funded by Fonds Wetenschappelijk Onderzoek, Grant no. 1805121N.

Data availability

The ELSO registry data are available from ELSO for investigators upon research requests.

Declarations

Conflicts of interest

The authors have no conflict of interest.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (DOCX 538 KB) (537.7KB, docx)

Data Availability Statement

The ELSO registry data are available from ELSO for investigators upon research requests.

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