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Purpose: To provide an overview and evaluate the performance of mortality prediction models for patients requiring extracorporeal membrane oxygenation (ECMO) support for refractory cardiocirculatory or respiratory failure. Methods: A systematic literature search was undertaken to identify studies developing and/or validating multivariable prediction models for all-cause mortality in adults requiring or receiving veno-arterial (V-A) or veno-venous (V-V) ECMO. Estimates of model performance (observed versus expected (O:E) ratio and c-statistic) were summarized using random effects models and sources of heterogeneity were explored by means of meta-regression. Risk of bias was assessed using the Prediction model Risk Of BiAS Tool (PROBAST). Results: Among 4905 articles screened, 96 studies described a total of 58 models and 225 external validations. Out of all 58 models which were specifically developed for ECMO patients, 14 (24%) were ever externally validated. Discriminatory ability of frequently validated models developed for ECMO patients (i.e., SAVE and RESP score) was moderate on average (pooled c-statistics between 0.66 and 0.70), and comparable to general intensive care population-based models (pooled c-statistics varying between 0.66 and 0.69 for the Simplified Acute Physiology Score II (SAPS II), Acute Physiology and Chronic Health Evaluation II (APACHE II) score and Sequential Organ Failure Assessment (SOFA) score). Nearly all models tended to underestimate mortality with a pooled O:E > 1. There was a wide variability in reported performance measures of external validations, reflecting a large between-study heterogeneity. Only 1 of the 58 models met the generally accepted Prediction model Risk Of BiAS Tool criteria of good quality. Importantly, all predicted outcomes were conditional on the fact that ECMO support had already been initiated, thereby reducing their applicability for patient selection in clinical practice. Conclusions: A large number of mortality prediction models have been developed for ECMO patients, yet only a minority has been externally validated. Furthermore, we observed only moderate predictive performance, large heterogeneity between-study populations and model performance, and poor methodological quality overall. Most importantly, current models are unsuitable to provide decision support for selecting individuals in whom initiation of ECMO would be most beneficial, as all models were developed in ECMO patients only and the decision to start ECMO had, therefore, already been made.

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/m(2)) 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/m(2) 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/m(2) 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/m(2), 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. This research was funded by Fonds Wetenschappelijk Onderzoek, Grant no. 1805121N. We thank Stefen 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.

Intensive care unit (ICU) patients with end-organ failure will require specialised machines or extracorporeal therapies to support the failing organs that would otherwise lead to death. ICU patients with severe acute kidney injury may require renal replacement therapy (RRT) to remove fluid and wastes from the body, and patients with severe cardiorespiratory failure will require extracorporeal membrane oxygenation (ECMO) to maintain adequate oxygen delivery whilst the underlying pathology is evaluated and managed. The presence of ECMO and RRT machines can further augment the existing pharmacokinetic (PK) alterations during critical illness. Significant changes in the apparent volume of distribution (V-d) and drug clearance (CL) for many important drugs have been reported during ECMO and RRT. Conventional antimicrobial dosing regimens rarely consider the impact of these changes and consequently, are unlikely to achieve effective antimicrobial exposures in critically ill patients receiving ECMO and/or RRT. Therefore, an in-depth understanding on potential PK changes during ECMO and/or RRT is required to inform antimicrobial dosing strategies in patients receiving ECMO and/or RRT. In this narrative review, we aim to discuss the potential impact of ECMO and RRT on the PK of antimicrobials and antimicrobial dosing requirements whilst receiving these extracorporeal therapies. The potential benefits of therapeutic drug monitoring (TDM) and dosing software to facilitate antimicrobial therapy for critically ill patients receiving ECMO and/or RRT are also reviewed and highlighted.

Rates of survival with functional recovery for both in-hospital and out-of-hospital cardiac arrest are notably low. Extracorporeal cardiopulmonary resuscitation (ECPR) is emerging as a modality to improve prognosis by augmenting perfusion to vital end-organs by utilizing extracorporeal membrane oxygenation (ECMO) during conventional CPR and stabilizing the patient for interventions aimed at reversing the aetiology of the arrest. Implementing this emergent procedure requires a substantial investment in resources, and even the most successful ECPR programs may nonetheless burden healthcare systems, clinicians, patients, and their families with unsalvageable patients supported by extracorporeal devices. Non-randomized and observational studies have repeatedly shown an association between ECPR and improved survival, versus conventional CPR, for in-hospital cardiac arrest in select patient populations. Recently, randomized controlled trials suggest benefit for ECPR over standard resuscitation, as well as the feasibility of performing such trials, in out-of-hospital cardiac arrest within highly coordinated healthcare delivery systems. Application of these data to clinical practice should be done cautiously, with outcomes likely to vary by the setting and system within which ECPR is initiated. ECPR introduces important ethical challenges, including whether it should be considered an extension of CPR, at what point it becomes sustained organ replacement therapy, and how to approach patients unable to recover or be bridged to heart replacement therapy. The economic impact of ECPR varies by health system, and has the potential to outstrip resources if used indiscriminately. Ideally, studies should include economic evaluations to inform health care systems about the cost-benefits of this therapy.