Interrater agreement in classifying infections during extracorporeal membrane oxygenation

Infectious complications are common during extracorporeal membrane oxygenation (ECMO) and may negatively impact outcomes. However, there is considerable variation in the reported rates of incidence, which hampers the use of infections as a quality benchmark for ECMO centers. To assess the contributing role of poor interrater agreement, three independent raters reviewed medical records from all intensive care unit (ICU) patients who received ECMO for &gt;24 h in our tertiary center between October 2019 and October 2021 for suspected episodes of infection, which were rated based on their date of onset and presumed site/diagnosis. To establish a gold standard, any discrepancies were resolved using an expert panel consisting of two intensivists/infectious disease specialists. During 83 ECMO-runs in 77 patients, we observed a total of 62 adjudicated infectious episodes (incidence rate 62, 95% CI: 48–80, per 1000 days at risk). Among 81 episodes suspected by at least one observer, 66 (81%) were identified by two, and only 44 (54%) by all three raters, resulting in Fleiss’ kappa of 0.10 (95% CI: 0.00–0.19; slight agreement). However, if raters concurred regarding infection onset, subsequent agreement on infection site was good (concordance 89%; kappa 0.85, 95% CI: 0.72–0.98; near perfect agreement). In conclusion, adjudication of infectious episodes during ECMO is associated with poor interrater agreement regarding occurrence—but not site—of infection. This finding might partially explain the significant disparities observed in reported infection rates during ECMO, emphasizing the need for caution when interpreting infection data in this particular population due to the potential for inherent measurement error.</p

Prognostic models for mortality risk in patients requiring ECMO

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