The physiology of venoarterial extracorporeal membrane oxygenation - A comprehensive clinical perspective.

Venoarterial extracorporeal membrane oxygenation (VA ECMO) has become a standard of care for severe cardiogenic shock, refractory cardiac arrest and related impending multiorgan failure. The widespread clinical use of this complex temporary circulatory support modality is still contrasted by a lack of formal scientific evidence in the current literature. This might at least in part be attributable to VA ECMO related complications, which may significantly impact on clinical outcome. In order to limit adverse effects of VA ECMO as much as possible an indepth understanding of the complex physiology during extracorporeally supported cardiogenic shock states is critically important. This review covers all relevant physiological aspects of VA ECMO interacting with the human body in detail. This, to provide a solid basis for health care professionals involved in the daily management of patients supported with VA ECMO and suffering from cardiogenic shock or cardiac arrest and impending multiorgan failure for the best possible care.

Leaving the hospital on time: hospital bed utilization and reasons for discharge delay in the Netherlands.

Inappropriate bed occupancy due to delayed hospital discharge affects both physical and psychological well-being in patients and can disrupt patient flow. The Dutch healthcare system is facing ongoing pressure, especially during the current coronavirus disease pandemic, intensifying the need for optimal use of hospital beds. The aim of this study was to quantify inappropriate patient stays and describe the underlying reasons for the delays in discharge. The Day of Care Survey (DoCS) is a validated tool used to gain information about appropriate and inappropriate bed occupancy in hospitals. Between February 2019 and January 2021, the DoCS was performed five times in three different hospitals within the region of Amsterdam, the Netherlands. All inpatients were screened, using standardized criteria, for their need for in-hospital care at the time of survey and reasons for discharge delay. A total of 782 inpatients were surveyed. Of these patients, 94 (12%) were planned for definite discharge that day. Of all other patients, 145 (21%, ranging from 14% to 35%) were without the need for acute in-hospital care. In 74% (107/145) of patients, the reason for discharge delay was due to issues outside the hospital; most frequently due to a shortage of available places in care homes (26%, 37/145). The most frequent reason for discharge delay inside the hospital was patients awaiting a decision or review by the treating physician (14%, 20/145). Patients who did not meet the criteria for hospital stay were, in general, older [median 75, interquartile range (IQR) 65-84 years, and 67, IQR 55-75 years, respectively, P < .001] and had spent more days in hospital (7, IQR 5-14 days, and 3, IQR 1-8 days respectively, P < .001). Approximately one in five admitted patients occupying hospital beds did not meet the criteria for acute in-hospital stay or care at the time of the survey. Most delays were related to issues outside the immediate control of the hospital. Improvement programmes working with stakeholders focusing on the transfer from hospital to outside areas of care need to be further developed and may offer potential for the greatest gain. The DoCS can be a tool to periodically monitor changes and improvements in patient flow.

Clinical decision support for ExtraCorporeal Membrane Oxygenation: Will we fly by wire?

Prognostic modelling techniques have rapidly evolved over the past decade and may greatly benefit patients supported with ExtraCorporeal Membrane Oxygenation (ECMO). Epidemiological and computational physiological approaches aim to provide more accurate predictive assessments of ECMO-related risks and benefits. Implementation of these approaches may produce predictive tools that can improve complex clinical decisions surrounding ECMO allocation and management. This Review describes current applications of prognostic models and elaborates on upcoming directions for their clinical applicability in decision support tools directed at improved allocation and management of ECMO patients. The discussion of these new developments in the field will culminate in a futuristic perspective leaving ourselves and the readers wondering whether we may "fly ECMO by wire" someday.

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 >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.

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.

Using machine learning to predict blood culture outcomes in the emergency department: a single-centre, retrospective, observational study.

OBJECTIVES: To develop predictive models for blood culture (BC) outcomes in an emergency department (ED) setting. DESIGN: Retrospective observational study. SETTING: ED of a large teaching hospital in the Netherlands between 1 September 2018 and 24 June 2020. PARTICIPANTS: Adult patients from whom BCs were collected in the ED. Data of demographic information, vital signs, administered medications in the ED and laboratory and radiology results were extracted from the electronic health record, if available at the end of the ED visits. MAIN OUTCOME MEASURES: The primary outcome was the performance of two models (logistic regression and gradient boosted trees) to predict bacteraemia in ED patients, defined as at least one true positive BC collected at the ED. RESULTS: In 4885 out of 51 399 ED visits (9.5%), BCs were collected. In 598/4885 (12.2%) visits, at least one of the BCs was true positive. Both a gradient boosted tree model and a logistic regression model showed good performance in predicting BC results with area under curve of the receiver operating characteristics of 0.77 (95% CI 0.73 to 0.82) and 0.78 (95% CI 0.73 to 0.82) in the test sets, respectively. In the gradient boosted tree model, the optimal threshold would predict 69% of BCs in the test set to be negative, with a negative predictive value of over 94%. CONCLUSIONS: Both models can accurately identify patients with low risk of bacteraemia at the ED in this single-centre setting and may be useful to reduce unnecessary BCs and associated healthcare costs. Further studies are necessary for validation and to investigate the potential clinical benefits and possible risks after implementation.