SynthesisSystematic reviews2023
Predictive models in extracorporeal membrane oxygenation (ECMO): a systematic review.
Synthesis in Systematic reviews, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Humans Versus Machine: Machine Learning-Based Individualized Treatment Effect Modeling for Extracorporeal Cardiopulmonary Resuscitation in Out-of-Hospital Cardiac Arrest.Journal of the American Heart Association · 2026Article
- Scoping review of contemporary adult venoarterial extracorporeal membrane oxygenation (VA ECMO) selection criteria and decision-making mechanisms.The journal of extra-corporeal technology · 2026Article
- Clinician Perspectives on the Extracorporeal Membrane Oxygenation Decision-Making Process.JAMA network open · 2026Article
- Healthcare professionals' views on training, standards, and resources for extracorporeal membrane oxygenation: a cross-sectional survey.Croatian medical journal · 2026Article
- Comparative Prognostic Performance of SOFA2.0 and SOFA for 28-Day Mortality in Patients Developing Sepsis During ECMO Support.Journal of inflammation research · 2026Article
- Development and validation of a machine learning model for predicting mortality risk in veno-arterial extracorporeal membrane oxygenation patients.Scientific reports · 2025Article
- A Novel, Interpretable Machine Learning Model to Predict Neurological Outcomes Following Venoarterial Extracorporeal Membrane Oxygenation.Neurocritical care · 2025Article
- Radiomics-enhanced modelling approach for predicting the need for ECMO in ARDS patients: a retrospective cohort study.Scientific reports · 2025Article
- Global Trends in Extracorporeal Membrane Oxygenation Support for Circulatory Failure: A Bibliometric Analysis.Healthcare (Basel, Switzerland) · 2025Review
- COMPREHENSIVE CHARACTERIZATION OF CYTOKINES IN PATIENTS UNDER EXTRACORPOREAL MEMBRANE OXYGENATION: EVIDENCE FROM INTEGRATED BULK AND SINGLE-CELL RNA SEQUENCING DATA USING MULTIPLE MACHINE LEARNING APPROACHES.Shock (Augusta, Ga.) · 2025Article
- Predictive Value of Serial Model of End-Stage Liver Disease Score Determination in Patients with Postcardiotomy Extracorporeal Membrane Oxygenation.Journal of clinical medicine · 2024Article
- A Comprehensive Review of Extra Corporeal Membrane Oxygenation: The Lifeline in Critical Moments.Cureus · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeExtracorporeal membrane oxygenation (ECMO) has been increasingly used in the last years to provide hemodynamic and respiratory support in critically ill patients. In this scenario, prognostic scores remain essential to choose which patients should initiate ECMO. This systematic review aims to assess the current landscape and inform subsequent efforts in the development of risk prediction tools for ECMO.
methodsPubMed, CINAHL, Embase, MEDLINE and Scopus were consulted. Articles between Jan 2011 and Feb 2022, including adults undergoing ECMO reporting a newly developed and validated predictive model for mortality, were included. Studies based on animal models, systematic reviews, case reports and conference abstracts were excluded. Data extraction aimed to capture study characteristics, risk model characteristics and model performance. The risk of bias was evaluated through the prediction model risk-of-bias assessment tool (PROBAST). The protocol has been registered in Open Science Framework ( https://osf.io/fevw5 ).
resultsTwenty-six prognostic scores for in-hospital mortality were identified, with a study size ranging from 60 to 4557 patients. The most common candidate variables were age, lactate concentration, creatinine concentration, bilirubin concentration and days in mechanical ventilation prior to ECMO. Five out of 16 venous-arterial (VA)-ECMO scores and 3 out of 9 veno-venous (VV)-ECMO scores had been validated externally. Additionally, one score was developed for both VA and VV populations. No score was judged at low risk of bias.
conclusionMost models have not been validated externally and apply after ECMO initiation; thus, some uncertainty whether ECMO should be initiated still remains. It has yet to be determined whether and to what extent a new methodological perspective may enhance the performance of predictive models for ECMO, with the ultimate goal to implement a model that positively influences patient outcomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.