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

Pladet, Lara C A; Luijken, Kim; Fresiello, Libera; Miranda, Dinís Dos Reis; Hermens, Jeannine A.J.M.; Smeden, Maarten van; Cremer, Olaf L.; Donker, Dirk W.; Meuwese, Christiaan L.

doi:10.1177/02676591231163688

Cited by 5 publications

(3 citation statements)

References 78 publications

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“…While the planning literature on healthcare systems has considered both physical resources and the generation and allocation of human resources, biomedically explicit approaches (such as estimates based on patients' immune profiles) have not yet been emphasized (26). While the need of personalized management has been articulated (13), it may be prevented by analyses that depend on relatively large sample sizes, i.e., data on population data where n > > 1. That is so because personalized clinical medicine involves n = 1 situations (8).…”

Section: Planning For the Usage Of Critical Hospital Resourcesmentioning

confidence: 99%

“…Given this background, here it is asked whether these informative capabilities may apply to manage and allocate critical hospital resources, such as mechanical ventilation (MV) and extracorporeal membrane oxygenation [ECMO (11,12)]. Calls for ECMO-related, personalized decision-making have recently been made (13). They are motivated by the poor predictability of models that explore ECMO use in COVID-19 patients (14)(15)(16).…”

Section: Introductionmentioning

confidence: 99%

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From numbers to medical knowledge: harnessing combinatorial data patterns to predict COVID-19 resource needs and distinguish patient subsets

Satashia,

Franco,

Rivas

et al. 2023

Front. Med.

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BackgroundThe COVID-19 pandemic intensified the use of scarce resources, including extracorporeal membrane oxygenation (ECMO) and mechanical ventilation (MV). The combinatorial features of the immune system may be considered to estimate such needs and facilitate continuous open-ended knowledge discovery.Materials and methodsComputer-generated distinct data patterns derived from 283 white blood cell counts collected within five days after hospitalization from 97 COVID-19 patients were used to predict patient’s use of hospital resources.ResultsAlone, data on separate cell types—such as neutrophils—did not identify patients that required MV/ECMO. However, when structured as multicellular indicators, distinct data patterns displayed by such markers separated patients later needing or not needing MV/ECMO. Patients that eventually required MV/ECMO also revealed increased percentages of neutrophils and decreased percentages of lymphocytes on admission.Discussion/conclusionFuture use of limited hospital resources may be predicted when combinations of available blood leukocyte-related data are analyzed. New methods could also identify, upon admission, a subset of COVID-19 patients that reveal inflammation. Presented by individuals not previously exposed to MV/ECMO, this inflammation differs from the well-described inflammation induced after exposure to such resources. If shown to be reproducible in other clinical syndromes and populations, it is suggested that the analysis of immunological combinations may inform more and/or uncover novel information even in the absence of pre-established questions.

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Section: Planning For the Usage Of Critical Hospital Resourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

From numbers to medical knowledge: harnessing combinatorial data patterns to predict COVID-19 resource needs and distinguish patient subsets

Satashia,

Franco,

Rivas

et al. 2023

Front. Med.

View full text Add to dashboard Cite

show abstract

“…We hope that a current randomized clinical trial investigating low-molecular-weight heparin and unfractionated heparin with different anticoagulation targets will provide further guidance regarding some of these important questions ( 10 ). For establishing specific anticoagulation monitoring targets, dedicated prediction tools could help assess the risk for bleeding and thrombosis and could further contribute to personalized care ( 11 ). Given the variability of bleeding risk across patients and within patients over time, such prediction tools would also need to be dynamic and able to update risk profiles over time by incorporating clinical (bleeding) events, degree of inflammation, and circuit changes.…”

mentioning

confidence: 99%

Optimizing Anticoagulation for Venovenous Extracorporeal Membrane Oxygenation: Finding the Right Balance

Meuwese,

Levy

2024

Am J Respir Crit Care Med

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The intelligent Impella: Future perspectives of artificial intelligence in the setting of Impella support

Consolo,

D'Andria Ursoleo,

Pieri

et al. 2024

ESC Heart Failure

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AimsArtificial intelligence (AI) has emerged as a potential useful tool to support clinical treatment of heart failure, including the setting of mechanical circulatory support (MCS). Modern Impella pumps are equipped with advanced technology (SmartAssist), enabling real‐time acquisition and display of data related to both pump performance and the patient's haemodynamic status. These data emerge as an ‘ideal’ source for data‐driven AI applications to predict the clinical course of an ongoing therapeutic protocol. Yet, no evidence of effective application of AI tools in the setting of Impella support is available. On this background, we aimed at identifying possible future applications of AI‐based tools in the setting of temporary MCS with an Impella device.MethodsWe explored the state of research and development at the intersection of AI and Impella support and derived future potential applications of AI in routine Impella clinical management.ResultsWe identified different areas where the future implementation of AI tools may contribute to addressing important clinical challenges in the setting of Impella support, including (i) early identification of the best suited pathway of care according to patients' conditions at presentation and intention to treat, (ii) prediction of therapy outcomes according to different possible therapeutic actions, (iii) optimization of device implantation procedures and evaluation of proper pump position over the whole course of support and (iv) prevention and/or rationale management of haemocompatibility‐related adverse events. For each of those areas, we discuss the potential advantages, challenges and implications of harnessing AI‐driven insights in the setting of MCS with an Impella device.ConclusionsTemporary MCS with an Impella device has great potential to benefit from the integration of AI‐based tools. Such tools may indeed translate into groundbreaking innovation supporting clinical decision‐making and therapy regulation, in particular in complex scenarios such as the multidevice MCS strategy.

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

Cited by 5 publications

References 78 publications

From numbers to medical knowledge: harnessing combinatorial data patterns to predict COVID-19 resource needs and distinguish patient subsets

From numbers to medical knowledge: harnessing combinatorial data patterns to predict COVID-19 resource needs and distinguish patient subsets

Optimizing Anticoagulation for Venovenous Extracorporeal Membrane Oxygenation: Finding the Right Balance

The intelligent Impella: Future perspectives of artificial intelligence in the setting of Impella support

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