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. 2023 Nov 22;13(1):20483.
doi: 10.1038/s41598-023-47452-7.

Defining predictors for successful mechanical ventilation weaning, using a data-mining process and artificial intelligence

Affiliations

Defining predictors for successful mechanical ventilation weaning, using a data-mining process and artificial intelligence

Juliette Menguy et al. Sci Rep. .

Abstract

Mechanical ventilation weaning within intensive care units (ICU) is a difficult process, while crucial when considering its impact on morbidity and mortality. Failed extubation and prolonged mechanical ventilation both carry a significant risk of adverse events. We aimed to determine predictive factors of extubation success using data-mining and artificial intelligence. A prospective physiological and biomedical signal data warehousing project. A 21-beds medical Intensive Care Unit of a University Hospital. Adult patients undergoing weaning from mechanical ventilation. Hemodynamic and respiratory parameters of mechanically ventilated patients were prospectively collected and combined with clinical outcome data. One hundred and eight patients were included, for 135 spontaneous breathing trials (SBT) allowing to identify physiological parameters either measured before or during the trial and considered as predictive for extubation success. The Early-Warning Score Oxygen (EWSO2) enables to discriminate patients deemed to succeed extubation, at 72-h and 7-days. Cut-off values for EWSO2 (AUC = 0.80; Se = 0.75; Sp = 0.76), mean arterial pressure and heart-rate variability parameters were determined. A predictive model for extubation success was established including body-mass index (BMI) on inclusion, occlusion pressure at 0,1 s. (P0.1) and heart-rate analysis parameters (LF/HF) both measured before SBT, and heart rate during SBT (global performance 62%; 83%). The data-mining process enabled to detect independent predictive factors for extubation success and to develop a dynamic predictive model using artificial intelligence. Such predictive tools may help clinicians to better discriminate patients deemed to succeed extubation and thus improve clinical performance.

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Conflict of interest statement

Dr. Juliette Menguy declares she has no conflict of interest related to this research. Dr Kahaia de Longeaux declares she has no conflict of interest related to this research. Dr Laetitia Bodenes declares she has no conflict of interest related to this research. Dr Baptiste Hourmant declares he has no conflict of interest related to this research. Pr L’Her is a consultant for GE Healthcare, Sedana Medical, Vygon; he is the coinventor of the FreeO2 patent, cofounder and shareholder of Oxynov Inc that commercializes FreeO2; he is also the the coinventor of the SDSP patent, cofounder and shareholder of Ivanae the spin-off company that develops SDSP.

Figures

Figure 1
Figure 1
Patients’ flow-chart. SBT: Spontaneous Breathing Test; MV: Mechanical Ventilation; EIT: Endotracheal Intubation; LOC: Lost of Contact; Success: Successful SBT and no reintubation within 72-h; Failure: failure of the SBT, or successful SBT but reintubation within 72-h.
Figure 2
Figure 2
Kaplan–Meier Curve for weaning Probability according to the classification group. Weaning classification in 3 groups according to the Consensus definition clearly depicts weaning outcome.

References

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