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Intelligent alert system for predicting invasive mechanical ventilation needs via noninvasive parameters: employing an integrated machine learning method with integration of multicenter databases

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Abstract

The use of invasive mechanical ventilation (IMV) is crucial in rescuing patients with respiratory dysfunction. Accurately predicting the demand for IMV is vital for clinical decision-making. However, current techniques are invasive and challenging to implement in pre-hospital and emergency rescue settings. To address this issue, a real-time prediction method utilizing only non-invasive parameters was developed to forecast IMV demand in this study. The model introduced the concept of real-time warning and leveraged the advantages of machine learning and integrated methods, achieving an AUC value of 0.935 (95% CI 0.933–0.937). The AUC value for the multi-center validation using the AmsterdamUMCdb database was 0.727, surpassing the performance of traditional risk adjustment algorithms (OSI(oxygenation saturation index): 0.608, P/F(oxygenation index): 0.558). Feature weight analysis demonstrated that BMI, Gcsverbal, and age significantly contributed to the model’s decision-making. These findings highlight the substantial potential of a machine learning real-time dynamic warning model that solely relies on non-invasive parameters to predict IMV demand. Such a model can provide technical support for predicting the need for IMV in pre-hospital and disaster scenarios.

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Authors and Affiliations

  1. Systems Engineering Institute, People’s Liberation Army, Academy of Military Sciences, Tianjin, 300161, China

    Guang Zhang, Guanjun Liu & Chen Su

  2. School of Life Sciences, Tiangong University, Tianjin, 300387, China

    Qingyan Xie, Chengyi Wang & Jiameng Xu

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  1. Guang Zhang
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Zhang, G., Xie, Q., Wang, C. et al. Intelligent alert system for predicting invasive mechanical ventilation needs via noninvasive parameters: employing an integrated machine learning method with integration of multicenter databases. Med Biol Eng Comput 62, 3445–3458 (2024). https://doi.org/10.1007/s11517-024-03143-7

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