• Yang-Han Lin, Ting-Chia Chang, Chung-Feng Liu, Chih-Cheng Lai, Chin-Ming Chen, and Willy Chou.
• Department of Intensive Care Medicine, Chi Mei Medical Center, Tainan City, Taiwan.
• Medicine (Baltimore). 2024 Mar 22; 103 (12): e37500e37500.

AbstractPatients admitted to intensive care units (ICU) and receiving mechanical ventilation (MV) may experience ventilator-associated adverse events and have prolonged ICU length of stay (LOS). We conducted a survey on adult patients in the medical ICU requiring MV. Utilizing big data and artificial intelligence (AI)/machine learning, we developed a predictive model to determine the optimal timing for weaning success, defined as no reintubation within 48 hours. An interdisciplinary team integrated AI into our MV weaning protocol. The study was divided into 2 parts. The first part compared outcomes before AI (May 1 to Nov 30, 2019) and after AI (May 1 to Nov 30, 2020) implementation in the medical ICU. The second part took place during the COVID-19 pandemic, where patients were divided into control (without AI assistance) and intervention (with AI assistance) groups from Aug 1, 2022, to Apr 30, 2023, and we compared their short-term outcomes. In the first part of the study, the intervention group (with AI, n = 1107) showed a shorter mean MV time (144.3 hours vs 158.7 hours, P = .077), ICU LOS (8.3 days vs 8.8 days, P = .194), and hospital LOS (22.2 days vs 25.7 days, P = .001) compared to the pre-intervention group (without AI, n = 1298). In the second part of the study, the intervention group (with AI, n = 88) exhibited a shorter mean MV time (244.2 hours vs 426.0 hours, P = .011), ICU LOS (11.0 days vs 18.7 days, P = .001), and hospital LOS (23.5 days vs 40.4 days, P < .001) compared to the control group (without AI, n = 43). The integration of AI into the weaning protocol led to improvements in the quality and outcomes of MV patients.Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.

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