Department of Anesthesiology and Perioperative Medicine, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan.
Hitachi Solutions East Japan, Ltd, Sendai, Miyagi, Japan.
Sci Rep. 2023 Oct 14;13(1):17479. doi: 10.1038/s41598-023-44970-2.
Machine learning tools have demonstrated viability in visualizing pain accurately using vital sign data; however, it remains uncertain whether incorporating individual patient baselines could enhance accuracy. This study aimed to investigate improving the accuracy by incorporating deviations from baseline patient vital signs and the concurrence of the predicted artificial intelligence values with the probability of critical care pain observation tool (CPOT) ≥ 3 after fentanyl administration. The study included adult patients in intensive care who underwent multiple pain-related assessments. We employed a random forest model, utilizing arterial pressure, heart rate, respiratory rate, gender, age, and Richmond Agitation-Sedation Scale score as explanatory variables. Pain was measured as the probability of CPOT scores of ≥ 3, and subsequently adjusted based on each patient's baseline. The study included 10,299 patients with 117,190 CPOT assessments. Of these, 3.3% had CPOT scores of ≥ 3. The random forest model demonstrated strong accuracy with an area under the receiver operating characteristic curve of 0.903. Patients treated with fentanyl were grouped based on CPOT score improvement. Those with ≥ 1-h of improvement after fentanyl administration had a significantly lower pain index (P = 0.020). Therefore, incorporating deviations from baseline patient vital signs improved the accuracy of pain visualization using machine learning techniques.
机器学习工具已证明在使用生命体征数据准确可视化疼痛方面具有可行性；然而，尚不确定是否可以通过纳入患者个体基线来提高准确性。本研究旨在通过纳入患者生命体征基线偏差以及预测人工智能值与芬太尼给药后疼痛观察工具（CPOT）≥3 的概率的一致性来提高准确性。该研究纳入了接受多次疼痛相关评估的重症监护成人患者。我们使用随机森林模型，利用动脉压、心率、呼吸率、性别、年龄和 Richmond 躁动镇静评分作为解释变量。疼痛测量为 CPOT 评分≥3 的概率，随后根据每位患者的基线进行调整。该研究共纳入了 10299 名患者，共进行了 117190 次 CPOT 评估。其中，3.3%的患者 CPOT 评分≥3。随机森林模型具有很强的准确性，其受试者工作特征曲线下面积为 0.903。根据 CPOT 评分的改善，对接受芬太尼治疗的患者进行分组。在芬太尼给药后改善≥1 小时的患者疼痛指数显著降低（P=0.020）。因此，通过纳入患者生命体征基线偏差，可以提高使用机器学习技术进行疼痛可视化的准确性。
