Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study

被引:6
作者
Kobayashi, Naoya [1 ]
Shiga, Takuya [1 ]
Ikumi, Saori [1 ]
Watanabe, Kazuki [2 ]
Murakami, Hitoshi [2 ]
Yamauchi, Masanori [1 ]
机构
[1] Tohoku Univ, Dept Anesthesiol & Perioperat Med, Grad Sch Med, Aoba Ku, 2-1 Seiryo Machi, Sendai, Miyagi 9508575, Japan
[2] Hitachi Solut East Japan Ltd, Sendai, Miyagi, Japan
关键词
OBSERVATION TOOL; CRITICAL ILLNESS; ILL PATIENTS; UNIT; DELIRIUM; ADULT; VALIDATION; MANAGEMENT; ANALGESIA; SEDATION;
D O I
10.1038/s41598-021-84714-8
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Monitoring the pain intensity in critically ill patients is crucial because intense pain can cause adverse events, including poor survival rates; however, continuous pain evaluation is difficult. Vital signs have traditionally been considered ineffective in pain assessment; nevertheless, the use of machine learning may automate pain assessment using vital signs. This retrospective observational study was performed at a university hospital in Sendai, Japan. Objective pain assessments were performed in eligible patients using the Critical-Care Pain Observation Tool (CPOT). Three machine-learning methods-random forest (RF), support vector machine (SVM), and logistic regression (LR)-were employed to predict pain using parameters, such as vital signs, age group, and sedation levels. Prediction accuracy was calculated as the harmonic mean of sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Furthermore, 117,190 CPOT assessments were performed in 11,507 eligible patients (median age: 65 years; 58.0% males). We found that pain prediction was possible with all three machine-learning methods. RF demonstrated the highest AUROC for the test data (RF: 0.853, SVM: 0.823, and LR: 0.787). With this method, pain can be objectively, continuously, and semi-automatically evaluated in critically ill patients.
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页数:7
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