Predictive analysis of delirium risk in ICU patients with cardiothoracic surgery by ensemble classification algorithm of random forest_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

CHEN Miao ,  CHEN Qing ,  YIN Xiaoqing

The First Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Changsha, 410007,P. R. China;

Corresponding?author：

CHEN Qing, Email: 3124617441@qq.com; YIN Xiaoqing, Email: xiaoqingyin62@163.com

Keywords：

Random forest; ensemble classification algorithm; delirium; prediction; artificial intelligence

DOI：

10.7507/1007-4848.202105058

Video：

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Objective To analyze the predictive value of ensemble classification algorithm of random forest for delirium risk in ICU patients with cardiothoracic surgery. Methods A total of 360 patients hospitalized in cardiothoracic ICU of our hospital from June 2019 to December 2020 were retrospectively analyzed. There were 193 males and 167 females, aged 18-80 (56.45±9.33) years. The patients were divided into a delirium group and a control group according to whether delirium occurred during hospitalization or not. The clinical data of the two groups were compared, and the related factors affecting the occurrence of delirium in cardiothoracic ICU patients were predicted by the multivariate logistic regression analysis and the ensemble classification algorithm of random forest respectively, and the difference of the prediction efficiency between the two groups was compared.Results Of the included patients, 19 patients fell out, 165 patients developed ICU delirium and were enrolled into the delirium group, with an incidence of 48.39% in ICU, and the remaining 176 patients without ICU delirium were enrolled into the control group. There was no statistical significance in gender, educational level, or other general data between the two groups (P>0.05). But compared with the control group, the patients of the delirium group were older, length of hospital stay was longer, and acute physiology and chronic health evaluationⅡ(APACHEⅡ) score, proportion of mechanical assisted ventilation, physical constraints, sedative drug use in the delirium group were higher (P<0.05). Multivariate logistic regression analysis showed that age (OR=1.162), length of hospital stay (OR=1.238), APACHEⅡ score (OR=1.057), mechanical ventilation (OR=1.329), physical constraints (OR=1.345) and sedative drug use (OR=1.630) were independent risk factors for delirium of cardiothoracic ICU patients. The variables in the random forest model for sorting, on top of important predictor variable were: age, length of hospital stay, APACHEⅡ score, mechanical ventilation, physical constraints and sedative drug use. The diagnostic efficiency of ensemble classification algorithm of random forest was obviously higher than that of multivariate logistic regression analysis. The area under receiver operating characteristic curve of ensemble classification algorithm of random forest was 0.87, and the one of multivariate logistic regression analysis model was 0.79.Conclusion The ensemble classification algorithm of random forest is more effective in predicting the occurrence of delirium in cardiothoracic ICU patients, which can be popularized and applied in clinical practice and contribute to early identification and strengthening nursing of high-risk patients.

Citation： CHEN Miao, CHEN Qing, YIN Xiaoqing. Predictive analysis of delirium risk in ICU patients with cardiothoracic surgery by ensemble classification algorithm of random forest. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(7): 886-891. doi: 10.7507/1007-4848.202105058 Copy

1.	魏月清, 李紅, 李蕓, 等. ICU后認知障礙風險預測模型的構建及驗證. 中華護理雜志, 2021, 56(1): 14-20.
2.	楊慧, 孫丹丹, 李尊柱, 等. 中文版理查茲-坎貝爾睡眠量表應用于ICU患者的信效度研究. 中國護理管理, 2017, 17(5): 601-604.
3.	Schulte PJ, Martin DP, Deljou A, et al. Effect of cognitive status on the receipt of procedures requiring anesthesia and critical care admissions in older adults. Mayo Clin Proc, 2018, 93(11): 1552-1562.
4.	Sakusic A, O'Horo JC, Dziadzko M, et al. Potentially modifiable risk factors for long-term cognitive impairment after critical illness: A systematic review. Mayo Clin Proc, 2018, 93(1): 68-82.
5.	Guo L, Lin F, Dai H, et al. Impact of sevoflurane versus propofol anesthesia on post-operative cognitive dysfunction in elderly cancer patients: A double-blinded randomized controlled trial. Med Sci Monit, 2020, 26: e919293.
6.	朱歡歡, 王建寧, 詹夢梅, 等. ICU護士對亞臨床譫妄認知現狀及對策的研究進展. 中國護理管理, 2021, 21(1): 136-140.
7.	Tieges Z, Evans JJ, Neufeld KJ, et al. The neuropsychology of delirium: Advancing the science of delirium assessment. Int J Geriatr Psychiatry, 2018, 33(11): 1501-1511.
8.	吳克河, 張英, 崔文超, 等. 一種基于隨機森林算法的MQTT異常流量檢測方法. 計算機與現代化, 2021, (1): 61-64, 119.
9.	Georganos S, Grippa T, Gadiaga AN, et al. Geographical random forests: A spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling. Geocarto Int, 2021, 36(2): 121-136.
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11.	湯鉑, 王小亭, 陳文勁, 等. 重癥患者譫妄管理專家共識. 中華內科雜志, 2019, 58(2): 108-118.
12.	Yamada C, Iwawaki Y, Harada K, et al. Frequency and risk factors for subsyndromal delirium in an intensive care unit. Intensive Crit Care Nurs, 2018, 47: 15-22.
13.	孫丹丹, 梁濤. 亞譫妄綜合征相關研究進展. 中國護理管理, 2016, 16(10): 1426-1429.
14.	Boettger S, Nu?ez DG, Meyer R, et al. Subsyndromal delirium in the intensive care setting: Phenomenological characteristics and discrimination of subsyndromal delirium versus no and full-syndromal delirium. Palliat Support Care, 2018, 16(1): 3-13.
15.	汪蓉, 楊寶義, 李龍倜, 等. ICU護士亞譫妄綜合征知信行現狀調查及影響因素分析. 湖北醫藥學院學報, 2020, 39(1): 93-95,100.
16.	Trogrli? Z, Ista E, Ponssen HH, et al. Attitudes, knowledge and practices concerning delirium: A survey among intensive care unit professionals. Nurs Crit Care, 2017, 22(3): 133-140.
17.	錢佳微, 孫秋華. 國內外亞譫妄綜合征研究現狀的文獻計量學分析. 中國實用護理雜志, 2019, 35(25): 1990-1995.
18.	Ndikom CM, Ofi BA, Omokhodion FO, et al. Effects of educational intervention on nurses' knowledge and attitude towards providing cervical cancer screening information in selected health facilities in Ibadan, Nigeria. J Cancer Educ, 2019, 34(1): 59-65.
19.	Soiza RL, Myint PK. The Scottish Intercollegiate Guidelines Network (SIGN) 157: Guidelines on risk reduction and management of delirium. Medicina (Kaunas), 2019, 55(8): 491.
20.	李京連, 馮雅笛, 姚菲, 等. 顱腦腫瘤術后ICU患者亞譫妄發生情況及影響因素研究. 中華現代護理雜志, 2019, 25(30): 3854-3858.
21.	DiLibero J, DeSanto-Madeya S, Dottery R, et al. Improving the accuracy of delirium assessments in neuroscience patients: Scaling a quality improvement program to improve nurses' skill, compliance, and accuracy in the use of the confusion assessment method in the intensive care unit tool. Dimens Crit Care Nurs, 2018, 37(1): 26-34.
22.	Niu B, Liang R, Zhou G, et al. Prediction for global Peste des Petits Ruminants outbreaks based on a combination of random forest algorithms and meteorological data. Front Vet Sci, 2021, 7: 570829.
23.	喬麗杰, 陳香萍, 勞月文, 等. ICU譫妄評估信息化模塊的設計及臨床應用. 中國實用護理雜志, 2019, 35(30): 2368-2373.
24.	李佳佳, 丁偉, 王伯偉, 等. 基于隨機森林的民俗體育對身體指標影響評估方法. 南京大學學報(自然科學), 2021, 57(1): 59-67.
25.	隋偉靜, 宮曉艷, 莊一渝. ICU譫妄風險預測模型的研究進展及趨勢分析. 護士進修雜志, 2020, 35(22): 2054-2060.

1. 魏月清, 李紅, 李蕓, 等. ICU后認知障礙風險預測模型的構建及驗證. 中華護理雜志, 2021, 56(1): 14-20.
2. 楊慧, 孫丹丹, 李尊柱, 等. 中文版理查茲-坎貝爾睡眠量表應用于ICU患者的信效度研究. 中國護理管理, 2017, 17(5): 601-604.
3. Schulte PJ, Martin DP, Deljou A, et al. Effect of cognitive status on the receipt of procedures requiring anesthesia and critical care admissions in older adults. Mayo Clin Proc, 2018, 93(11): 1552-1562.
4. Sakusic A, O'Horo JC, Dziadzko M, et al. Potentially modifiable risk factors for long-term cognitive impairment after critical illness: A systematic review. Mayo Clin Proc, 2018, 93(1): 68-82.
5. Guo L, Lin F, Dai H, et al. Impact of sevoflurane versus propofol anesthesia on post-operative cognitive dysfunction in elderly cancer patients: A double-blinded randomized controlled trial. Med Sci Monit, 2020, 26: e919293.
6. 朱歡歡, 王建寧, 詹夢梅, 等. ICU護士對亞臨床譫妄認知現狀及對策的研究進展. 中國護理管理, 2021, 21(1): 136-140.
7. Tieges Z, Evans JJ, Neufeld KJ, et al. The neuropsychology of delirium: Advancing the science of delirium assessment. Int J Geriatr Psychiatry, 2018, 33(11): 1501-1511.
8. 吳克河, 張英, 崔文超, 等. 一種基于隨機森林算法的MQTT異常流量檢測方法. 計算機與現代化, 2021, (1): 61-64, 119.
9. Georganos S, Grippa T, Gadiaga AN, et al. Geographical random forests: A spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling. Geocarto Int, 2021, 36(2): 121-136.
10. Bastos AS, Beccaria LM, Silva DCD, et al. Identification of delirium and subsyndromal delirium in intensive care patients. Rev Bras Enferm, 2019, 72(2): 463-467.
11. 湯鉑, 王小亭, 陳文勁, 等. 重癥患者譫妄管理專家共識. 中華內科雜志, 2019, 58(2): 108-118.
12. Yamada C, Iwawaki Y, Harada K, et al. Frequency and risk factors for subsyndromal delirium in an intensive care unit. Intensive Crit Care Nurs, 2018, 47: 15-22.
13. 孫丹丹, 梁濤. 亞譫妄綜合征相關研究進展. 中國護理管理, 2016, 16(10): 1426-1429.
14. Boettger S, Nu?ez DG, Meyer R, et al. Subsyndromal delirium in the intensive care setting: Phenomenological characteristics and discrimination of subsyndromal delirium versus no and full-syndromal delirium. Palliat Support Care, 2018, 16(1): 3-13.
15. 汪蓉, 楊寶義, 李龍倜, 等. ICU護士亞譫妄綜合征知信行現狀調查及影響因素分析. 湖北醫藥學院學報, 2020, 39(1): 93-95,100.
16. Trogrli? Z, Ista E, Ponssen HH, et al. Attitudes, knowledge and practices concerning delirium: A survey among intensive care unit professionals. Nurs Crit Care, 2017, 22(3): 133-140.
17. 錢佳微, 孫秋華. 國內外亞譫妄綜合征研究現狀的文獻計量學分析. 中國實用護理雜志, 2019, 35(25): 1990-1995.
18. Ndikom CM, Ofi BA, Omokhodion FO, et al. Effects of educational intervention on nurses' knowledge and attitude towards providing cervical cancer screening information in selected health facilities in Ibadan, Nigeria. J Cancer Educ, 2019, 34(1): 59-65.
19. Soiza RL, Myint PK. The Scottish Intercollegiate Guidelines Network (SIGN) 157: Guidelines on risk reduction and management of delirium. Medicina (Kaunas), 2019, 55(8): 491.
20. 李京連, 馮雅笛, 姚菲, 等. 顱腦腫瘤術后ICU患者亞譫妄發生情況及影響因素研究. 中華現代護理雜志, 2019, 25(30): 3854-3858.
21. DiLibero J, DeSanto-Madeya S, Dottery R, et al. Improving the accuracy of delirium assessments in neuroscience patients: Scaling a quality improvement program to improve nurses' skill, compliance, and accuracy in the use of the confusion assessment method in the intensive care unit tool. Dimens Crit Care Nurs, 2018, 37(1): 26-34.
22. Niu B, Liang R, Zhou G, et al. Prediction for global Peste des Petits Ruminants outbreaks based on a combination of random forest algorithms and meteorological data. Front Vet Sci, 2021, 7: 570829.
23. 喬麗杰, 陳香萍, 勞月文, 等. ICU譫妄評估信息化模塊的設計及臨床應用. 中國實用護理雜志, 2019, 35(30): 2368-2373.
24. 李佳佳, 丁偉, 王伯偉, 等. 基于隨機森林的民俗體育對身體指標影響評估方法. 南京大學學報(自然科學), 2021, 57(1): 59-67.
25. 隋偉靜, 宮曉艷, 莊一渝. ICU譫妄風險預測模型的研究進展及趨勢分析. 護士進修雜志, 2020, 35(22): 2054-2060.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Predictive analysis of delirium risk in ICU patients with cardiothoracic surgery by ensemble classification algorithm of random forest

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