Construction and empirical test of shunt safety evaluation model for patients in emergency intensive care unit_West China Medical Journal

Authors：

LI Ruixin ^1,2 , TANG Songling ^1,2 , MA Wen ^1,2 , HE Yarong ^1,2 , TANG Shiyuan ^1,2 ,  CAO Yu ^1,2

1. Department of Emergency Medicine / Laboratory of Emergency Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. Disaster Medical Center, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

CAO Yu, Email: yuyuer@126.com

Keywords：

Emergency intensive care unit; Shunt safety; Evaluation model

DOI：

10.7507/1002-0179.202109002

Video：

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Objective To explore factors affecting the shunt safety of patients in emergency intensive care unit (EICU), construct a shunt safety evaluation model, and evaluate its prediction effectiveness, so as to provide a theoretical basis for the decision-making of shunt safety in EICU. Methods The demographic data, vital signs, laboratory examinations and other indicators of patients transferred to the general ward from the EICU of West China Hospital of Sichuan University from 0:00 on August 1, 2019 to 23:59 on May 31, 2021 were collected and analyzed. The short-term poor prognosis after being transferred out of the EICU was regarded as the end-point event. Of the patients, 70% were randomly selected as the model construction cohort, and 30% were the model validation cohort. In the model construction cohort, multivariate logistic regression analysis was used to screen the influencing factors affecting shunt safety, and the shunt safety evaluation model of patients in EICU was constructed. In the validation cohort, receiver operating characteristic curve was used to evaluate the effectiveness of the model in evaluating the shunt safety of patients in EICU. Results A total of 582 patients were included, of whom 59 patients (10.1%) had a poor short-term prognosis. Multivariate logistic regression analysis showed that the patients’ respiratory rate when leaving the EICU [odds ratio (OR)=0.863, 95% confidence interval (CI) (0.794, 0.938), P=0.001], Glasgow Coma Scale scores [OR=1.575, 95%CI (1.348, 1.841), P<0.001], albumin [OR=1.137, 95%CI (1.008, 1.282), P=0.036], prothrombin time [OR=0.956, 95%CI (0.914, 1.000), P=0.048] were the influencing factors of shunt safety. Based on the above indicators, a shunt safety evaluation model for patients in EICU was created. The area under the curve for the shunt safety assessment model to predict poor short-term prognosis was 0.815, the best cut-off value was 4 points, the sensitivity was 93.3%, and the specificity was 61.5%. Conclusions The patients’ respiratory rate when leaving EICU, Glasgow Coma Scale scores, albumin and prothrombin time are factors affecting the shunt safety for patients in EICU. The shunt safety assessment model can better predict the short-term poor prognosis of patients transferred from EICU to general ward.

Citation： LI Ruixin, TANG Songling, MA Wen, HE Yarong, TANG Shiyuan, CAO Yu. Construction and empirical test of shunt safety evaluation model for patients in emergency intensive care unit. West China Medical Journal, 2021, 36(11): 1515-1521. doi: 10.7507/1002-0179.202109002 Copy

1.	An Y, Tian ZR, Li F, et al. Risk prediction using the National Early Warning Score and the Worthing Physiological Scoring System in patients who were transported to the intensive care unit from the emergency department: a cohort study. Intensive Crit Care Nurs, 2021, 64: 103015.
2.	Jiang Z, Bo L, Xu Z, et al. An explainable machine learning algorithm for risk factor analysis of in-hospital mortality in sepsis survivors with ICU readmission. Comput Methods Programs Biomed, 2021, 204: 106040.
3.	Xue Y, Klabjan D, Luo Y. Predicting ICU readmission using grouped physiological and medication trends. Artif Intell Med, 2019, 95: 27-37.
4.	Tuncer G, Surme S, Bayramlar OF, et al. National Early Warning Score 2 and laboratory predictors correlate with clinical deterioration in hospitalized patients with COVID-19. Biomark Med, 2021, 15(11): 807-820.
5.	Loreto M, Lisboa T, Moreira VP. Early prediction of ICU readmissions using classification algorithms. Comput Biol Med, 2020, 118: 103636.
6.	Rojas JC, Carey KA, Edelson DP, et al. Predicting intensive care unit readmission with machine learning using electronic health record data. Ann Am Thorac Soc, 2018, 15(7): 846-853.
7.	Martin LA, Kilpatrick JA, Al-Dulaimi R, et al. Predicting ICU readmission among surgical ICU patients: development and validation of a clinical nomogram. Surgery, 2019, 165(2): 373-380.
8.	Grmec S, Gasparovic V. Comparison of APACHE Ⅱ, MEES and Glasgow Coma Scale in patients with nontraumatic coma for prediction of mortality. Acute physiology and chronic health evaluation. Mainz emergency evaluation system. Crit Care, 2001, 5(1): 19-23.
9.	Hu H, Du X, He Y, et al. Application of SOFA score as a predict tool for mortality of delayed admission to ICU on patients with acute respiratory failure. Am J Emerg Med, 2017, 35(6): 914-915.
10.	Qin Q, Xia Y, Cao Y. Clinical study of a new modified early warning system scoring system for rapidly evaluating shock in adults. J Crit Care, 2017, 37: 50-55.
11.	Koh TL, Canet E, Amjad S, et al. Prognostic performance of qSOFA in oncology patients admitted to the emergency department with suspected infection. Asia Pac J Clin Oncol, 2021, 17(1): 94-100.
12.	杨冬冬, 张尊胜. 急性缺血性脑卒中相关性肺炎的危险因素分析及预测评分模型构建. 神经疾病与精神卫生, 2021, 21(2): 121-127.
13.	Wong EG, Parker AM, Leung DG, et al. Association of severity of illness and intensive care unit readmission: a systematic review. Heart Lung, 2016, 45(1): 3-9.e2.
14.	Itenov TS, Jensen JU. Readmission after intensive care: frequent, hazardous, and possibly preventable. Crit Care Med, 2015, 43(2): 504-505.
15.	Azabou E, Fischer C, Guerit JM, et al. Neurophysiological assessment of brain dysfunction in critically ill patients: an update. Neurol Sci, 2017, 38(5): 715-726.
16.	Sharshar T, Gray F, Poron F, et al. Multifocal necrotizing leukoencephalopathy in septic shock. Crit Care Med, 2002, 30(10): 2371-2375.
17.	Prescott HC, Langa KM, Iwashyna TJ. Readmission diagnoses after hospitalization for severe sepsis and other acute medical conditions. JAMA, 2015, 313(10): 1055-1057.
18.	Davis DP, Vadeboncoeur TF, Ochs M, et al. The association between field Glasgow Coma Scale score and outcome in patients undergoing paramedic rapid sequence intubation. J Emerg Med, 2005, 29(4): 391-397.
19.	Mouri S, Tripon S, Rudler M, et al. FOUR score, a reliable score for assessing overt hepatic encephalopathy in cirrhotic patients. Neurocrit Care, 2015, 22(2): 251-257.
20.	AbuSara AK, Nazer LH, Hawari FI. ICU readmission of patients with cancer: Incidence, risk factors and mortality. J Crit Care, 2019, 51: 84-87.
21.	Kleinpell R. Promoting early identification of sepsis in hospitalized patients with nurse-led protocols. Crit Care, 2017, 21(1): 10.
22.	Yin M, Si L , Qin W, et al. Predictive value of serum albumin level for the prognosis of severe sepsis without exogenous human albumin administration. J Intensive Care Med, 2016: 088506661668530.
23.	Wang S, Ding S, Luo H, et al. International normalized ratio to albumin ratio (PTAR): an objective risk stratification tool in patients with sepsis. Int J Gen Med, 2021, 14: 1829-1841.
24.	Bonventre JV, Yang L. Cellular pathophysiology of ischemic acute kidney injury. J Clin Invest, 2011, 121(11): 4210-4221.
25.	Ramadori G. Albumin infusion in critically ill COVID-19 patients: hemodilution and anticoagulation. Int J Mol Sci, 2021, 22(13): 7126.
26.	Zeng R, Li D, Deng L, et al. Hypoalbuminemia predicts clinical outcome in patients with type B acute aortic dissection after endovascular therapy. Am J Emerg Med, 2016, 34(8): 1369-1372.
27.	Jensen JS, Peters L, Itenov TS, et al. Biomarker-assisted identification of sepsis-related acute liver impairment: a frequent and deadly condition in critically ill patients. Clin Chem Lab Med, 2019, 57(9): 1422-1431.
28.	Ponzoni CR, Corrêa TD, Filho RR, et al. Readmission to the intensive care unit: incidence, risk factors, resource use, and outcomes. A retrospective cohort study. Ann Am Thorac Soc, 2017, 14(8): 1312-1319.
29.	Aziz M, Fatima R, Lee-Smith W, et al. The association of low serum albumin level with severe COVID-19: a systematic review and meta-analysis. Crit Care, 2020, 24(1): 255.
30.	Stanworth SJ, Desborough MJR, Simons G, et al. Clinical bleeding and thrombin generation in admissions to critical care with prolonged prothrombin time: an exploratory study. Transfusion, 2018, 58(6): 1388-1398.
31.	Panigada M, Bottino N, Tagliabue P, et al. Hypercoagulability of COVID-19 patients in intensive care unit: a report of thromboelastography findings and other parameters of hemostasis. J Thromb Haemost, 2020, 18(7): 1738-1742.
32.	Taylor FB Jr, Toh CH, Hoots WK, et al. Towards definition, clinical and laboratory criteria, and a scoring system for disseminated intravascular coagulation. Thromb Haemost, 2001, 86(5): 1327-1330.

1. An Y, Tian ZR, Li F, et al. Risk prediction using the National Early Warning Score and the Worthing Physiological Scoring System in patients who were transported to the intensive care unit from the emergency department: a cohort study. Intensive Crit Care Nurs, 2021, 64: 103015.
2. Jiang Z, Bo L, Xu Z, et al. An explainable machine learning algorithm for risk factor analysis of in-hospital mortality in sepsis survivors with ICU readmission. Comput Methods Programs Biomed, 2021, 204: 106040.
3. Xue Y, Klabjan D, Luo Y. Predicting ICU readmission using grouped physiological and medication trends. Artif Intell Med, 2019, 95: 27-37.
4. Tuncer G, Surme S, Bayramlar OF, et al. National Early Warning Score 2 and laboratory predictors correlate with clinical deterioration in hospitalized patients with COVID-19. Biomark Med, 2021, 15(11): 807-820.
5. Loreto M, Lisboa T, Moreira VP. Early prediction of ICU readmissions using classification algorithms. Comput Biol Med, 2020, 118: 103636.
6. Rojas JC, Carey KA, Edelson DP, et al. Predicting intensive care unit readmission with machine learning using electronic health record data. Ann Am Thorac Soc, 2018, 15(7): 846-853.
7. Martin LA, Kilpatrick JA, Al-Dulaimi R, et al. Predicting ICU readmission among surgical ICU patients: development and validation of a clinical nomogram. Surgery, 2019, 165(2): 373-380.
8. Grmec S, Gasparovic V. Comparison of APACHE Ⅱ, MEES and Glasgow Coma Scale in patients with nontraumatic coma for prediction of mortality. Acute physiology and chronic health evaluation. Mainz emergency evaluation system. Crit Care, 2001, 5(1): 19-23.
9. Hu H, Du X, He Y, et al. Application of SOFA score as a predict tool for mortality of delayed admission to ICU on patients with acute respiratory failure. Am J Emerg Med, 2017, 35(6): 914-915.
10. Qin Q, Xia Y, Cao Y. Clinical study of a new modified early warning system scoring system for rapidly evaluating shock in adults. J Crit Care, 2017, 37: 50-55.
11. Koh TL, Canet E, Amjad S, et al. Prognostic performance of qSOFA in oncology patients admitted to the emergency department with suspected infection. Asia Pac J Clin Oncol, 2021, 17(1): 94-100.
12. 杨冬冬, 张尊胜. 急性缺血性脑卒中相关性肺炎的危险因素分析及预测评分模型构建. 神经疾病与精神卫生, 2021, 21(2): 121-127.
13. Wong EG, Parker AM, Leung DG, et al. Association of severity of illness and intensive care unit readmission: a systematic review. Heart Lung, 2016, 45(1): 3-9.e2.
14. Itenov TS, Jensen JU. Readmission after intensive care: frequent, hazardous, and possibly preventable. Crit Care Med, 2015, 43(2): 504-505.
15. Azabou E, Fischer C, Guerit JM, et al. Neurophysiological assessment of brain dysfunction in critically ill patients: an update. Neurol Sci, 2017, 38(5): 715-726.
16. Sharshar T, Gray F, Poron F, et al. Multifocal necrotizing leukoencephalopathy in septic shock. Crit Care Med, 2002, 30(10): 2371-2375.
17. Prescott HC, Langa KM, Iwashyna TJ. Readmission diagnoses after hospitalization for severe sepsis and other acute medical conditions. JAMA, 2015, 313(10): 1055-1057.
18. Davis DP, Vadeboncoeur TF, Ochs M, et al. The association between field Glasgow Coma Scale score and outcome in patients undergoing paramedic rapid sequence intubation. J Emerg Med, 2005, 29(4): 391-397.
19. Mouri S, Tripon S, Rudler M, et al. FOUR score, a reliable score for assessing overt hepatic encephalopathy in cirrhotic patients. Neurocrit Care, 2015, 22(2): 251-257.
20. AbuSara AK, Nazer LH, Hawari FI. ICU readmission of patients with cancer: Incidence, risk factors and mortality. J Crit Care, 2019, 51: 84-87.
21. Kleinpell R. Promoting early identification of sepsis in hospitalized patients with nurse-led protocols. Crit Care, 2017, 21(1): 10.
22. Yin M, Si L , Qin W, et al. Predictive value of serum albumin level for the prognosis of severe sepsis without exogenous human albumin administration. J Intensive Care Med, 2016: 088506661668530.
23. Wang S, Ding S, Luo H, et al. International normalized ratio to albumin ratio (PTAR): an objective risk stratification tool in patients with sepsis. Int J Gen Med, 2021, 14: 1829-1841.
24. Bonventre JV, Yang L. Cellular pathophysiology of ischemic acute kidney injury. J Clin Invest, 2011, 121(11): 4210-4221.
25. Ramadori G. Albumin infusion in critically ill COVID-19 patients: hemodilution and anticoagulation. Int J Mol Sci, 2021, 22(13): 7126.
26. Zeng R, Li D, Deng L, et al. Hypoalbuminemia predicts clinical outcome in patients with type B acute aortic dissection after endovascular therapy. Am J Emerg Med, 2016, 34(8): 1369-1372.
27. Jensen JS, Peters L, Itenov TS, et al. Biomarker-assisted identification of sepsis-related acute liver impairment: a frequent and deadly condition in critically ill patients. Clin Chem Lab Med, 2019, 57(9): 1422-1431.
28. Ponzoni CR, Corrêa TD, Filho RR, et al. Readmission to the intensive care unit: incidence, risk factors, resource use, and outcomes. A retrospective cohort study. Ann Am Thorac Soc, 2017, 14(8): 1312-1319.
29. Aziz M, Fatima R, Lee-Smith W, et al. The association of low serum albumin level with severe COVID-19: a systematic review and meta-analysis. Crit Care, 2020, 24(1): 255.
30. Stanworth SJ, Desborough MJR, Simons G, et al. Clinical bleeding and thrombin generation in admissions to critical care with prolonged prothrombin time: an exploratory study. Transfusion, 2018, 58(6): 1388-1398.
31. Panigada M, Bottino N, Tagliabue P, et al. Hypercoagulability of COVID-19 patients in intensive care unit: a report of thromboelastography findings and other parameters of hemostasis. J Thromb Haemost, 2020, 18(7): 1738-1742.
32. Taylor FB Jr, Toh CH, Hoots WK, et al. Towards definition, clinical and laboratory criteria, and a scoring system for disseminated intravascular coagulation. Thromb Haemost, 2001, 86(5): 1327-1330.

West China Medical Journal

Construction and empirical test of shunt safety evaluation model for patients in emergency intensive care unit

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