The risk prediction models of ICU readmissions: a systematic review_Chinese Journal of Evidence-Based Medicine

Authors：

YANG Xiao ,  ZHOU Huilan , WANG Ling , FANG Jie , YANG Maofan , CHEN Keyu , GAO Jie

Department of Critical Care Medicine, North Sichuan Medical College, Nanchong 637000, P. R. China;

Corresponding author：

ZHOU Huilan, Email: 472260181@qq.com

Keywords：

ICU readmission; Risk prediction; Model; Systematic review

DOI：

10.7507/1672-2531.202207148

Video：

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Objective To systematically review the risk prediction model of intensive care unit (ICU) readmissions. Methods CNKI, WanFang Data, VIP, CBM, PubMed, EMbase, Web of Science and The Cochrane Library databases were electronically searched to collect the related studies on risk prediction models of ICU readmissions from inception to June 12^th, 2022. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies; then, the qualitative systematic review was performed. Results A total of 15 studies involving 23 risk prediction models were included. The area under the ROC curve of the models was 0.609-0.924. The most common five predictors of the included model were age, length of ICU hospitalization, heart rate, respiration, and admission diagnosis. Conclusion The overall prediction performance of the risk prediction model of ICU readmissions is good; however, there are differences in research types and outcomes, and the clinical value of the model needs to be further studied.

Citation： YANG Xiao, ZHOU Huilan, WANG Ling, FANG Jie, YANG Maofan, CHEN Keyu, GAO Jie. The risk prediction models of ICU readmissions: a systematic review. Chinese Journal of Evidence-Based Medicine, 2023, 23(1): 67-74. doi: 10.7507/1672-2531.202207148 Copy

1.	Morgan M, Vernon T, Bradburn EH, et al. A comprehensive review of the outcome for patients readmitted to the icu following trauma and strategies to decrease readmission rates. J Intensive Care Med, 2020, 35(10): 936-942.
2.	Oh TK, Song IA, Jeon YT. Impact of Glasgow coma scale scores on unplanned intensive care unit readmissions among surgical patients. Ann Transl Med, 2019, 7(20): 520.
3.	李玉萍, 谢冬, 杨健, 等. 普胸外科重返重症监护室患者术后并发症分析. 中华胸心血管外科杂志, 2015, 31(9): 545-548.
4.	Maharaj R, Terblanche M, Vlachos S. The utility of ICU readmission as a quality indicator and the effect of selection. Crit Care Med, 2018, 46(5): 749-756.
5.	Nguyen MC, Strosberg DS, Jones TS, et al. Mortality and readmission of outcomes after discharge from the surgical intensive care unit to long-term, acute-care hospitals. Surgery, 2017, 161(5): 1367-1375.
6.	Grochla M, Saucha W, Borkowski J, et al. Readmission to the intensive care unit - epidemiology, prediction and clinical consequences. Wiad Lek, 2019, 72(7): 1387-1396.
7.	Al-Jaghbeer MJ, Tekwani SS, Gunn SR, et al. Incidence and etiology of potentially preventable ICU readmissions. Crit Care Med, 2016, 44(9): 1704-1709.
8.	周娥, 刘胤佃, 何金爱. 病人非计划重返重症监护室危险因素的Meta分析. 循证护理, 2022, 8(8): 1001-1008.
9.	Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
10.	胡佳民, 邹圣强, 江竹月, 等. 非计划重返ICU风险预测模型的构建与验证. 中国护理管理, 2020, 20(4): 519-524.
11.	郝芳芳, 柳文娟, 林辉, 等. 肝胆胰术后患者非计划重返SICU的危险因素及预测模型. 中华危重病急救医学, 2019, 31(3): 350-354.
12.	林瑜, 吴静依, 蔺轲, 等. 基于集成学习模型预测重症患者再入重症监护病房的风险. 北京大学学报(医学版), 2021, 53(3): 566-572.
13.	Badawi O, Breslow MJ. Readmissions and death after ICU discharge: development and validation of two predictive models. PLoS One, 2012, 7(11): e48758.
14.	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.
15.	Pakbin A, Rafi P, Hurley N, et al. Prediction of ICU Readmissions using data at patient discharge. Annu Int Conf IEEE Eng Med Biol Soc, 2018, 2018: 4932-4935.
16.	Desautels T, Das R, Calvert J, et al. Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach. BMJ Open, 2017, 7(9): e017199.
17.	Thoral PJ, Fornasa M, de Bruin DP, et al. Explainable machine learning on AmsterdamUMCdb for ICU discharge decision support: uniting intensivists and data scientists. Crit Care Explor, 2021, 3(9): e0529.
18.	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.
19.	Kaur H, Naessens JM, Hanson AC, et al. PROPER: development of an early pediatric intensive care unit readmission risk prediction tool. J Intensive Care Med, 2018, 33(1): 29-36.
20.	Frost SA, Tam V, Alexandrou E, et al. Readmission to intensive care: development of a nomogram for individualising risk. Crit Care Resusc, 2010, 12(2): 83-89.
21.	徐剑锋, 陈昊天, 毛越, 等. 心胸外科术后患者呼吸相关性ICU重回可视化预测模型构建. 中华急诊医学杂志, 2020, 29(11): 1456-1460.
22.	Gajic O, Malinchoc M, Comfere TB, et al. The stability and workload index for transfer score predicts unplanned intensive care unit patient readmission: initial development and validation. Crit Care Med, 2008, 36(3): 676-682.
23.	Haribhakti N, Agarwal P, Vida J, et al. A simple scoring tool to predict medical intensive care unit readmissions based on both patient and process factors. J Gen Intern Med, 2021, 36(4): 901-907.
24.	Hammer M, Grabitz SD, Teja B, et al. A tool to predict readmission to the intensive care unit in surgical critical care patients-the RISC score. J Intensive Care Med, 2021, 36(11): 1296-1304.
25.	陶立元, 刘珏, 曾琳, 等. 针对个体的预后或诊断多因素预测模型报告规范(TRIPOD)解读. 中华医学杂志, 2018, 98(44): 3556-3560.
26.	吴秋硕, 陆宗庆, 刘瑜, 等. 机器学习应用于心脏骤停早期预测模型的系统评价. 中国循证医学杂志, 2021, 21(8): 942-952.
27.	Ogundimu EO, Altman DG, Collins GS. Adequate sample size for developing prediction models is not simply related to events per variable. J Clin Epidemiol, 2016, 76: 175-182.
28.	Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med, 2016, 35(2): 214-226.
29.	Pavlou M, Ambler G, Seaman SR, et al. How to develop a more accurate risk prediction model when there are few events. BMJ, 2015, 351: h3868.
30.	Zhou ZR, Wang WW, Li Y, et al. In-depth mining of clinical data: the construction of clinical prediction model with R. Ann Transl Med, 2019, 7(23): 796.

1. Morgan M, Vernon T, Bradburn EH, et al. A comprehensive review of the outcome for patients readmitted to the icu following trauma and strategies to decrease readmission rates. J Intensive Care Med, 2020, 35(10): 936-942.
2. Oh TK, Song IA, Jeon YT. Impact of Glasgow coma scale scores on unplanned intensive care unit readmissions among surgical patients. Ann Transl Med, 2019, 7(20): 520.
3. 李玉萍, 谢冬, 杨健, 等. 普胸外科重返重症监护室患者术后并发症分析. 中华胸心血管外科杂志, 2015, 31(9): 545-548.
4. Maharaj R, Terblanche M, Vlachos S. The utility of ICU readmission as a quality indicator and the effect of selection. Crit Care Med, 2018, 46(5): 749-756.
5. Nguyen MC, Strosberg DS, Jones TS, et al. Mortality and readmission of outcomes after discharge from the surgical intensive care unit to long-term, acute-care hospitals. Surgery, 2017, 161(5): 1367-1375.
6. Grochla M, Saucha W, Borkowski J, et al. Readmission to the intensive care unit - epidemiology, prediction and clinical consequences. Wiad Lek, 2019, 72(7): 1387-1396.
7. Al-Jaghbeer MJ, Tekwani SS, Gunn SR, et al. Incidence and etiology of potentially preventable ICU readmissions. Crit Care Med, 2016, 44(9): 1704-1709.
8. 周娥, 刘胤佃, 何金爱. 病人非计划重返重症监护室危险因素的Meta分析. 循证护理, 2022, 8(8): 1001-1008.
9. Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
10. 胡佳民, 邹圣强, 江竹月, 等. 非计划重返ICU风险预测模型的构建与验证. 中国护理管理, 2020, 20(4): 519-524.
11. 郝芳芳, 柳文娟, 林辉, 等. 肝胆胰术后患者非计划重返SICU的危险因素及预测模型. 中华危重病急救医学, 2019, 31(3): 350-354.
12. 林瑜, 吴静依, 蔺轲, 等. 基于集成学习模型预测重症患者再入重症监护病房的风险. 北京大学学报(医学版), 2021, 53(3): 566-572.
13. Badawi O, Breslow MJ. Readmissions and death after ICU discharge: development and validation of two predictive models. PLoS One, 2012, 7(11): e48758.
14. 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.
15. Pakbin A, Rafi P, Hurley N, et al. Prediction of ICU Readmissions using data at patient discharge. Annu Int Conf IEEE Eng Med Biol Soc, 2018, 2018: 4932-4935.
16. Desautels T, Das R, Calvert J, et al. Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach. BMJ Open, 2017, 7(9): e017199.
17. Thoral PJ, Fornasa M, de Bruin DP, et al. Explainable machine learning on AmsterdamUMCdb for ICU discharge decision support: uniting intensivists and data scientists. Crit Care Explor, 2021, 3(9): e0529.
18. 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.
19. Kaur H, Naessens JM, Hanson AC, et al. PROPER: development of an early pediatric intensive care unit readmission risk prediction tool. J Intensive Care Med, 2018, 33(1): 29-36.
20. Frost SA, Tam V, Alexandrou E, et al. Readmission to intensive care: development of a nomogram for individualising risk. Crit Care Resusc, 2010, 12(2): 83-89.
21. 徐剑锋, 陈昊天, 毛越, 等. 心胸外科术后患者呼吸相关性ICU重回可视化预测模型构建. 中华急诊医学杂志, 2020, 29(11): 1456-1460.
22. Gajic O, Malinchoc M, Comfere TB, et al. The stability and workload index for transfer score predicts unplanned intensive care unit patient readmission: initial development and validation. Crit Care Med, 2008, 36(3): 676-682.
23. Haribhakti N, Agarwal P, Vida J, et al. A simple scoring tool to predict medical intensive care unit readmissions based on both patient and process factors. J Gen Intern Med, 2021, 36(4): 901-907.
24. Hammer M, Grabitz SD, Teja B, et al. A tool to predict readmission to the intensive care unit in surgical critical care patients-the RISC score. J Intensive Care Med, 2021, 36(11): 1296-1304.
25. 陶立元, 刘珏, 曾琳, 等. 针对个体的预后或诊断多因素预测模型报告规范(TRIPOD)解读. 中华医学杂志, 2018, 98(44): 3556-3560.
26. 吴秋硕, 陆宗庆, 刘瑜, 等. 机器学习应用于心脏骤停早期预测模型的系统评价. 中国循证医学杂志, 2021, 21(8): 942-952.
27. Ogundimu EO, Altman DG, Collins GS. Adequate sample size for developing prediction models is not simply related to events per variable. J Clin Epidemiol, 2016, 76: 175-182.
28. Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med, 2016, 35(2): 214-226.
29. Pavlou M, Ambler G, Seaman SR, et al. How to develop a more accurate risk prediction model when there are few events. BMJ, 2015, 351: h3868.
30. Zhou ZR, Wang WW, Li Y, et al. In-depth mining of clinical data: the construction of clinical prediction model with R. Ann Transl Med, 2019, 7(23): 796.

Chinese Journal of Evidence-Based Medicine

The risk prediction models of ICU readmissions: a systematic review

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