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, 謝冬, 楊健, 等. 普胸外科重返重癥監護室患者術后并發癥分析. 中華胸心血管外科雜志, 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, 劉胤佃, 何金愛. 病人非計劃重返重癥監護室危險因素的Meta分析. 循證護理, 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, 鄒圣強, 江竹月, 等. 非計劃重返ICU風險預測模型的構建與驗證. 中國護理管理, 2020, 20(4): 519-524.
11.	郝芳芳, 柳文娟, 林輝, 等. 肝膽胰術后患者非計劃重返SICU的危險因素及預測模型. 中華危重病急救醫學, 2019, 31(3): 350-354.
12.	林瑜, 吳靜依, 藺軻, 等. 基于集成學習模型預測重癥患者再入重癥監護病房的風險. 北京大學學報(醫學版), 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.	徐劍鋒, 陳昊天, 毛越, 等. 心胸外科術后患者呼吸相關性ICU重回可視化預測模型構建. 中華急診醫學雜志, 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, 劉玨, 曾琳, 等. 針對個體的預后或診斷多因素預測模型報告規范(TRIPOD)解讀. 中華醫學雜志, 2018, 98(44): 3556-3560.
26.	吳秋碩, 陸宗慶, 劉瑜, 等. 機器學習應用于心臟驟停早期預測模型的系統評價. 中國循證醫學雜志, 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, 謝冬, 楊健, 等. 普胸外科重返重癥監護室患者術后并發癥分析. 中華胸心血管外科雜志, 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, 劉胤佃, 何金愛. 病人非計劃重返重癥監護室危險因素的Meta分析. 循證護理, 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, 鄒圣強, 江竹月, 等. 非計劃重返ICU風險預測模型的構建與驗證. 中國護理管理, 2020, 20(4): 519-524.
11. 郝芳芳, 柳文娟, 林輝, 等. 肝膽胰術后患者非計劃重返SICU的危險因素及預測模型. 中華危重病急救醫學, 2019, 31(3): 350-354.
12. 林瑜, 吳靜依, 藺軻, 等. 基于集成學習模型預測重癥患者再入重癥監護病房的風險. 北京大學學報(醫學版), 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. 徐劍鋒, 陳昊天, 毛越, 等. 心胸外科術后患者呼吸相關性ICU重回可視化預測模型構建. 中華急診醫學雜志, 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, 劉玨, 曾琳, 等. 針對個體的預后或診斷多因素預測模型報告規范(TRIPOD)解讀. 中華醫學雜志, 2018, 98(44): 3556-3560.
26. 吳秋碩, 陸宗慶, 劉瑜, 等. 機器學習應用于心臟驟停早期預測模型的系統評價. 中國循證醫學雜志, 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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