缺血性脑卒中患者31天内非计划性再入院风险因素研究:随机森林模型
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摘要
目的利用随机森林模型研究缺血性脑卒中患者31天非计划性再入院风险的危险因素。方法搜集北京地区24家医院2015～2016年间首次因缺血性脑卒中住院和31天内再入院患者记录,按是否存在31天内非计划性再入院分为两组,利用卡方检验或Mann-Whitney U检验筛选出两组差异有统计学(P<0.05)或临床意义的变量进入随机森林模型,利用精确系数和基尼系数综合评估所有变量的重要程度,选取重要性较高的变量并利用边际效应评估其在不同水平上的相对危险度。结果共纳入3 473例患者,其中960例患者(27.64%)在31天内发生非计划性再入院。随机森林模型分析结果显示:住院天数、年龄、医疗付费方式、医院等级和职业是非计划再入院最重要的影响因素。住院天数在一个月内时,10天左右的住院患者出院后再入院风险最低,而住院天数更短或更长都会增加再入院风险;年龄越小的患者再入院风险越高;三级医院就诊患者再入院风险高于二级医院就诊患者;医疗费用支付方式为公费者和职业为企事业单位职工的患者较其他患者再入院风险更高。结论首次缺血性脑卒中患者在出院后31天内非计划再入院的风险不仅与疾病严重程度相关,还与患者个人社会、经济环境因素相关。这提示我们对脑卒中患者的恢复不仅需要关注医疗过程,还应对其个人和家庭情况进行关怀。
Objectives To investigate risk factors for unplanned readmission in ischemic stroke patients within 31 days by using random forest algorithm. Methods The record of readmission patients with ischemic stroke within 31 days from 24 hospitals in Beijing between between 2015 and 2016 were collected. Patients were divided into two groups according to the occurrence of readmission within 31 days or not. Chi-squared or Mann-Whitney U test was used to select variables into the random forest algorithm. The precision coefficient and the Gini coefficient were used to comprehensively assess the importance of all variables, and select the more important variables and use the margind effect to assess relative risk of different levels. Results A total of 3 473 patients were included, among them 960(27.64%)were readmitted within 31 days after stroke hospitalization. Based on the result of random forest, the most important variables affecting the risk of unplanned readmission within 31 days included the length of hospital stay, age, medical expense payment, rank of hospital, and occupation. When hospitalization was within 1 month, 10-day-hospitalization-stay patients had the lowest risk of rehospitalization; the younger the patients was, the higher the risk of readmission was. For ranks of hospital, patients from tertiary hospital had higher risk than secondary hospital. Furthermore, patients whose medical expenses were paid by free medical service and whose occupations were managers or staffs had higher risk of readmission within 31 days. Conclusions The unplanned readmission risk within 31 days of discharged ischemic stroke patients was connected not only with disease, but also with personal social and economic factors. Thus, more attention should be paid to both the medical process and the personal and family factors of stroke patients.
Objectives To investigate risk factors for unplanned readmission in ischemic stroke patients within 31 days by using random forest algorithm. Methods The record of readmission patients with ischemic stroke within 31 days from 24 hospitals in Beijing between between 2015 and 2016 were collected. Patients were divided into two groups according to the occurrence of readmission within 31 days or not. Chi-squared or Mann-Whitney U test was used to select variables into the random forest algorithm. The precision coefficient and the Gini coefficient were used to comprehensively assess the importance of all variables, and select the more important variables and use the margind effect to assess relative risk of different levels. Results A total of 3 473 patients were included, among them 960(27.64%)were readmitted within 31 days after stroke hospitalization. Based on the result of random forest, the most important variables affecting the risk of unplanned readmission within 31 days included the length of hospital stay, age, medical expense payment, rank of hospital, and occupation. When hospitalization was within 1 month, 10-day-hospitalization-stay patients had the lowest risk of rehospitalization; the younger the patients was, the higher the risk of readmission was. For ranks of hospital, patients from tertiary hospital had higher risk than secondary hospital. Furthermore, patients whose medical expenses were paid by free medical service and whose occupations were managers or staffs had higher risk of readmission within 31 days. Conclusions The unplanned readmission risk within 31 days of discharged ischemic stroke patients was connected not only with disease, but also with personal social and economic factors. Thus, more attention should be paid to both the medical process and the personal and family factors of stroke patients.
引文
1世界卫生组织.前十位死亡原因. Available at:http://www.who.int/zh/news-room/fact-sheets/detail/the-top-10-causes-of-death.
2陈伟伟,高润霖,刘力生,等.《中国心血管病报告2017》概要.中国循环杂志, 2018, 33(1):1-8.
3国家卫生和计划生育委员会. 2016中国卫生和计划生育统计年鉴.北京:中国协和医科大学出版社, 2016.
4Ma Y, Xiong X, Li J, et al. Effect of health insurance on direct hospitalisation costs for in-patients with ischaemic stroke in China.Aust Health Rev, 2018, 42(1):39-44.
5刘娟,杨栋,李系仁,等.某院31天内再入院现状与影响因素分析.中国卫生质量管理, 2011, 18(6):22-25.
6Kind AJ, Smith MA, Liou JI, et al. The price of bouncing back:oneyear mortality and payments for acute stroke patients with 30-day bounce-backs. J Am Geriatr Soc, 2008, 56(6):999-1005.
7Kilkenny MF, Longworth M, Pollack M, et al. Factors associated with 28-day hospital readmission after stroke in Australia. Stroke,2013, 44(8):2260-2268.
8Thomas JW, Holloway JJ. Investigating early readmission as an indicator for quality of care studies. Med Care, 1991, 29(4):377-394.
9刘玉琦,郝晓刚,王洪丽,等.某院出院患者31天内再住院分析与讨论.中国卫生质量管理, 2014, 21(4):40-41.
10Bhattacharya P, Khanal D, Madhavan R, et al. Why do ischemic stroke and transient ischemic attack patients get readmitted? J Neurol Sci, 2011, 307(1-2):50-54.
11唐爱丰,祝东林,谢薇,等.脑卒中再入院的原因及其危险因素分析.中华脑血管病杂志(电子版), 2013, 7(1):376-381.
12Rao A, Barrow E, Vuik S, et al. Systematic review of hospital readmissions in stroke patients. Stroke Res Treat, 2016, 2016(1):1-11.
13Tseng MC, Lin HJ. Readmission after hospitalization for stroke in Taiwan:results from a national sample. J Neurol Sci, 2009, 284(1-2):52-55.
14 Lewsey J, Ebueku O, Jhund PS, et al. Temporal trends and risk factors for readmission for infections, gastrointestinal and immobility complications after an incident hospitalisation for stroke in Scotland between 1997 and 2005. BMC Neurol, 2015, 15:3.
15李欣海.随机森林模型在分类与回归分析中的应用.应用昆虫学报, 2013, 50(4):1190-1197.
16卫生部医政司.神经内科临床路径.北京:人民卫生出版社, 2012.
17中华医学会.缺血性中风临床路径. Available at:http://www.cma.org.cn/attachment/2016612/1465718140116.docx.
18王亚静.用归一法分析某院2011年床位工作效率.中国卫生统计, 2013, 30(6):915-916.
19施霁桉,赵丽.对病床周转率的反思.中外医学研究, 2015, 13(22):148-149, 150.
20Singhal AB, Biller J, Elkind MS, et al. Recognition and management of stroke in young adults and adolescents. Neurology, 2013, 81(12):1089-1097.
21Smajlovi?D. Strokes in young adults:epidemiology and prevention. Vasc Health Risk Manag, 2015, 11:157-164.
22 Renna R, Pilato F, Profice P, et al. Risk factor and etiology analysis of ischemic stroke in young adult patients. J Stroke Cerebrovasc Dis, 2014, 23(3):e221-e227.
23Lewington S, Lacey B, Clarke R, et al. The burden of hypertension and associated risk for cardiovascular mortality in China. JAMA Intern Med, 2016, 176(4):524-532.
24Qin Y, Wang R, Ma X, et al. Prevalence, awareness, treatment and control of diabetes mellitus-A population based study in Shanghai,China. Int J Environ Res Public Health, 2016, 13(5):E512.
25杨功焕. 2010年全球成人烟草调查-中国报告.北京:中国三峡出版社, 2011.
26聂静,向阳.对某三甲综合医院住院天数与住院费用的分析.新疆医学, 2015, 45(9):1354-1356.
27方黎明.新型农村合作医疗和农村医疗救助制度对农村贫困居民就医经济负担的影响.中国农村观察, 2013,(2):80-92.
28 于长永.疾病类型、医疗保险与农民就医机构选择行为研究.农业技术经济, 2017,(2):82-92.
2陈伟伟,高润霖,刘力生,等.《中国心血管病报告2017》概要.中国循环杂志, 2018, 33(1):1-8.
3国家卫生和计划生育委员会. 2016中国卫生和计划生育统计年鉴.北京:中国协和医科大学出版社, 2016.
4Ma Y, Xiong X, Li J, et al. Effect of health insurance on direct hospitalisation costs for in-patients with ischaemic stroke in China.Aust Health Rev, 2018, 42(1):39-44.
5刘娟,杨栋,李系仁,等.某院31天内再入院现状与影响因素分析.中国卫生质量管理, 2011, 18(6):22-25.
6Kind AJ, Smith MA, Liou JI, et al. The price of bouncing back:oneyear mortality and payments for acute stroke patients with 30-day bounce-backs. J Am Geriatr Soc, 2008, 56(6):999-1005.
7Kilkenny MF, Longworth M, Pollack M, et al. Factors associated with 28-day hospital readmission after stroke in Australia. Stroke,2013, 44(8):2260-2268.
8Thomas JW, Holloway JJ. Investigating early readmission as an indicator for quality of care studies. Med Care, 1991, 29(4):377-394.
9刘玉琦,郝晓刚,王洪丽,等.某院出院患者31天内再住院分析与讨论.中国卫生质量管理, 2014, 21(4):40-41.
10Bhattacharya P, Khanal D, Madhavan R, et al. Why do ischemic stroke and transient ischemic attack patients get readmitted? J Neurol Sci, 2011, 307(1-2):50-54.
11唐爱丰,祝东林,谢薇,等.脑卒中再入院的原因及其危险因素分析.中华脑血管病杂志(电子版), 2013, 7(1):376-381.
12Rao A, Barrow E, Vuik S, et al. Systematic review of hospital readmissions in stroke patients. Stroke Res Treat, 2016, 2016(1):1-11.
13Tseng MC, Lin HJ. Readmission after hospitalization for stroke in Taiwan:results from a national sample. J Neurol Sci, 2009, 284(1-2):52-55.
14 Lewsey J, Ebueku O, Jhund PS, et al. Temporal trends and risk factors for readmission for infections, gastrointestinal and immobility complications after an incident hospitalisation for stroke in Scotland between 1997 and 2005. BMC Neurol, 2015, 15:3.
15李欣海.随机森林模型在分类与回归分析中的应用.应用昆虫学报, 2013, 50(4):1190-1197.
16卫生部医政司.神经内科临床路径.北京:人民卫生出版社, 2012.
17中华医学会.缺血性中风临床路径. Available at:http://www.cma.org.cn/attachment/2016612/1465718140116.docx.
18王亚静.用归一法分析某院2011年床位工作效率.中国卫生统计, 2013, 30(6):915-916.
19施霁桉,赵丽.对病床周转率的反思.中外医学研究, 2015, 13(22):148-149, 150.
20Singhal AB, Biller J, Elkind MS, et al. Recognition and management of stroke in young adults and adolescents. Neurology, 2013, 81(12):1089-1097.
21Smajlovi?D. Strokes in young adults:epidemiology and prevention. Vasc Health Risk Manag, 2015, 11:157-164.
22 Renna R, Pilato F, Profice P, et al. Risk factor and etiology analysis of ischemic stroke in young adult patients. J Stroke Cerebrovasc Dis, 2014, 23(3):e221-e227.
23Lewington S, Lacey B, Clarke R, et al. The burden of hypertension and associated risk for cardiovascular mortality in China. JAMA Intern Med, 2016, 176(4):524-532.
24Qin Y, Wang R, Ma X, et al. Prevalence, awareness, treatment and control of diabetes mellitus-A population based study in Shanghai,China. Int J Environ Res Public Health, 2016, 13(5):E512.
25杨功焕. 2010年全球成人烟草调查-中国报告.北京:中国三峡出版社, 2011.
26聂静,向阳.对某三甲综合医院住院天数与住院费用的分析.新疆医学, 2015, 45(9):1354-1356.
27方黎明.新型农村合作医疗和农村医疗救助制度对农村贫困居民就医经济负担的影响.中国农村观察, 2013,(2):80-92.
28 于长永.疾病类型、医疗保险与农民就医机构选择行为研究.农业技术经济, 2017,(2):82-92.