提高危重症患者风险预测模型质量的数据关联研究

P. Ferrando-Vivas, M. Shankar-Hari, K. Thomas, J. Doidge, F. Caskey, L. Forni, S. Harris, M. Ostermann, I. Gornik, N. Holman, N. Lone, B. Young, D. Jenkins, S. Webb, J. Nolan, J. Soar, K. Rowan, D. Harrison
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Second, to improve risk models for cardiothoracic critical care by enhancing risk factor data and developing models for longer-term mortality. Third, to improve risk models for in-hospital cardiac arrest by enhancing risk factor data and developing models for longer-term mortality and critical care utilisation.\n \n \n \n Risk modelling study linking existing data.\n \n \n \n NHS adult critical care units and acute hospitals in England.\n \n \n \n Patients admitted to an adult critical care unit or experiencing an in-hospital cardiac arrest.\n \n \n \n None.\n \n \n \n Mortality at hospital discharge, 30 days, 90 days and 1 year following critical care unit admission; mortality at 1 year following discharge from acute hospital; new diagnosis of end-stage renal disease or type 2 diabetes; hospital resource use and costs; return of spontaneous circulation sustained for > 20 minutes; survival to hospital discharge and 1 year; and length of stay in critical care following in-hospital cardiac arrest.\n \n \n \n Case Mix Programme, National Cardiac Arrest Audit, UK Renal Registry, National Diabetes Audit, National Adult Cardiac Surgery Audit, Hospital Episode Statistics and Office for National Statistics.\n \n \n \n Data were linked for 965,576 critical care admissions between 1 April 2009 and 31 March 2016, and 83,939 in-hospital cardiac arrests between 1 April 2011 and 31 March 2016. For admissions to adult critical care units, models for 30-day mortality had similar predictors and performance to those for hospital mortality and did not reduce heterogeneity. Models for longer-term outcomes reflected increasing importance of chronic over acute predictors. New models for end-stage renal disease and diabetes will allow benchmarking of critical care units against these important outcomes and identification of patients requiring enhanced follow-up. The strongest predictors of health-care costs were prior hospitalisation, prior dependency and chronic conditions. Adding pre- and intra-operative risk factors to models for cardiothoracic critical care gave little improvement in performance. 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引用次数: 0

摘要

国家卫生与保健研究所先前的一项研究【Harrison DA,Ferrando Vivas P,Shahin J,Rowan KM。确保医疗保健提供者的比较是公平的:开发和验证危重患者的风险预测模型。卫生服务Deliv Res 2015;3(41)]确定需要进行更多的研究,以了解重症监护的风险因素和后果以及随后的结果。首先,通过开发固定时间点和事件发生时间的死亡率、终末期肾病、2型糖尿病、医疗保健利用率和成本的模型,改进成人普通重症监护的风险模型。其次,通过增强风险因素数据和开发长期死亡率模型来改进心胸重症监护的风险模型。第三,通过增强风险因素数据和开发长期死亡率和重症监护利用率模型,改进院内心脏骤停的风险模型。连接现有数据的风险建模研究。英国国家医疗服务体系成人重症监护室和急诊医院。入住成人重症监护室或经历住院心脏骤停的患者。没有一个重症监护病房入院后30天、90天和1年的出院死亡率;急性出院后1年的死亡率;终末期肾病或2型糖尿病的新诊断;医院资源使用和成本;自发循环的恢复持续> 20分钟;存活至出院和1年;以及住院心脏骤停后在重症监护室的住院时间。病例混合计划、国家心脏骤停审计、英国肾脏登记处、国家糖尿病审计、国家成人心脏外科审计、医院事件统计和国家统计局。数据关联了2009年4月1日至2016年3月31日期间965576名重症监护入院患者和2011年4月31日至2016年间83939名住院心脏骤停患者。对于进入成人重症监护室的患者,30天死亡率模型的预测因子和表现与医院死亡率模型相似,并且没有减少异质性。长期结果的模型反映了慢性预测因素比急性预测因素更重要。终末期肾病和糖尿病的新模型将使重症监护室能够根据这些重要结果进行基准测试,并确定需要加强随访的患者。医疗费用最有力的预测因素是既往住院、既往依赖和慢性病。在心胸重症监护模型中添加术前和术中风险因素对表现几乎没有改善。在住院心脏骤停模型中添加合并症提供了适度的改善,但对长期结果更为重要。延迟获取相关数据导致使用的数据在发布时已有5年历史:模型已经需要重新校准。数据链接以额外的预测因素和新的结果衡量标准的形式,增强了支持国家临床审计的风险模型。本报告中开发的新模型可能有助于为患者及其家人提供潜在结果的客观估计。(1) 针对最需要的人,制定和测试危重症后康复的护理途径;(2) 探索其他相关数据来源,以取得长期成果;(3) 扩大资源使用和成本与初级保健、门诊和急诊科数据的数据链接。本研究注册号为NCT02454257。该项目由国家卫生与护理研究所(NIHR)卫生与社会护理提供研究计划资助,并将在《卫生与社会保健提供研究》上全文发表;第10卷,第39期。有关更多项目信息,请访问NIHR期刊图书馆网站。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving risk prediction model quality in the critically ill: data linkage study
A previous National Institute for Health and Care Research study [Harrison DA, Ferrando-Vivas P, Shahin J, Rowan KM. Ensuring comparisons of health-care providers are fair: development and validation of risk prediction models for critically ill patients. Health Serv Deliv Res 2015;3(41)] identified the need for more research to understand risk factors and consequences of critical care and subsequent outcomes. First, to improve risk models for adult general critical care by developing models for mortality at fixed time points and time-to-event outcomes, end-stage renal disease, type 2 diabetes, health-care utilisation and costs. Second, to improve risk models for cardiothoracic critical care by enhancing risk factor data and developing models for longer-term mortality. Third, to improve risk models for in-hospital cardiac arrest by enhancing risk factor data and developing models for longer-term mortality and critical care utilisation. Risk modelling study linking existing data. NHS adult critical care units and acute hospitals in England. Patients admitted to an adult critical care unit or experiencing an in-hospital cardiac arrest. None. Mortality at hospital discharge, 30 days, 90 days and 1 year following critical care unit admission; mortality at 1 year following discharge from acute hospital; new diagnosis of end-stage renal disease or type 2 diabetes; hospital resource use and costs; return of spontaneous circulation sustained for > 20 minutes; survival to hospital discharge and 1 year; and length of stay in critical care following in-hospital cardiac arrest. Case Mix Programme, National Cardiac Arrest Audit, UK Renal Registry, National Diabetes Audit, National Adult Cardiac Surgery Audit, Hospital Episode Statistics and Office for National Statistics. Data were linked for 965,576 critical care admissions between 1 April 2009 and 31 March 2016, and 83,939 in-hospital cardiac arrests between 1 April 2011 and 31 March 2016. For admissions to adult critical care units, models for 30-day mortality had similar predictors and performance to those for hospital mortality and did not reduce heterogeneity. Models for longer-term outcomes reflected increasing importance of chronic over acute predictors. New models for end-stage renal disease and diabetes will allow benchmarking of critical care units against these important outcomes and identification of patients requiring enhanced follow-up. The strongest predictors of health-care costs were prior hospitalisation, prior dependency and chronic conditions. Adding pre- and intra-operative risk factors to models for cardiothoracic critical care gave little improvement in performance. Adding comorbidities to models for in-hospital cardiac arrest provided modest improvements but were of greater importance for longer-term outcomes. Delays in obtaining linked data resulted in the data used being 5 years old at the point of publication: models will already require recalibration. Data linkage provided enhancements to the risk models underpinning national clinical audits in the form of additional predictors and novel outcomes measures. The new models developed in this report may assist in providing objective estimates of potential outcomes to patients and their families. (1) Develop and test care pathways for recovery following critical illness targeted at those with the greatest need; (2) explore other relevant data sources for longer-term outcomes; (3) widen data linkage for resource use and costs to primary care, outpatient and emergency department data. This study is registered as NCT02454257. This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in full in Health and Social Care Delivery Research; Vol. 10, No. 39. See the NIHR Journals Library website for further project information.
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