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利用临床路径模拟和机器学习确定关键杠杆,以最大限度地提高急性脑卒中静脉溶栓的获益。

Use of Clinical Pathway Simulation and Machine Learning to Identify Key Levers for Maximizing the Benefit of Intravenous Thrombolysis in Acute Stroke.

机构信息

Medical School, University of Exeter, St Luke's Campus, United Kingdom (M.A., C.J., J.F., K.L., K.P., T.M., K.S.).

Computer Science, University of Exeter, Streatham Campus, United Kingdom (R.E.).

出版信息

Stroke. 2022 Sep;53(9):2758-2767. doi: 10.1161/STROKEAHA.121.038454. Epub 2022 Jul 15.

Abstract

BACKGROUND

Expert opinion is that about 20% of emergency stroke patients should receive thrombolysis. Currently, 11% to 12% of patients in England and Wales receive thrombolysis, ranging from 2% to 24% between hospitals. The aim of this study was to assess how much variation is due to differences in local patient populations, and how much is due to differences in clinical decision-making and stroke pathway performance, while estimating a realistic target thrombolysis use.

METHODS

Anonymised data for 246 676 emergency stroke admissions to 132 acute hospitals in England and Wales between 2016 and 2018 was obtained from the Sentinel Stroke National Audit Programme data. We used machine learning to learn decisions on who to give thrombolysis to at each hospital. We used clinical pathway simulation to model effects of changing pathway performance. Qualitative research was used to assess clinician attitudes to these methods. Three changes were modeled: (1) arrival-to-treatment in 30 minutes, (2) proportion of patients with determined stroke onset times set to at least the national upper quartile, (3) thrombolysis decisions made based on majority vote of a benchmark set of hospitals.

RESULTS

Of the modeled changes, any single change was predicted to increase national thrombolysis use from 11.6% to between 12.3% to 14.5% (clinical decision-making having the most effect). Combined, these changes would be expected to increase thrombolysis to 18.3%, but there would still be significant variation between hospitals depending on local patient population. Clinicians engaged well with the modeling, but those from hospitals with lower thrombolysis use were most cautious about the methods.

CONCLUSIONS

Machine learning and clinical pathway simulation may be applied at scale to national stroke audit data, allowing extended use and analysis of audit data. Stroke thrombolysis rates of at least 18% look achievable in England and Wales, but each hospital should have its own target.

摘要

背景

专家意见认为,约 20%的急诊脑卒中患者应接受溶栓治疗。目前,英格兰和威尔士有 11%至 12%的患者接受溶栓治疗,医院之间的溶栓治疗率在 2%至 24%之间。本研究旨在评估有多少差异是由于当地患者人群的差异造成的,以及有多少差异是由于临床决策和脑卒中通路表现的差异造成的,同时估计一个实际的溶栓治疗目标。

方法

从 Sentinel Stroke National Audit Programme 数据中获取了 2016 年至 2018 年间英格兰和威尔士 132 家急性医院的 246676 例急诊脑卒中入院的匿名数据。我们使用机器学习来学习每家医院给谁进行溶栓治疗的决策。我们使用临床路径模拟来模拟改变路径性能的效果。定性研究用于评估临床医生对这些方法的态度。模拟了三种变化:(1)30 分钟内到达治疗,(2)将确定脑卒中发病时间的患者比例设定为至少全国四分位数以上,(3)根据基准医院集的多数投票做出溶栓治疗决策。

结果

在建模的变化中,任何单一的变化都预计会将全国溶栓治疗使用率从 11.6%提高到 12.3%至 14.5%(临床决策的影响最大)。这些变化的综合影响预计将使溶栓治疗增加到 18.3%,但由于当地患者人群的不同,医院之间仍会存在显著的差异。临床医生对建模的反应良好,但溶栓治疗使用率较低的医院的医生对这些方法最为谨慎。

结论

机器学习和临床路径模拟可以大规模应用于国家脑卒中审计数据,从而允许对审计数据进行扩展使用和分析。英格兰和威尔士至少有 18%的脑卒中溶栓治疗率是可行的,但每个医院都应该有自己的目标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/765b/9389935/7b69d61572f8/str-53-2758-g001.jpg

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