Acute, Respiratory & Critical Care Medicine, Ysbyty Gwynedd, United Kingdom; School of Medical Sciences, Bangor University, Bangor LL57 2AS, United Kingdom.
North Wales Organisation for Randomised Trials in Health, School of Healthcare Sciences, Bangor University, Bangor LL57 2PZ, United Kingdom.
Eur J Intern Med. 2017 Nov;45:74-77. doi: 10.1016/j.ejim.2017.09.029. Epub 2017 Sep 30.
Acute admissions to hospital are rising. As a part of a service evaluation we examined pathways of patients following hospital discharge depending on data available on admission to hospital.
We merged data available on admission to the Wrexham Maelor hospital from an existing data-base in the Acute Medical Unit with follow up data from local social services as part of a data sharing agreement. Patients requiring support by social services post-discharge were matched with patients not requiring social services from the same post-code.
Stepwise logistic regression analysis identified candidate variables predicting likely support need. Decision tree analysis identified sub-groups of patients with higher likelihood to require support by social services after discharge from hospital. We found patients with normal physiology on admission as evidenced by a value of zero for the National Early Warning Score who were frail or older than 85years were most likely to require support after discharge.
Information available on admission to hospital might inform long term care needs. Prospective testing is needed. The algorithms are prone to be dependent on availability of local services but our methodology is expected to be transferable to other organizations.
医院的急性入院人数正在上升。作为服务评估的一部分,我们根据入院时可获得的数据,检查了患者出院后的途径。
我们将威勒姆梅尔医院入院时现有的数据库中的数据与当地社会服务部门的随访数据合并,这是数据共享协议的一部分。需要社会服务支持的出院患者与来自同一邮政编码但不需要社会服务的患者相匹配。
逐步逻辑回归分析确定了预测可能需要支持的候选变量。决策树分析确定了出院后更有可能需要社会服务支持的亚组患者。我们发现入院时生理正常(国家早期预警评分值为 0)、身体虚弱或年龄超过 85 岁的患者最有可能在出院后需要支持。
入院时可获得的信息可能可以提供长期护理需求的信息。需要进行前瞻性测试。这些算法可能取决于当地服务的可用性,但我们的方法预计可以转移到其他组织。