King Jessica, Bains Vikram, Doidge James, Van Der Meulen Jan, Walker Kate, Bernal William
Department of Health Services Research & Policy, London School of Hygiene & Tropical Medicine, London, UK.
Clinical Effectiveness Unit, Royal College of Surgeons of England, London, UK.
JHEP Rep. 2024 Dec 31;7(5):101322. doi: 10.1016/j.jhepr.2024.101322. eCollection 2025 May.
BACKGROUND & AIMS: Patients with chronic liver disease (CLD) are often first diagnosed during an emergency hospital admission, when their disease is advanced and survival is very poor. Evaluating their care and outcomes is a clinical research priority, but methods are needed to identify them in routine data.
We analysed national administrative hospital data in the English National Health Service. We used existing literature, expert clinical opinion, and data-driven approaches to develop three algorithms to identify first-time emergency admissions in 2017-2018. We validated these in 2018-2019 data by assessing the distributions of predictive factors, treatments, and outcomes associated with CLD in the patients captured by each algorithm.
Our most specific algorithm identified 10,719 patients with CLD who first presented through emergency hospital admission from April 2018 to March 2019. Alternative, less specific or more sensitive algorithms identified 12,867 or 20,828 patient, respectively. Additional patients identified by more sensitive algorithms had more comorbidities, were less likely to die from CLD, and were less likely to be treated by a gastroenterologist or hepatologist.
Three algorithms are provided that successfully identified patients in administrative hospital data with a first emergency admission for CLD. The choice of algorithm should reflect the aims of the research.
The more and most sensitive algorithms are recommended in studies when it is important to minimise the number of patients with CLD erroneously missed from the cohort, such as studies measuring disease burden. The most specific algorithms might miss patients whose primary reason for admission is recorded as a sign, symptom, or complication of CLD, but is recommended when the interest is strictly in patients whose primary reason for emergency admission is CLD.
慢性肝病（CLD）患者常在紧急住院期间首次被诊断出来，此时他们的病情已发展到晚期，生存率很低。评估他们的治疗情况和预后是临床研究的重点，但需要在常规数据中识别他们的方法。
我们分析了英国国家医疗服务体系的全国行政医院数据。我们利用现有文献、专家临床意见和数据驱动方法开发了三种算法，以识别2017 - 2018年首次紧急住院的患者。我们通过评估每种算法捕获的患者中与CLD相关的预测因素、治疗方法和预后的分布情况，在2018 - 2019年的数据中对这些算法进行了验证。
我们最具特异性的算法识别出10719例CLD患者，这些患者于2018年4月至2019年3月首次通过紧急住院就诊。另外，特异性较低或敏感性较高的算法分别识别出12867例或20828例患者。敏感性较高的算法识别出的额外患者有更多的合并症，死于CLD的可能性较小，由胃肠病学家或肝病学家治疗的可能性也较小。
提供了三种算法，它们成功地在行政医院数据中识别出首次因CLD紧急住院的患者。算法的选择应反映研究目的。
在研究中，当尽量减少队列中错误遗漏的CLD患者数量很重要时，例如在测量疾病负担的研究中，建议使用敏感性较高和最高的算法。最具特异性的算法可能会遗漏那些入院主要原因被记录为CLD的体征、症状或并发症的患者，但当严格关注因CLD紧急入院的患者时，推荐使用该算法。
