Identifying emergency presentations of chronic liver disease using routinely collected administrative hospital data

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. Methods: 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. Results: 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. Conclusions: 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. Impact and implications: 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.