Alcohol misuse is associated with a wide range of physical, psychological and social harms. Liver damage caused by alcohol (alcohol-related liver disease, ARLD) remains a leading cause of morbidity and premature mortality. Poor engagement with elective health services, self-neglect and impaired judgement can limit preventive strategies. An emergency admission to hospital with life-threatening complications is often the first time a patient is diagnosed with ARLD and over one in ten patients will die during that first admission. This represents a crucial point in the patient journey an opportunity for co-ordinated intervention by acute liver services and alcohol care teams to reduce avoidable in-hospital morbidity and mortality, and set the patient on the path to long term abstinence and recovery. This project seeks to develop better methods to analyse routinely collected data from primary and secondary care to gain insights into patient journeys through the health system. We wish to describe patterns of contacts with primary care before and after first admission with ARLD. We will compare the characteristics of those patients that do, or do not have contacts, and the type of practice they are registered with. After adjusting for case mix, we will test whether outcomes of first emergency admission for ARLD are associated with contacts with primary care.
Identifying and characterising patients with specific conditions from routinely collected healthcare datasets requires the generation of code lists, rules and algorithms (diagnostic algorithms). In complex chronic diseases, where patients may present with a multitude of symptoms, signs and disease manifestations, there are significant challenges. We have developed an algorithm for the identification of patients admitted to hospital with alcohol-related liver disease (ARLD) within hospital discharge coding. Compared to the standard approach (focused on a list of six specific primary diagnosis codes), our method identifies almost twice the number of cases and admissions. We wish to validate this algorithm by comparing cases of ARLD identified in hospital data with matched primary care records (and vice versa) and develop a new algorithm (combining data from both primary and secondary care datasets). We will compare estimates of incidence and prevalence using alternative methods to illustrate the implications of employing different methodologies. After optimising the diagnostic algorithm for cohort selection, we will deploy the methodology to examine specific questions about the role of primary care contacts in the year before and after a patients first admissions with ARLD. We will describe patterns of primary care contacts among this patient population and explore associations between contacts with primary care and outcomes of emergency inpatient admissions. Hypotheses will be tested relating to whether engagement with primary care is associated with outcomes, and specifically whether patients without evidence of consultations in the community are at highest risk for in-hospital mortality and prospective readmission. This work will inform strategies for stratifying and targeting of high risk cases for preventive action and alternative models of care.
Annual incidence rates and period prevalence of alcohol related liver disease (ARLD) in population-based sample of patients with alcohol-related presentations calculated by including cases drawn from primary care, secondary care and combined datasets; diagnostic code for ARLD assigned to patient in primary or secondary care data or both; disposal method of first unplanned admission for ARLD (discharged alive vs. died in hospital); 7-, 30- and 90-day all-cause readmission following first unplanned admission for ARLD; all-cause mortality following first unplanned admission for ARLD; time to readmission following first unplanned admission for ARLD; time to death following first unplanned admission for ARLD.
Keith Bodger - Chief Investigator - University of Liverpool
Constantinos Kallis - Corresponding Applicant - Imperial College London
Kate Fleming - Collaborator - University of Liverpool
Peter Dixon - Collaborator - University of Liverpool
Benjamin Silberberg - Corresponding Applicant - Not from an Organisation
HES Accident and Emergency;HES Admitted Patient Care;HES Outpatient;ONS Death Registration Data;Patient Level Index of Multiple Deprivation