Big data analysis of electronic hospital records: inpatient trajectories and pharmacological exposures associated with mortality in older adults.

点击翻译按钮获取中文摘要

Admissions to National Health Service (NHS) hospitals in England have increased by 28% over the last decade and nearly half of all adults admitted are >=65 years old. Some older adults age robustly but others develop frailty, a condition characterised by reduced ability to withstand stressors such as illness. In addition, approximately a third of adult inpatients have one or more chronic health conditions, and many patients are prescribed multiple long term medications, so called 'polypharmacy'. We aim to understand how older adults use emergency hospital services and journey through the hospital from admission to discharge. We will also explore factors, particularly related to prescription medications, associated with poor hospital outcomes such as inpatient death. The World Health Organisation declared 'Medication without harm' its 3rd global patient safety challenge in 2017 and a report commissioned by the Department of Health Policy Research Programme estimated medication errors cost the NHS £98.5 million per year. Whilst medication related harm is widely recognised, more information is needed about which drugs confer the highest risk in which patients to inform safer prescribing practices. We will use data from 80 000 inpatient episodes of older adults (>/=65 years old) admitted as an emergency to one tertiary NHS hospital (Addenbrooke's Hospital, Cambridge) over four years. Data is available for large scale retrospective analysis after an electronic patient record system was introduced in 2014. Information describing all aspects of admission from patient characteristics such as age group to information pertaining to bedside observations, prescription medications and blood tests are available. These data will have many repeated measurements over the admission duration, for example blood pressure measurements taken several times each day, leading to a very large and complex dataset. Therefore, anonymised patient records will be transferred to the European Bioinformatics Institute (EBI; Wellcome Genome Campus, Hinxton, Cambridge). EBI is a leading research institution focused on developing cutting-edge technologies to process and manage 'big' data. We will employ machine learning (ML), a type of artificial intelligence capable of visualising patterns within complex data, to explore the thousands of patient examples in our dataset. We will firstly define how many different types of hospital admission describe the majority of admissions in older adults and characterise these inpatient trajectories. For example, admission episodes may be characterised by their length (short versus prolonged) or hospital operational factors such as number of ward moves. Secondly, we will use ML to study how different prescribed medications, or combinations of medications, represent a pattern that is consistently associated with inpatient death. We can use known associations, such as the use of blood thinning medications and higher likelihood of death from bleeding, to educate the ML process. ML can then identify other prescribing patterns associated with inpatient death and explore whether certain patient characteristics or types of admission make patients more vulnerable. This will build a comprehensive picture of patient, treatment and hospital factors that impact on the eventual hospital outcome. Inpatient death is our primary outcome but other outcomes such as new admission to a care home following discharge can be considered. Finally, ML can simulate how the hospital outcome might change if a hypothetical alternative treatment plan was employed. For example, medications can be substituted with an alternative treatment to see how this would change the likelihood of death occurring. This research will describe use of acute hospital services by older adults and identify potentially inappropriate medications for further study. The Northeast-Newcastle & North Tyneside research ethics service committee approved the study.