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

在过去十年中，英国国民健康服务（NHS）医院的入院人数增加了28%，入院的成年人中有近一半的人年龄在65岁以上。一些老年人身体健壮，但另一些人则变得虚弱，这种状况的特点是承受疾病等压力的能力下降。此外，大约三分之一的成年住院病人患有一种或多种慢性疾病，许多病人需要服用多种长期药物，即所谓的“综合用药”。我们的目标是了解老年人如何使用紧急医院服务，以及从入院到出院的整个过程。我们还将探讨与住院病人死亡等不良住院结果相关的因素，特别是与处方药相关的因素。世界卫生组织在2017年宣布“无伤害用药”是其第三个全球患者安全挑战，卫生政策研究计划部委托撰写的一份报告估计，药物错误每年使NHS损失9850万英镑。虽然与药物有关的危害已得到广泛认识，但需要更多的信息，了解哪些药物会带来最高风险，哪些患者应告知更安全的处方做法。我们将使用一所三级NHS医院（剑桥阿登布鲁克医院）在四年内作为急诊收治的80,000例住院老年人（bb0 /=65岁）的数据。2014年引入电子病历系统后，数据可用于大规模回顾性分析。信息描述入院的各个方面，从病人的特点，如年龄组的信息有关床边观察，处方药物和血液检查。这些数据将在入院期间进行多次重复测量，例如每天多次测量血压，从而形成非常大而复杂的数据集。因此，匿名患者记录将被转移到欧洲生物信息学研究所（EBI; Wellcome Genome Campus, Hinxton, Cambridge）。EBI是一家领先的研究机构，专注于开发处理和管理“大”数据的尖端技术。我们将使用机器学习（ML），一种能够在复杂数据中可视化模式的人工智能，来探索我们数据集中数千个患者的例子。我们将首先定义有多少种不同的住院类型描述了大多数老年人的住院情况，并描述了这些住院轨迹。例如，住院事件的特征可能是其长度（短与长）或医院的操作因素，如病房移动的数量。其次，我们将使用ML来研究不同的处方药物或药物组合如何代表与住院患者死亡一致相关的模式。我们可以使用已知的关联，例如使用血液稀释药物和出血死亡的可能性更高，来教育ML过程。然后，ML可以识别与住院患者死亡相关的其他处方模式，并探索某些患者特征或入院类型是否使患者更容易受到伤害。这将建立对最终医院结果产生影响的患者、治疗和医院因素的全面图景。住院患者死亡是我们的主要结果，但其他结果，如出院后再次入住养老院也可以考虑。最后，机器学习可以模拟如果采用假设的替代治疗方案，医院的结果可能会如何变化。例如，可以用另一种治疗方法代替药物，看看这将如何改变死亡发生的可能性。本研究将描述老年人急性住院服务的使用情况，并确定可能不适当的药物以供进一步研究。东北-纽卡斯尔和北泰恩赛德研究伦理服务委员会批准了这项研究。

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.