Inpatient trajectories and drug exposures of older adults

• Research type

  Research Study

• Full title

  Analysis of longitudinal physiological, biochemical and pharmacological data from the anonymised electronic hospital records of non-elective inpatient admission episodes in older adults: characterisation of different inpatient trajectories and pharmacological exposures associated with inpatient mortality

• IRAS ID

  253457

• Contact name

  John Bradley

• Sponsor organisation

  Cambridge University Hospitals NHS Foundation Trust

• Duration of Study in the UK

  2 years, 0 months, 1 days

• Research summary

  Older inpatient adults (≥65 years old) are a heterogenous population group at high risk of both adverse drug reactions and adverse outcomes of their hospital admissions (e.g., death during hospitalisation, prolonged stay or readmission). One of the reasons contributing to this higher risk is the prescription of drugs that, although correctly indicated for an appropriate condition, have a higher risk of causing adverse reactions in certain older adults that outweighs the potential benefits of the treatment. Current guidelines to avoid these potentially inappropriate medications are generalised and do not consider the heterogeneity of older adults that might put certain groups at higher risk than others.
  Electronic Health Record systems collect all clinically relevant information about patients, including physiological measurements (such as blood pressure or temperature), blood test results and medications, providing a time-based description of their clinical progress from hospital admission to discharge. This large-scale data provides a unique source of information for the study of the relationships between clinical observations, treatments and final outcomes of hospitalisation, such as whether or not a patient dies during the admission episode. While traditional statistical techniques cannot study these multiple and interrelated relationships in their entirety, advanced statistical methods used by machine learning algorithms can recognise patterns within these data and relate multiple patient characteristics to defined outcomes, such as inpatient death.
  This project is a collaboration between Addenbrooke’s Hospital (as part of Cambridge University Hospitals) and the European Bioinformatics Institute (EMBL-EBI) to bring together clinical and technical expertise for the development of machine learning models that can process large amounts of complex clinical data collected over time during hospital admission episodes. We aim to identify medications that, solely or in combination with some patient characteristics, are associated with a higher risk of adverse clinical outcomes in the older inpatient population.

• REC name

  North East - Newcastle & North Tyneside 1 Research Ethics Committee

• REC reference

  19/NE/0013

• Date of REC Opinion

  4 Jan 2019

• REC opinion

  Favourable Opinion