Sequence Analysis to Phenotype Healthcare Patterns in Adults with Musculoskeletal Conditions Using Primary Care Electronic Health Records.

Objective: The aim of this study was to apply sequence analysis (SA) to phenotype healthcare patterns of adult patients with musculoskeletal (MSK) conditions using primary care electronic health records and to investigate the association between these healthcare patterns and post-consultation patient's self-reported outcome.

Methods: Data from the Multi-level Integrated Data for musculoskeletal health intelligence and ActionS (MIDAS) programme conducted in North Staffordshire and Stoke-on-Trent, UK was utilised. The study included patients aged ≥18 years who consulted primary care for MSK conditions between September 2021 and July 2022.

Developing an electronic surprise question to predict end-of-life prognosis in a prospective cohort study of acute hospital admissions.

Objective: Determining the accuracy of a method calculating the Gold Standards Framework Surprise Question (GSFSQ) equivalent end-of-life prognosis amongst hospital inpatients.

Design: A prospective cohort study with regression calculated 1-year mortality probability. Probability cut points triaged unknown prognosis into the GSFSQ equivalent 'Yes' or 'No' survival categories (> or < 1-year respectively), with subsidiary classification of 'No'.

Expanding PFAS Identification with Transformation Product Libraries: Nontargeted Analysis Reveals Biotransformation Products in Mice.

Per- and polyfluoroalkyl substances (PFAS) are widely used persistent synthetic chemicals that have been linked to adverse health effects. While the behavior of PFAS has been evaluated in the environment, our understanding of reaction products in mammalian systems is limited. This study identified biological PFAS transformation products and generated mass spectral libraries to facilitate an automated search and identification.

Improving event prediction using general practitioner clinical judgement in a digital risk stratification model: a pilot study.

Background: Numerous tools based on electronic health record (EHR) data that predict risk of unscheduled care and mortality exist. These are often criticised due to lack of external validation, potential for low predictive ability and the use of thresholds that can lead to large numbers being escalated for assessment that would not have an adverse outcome leading to unsuccessful active case management. Evidence supports the importance of clinical judgement in risk prediction particularly when ruling out disease.