Correcting for case-mix shift when developing clinical prediction models.

Background: When developing a clinical prediction model (CPM), a case-mix shift could occur in the development dataset where the distribution of individual predictors changes, potentially affecting model performance. This study exploits the case-mix shift that is already observed in the development dataset to address the case-mix shift between the development and deployment phase of a CPM.

Methods: We propose a Membership-based method to correct for case-mix shift in the development phase of CPMs.

Integrating clinical predictors and glial fibrillary acidic protein in prediction models for the prehospital identification of intracerebral haemorrhage in suspected stroke.

Introduction: Accurate and timely differentiation of intracerebral haemorrhage (ICH) from other suspected stroke cases is crucial in prehospital settings, where early blood pressure reduction in the ambulance can improve outcomes. This study aims to assess whether combining clinical predictors and glial fibrillary acidic protein (GFAP) in prediction models can effectively distinguish ICH from other suspected stroke cases.

Methods: Data were derived from the Testing for Identification Markers of Stroke trial, a prospective diagnostic accuracy study.

Clinical prediction models for medication adverse events in patients with rheumatic and musculoskeletal conditions: A systematic literature review.

Objectives: This systematic review aims to identify, summarize, and evaluate the methodological quality of existing clinical prediction models (CPMs) that predict adverse events (AEs) associated with medications prescribed for rheumatic and musculoskeletal diseases (RMDs).

Methods: We searched PubMed, Embase, and Medline databases up to March 2024. Studies were included if they developed multivariable CPM predicting AEs in adult patients using RMD medications.

A systematic review of machine learning applications in predicting opioid associated adverse events.

Machine learning has increasingly been applied to predict opioid-related harms due to its ability to handle complex interactions and generating actionable predictions. This review evaluated the types and quality of ML methods in opioid safety research, identifying 44 studies using supervised ML through searches of Ovid MEDLINE, PubMed and SCOPUS databases. Commonly predicted outcomes included postoperative opioid use (n = 15, 34%) opioid overdose (n = 8, 18%), opioid use disorder (n = 8, 18%) and persistent opioid use (n = 5, 11%) with varying definitions.

Prehospital video triage of suspected stroke patients in Greater Manchester: pilot project report.

Introduction: Stroke is a leading cause of mortality and morbidity, demanding prompt and accurate identification. However, prehospital diagnosis is challenging, with up to 50% of suspected strokes having other diagnoses. A prehospital video triage (PHVT) system was piloted in Greater Manchester to improve prehospital diagnostic accuracy and appropriate conveyance decisions.

Updating methods for artificial intelligence-based clinical prediction models: a scoping review.

Objectives: To give an overview of methods for updating artificial intelligence (AI)-based clinical prediction models based on new data.

Study Design And Setting: We comprehensively searched Scopus and Embase up to August 2022 for articles that addressed developments, descriptions, or evaluations of prediction model updating methods. We specifically focused on articles in the medical domain involving AI-based prediction models that were updated based on new data, excluding regression-based updating methods as these have been extensively discussed elsewhere.

Development and internal validation of prehospital prediction models for identifying intracerebral haemorrhage in suspected stroke patients.

Introduction: Distinguishing patients with intracerebral haemorrhage (ICH) from other suspected stroke cases in the prehospital setting is crucial for determining the appropriate level of care and minimising the onset-to-treatment time, thereby potentially improving outcomes. Therefore, we developed prehospital prediction models to identify patients with ICH among suspected stroke cases.

Methods: Data were obtained from the Field Administration of Stroke Therapy-Magnesium prehospital stroke trial, where paramedics evaluated multiple variables in suspected stroke cases within the first 2 hours from the last known well time.

Factors associated with long-term opioid use among patients with axial spondyloarthritis or psoriatic arthritis who initiate opioids.

Objective: Up to one in five patients with axial spondyloarthritis (AxSpA) or psoriatic arthritis (PsA) newly initiated on opioids transition to long-term use within the first year. This study aimed to investigate individual factors associated with long-term opioid use among opioid new users with AxSpA/PsA.

Methods: Adult patients with AxSpA/PsA and without prior cancer who initiated opioids between 2006 and 2021 were included from Clinical Practice Research Datalink Gold, a national UK primary care database.

The prevalence of long COVID in people with diabetes mellitus-evidence from a UK cohort.

Background: It was apparent from the early phase of the SARS-CoV-2 virus (COVID-19) pandemic that a multi-system syndrome can develop in the weeks following a COVID-19 infection, now referred to as Long COVID. Given that people living with diabetes are at increased risk of hospital admission/poor outcomes following COVID-19 infection we hypothesised that they may also be more susceptible to developing Long COVID. We describe here the prevalence of Long COVID in people living with diabetes when compared to matched controls in a Northwest UK population.

Machine learning identifies risk factors associated with long-term opioid use in fibromyalgia patients newly initiated on an opioid.

Objectives: Fibromyalgia is frequently treated with opioids due to limited therapeutic options. Long-term opioid use is associated with several adverse outcomes. Identifying factors associated with long-term opioid use is the first step in developing targeted interventions.

Associations of the glycaemic index and the glycaemic load with risk of type 2 diabetes in 127 594 people from 20 countries (PURE): a prospective cohort study.

Background: The association between the glycaemic index and the glycaemic load with type 2 diabetes incidence is controversial. We aimed to evaluate this association in an international cohort with diverse glycaemic index and glycaemic load diets.

Methods: The PURE study is a prospective cohort study of 127 594 adults aged 35-70 years from 20 high-income, middle-income, and low-income countries.

Comparing Predictive Performance of Time Invariant and Time Variant Clinical Prediction Models in Cardiac Surgery.

Clinical prediction models are increasingly used across healthcare to support clinical decision making. Existing methods and models are time-invariant and thus ignore the changes in populations and healthcare practice that occur over time. We aimed to compare the performance of time-invariant with time-variant models in UK National Adult Cardiac Surgery Audit data from Manchester University NHS Foundation Trust between 2009 and 2019.

Sars-Cov-2 Infection in People with Type 1 Diabetes and Hospital Admission: An Analysis of Risk Factors for England.

Introduction: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus (coronavirus disease 2019 [COVID-19]) pandemic revealed the vulnerability of specific population groups in relation to susceptibility to acute deterioration in their health, including hospital admission and mortality. There is less data on outcomes for people with type 1 diabetes (T1D) following SARS-CoV-2 infection than for those with type 2 diabetes (T2D). In this study we set out to determine the relative likelihood of hospital admission following SARS-CoV-2 infection in people with T1D when compared to those without T1D.

Trends for opioid prescribing and the impact of the COVID-19 pandemic in patients with rheumatic and musculoskeletal diseases between 2006 and 2021.

Objective: To investigate opioid prescribing trends and assess the impact of the COVID-19 pandemic on opioid prescribing in rheumatic and musculoskeletal diseases (RMDs).

Methods: Adult patients with RA, PsA, axial spondyloarthritis (AxSpA), SLE, OA and FM with opioid prescriptions between 1 January 2006 and 31 August 2021 without cancer in UK primary care were included. Age- and gender-standardized yearly rates of new and prevalent opioid users were calculated between 2006 and 2021.

COVID-19 Vaccination and Diabetes Mellitus: How Much Has It Made a Difference to Outcomes Following Confirmed COVID-19 Infection?

Introduction: Since early 2020 the whole world has been challenged by the SARS-CoV-2 virus (COVID-19), its successive variants and the associated pandemic caused. We have previously shown that for people living with type 2 diabetes (T2DM), the risk of being admitted to hospital or dying following a COVID-19 infection progressively decreased through the first months of 2021. In this subsequent analysis we have examined how the UK COVID-19 vaccination programme impacted differentially on COVID-19 outcomes in people with T1DM or T2DM compared to appropriate controls.

Longitudinal lung function and gas transfer in individuals with idiopathic pulmonary fibrosis: a genome-wide association study.

Background: Idiopathic pulmonary fibrosis (IPF) is an incurable lung disease characterised by progressive scarring leading to alveolar stiffness, reduced lung capacity, and impeded gas transfer. We aimed to identify genetic variants associated with declining lung capacity or declining gas transfer after diagnosis of IPF.

Methods: We did a genome-wide meta-analysis of longitudinal measures of forced vital capacity (FVC) and diffusing capacity of the lung for carbon monoxide (DLCO) in individuals diagnosed with IPF.

Mortality in People with Type 2 Diabetes Following SARS-CoV-2 Infection: A Population Level Analysis of Potential Risk Factors.

Introduction: Research is ongoing to increase our understanding of how much a previous diagnosis of type 2 diabetes mellitus (T2DM) affects someone's risk of becoming seriously unwell following a COVID-19 infection. In this study we set out to determine the relative likelihood of death following COVID-19 infection in people with T2DM when compared to those without T2DM. This was conducted as an urban population study and based in the UK.

The Risk Factors Potentially Influencing Hospital Admission in People with Diabetes, Following SARS-CoV-2 Infection: A Population-Level Analysis.

Introduction: Since early 2020 the whole world has been challenged by the SARS-CoV-2 virus and the associated global pandemic (Covid-19). People with diabetes are particularly at high risk of becoming seriously unwell after contracting this virus.

Methods: This population-based study included people living in the Greater Manchester conurbation who had a recorded diagnosis of type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) and subsequent Covid-19 infection.