Publications by authors named "Niels Peek"

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.

View Article and Find Full Text PDF
Article Synopsis
  • Prediction models help make medical decisions by estimating risks, advising high-risk individuals to undergo interventions while suggesting low-risk individuals avoid them.
  • Traditional models may overlook the complexities of interventions since they often assess risk at just one point in time, while in reality, decisions are made repeatedly and may change over time.
  • The article discusses how to formulate estimands for making better sequential predictions about interventions, using the example of choosing between vaginal delivery and cesarean section to inform future research and improve decision-making in clinical practice.
View Article and Find Full Text PDF

Background: There has been a substantial increase in the development of artificial intelligence (AI) tools for clinical decision support. Historically, these were mostly knowledge-based systems, but recent advances include non-knowledge-based systems using some form of machine learning. The ability of health care professionals to trust technology and understand how it benefits patients or improves care delivery is known to be important for their adoption of that technology.

View Article and Find Full Text PDF
Article Synopsis
  • This study looks at how well doctors can predict sudden cardiac death after someone has a heart attack using a measurement called left ventricular ejection fraction (LVEF).
  • They combined information from over 140,000 heart attack patients to see if LVEF alone is good enough for deciding who should get a heart device called a defibrillator.
  • The results showed that LVEF didn't do a great job at predicting sudden cardiac death, which means doctors need better ways to tell who is at risk.
View Article and Find Full Text PDF
Article Synopsis
  • - This study focused on classifying patients with heart failure (HF) and preserved or mildly reduced ejection fraction into specific phenogroups to improve targeted treatment options.
  • - Researchers analyzed data from over 2,000 patients across five UK hospitals using advanced machine learning techniques and found three distinct phenogroups, each with different clinical traits and survival outcomes.
  • - The findings revealed that survival rates declined from the first phenogroup to the third, highlighting the importance of phenogroup membership in predicting survival better than traditional factors, though it did not predict hospitalisation for HF.
View Article and Find Full Text PDF

Background: Pregnancy acts as a cardiovascular stress test. Although many complications resolve following birth, women with hypertensive disorder of pregnancy have an increased risk of developing cardiovascular disease (CVD) long-term. Monitoring postnatal health can reduce this risk but requires better methods to identity high-risk women for timely interventions.

View Article and Find Full Text PDF
Article Synopsis
  • The study compares patterns of multimorbidity in over 103,000 individuals with rheumatic and musculoskeletal diseases (RMDs) to 2.9 million people without RMDs from 2010 to 2019.
  • The research found that those with RMDs had significantly higher odds of various comorbidities, such as hypertension and diabetes, with 81% experiencing multiple conditions compared to 73% in the non-RMD group by 2019.
  • The findings suggest that individuals with RMDs are about 1.5 times more likely to have additional health issues, indicating a need for targeted healthcare interventions for this high-risk population.
View Article and Find Full Text PDF

Introduction: There is currently no guidance on how to assess the calibration of multistate models used for risk prediction. We introduce several techniques that can be used to produce calibration plots for the transition probabilities of a multistate model, before assessing their performance in the presence of random and independent censoring through a simulation.

Methods: We studied pseudo-values based on the Aalen-Johansen estimator, binary logistic regression with inverse probability of censoring weights (BLR-IPCW), and multinomial logistic regression with inverse probability of censoring weights (MLR-IPCW).

View Article and Find Full Text PDF

Background: Most existing clinical prediction models do not allow predictions under interventions. Such predictions allow predicted risk under different proposed strategies to be compared and are therefore useful to support clinical decision making. We aimed to compare methodological approaches for predicting individual level cardiovascular risk under three interventions: smoking cessation, reducing blood pressure, and reducing cholesterol.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

Collaboration across disciplinary boundaries is vital to address the complex challenges and opportunities in Digital Health. We present findings and experiences of applying the principles of Team Science to a digital health research project called 'The Wearable Clinic'. Challenges faced were a lack of shared understanding of key terminology and concepts, and differences in publication cultures between disciplines.

View Article and Find Full Text PDF

Careful handling of missing data is crucial to ensure that clinical prediction models are developed, validated, and implemented in a robust manner. We determined the bias in estimating predictive performance of different combinations of approaches for handling missing data across validation and implementation. We found four strategies that are compatible across the model pipeline and have provided recommendations for handling missing data between model validation and implementation under different missingness mechanisms.

View Article and Find Full Text PDF

Aims: Population-wide, person-level, linked electronic health record data are increasingly used to estimate epidemiology, guide resource allocation, and identify events in clinical trials. The accuracy of data from NHS Digital (now part of NHS England) for identifying hospitalization for heart failure (HHF), a key HF standard, is not clear. This study aimed to evaluate the accuracy of NHS Digital data for identifying HHF.

View Article and Find Full Text PDF

Background: Online consultation systems (OCSs) allow patients to contact their healthcare teams online. Since 2020 they have been rapidly rolled out in primary care following policy initiatives and the COVID-19 pandemic. In-depth research of patients' experiences using OCSs is lacking.

View Article and Find Full Text PDF

Rates of Multimorbidity (also called Multiple Long Term Conditions, MLTC) are increasing in many developed nations. People with multimorbidity experience poorer outcomes and require more healthcare intervention. Grouping of conditions by health service utilisation is poorly researched.

View Article and Find Full Text PDF

Objectives: Multimorbidity, the presence of two or more long-term conditions, is a growing public health concern. Many studies use analytical methods to discover multimorbidity patterns from data. We aimed to review approaches used in published literature to validate these patterns.

View Article and Find Full Text PDF

In 2020, we published an editorial about the massive disruption of health and care services caused by the COVID-19 pandemic and the rapid changes in digital service delivery, artificial intelligence and data sharing that were taking place at the time. Now, 3 years later, we describe how these developments have progressed since, reflect on lessons learnt and consider key challenges and opportunities ahead by reviewing significant developments reported in the literature. As before, the three key areas we consider are digital transformation of services, realising the potential of artificial intelligence and wise data sharing to facilitate learning health systems.

View Article and Find Full Text PDF

International deployment of remote monitoring and virtual care (RMVC) technologies would efficiently harness their positive impact on outcomes. Since Canada and the United Kingdom have similar populations, health care systems, and digital health landscapes, transferring digital health innovations between them should be relatively straightforward. Yet examples of successful attempts are scarce.

View Article and Find Full Text PDF

Background: Understanding and quantifying the differences in disease development in different socioeconomic groups of people across the lifespan is important for planning healthcare and preventive services. The study aimed to measure chronic disease accrual, and examine the differences in time to individual morbidities, multimorbidity, and mortality between socioeconomic groups in Wales, UK.

Methods: Population-wide electronic linked cohort study, following Welsh residents for up to 20 years (2000-2019).

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

Introduction: This study considers the prediction of the time until two survival outcomes have both occurred. We compared a variety of analytical methods motivated by a typical clinical problem of multimorbidity prognosis.

Methods: We considered five methods: product (multiply marginal risks), dual-outcome (directly model the time until both events occur), multistate models (msm), and a range of copula and frailty models.

View Article and Find Full Text PDF

In clinical prediction modelling, missing data can occur at any stage of the model pipeline; development, validation or deployment. Multiple imputation is often recommended yet challenging to apply at deployment; for example, the outcome cannot be in the imputation model, as recommended under multiple imputation. Regression imputation uses a fitted model to impute the predicted value of missing predictors from observed data, and could offer a pragmatic alternative at deployment.

View Article and Find Full Text PDF

Automatic extraction of relations between gene mutations and cancer entities occurring in the cancer literature using text mining can rapidly provide vital information to support precision cancer medicine. However, mutation-cancer relation extraction is more challenging than general relation extraction from free text, since it is often not possible without cancer-specific background knowledge and thus the model replies on a deeper understanding of complex surrounding tokens. We propose a deep learning model that jointly extracts mutations and their associated cancers.

View Article and Find Full Text PDF

A PHP Error was encountered

Severity: Warning

Message: fopen(/var/lib/php/sessions/ci_sessiond5nik5p4qgjgv9e6ti06iagbmleutjkl): Failed to open stream: No space left on device

Filename: drivers/Session_files_driver.php

Line Number: 177

Backtrace:

File: /var/www/html/index.php
Line: 316
Function: require_once

A PHP Error was encountered

Severity: Warning

Message: session_start(): Failed to read session data: user (path: /var/lib/php/sessions)

Filename: Session/Session.php

Line Number: 137

Backtrace:

File: /var/www/html/index.php
Line: 316
Function: require_once