Kym Snell

Associate Professor of Biostatistics

University of Birmingham

United Kingdom

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Dr. Kym Snell is an Associate Professor of Biostatistics at the University of Birmingham, UK. His research focuses on developing and validating prediction models in healthcare, with recent articles addressing topics such as pain reduction post-surgery, musculoskeletal health, and sample size implications for AI-based models. His work aims to enhance clinical decision-making and patient outcomes in various medical contexts.

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Collaborators (23)

James Sheppard

University of Oxford

UNITED KINGDOM

Kym Snell

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Articles (20)

Uncertainty of risk estimates from clinical prediction models: rationale, challenges, and approaches.

Importance of sample size on the quality and utility of AI-based prediction models for healthcare

Predicting pain reduction following laparoscopic surgery for endometriosis: a retrospective cohort study using UK national and research databases

Larger sample sizes are needed when developing a clinical prediction model using machine learning in oncology: methodological systematic review

Developing prediction models for electrolyte abnormalities in patients indicated for antihypertensive therapy: evidence-based treatment and monitoring recommendations.

Musculoskeletal Health and Work: Development and Internal–External Cross-Validation of a Model to Predict Risk of Work Absence and Presenteeism in People Seeking Primary Healthcare

Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies

Development and internal validation of prognostic models for event-free survival in newly diagnosed Ewing sarcoma patients based on routinely collected clinical characteristics from the European randomised controlled trial, EE2012

The Value of Clinical Prediction Models in General Practice: A Qualitative Study Exploring the Perspectives of People With Lived Experience of Depression and General Practitioners.

Trends in clinical workload in UK primary care 2005–2019: a retrospective cohort study

Predicting the risk of acute kidney injury in primary care: derivation and validation of STRATIFY-AKI

Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)

Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration.

Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist.

Anticholinergic burden measures, symptoms, and fall-associated risk in older adults with polypharmacy: Development and validation of a prognostic model.

Prognostic factors associated with outcome following an epidural steroid injection for disc-related sciatica: a systematic review and narrative synthesis.

Effects of race and ethnicity on perinatal outcomes in high-income and upper-middle-income countries: an individual participant data meta-analysis of 2 198 655 pregnancies.

Development and external validation of a risk prediction model for falls in patients with an indication for antihypertensive treatment: retrospective cohort study

Diagnostic Models Combining Clinical Information, Ultrasound and Biochemical Markers for Ovarian Cancer: Cochrane Systematic Review and Meta-Analysis.

Menopausal status, ultrasound and biomarker tests in combination for the diagnosis of ovarian cancer in symptomatic women.

Collaborators (23)

James Sheppard

Jonathan Hill

Gwenllian Wynne-Jones

Danielle van der Windt

Ariel Wang

Shona Kirtley

Nadine Foster

Andrew Clegg

Rebecca Whittle

Jaime Delgadillo

Richard D Riley

Rupert Payne

Clare Davenport

Lucinda Archer

Carol Rivas

George Peat

Ulrich Thiem

Thomas P. A. Debray

Juliet A. Usher-Smith

Sudha Sundar

Amitava Banerjee

Annette Bishop

Matthew Sperrin

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