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☐ ☆ ✇ BMJ Open

Protocol for the PROSECCA study: a new approach for predicting radiotherapy outcome using artificial intelligence and electronic population-based healthcare data

Por: Nailon · W. H. · Noble · D. J. · Harrison · E. · Yang · Z. · Elliot · S. · MacNair · A. · Beckett · G. · Hallam · A. · Sheikh · A. · Mills · N. · Halliday · R. · Morrison · D. · Chalmers · A. · Cameron · D. · Gourley · C. · Hall · P. · Lilley · C. · Carruthers · L. J. · Trainer · M. · Burns — Febrero 2nd 2026 at 16:02
Introduction

Within the UK there are 33 deaths every day from prostate cancer, second only to lung cancer as the most common cause of cancer death in males in the UK. Of the 55 000 new cases each year, up to 50% of these patients will receive radiotherapy either alone or after prostatectomy. Although there have been significant improvements in the accuracy of radiotherapy delivery leading to better tumour targeting and a reduction in dose to normal tissues, significant permanent genito-urinary or gastrointestinal-related side effects are all too common. With nearly 80% of patients with prostate cancer surviving for 10 years or more, minimising life-limiting radiation damage to normal tissues is vitally important. However, at present, it is not possible to identify which patients will suffer a poorer outcome after radiotherapy. The aim of this study, improving radiotherapy in PROState cancer using EleCtronic population-based healthCAre data (PROSECCA), is to do this by using the existing information in a patient’s digital healthcare record. By linking primary, secondary and tertiary clinical data, including digital image information, with radiotherapy treatment plans and outcome data, the PROSECCA study will identify de novo predictive biomarkers of radiation response and provide clinicians with a tool to individualise a radiotherapy dose and plan to maximise cure and minimise toxicity.

Methods and analysis

The PROSECCA study is a large multidisciplinary project, the purpose of which is to analyse healthcare records from up to 15 000 patients with prostate cancer who underwent radiotherapy in the treatment of their cancer in Scotland between 2010 and 2022. Through the linkage of data obtained specifically for radiotherapy and data held within each patient’s unique electronic health record (EHR), the factors that indicate why some patients have a poor response to treatment, or an increased risk of side effects from radiation, will be identified. This will be made possible by the use of artificial intelligence and machine learning (AL/ML), which will help to identify at-risk patients earlier and allow adaptation of their treatment accordingly.

Ethics and dissemination

The study is being conducted in accordance with the ethical principles set out in the Declaration of Helsinki and Good Clinical Practice that respects and protects the rights, and maintains confidentiality, of all trial participants. The study protocol (V.1.0) was reviewed by the South Central Oxford A Research Ethics Committee (REC) on 13 December 2021 and received a favourable opinion subject to each National Health Service (NHS) organisation confirming permission for patients treated within their area. Approval for the use of unconsented healthcare record data for patients included in the study and treated at one of the five Scottish Cancer Centres required an application to the NHS Scotland Public Benefit and Privacy Panel for Health and Social Care (HSC-PBPP). Full approval from the HSC-PBPP panel was received on 1 July 2024, which covered the use of pseudoanonymised EHR data for all patients participating in the study. The study is publicly listed on the NHS Health Research Authority site, with IRAS ID 306245 and REC reference 21/SC/0402. Dissemination of the study findings will take place through field-leading cancer, radiation oncology and medical physics journals. All manuscripts will be approved by the main study team and authorship determined by mutual agreement.

Trial registration number

NCT06714630.

☐ ☆ ✇ BMJ Open

Patterns of follow-up testing of abnormal eGFR and UACR for the detection of chronic kidney disease in Australian primary care: analysis of a national general practice dataset

Por: Li · A. K. · Kotwal · S. · Wallace · H. · Ketema · D. B. · Wick · J. · Neuen · B. L. · Falster · M. O. · Lin · J. · Pearson · S.-A. · Peiris · D. · Jardine · M. J. · Woodward · M. · Chalmers · J. · Ronksley · P. E. · Jun · M. — Octubre 15th 2025 at 09:50
Objective

To evaluate the patterns of abnormal estimated glomerular filtration rate (eGFR) and urine albumin–creatinine ratio (UACR) follow-up testing for the detection of chronic kidney disease (CKD) in Australian general practices.

Design

Retrospective, population-based observational study.

Setting and participants

2 717 966 adults who visited a MedicineInsight participating general practice between 1 January 2012 and 31 December 2020, had ≥1 serum creatinine measurement (with or without a UACR measurement) and did not have CKD at baseline.

Main outcome measure

‘Guideline-concordant follow-up’ was defined as having a record of a repeat eGFR or UACR testing (assessed separately) within 6 months following the abnormal (eGFR2; UACR≥2.5 mg/mmol in males, ≥3.5 mg/mmol in females) incident result. Multivariable logistic regression was used to identify patient factors associated with receiving appropriate follow-up testing.

Results

A total of 220 841 and 114 889 patients with an abnormal incident eGFR and UACR result, respectively, were identified. Nearly half (45.0%) of the patients with an abnormal eGFR result and over two-thirds (69.7%) of the patients with an abnormal UACR result did not have a follow-up test within 6 months. Patient factors associated with a higher likelihood of follow-up eGFR testing included indicators of poorer baseline health and greater CKD risk, such as comorbid diabetes (adjusted OR 1.36, 95% CI 1.32 to 1.40) or more severe incident eGFR (adjusted ORs for eGFR categories 30–44, 15–29 and

Conclusions

In this large, population-based study, we observed substantial gaps in the follow-up of abnormal eGFR and UACR for the detection of CKD in primary care settings. Effective strategies to optimise follow-up testing for CKD detection are needed.

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