Comparative performance and external validation of the multivariable PREDICT Prostate tool for non-metastatic prostate cancer: a study in 69,206 men from Prostate Cancer data Base Sweden (PCBaSe).

David Thurtle, Ola Bratt, Pär Stattin, Paul Pharoah, Vincent Gnanapragasam

Abstract read

In one paragraph

Article in BMC medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it

0cells of the map it votes in

7citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

Optimizing clinical risk stratification of localized prostate cancer.Current opinion in urology · 2025
Review
Feasibility study of using the PREDICT kidney tool for patients with localised renal cell carcinoma.BJUI compass · 2025
Article
Combining Molecular Subtypes with Multivariable Clinical Models Has the Potential to Improve Prediction of Treatment Outcomes in Prostate Cancer at Diagnosis.Current oncology (Toronto, Ont.) · 2022
Article
Assessing the impact of MRI based diagnostics on pre-treatment disease classification and prognostic model performance in men diagnosed with new prostate cancer from an unscreened population.BMC cancer · 2022
Article
Risk stratification tools in prostate cancer, where do we stand?Translational andrology and urology · 2021
Article
PREDICT Prostate, a useful tool in men with low- and intermediate-risk prostate cancer who are hesitant between conservative management and active treatment.BMC medicine · 2020
Article
Understanding of prognosis in non-metastatic prostate cancer: a randomised comparative study of clinician estimates measured against the PREDICT prostate prognostic model.British journal of cancer · 2019
Article

4 · The record

Corrections and comments

Commented on by
PREDICT Prostate, a useful tool in men with low- and intermediate-risk prostate cancer who are hesitant between conservative management and active treatment.2020

5 · Who and what money

Authors and funding

5 authors.

David ThurtleAcademic Urology Group, University of Cambridge, Norman Bleehan Offices, Addenbrookes Hospital, Hills Road, Cambridge, CB2 0QQ, UK. Dt433@cam.ac.uk.

Ola BrattDepartment of Urology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, and Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden.

Pär StattinDepartment of Surgical Sciences, Uppsala University, Uppsala, Sweden.

Paul PharoahDepartment of Cancer Epidemiology, University of Cambridge, Cambridge, UK.

Vincent GnanapragasamVincent Gnanapragasam, Academic Urology Group, University of Cambridge, Cambridge, UK.

Funding

Cancer Research UK

6 · The paper itself

Abstract

backgroundPREDICT Prostate is an endorsed prognostic model that provides individualised long-term prostate cancer-specific and overall survival estimates. The model, derived from UK data, estimates potential treatment benefit on overall survival. In this study, we externally validated the model in a large independent dataset and compared performance to existing models and within treatment groups.

methodsMen with non-metastatic prostate cancer and prostate-specific antigen (PSA) < 100 ng/ml diagnosed between 2000 and 2010 in the nationwide population-based Prostate Cancer data Base Sweden (PCBaSe) were included. Data on age, PSA, clinical stage, grade group, biopsy involvement, primary treatment and comorbidity were retrieved. Sixty-nine thousand two hundred six men were included with 13.9 years of median follow-up. Fifteen-year survival estimates were calculated using PREDICT Prostate for prostate cancer-specific mortality (PCSM) and all-cause mortality (ACM). Discrimination was assessed using Harrell's concordance (c)-index in R. Calibration was evaluated using cumulative available follow-up in Stata (TX, USA).

resultsOverall discrimination of PREDICT Prostate was good with c-indices of 0.85 (95% CI 0.85-0.86) for PCSM and 0.79 (95% CI 0.79-0.80) for ACM. Overall calibration of the model was excellent with 25,925 deaths predicted and 25,849 deaths observed. Within the conservative management and radical treatment groups, c-indices for 15-year PCSM were 0.81 and 0.78, respectively. Calibration also remained good within treatment groups. The discrimination of PREDICT Prostate significantly outperformed the EAU, NCCN and CAPRA scores for both PCSM and ACM within this cohort overall. A key limitation is the use of retrospective cohort data.

conclusionsThis large external validation demonstrates that PREDICT Prostate is a robust and generalisable model to aid clinical decision-making.

Indexed as

AgedCohort StudiesHumansMalePrognosisProstatic NeoplasmsRetrospective StudiesSwedenCompeting risksDecision aidOverall mortalityPCSMPrognosisProstate cancerProstate cancer-specific mortalitySurvival

Identifiers

PMID32539712

DOI10.1186/s12916-020-01606-w

PMCPMC7296776

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.