Search

Background: Patients with multiple conditions present a growing challenge for healthcare provision. Measures of multimorbidity may support clinical management, healthcare resource allocation and accounting for the health of participants in purpose-designed cohorts. The recently developed Cambridge Multimorbidity scores (CMS) have the potential to achieve these aims using primary care records, however, they have not yet been validated outside of their development cohort., Methods: The CMS, developed in the Clinical Research Practice Dataset (CPRD), were validated in UK Biobank participants whose data is not available in CPRD (the cohort used for CMS development) with available primary care records (n = 111,898). This required mapping of the 37 pre-existing conditions used in the CMS to the coding frameworks used by UK Biobank data providers. We used calibration plots and measures of discrimination to validate the CMS for two of the three outcomes used in the development study (death and primary care consultation rate) and explored variation by age and sex. We also examined the predictive ability of the CMS for the outcome of cancer diagnosis. The results were compared to an unweighted count score of the 37 pre-existing conditions., Results: For all three outcomes considered, the CMS were poorly calibrated in UK Biobank. We observed a similar discriminative ability for the outcome of primary care consultation rate to that reported in the development study (C-index: 0.67 (95%CI:0.66-0.68) for both, 5-year follow-up); however, we report lower discrimination for the outcome of death than the development study (0.69 (0.68-0.70) and 0.89 (0.88-0.90) respectively). Discrimination for cancer diagnosis was adequate (0.64 (0.63-0.65)). The CMS performs favourably to the unweighted count score for death, but not for the outcomes of primary care consultation rate or cancer diagnosis., Conclusions: In the UK Biobank, CMS discriminates reasonably for the outcomes of death, primary care consultation rate and cancer diagnosis and may be a valuable resource for clinicians, public health professionals and data scientists. However, recalibration will be required to make accurate predictions when cohort composition and risk levels differ substantially from the development cohort. The generated resources (including codelists for the conditions and code for CMS implementation in UK Biobank) are available online., (© 2024. The Author(s).)

In the absence of population-based screening, addition of screening for kidney cancer to lung cancer screening could provide an efficient and low-resource means to improve early detection. In this study, we used the UK Biobank cohort (n = 442 865) to determine the performance of the Yorkshire Lung Cancer Screening Trial (YLST) eligibility criteria for selecting individuals for kidney cancer screening. We measured the performance of two models widely used to determine eligibility for lung cancer screening (PLCO[m2012] and the Liverpool-Lung-Project-v2) and the performance of the combined YLST criteria. We found that the lung cancer models have discrimination (area under the receiver operating curve) between 0.60 and 0.68 for kidney cancer. In the UK, one in four cases (25%) of kidney cancer cases is expected to occur in those eligible for lung cancer screening, and one case of kidney cancer detected for every 200 people invited to lung cancer screening. These results suggest that adding kidney cancer screening to lung cancer screening would be an effective strategy to improve early detection rates of kidney cancer. However, most kidney cancers would not be picked up by this approach. This analysis does not address other important considerations about kidney cancer screening, such as overdiagnosis. PATIENT SUMMARY: It has been proposed that adding-on kidney cancer screening to lung cancer screening (both carried out by a computed tomography scan of the chest/abdomen) would be an easy and low-cost way of detecting cases of kidney cancer earlier, when these can be treated more easily. Lung cancer screening is usually targeted at people who are at a high risk (eg, older smokers); therefore, here we look at whether the same group of people are also at a high risk of kidney cancer. Our analysis shows that one in four people later diagnosed with kidney cancer are also at a high risk of lung cancer; hence, a combined screening programme could detect up to a quarter of kidney cancers., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objectives: To externally validate risk models for the detection of kidney cancer, as early detection of kidney cancer improves survival and stratifying the population using risk models could enable an individually tailored screening programme., Methods: We validated the performance of 30 existing phenotypic models predicting the risk of kidney cancer in the UK Biobank cohort (n = 450 687). We compared the discrimination and calibration of models for men, women, and a mixed-sex cohort. Population level data were used to estimate model performance in a screening scenario for a range of risk thresholds (6-year risk: 0.1-1.0%)., Results: In all, 10 models had reasonable discrimination (area under the receiver-operating characteristic curve >0.60), although some had poor calibration. Modelling demonstrated similar performance of the best models over a range of thresholds. The models showed an improvement in ability to identify cases compared to age- and sex-based screening. All the models performed less well in women than men., Conclusions: The present study is the first comprehensive external validation of risk models for kidney cancer. The best-performing models are better at identifying individuals at high risk of kidney cancer than age and sex alone; however, the benefits are relatively small. Feasibility studies are required to determine applicability to a screening programme., (© 2021 The Authors. BJU International published by John Wiley & Sons Ltd on behalf of BJU International.)