BackgroundClinical and imaging surveillance practices following endovascular aneurysm repair (EVAR) for intact abdominal aortic aneurysm (AAA) vary considerably and compliance with recommended lifelong surveillance is poor. The aim of this study was to develop a dynamic prognostic model to enable stratification of patients at risk of future secondary aortic rupture or the need for intervention to prevent rupture (rupture‐preventing reintervention) to enable the development of personalized surveillance intervals.MethodsBaseline data and repeat measurements of postoperative aneurysm sac diameter from the EVAR‐1 and EVAR‐2 trials were used to develop the model, with external validation in a cohort from a single‐centre vascular database. Longitudinal mixed‐effects models were fitted to trajectories of sac diameter, and model‐predicted sac diameter and rate of growth were used in prognostic Cox proportional hazards models.ResultsSome 785 patients from the EVAR trials were included, of whom 155 (19·7 per cent) experienced at least one rupture or required a rupture‐preventing reintervention during follow‐up. An increased risk was associated with preoperative AAA size, rate of sac growth and the number of previously detected complications. A prognostic model using predicted sac growth alone had good discrimination at 2 years (C‐index 0·68), 3 years (C‐index 0·72) and 5 years (C‐index 0·75) after operation and had excellent external validation (C‐index 0·76–0·79). More than 5 years after operation, growth rates above 1 mm/year had a sensitivity of over 80 per cent and specificity over 50 per cent in identifying events occurring within 2 years.ConclusionSecondary sac growth is an important predictor of rupture or rupture‐preventing reintervention to enable the development of personalized surveillance intervals. A dynamic prognostic model has the potential to tailor surveillance by identifying a large proportion of patients who may require less intensive follow‐up.
Background: Predict Breast (www.breast.predict.nhs.uk) is an online prognostication and treatment benefit tool for early invasive breast cancer. However, the most recent version of PREDICT Breast (v2.2) was based on data for breast cancer cases diagnosed from 1999 to 2003 and did not incorporate the benefits of radiotherapy or the harms associated with theray. Since then, there has been a substantial improvement in the outcomes for breast cancer cases. The aim of this study was to update PREDICT Breast to ensure that the underlying model is appropriate for contemporary patients. Methods: Data from 4,644 ER-negative and 30,830 ER-positive breast cancer cases diagnosed from 2000 to 2017 in the region served by the Eastern Cancer Registry were used for model development. Multivariable fractional polynomials in a Cox proportional hazards framework were used to estimate the prognostic effects of year of diagnosis, age at diagnosis, tumour size, tumour grade and number of positive nodes and to compute the baseline hazard functions. Separate models were developed for ER-positive and ER-negative disease. Data on 32,408 breast cancer patients from the West Midlands Cancer Registry and from 100,551 breast cancer cases from the other English Cancer Registries combined were used to determine the discriminative power, calibration, and reclassification of the new version of PREDICT Breast (v3.0). Results: The new model (v3.0) was well-calibrated; predicted numbers of 5-, 10- and 15-year breast cancer deaths were within 10 per cent of the observed number in both model development and model validation data sets. In contrast, PREDICT Breast v2.2 was found to substantially over-predict the number of deaths. Discrimination was also good: The AUC for 15-year breast cancer survival was 0. 824 in the model development data, 0.809 in the West Midlands data set and 0.846 in the data set for the other registries. There figures were slightly better than those for PREDICT Breast v2.2 Conclusion: Incorporating the prognostic effect of year of diagnosis, updating the prognostic effects of all risk factors and amending the baseline hazard functions have led to an improvement of model performance of PREDICT Breast. The new model will be implemented in the online tool which should lead to more accurate absolute treatment benefit predictions for individual patients.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.