Incorporating progesterone receptor expression into the PREDICT breast prognostic model

Background: Predict Breast (www.predict.nhs.uk) is an online prognostication and treatment benefit tool for early invasive breast cancer. The aim of this study was to incorporate the prognostic effect of progesterone receptor (PR) status into a new version of PREDICT and to compare its performance to the current version (2.2).Method: The prognostic effect of PR status was based on the analysis of data from 45,088 European patients with breast cancer from 49 studies in the Breast Cancer Association Consortium. Cox proportional hazard models were used to estimate the hazard ratio for PR status. Data from a New Zealand study of 11,365 patients with early invasive breast cancer were used for external validation. Model calibration and discrimination were used to test the model performance.Results: Having a PR-positive tumour was associated with a 23% and 28% lower risk of dying from breast cancer for women with oestrogen receptor (ER)-negative and ER-positive breast cancer, respectively. The area under the ROC curve increased with the addition of PR status from 0.807 to 0.809 for patients with ER-negative tumours (p = 0.023) and from 0.898 to 0. 902 for patients with ER-positive tumours (p = 2.3 x 10(-6)) in the New Zealand cohort. Model calibration was modest with 940 observed deaths compared to 1151 predicted.Conclusion: The inclusion of the prognostic effect of PR status to PREDICT Breast has led to an improvement of model performance and more accurate absolute treatment benefit predic-tions for individual patients. Further studies should determine whether the baseline hazard function requires recalibration. (C) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

Evidence of ventilation changes in the Arabian Sea during the late Quaternary:Implication for denitrification and nitrous oxide emission

Modern seawater profiles of oxygen, nitrate deficit, and nitrogen isotopes reveal the spatial decoupling of summer monsoon-related productivity and denitrification maxima in the Arabian Sea (AS) and raise the possibility that winter monsoon and/or ventilation play a crucial role in modulating denitrification in the northeastern AS, both today and through the past. A new high-resolution 50-ka record of delta(15) N from the Pakistan margin is compared to five other denitrification records distributed across the AS. This regional comparison unveils the persistence of east-west heterogeneities in denitrification intensity across millennial-scale climate shifts and throughout the Holocene. The oxygen minimum zone (OMZ) experienced east-west swings across Termination I and throughout the Holocene. Probable causes are (1) changes in ventilation due to millennial-scale variations in Antarctic Intermediate Water formation and (2) postglacial reorganization of intermediate circulation in the northeastern AS following sea level rise. Whereas denitrification in the world's OMZs, including the western AS, gradually declined following the deglacial maximum (10-9 ka BP), the northeastern AS record clearly witnesses increasing denitrification from about 8 ka BP. This would have impacted the global Holocene climate through sustained N2O production and marine nitrogen loss

Incorporating progesterone receptor expression into the PREDICT breast prognostic model

Background: Predict Breast (www.predict.nhs.uk) is an online prognostication and treatment benefit tool for early invasive breast cancer. The aim of this study was to incorporate the prognostic effect of progesterone receptor (PR) status into a new version of PREDICT and to compare its performance to the current version (2.2).Method: The prognostic effect of PR status was based on the analysis of data from 45,088 European patients with breast cancer from 49 studies in the Breast Cancer Association Consortium. Cox proportional hazard models were used to estimate the hazard ratio for PR status. Data from a New Zealand study of 11,365 patients with early invasive breast cancer were used for external validation. Model calibration and discrimination were used to test the model performance.Results: Having a PR-positive tumour was associated with a 23% and 28% lower risk of dying from breast cancer for women with oestrogen receptor (ER)-negative and ER-positive breast cancer, respectively. The area under the ROC curve increased with the addition of PR status from 0.807 to 0.809 for patients with ER-negative tumours (p = 0.023) and from 0.898 to 0. 902 for patients with ER-positive tumours (p = 2.3 x 10(-6)) in the New Zealand cohort. Model calibration was modest with 940 observed deaths compared to 1151 predicted.Conclusion: The inclusion of the prognostic effect of PR status to PREDICT Breast has led to an improvement of model performance and more accurate absolute treatment benefit predic-tions for individual patients. Further studies should determine whether the baseline hazard function requires recalibration. (C) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

The Development of the Prognostic Breast Cancer Model PREDICT

Background: PREDICT Breast is an online prognostication and treatment benefit tool to aid clinical decision making for patients with early invasive breast cancer. Since its development in 2010, the model has shown a variety of prognostic outcomes among numerous studies. Due to improvements in breast cancer survival and advancements in cancer treatments, the model might be outdated and could lead to imprecise survival predictions. The aim of this doctoral research was to update the model and enhance its model performance in order to contribute to more accurate predictions for individual patients. The first aim was to investigate and incorporate the prognostic effect of the biomarker progesterone receptor (PR) into the model. Another objective was to address the underestimation and overestimation of breast cancer mortality by amending the prognostic tool with more recent data. Methods: The prognostic effect of PR status was based on the analysis of data from 45,088 breast cancer patients of European descent from 49 studies in the Breast Cancer Association Consortium (BCAC). Cox proportional hazards models were used to obtain estimates of the relative hazard for breast cancer-specific mortality associated with PR status after adjusting for the prognostic factors found in version 2.2. Separate models were derived for oestrogen receptor (ER)-negative cases and ER-positive cases. Data from an independent cohort of 11,365 breast cancer patients from New Zealand were utilised for external validation. Model calibration, discrimination and reclassification were used to test the model performance. Data from 34,265 ER-positive cases and 5,484 ER-negative cases diagnosed from 2000 to 2017 in the regions served by the Eastern cancer registry were used for model development of the updated version of PREDICT Breast. Various statistical fitting methods were applied to enhance the ability to capture the shape of the survival data and examine for any non-linear effects in the continuous prognostic factors, and to improve model performance relative to the previous version (v 2.2). These techniques were compared with each other based on the Akaike Information Criterion (AIC) value. Subsequently, Cox proportional hazards models with the optimal modelling method were fitted to estimate the prognostic effects of the risk factors found in PREDICT Breast and to compute the baseline hazard functions for the ER-specific cases separately. For external validation, data from West Midlands cancer registry on 32,408 breast cancer patients were used to determine the discriminative power, calibration and reclassification of the new version of PREDICT Breast. Findings: Having a PR-positive tumour was associated with a 23% and 28% lower risk of dying from breast cancer for women with ER-negative and ER-positive breast cancer, respectively. The area under the ROC curve increased with the addition of PR status from 0.807 to 0.809 for patients with ER-negative tumours (p = 0.023) and from 0.898 to 0.902 for patients with ER-positive tumours (p = 2.3×10−6) in the New Zealand cohort. Model calibration was modest with 940 observed deaths compared to 1,151 predicted. The AIC measurements showed that multiple fractional polynomials best capture the non-linear effects of the continuous risk factors and were used to estimate the non-linear transformations. The new model shows to be well-calibrated. 10-year breast cancer deaths were slightly under-predicted in the Eastern cancer registry (ER-: -2.5%, ER+: -1.4%) and over-predicted in the West Midlands data (ER: 0.1%, ER+: 6.8%). The AUC for 15-year breast cancer survival improved from 0.833 to 0.836 (p = 2.3x10−4) in the Eastern cancer registry data and from 0.810 to 0.812 (p = 0.098) in the West Midlands data. Conclusion: Incorporating the prognostic effects of PR status and 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 and resulted in more accurate absolute treatment benefit predictions for individual patients. I developed an enhanced version of the prognostic tool and successfully validated it utilising several independent data sets. The updated version will shortly be implemented online

An updated PREDICT breast cancer prognostic model including the benefits and harms of radiotherapy

Acknowledgements: We thank Alex Freeman, David Speigelhalter and Gabriel Recchia for helpful discussion on the development and implementation of the model; and Julia Brown of Public Health England for help in accessing the national cancer registration data set. Isabelle Grootes was funded by the Mark Foundation Institute for Integrated Cancer Medicine at the University of Cambridge.Funder: Bergmark Foundation; doi: https://doi.org/10.13039/100009536Funder: Mark FoundationAbstractPREDICT Breast (www.breast .predict.nhs.uk) is a prognostication tool for early invasive breast cancer. The current version was based on cases diagnosed in 1999–2003 and did not incorporate the benefits of radiotherapy or the harms associated with therapy. 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. Data from the England National Cancer Registration and Advisory Service for invasive breast cancer cases diagnosed 2000–17 were used for model development and validation. Model development was based on 35,474 cases diagnosed and registered by the Eastern Cancer Registry. A Cox model was used to estimate the prognostic effects of the year of diagnosis, age at diagnosis, tumour size, tumour grade and number of positive nodes. Separate models were developed for ER-positive and ER-negative disease. Data on 32,408 cases from the West Midlands Cancer Registry and 100,551 cases from other cancer registries were used for validation. The new model was well-calibrated; predicted breast cancer deaths at 5-, 10- and 15-year were within 10 per cent of the observed validation data. Discrimination was also good: The AUC for 15-year breast cancer survival was 0.809 in the West Midlands data set and 0.846 in the data set for the other registries. The new PREDICT Breast model outperformed the current model and will be implemented in the online tool which should lead to more accurate absolute treatment benefit predictions for individual patients.</jats:p

Predicting risk of rupture and rupture-preventing re-interventions utilising repeated measures on aneurysm sac diameter following endovascular abdominal aortic aneurysm repair

Clinical 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. This study developed a dynamic prognostic model to enable stratification of patients at risk of future secondary rupture or rupture preventing re-intervention (RPR) to enable the development of personalised surveillance intervals. Baseline data and repeat measurements of post-operative aneurysm sac diameter from the EVAR-1 and EVAR-2 trials were used to develop the model with external validation in a cohort from Helsinki. 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. 785 patients from the EVAR trials were included of which 155 (20%) suffered at least one rupture or RPR during follow-up. An increased risk was associated with pre-operative AAA size, rate of sac growth, and the number of previously detected complications. A prognostic model using only predicted sac growth had good discrimination at 2-years (C-index = 0.68), 3-years (C-index= 0.72) and 5-years (C-index= 0.75) post-operation and had excellent external validation (C-indices 0.76 to 0.79). After 5-years post-operation, growth rates above 1mm/year had a sensitivity of over 80% and specificity over 50% in identifying events occurring within 2 years. Secondary sac growth is an important predictor of rupture or RPR. A dynamic prognostic model has the potential to tailor surveillance by identifying a large proportion of patients who may require less intensive follow-up

Incorporating progesterone receptor expression into the PREDICT Breast prognostic model

Background: Predict Breast (www.predict.nhs.uk) is an online prognostication and treatment benefit tool for early invasive breast cancer. The aim of this study was to incorporate the prognostic effect of progesterone receptor (PR) status into a new version of PREDICT and to compare its performance to the current version (2.2).Method: the prognostic effect of PR status was based on the analysis of data from 45,088 European patients with breast cancer from 49 studies in the Breast Cancer Association Consortium. Cox proportional hazard models were used to estimate the hazard ratio for PR status. Data from a New Zealand study of 11,365 patients with early invasive breast cancer were used for external validation. Model calibration and discrimination were used to test the model performance.Results: having a PR-positive tumour was associated with a 23% and 28% lower risk of dying from breast cancer for women with oestrogen receptor (ER)-negative and ER-positive breast cancer, respectively. The area under the ROC curve increased with the addition of PR status from 0.807 to 0.809 for patients with ER-negative tumours (p = 0.023) and from 0.898 to 0.902 for patients with ER-positive tumours (p = 2.3 × 10−6) in the New Zealand cohort. Model calibration was modest with 940 observed deaths compared to 1151 predicted.Conclusion: the inclusion of the prognostic effect of PR status to PREDICT Breast has led to an improvement of model performance and more accurate absolute treatment benefit predictions for individual patients. Further studies should determine whether the baseline hazard function requires recalibration

Incorporating progesterone receptor expression into the PREDICT breast prognostic model

BACKGROUND: Predict Breast (www.predict.nhs.uk) is an online prognostication and treatment benefit tool for early invasive breast cancer. The aim of this study was to incorporate the prognostic effect of progesterone receptor (PR) status into a new version of PREDICT and to compare its performance to the current version (2.2). METHOD: The prognostic effect of PR status was based on the analysis of data from 45,088 European patients with breast cancer from 49 studies in the Breast Cancer Association Consortium. Cox proportional hazard models were used to estimate the hazard ratio for PR status. Data from a New Zealand study of 11,365 patients with early invasive breast cancer were used for external validation. Model calibration and discrimination were used to test the model performance. RESULTS: Having a PR-positive tumour was associated with a 23% and 28% lower risk of dying from breast cancer for women with oestrogen receptor (ER)-negative and ER-positive breast cancer, respectively. The area under the ROC curve increased with the addition of PR status from 0.807 to 0.809 for patients with ER-negative tumours (p = 0.023) and from 0.898 to 0.902 for patients with ER-positive tumours (p = 2.3 × 10-6) in the New Zealand cohort. Model calibration was modest with 940 observed deaths compared to 1151 predicted. CONCLUSION: The inclusion of the prognostic effect of PR status to PREDICT Breast has led to an improvement of model performance and more accurate absolute treatment benefit predictions for individual patients. Further studies should determine whether the baseline hazard function requires recalibration