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The AUGIS survival predictor: prediction of long-term and conditional survival after esophagectomy using Random Survival Forests

The AUGIS survival predictor: prediction of long-term and conditional survival after esophagectomy using Random Survival Forests
The AUGIS survival predictor: prediction of long-term and conditional survival after esophagectomy using Random Survival Forests
Background: for patients with esophageal cancer, accurately predicting long-term survival after esophagectomy is challenging. This study investigated survival prediction after esophagectomy using a Random Survival Forest (RSF) model derived from routine data from a large, well curated, national dataset. Methods: patients diagnosed with esophageal adenocarcinoma or squamous cell carcinoma between 2012 and 2018 in England and Wales who underwent an esophagectomy were included. Prediction models for overall survival were developed using the RSF method and Cox regression from 41 patient and disease characteristics. Calibration and discrimination (time dependent AUC) were validated internally using bootstrap resampling. Results: the study analysed 6399 patients, with 2625 deaths during follow-up. Median follow-up was 41 months. Overall survival was 47.1% at 5 years. The final RSF model included 14 variables and had excellent discrimination with a 5-year tAUC of 83.9% (95%CI 82.6-84.9%), compared to 82.3% (95%CI 81.1-83.3%) for the Cox model. The most important variables were lymph node involvement, pT stage, CRM involvement (tumour at <1mm from cut edge) and age. There was a wide range of survival estimates even within TNM staging groups, with quintiles of prediction within Stage 3b ranging from 12.2-44.7% survival at 5 years. Conclusions: an RSF model for long-term survival after esophagectomy exhibited excellent discrimination and well calibrated predictions. At a patient level, it provides more accuracy than TNM staging alone and could help in the delivery of tailored treatment and follow-up.
0003-4932
Rahman, Saqib A
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Walker, Robert C
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Maynard, Nick
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Trudgill, Nigel
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Crosby, Tom
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Cromwell, David
0e781623-7ab3-4ee3-b56f-d493258a697a
Underwood, Timothy
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Rahman, Saqib A
e2b565d4-df7f-4496-8cc3-80fc63a9e4cd
Walker, Robert C
c8fbfe1c-349d-497f-b24e-0295c84c4634
Maynard, Nick
b1551de8-a068-4e5d-89d2-9d47540c0dff
Trudgill, Nigel
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Crosby, Tom
d641cb6d-efc6-45ae-b083-a21c599a032c
Cromwell, David
0e781623-7ab3-4ee3-b56f-d493258a697a
Underwood, Timothy
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Rahman, Saqib A, Walker, Robert C, Maynard, Nick, Trudgill, Nigel, Crosby, Tom, Cromwell, David and Underwood, Timothy (2021) The AUGIS survival predictor: prediction of long-term and conditional survival after esophagectomy using Random Survival Forests. Annals of Surgery. (doi:10.1097/SLA.0000000000004794).

Record type: Article

Abstract

Background: for patients with esophageal cancer, accurately predicting long-term survival after esophagectomy is challenging. This study investigated survival prediction after esophagectomy using a Random Survival Forest (RSF) model derived from routine data from a large, well curated, national dataset. Methods: patients diagnosed with esophageal adenocarcinoma or squamous cell carcinoma between 2012 and 2018 in England and Wales who underwent an esophagectomy were included. Prediction models for overall survival were developed using the RSF method and Cox regression from 41 patient and disease characteristics. Calibration and discrimination (time dependent AUC) were validated internally using bootstrap resampling. Results: the study analysed 6399 patients, with 2625 deaths during follow-up. Median follow-up was 41 months. Overall survival was 47.1% at 5 years. The final RSF model included 14 variables and had excellent discrimination with a 5-year tAUC of 83.9% (95%CI 82.6-84.9%), compared to 82.3% (95%CI 81.1-83.3%) for the Cox model. The most important variables were lymph node involvement, pT stage, CRM involvement (tumour at <1mm from cut edge) and age. There was a wide range of survival estimates even within TNM staging groups, with quintiles of prediction within Stage 3b ranging from 12.2-44.7% survival at 5 years. Conclusions: an RSF model for long-term survival after esophagectomy exhibited excellent discrimination and well calibrated predictions. At a patient level, it provides more accuracy than TNM staging alone and could help in the delivery of tailored treatment and follow-up.

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oesmodel_ann_surg_accepted_25.01.21 - Accepted Manuscript
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Accepted/In Press date: 24 January 2021
e-pub ahead of print date: 17 February 2021

Identifiers

Local EPrints ID: 446527
URI: http://eprints.soton.ac.uk/id/eprint/446527
ISSN: 0003-4932
PURE UUID: 442a1ba2-492b-469d-9989-23a6b999618c
ORCID for Timothy Underwood: ORCID iD orcid.org/0000-0001-9455-2188

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Date deposited: 12 Feb 2021 17:31
Last modified: 17 Mar 2024 06:17

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Contributors

Author: Saqib A Rahman
Author: Robert C Walker
Author: Nick Maynard
Author: Nigel Trudgill
Author: Tom Crosby
Author: David Cromwell

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