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Erschienen in:

01.08.2014 | Review Article

Individual Risk Modelling for Esophagectomy: A Systematic Review

verfasst von: John M. Findlay, Richard S. Gillies, Bruno Sgromo, Robert E. K. Marshall, Mark R. Middleton, Nicholas D. Maynard

Erschienen in: Journal of Gastrointestinal Surgery | Ausgabe 8/2014

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Abstract

Introduction

A number of models have been applied to predict outcomes from esophagectomy. This systematic review aimed to compare their clinical credibility, methodological quality and performance.

Methods

A systematic review of the PubMed, EMBASE and Cochrane databases was performed in October 2012. Model and study quality were appraised using the framework of Minne et al.

Results

Twenty studies were included in total; these were heterogeneous, retrospective and conducted over a number of years; all models were generated via logistic regression. Overall mortality was high, and consequently not representative of current practice. Clinical credibility and methodological quality were variable, with frequent failure to perform internal validation and variable presentation of calibration and discrimination metrics. P-POSSUM demonstrated the best calibration and discrimination for predicting mortality. Other than the Southampton score (which has yet to be externally validated) and the Amsterdam score, no studies had utility in predicting complications.

Conclusion

Whilst a number of models have been developed, adapted or trialled, due to numerous limitations, larger and more contemporary studies are required to develop and validate models further. The role of alternative techniques such as decision tree analysis and artificial neural networks is not known.

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Titel: Individual Risk Modelling for Esophagectomy: A Systematic Review
verfasst von: John M. Findlay
Richard S. Gillies
Bruno Sgromo
Robert E. K. Marshall
Mark R. Middleton
Nicholas D. Maynard
Publikationsdatum: 01.08.2014
Verlag: Springer US
Erschienen in: Journal of Gastrointestinal Surgery / Ausgabe 8/2014
Print ISSN: 1091-255X
Elektronische ISSN: 1873-4626
DOI: https://doi.org/10.1007/s11605-014-2524-2

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Abstract

Introduction

Methods

Results

Conclusion

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