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01.11.2010 | 2010 SSAT Quick Shot Presentation | Ausgabe 11/2010

Journal of Gastrointestinal Surgery 11/2010

Pre-operative Nomogram to Predict Risk of Peri-operative Mortality following Liver Resections for Malignancy

Zeitschrift:
Journal of Gastrointestinal Surgery > Ausgabe 11/2010
Autoren:
Mashaal Dhir, Lynette M. Smith, Fred Ullrich, Premila D. Leiphrakpam, Quan P. Ly, Aaron R. Sasson, Chandrakanth Are

Abstract

Introduction

The majority of liver resections for malignancy are performed in older patient with major co-morbidities. There is currently no pre-operative, patient-specific method to determine the likely peri-operative mortality for each individual patient. The aim of this study was to develop a pre-operative nomogram based on the presence of co-morbidities to predict risk of peri-operative mortality following liver resections for malignancy.

Methods

The Nationwide Inpatient Sample database was queried to identify adult patients that underwent liver resection for malignancy. The pre-operative co-morbidities, identified as predictors were used and a nomogram was created with multivariate regression using Taylor expansion method in SAS software, SURVEYLOGISTIC procedure. Training set (years 2000–2004) was utilized to develop the model and validation set (year 2005) was utilized to validate this model.

Results

A total of 3,947 and 972 patients were included in training and validation sets, respectively. The overall actual-observed peri-operative mortality rates for training and validation sets were 4.1% and 3.2%, respectively. The decile-based calibration plots for the training set revealed good agreement between the observed probabilities and nomogram-predicted probabilities. Similarly, the quartile-based calibration plot for the validation set revealed good agreement between the observed and predicted probabilities. The accuracy of the nomogram was further reinforced by a good concordance index of 0.80 with a 95% confidence interval of 0.72 and 0.87.

Conclusions

This pre-operative nomogram may be utilized to predict the risk of peri-operative mortality following liver resection for malignancy.

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