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J Thorac Cardiovasc Surg. 2007 Feb;133(2):325-32. Epub 2007 Jan 9.

The Thoracic Surgery Scoring System (Thoracoscore): risk model for in-hospital death in 15,183 patients requiring thoracic surgery.

Author information

1
Department of Thoracic and Cardiovascular Surgery, Jean-Minjoz Hospital, Besançon, France. pierre-emmanuel.falcoz@wanadoo.fr

Abstract

OBJECTIVE:

This study was undertaken to determine factors associated with in-hospital mortality among patients after general thoracic surgery and to construct a risk model.

METHODS:

Data from a nationally representative thoracic surgery database were collected prospectively between June 2002 and July 2005. Logistic regression analysis was used to predict the risk of in-hospital death. A risk model was developed with a training set of data (two thirds of patients) and validated on an independent test set (one third of patients). Model fit was assessed by the Hosmer-Lemeshow test; predictive accuracy was assessed by the c-index.

RESULTS:

Of the 15,183 original patients, 338 (2.2%) died during the same hospital admission. Within the data used to develop the model, these factors were found to be significantly associated with the occurrence of in-hospital death in a multivariate analysis: age, sex, dyspnea score, American Society of Anesthesiologists score, performance status classification, priority of surgery, diagnosis group, procedure class, and comorbid disease. The model was reliable (Hosmer-Lemeshow test 3.22; P = .92) and accurate, with a c-index of 0.85 (95% confidence interval 0.83-0.87) for the training set and 0.86 (95% confidence interval 0.83-0.89) for the test set of data. The correlation between the expected and observed number of deaths was 0.99.

CONCLUSIONS:

The validated multivariate model Thoracoscore, described in this report for risk of in-hospital death among adult patients after general thoracic surgery was developed with national data, uses only 9 variables, and has good performance characteristics. It appears to be a valid clinical tool for predicting the risk of death.

PMID:
17258556
DOI:
10.1016/j.jtcvs.2006.09.020
[Indexed for MEDLINE]
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