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. 2003 Nov 22;327(7425):1196-201.
doi: 10.1136/bmj.327.7425.1196.

Operative mortality in colorectal cancer: prospective national study

Paris P Tekkis  1 , Jan D Poloniecki, Michael R Thompson, Jeffrey D Stamatakis
Affiliations

Operative mortality in colorectal cancer: prospective national study

Paris P Tekkis et al. BMJ. .

Abstract

Objective: To develop a mathematical model that will predict the probability of death after surgery for colorectal cancer.

Design: Descriptive study using routinely collected clinical data.

Data source: The database of the Association of Coloproctology of Great Britain and Ireland (ACPGBI), encompassing 8077 patients with a new diagnosis of colorectal cancer in 73 hospitals during a 12 month period.

Statistical analysis: A three level hierarchical logistic regression model was used to identify independent predictors of operative mortality. The model was developed on 60% of the patient population and its validity tested on the remaining 40%.

Results: Overall postoperative mortality was 7.5% (95% confidence interval 6.9% to 8.1%). Independent predictors of death were age, American Society of Anesthesiology (ASA) grade, Dukes's stage, urgency of the operation, and cancer excision. When tested the predictive model showed good discrimination (area under the receiver operating characteristic curve = (0.775) and calibration (comparison of observed with expected mortality across different procedures; Hosmer-Lemeshow statistic = 6.34, 8 df, P = 0.610).

Conclusions: Clinicians can predict postoperative death by using a simple numerical table derived from the statistical model of the ACPGBI. The model can be used in everyday practice for preoperative counselling of patients and their carers as a part of multidisciplinary care. It may also be used to compare the outcomes between multidisciplinary teams for colorectal cancer.

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Figures

Fig 1
Fig 1
Variation of 30 day operative mortality with age across the strata of ASA grade. Individual prediction lines are provided for individual hospitals parallel to each other but with different intercepts on the vertical axis. The variability in the intercept represents the “hospital effect,” which is an indirect proxy of the structure and process of care in each hospital
Fig 2
Fig 2
Variation of operative mortality by age, ASA grade, and cancer resection. The regression lines have been adjusted for age, ASA, and the interaction term ASA×cancer resection by using a three level logistic regression model
Fig 3
Fig 3
Calibration chart of the observed and model predicted operative mortality for the ACPGBI colorectal cancer model based on the validation set (n=3000)
Fig 4
Fig 4
Comparison of observed and predicted 30 day operative mortality by type of procedure on the validation set of 3000 cases (Hosmer-Lemeshow statistic 6.34, 8 df, P=0.610). For each procedure the number of cases, the observed 30 day operative mortality (%), and its 95% confidence intervals are displayed
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References

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