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. 2010;1(1):38-49.
doi: 10.4338/ACI-2009-12-RA-0026.

Developing a multivariable prognostic model for pancreatic endocrine tumors using the clinical data warehouse resources of a single institution

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Developing a multivariable prognostic model for pancreatic endocrine tumors using the clinical data warehouse resources of a single institution

Taxiarchis Botsis et al. Appl Clin Inform. 2010.

Abstract

OBJECTIVE: Current staging systems are not accurate for classifying pancreatic endocrine tumors (PETs) by risk. Here, we developed a prognostic model for PETs and compared it to the WHO classification system. METHODS: We identified 98 patients diagnosed with PET at NewYork-Presbyterian Hospital/Columbia University Medical Center (1999 to 2009). Tumor and clinical characteristics were retrieved and associations with survival were assessed by univariate Cox analysis. A multivariable model was constructed and a risk score was calculated; the prognostic strength of our model was assessed with the concordance index. RESULTS: Our cohort had median age of 60 years and consisted of 61.2% women; median follow-up time was 10.4 months (range: 0.1-99.6) with a 5-year survival of 61.5%. The majority of PETs were non-functional and no difference was observed between functional and non-functional tumors with respect to WHO stage, age, pathologic characteristics or survival. Distant metastases, aspartate aminotransferase-AST and surgical resection (HR=3.39, 95% CI: 1.38-8.35, p=0.008, HR=3.73, 95% CI: 1.20-11.57, p=0.023 and HR=0.20, 95% CI: 0.08-0.51, p<0.001 respectively) were the strongest predictors in the univariate analysis. Age, perineural and/or lymphovascular invasion, distant metastases and AST were the independent prognostic factors in the final multivariable model; a risk score was calculated and classified patients into low (n=40), intermediate (n=48) and high risk (n=10) groups. The concordance index of our model was 0.93 compared to 0.72 for the WHO system. CONCLUSION: Our prognostic model was highly accurate in stratifying patients by risk; novel approaches as such could thus be incorporated into clinical decisions.

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Figures

Figure 1
Figure 1
Disease outcome by the (A) proposed multivariable model with low risk patients showing an exceptional benefit towards survival compared to intermediate (log rank p<0.001) and high risk patients (log rank p<0.001); intermediate vs high risk patients also have better outcome (log rank p = 0.002) and (B) WHO staging system does not classify patients accurately.
Figure 2
Figure 2
Disease outcome by (A) TNM staging with early stages not showing any survival benefit towards late stages (log rank p=0.679) and (B) Hochwald et al grading system with no significant survival difference (log rank p=0.561) between the patients of the two groups (low grade vs. intermediate grade).

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