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      Modeling Prognostic Factors in Resectable Pancreatic Adenocarcinomas

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          Abstract

          Background:

          The accurate prognosis for patients with resectable pancreatic adenocarcinomas requires the incorporation of more factors than those included in AJCC TNM system.

          Methods:

          We identified 218 patients diagnosed with stage I and II pancreatic adenocarcinoma 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 prognostic score was calculated; the prognostic strength of our model was assessed with the concordance index.

          Results:

          Our cohort had a median age of 67 years and consisted of 49% men; the median follow-up time was 14.3 months and the 5-year survival 3.6%. Age, tumor differentiation and size, alkaline phosphatase, albumin and CA 19-9 were the independent factors of the final multivariable model; patients were thus classified into low (n = 14, median survival = 53.7 months), intermediate (n = 124, median survival = 19.7 months) and high risk groups (n = 80, median survival = 12.3 months). The prognostic classification of our model remained significant after adjusting for adjuvant chemotherapy and the concordance index was 0.73 compared to 0.59 of the TNM system.

          Conclusion:

          Our prognostic model was accurate in stratifying patients by risk and could be incorporated into clinical decisions.

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          Most cited references21

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          1423 pancreaticoduodenectomies for pancreatic cancer: A single-institution experience.

          Pancreaticoduodenectomy (PD) with the possible addition of neoadjuvant or adjuvant therapy is the standard of care in the United States for adenocarcinoma originating in the pancreatic head, neck, and uncinate process. We reviewed 1423 patients who underwent a PD for a malignancy originating in the pancreas at our institution between 1970 and 2006. We examined 1175 PDs for ductal adenocarcinomas in greater detail. Eighteen different histological types of pancreatic cancer were identified; the most common diagnoses included ductal adenocarcinoma, neuroendocrine carcinoma, and IPMN with invasive cancer. Patients with ductal adenocarcinoma were analyzed in detail. The median age was 66 years, with patients in the present decade significantly older (68 years), on average, than patients in the three prior decades (e.g., 60 years in 1970, P = 0.02). The median tumor diameter was 3 cm; 42% of the resections had positive margins and 78% had positive lymph nodes. The perioperative morbidity was 38%. The median postoperative stay declined over time, from 16 days in the 1980s to 8 days in the 2000s (P < 0.001). The perioperative mortality declined from 30% in the 1970s to 1% in the 2000s (P < 0.001). The median survival for all patients with ductal adenocarcinoma was 18 months (1-year survival = 65 %, 2-year survival = 37%, 5-year survival = 18%). In a Cox proportional hazards model, pathological factors having a significant impact on survival included tumor diameter, resection margin status, lymph node status, and histologic grade. This is the largest single-institution experience with PD for pancreatic cancer. Patients who have cancers with favorable pathological features have a statistically significant improved long-term survival.
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            Perioperative CA19-9 levels can predict stage and survival in patients with resectable pancreatic adenocarcinoma.

            Different prognostic factors stratify patients with pancreatic adenocarcinoma. The purpose of this study was to determine whether preoperative CA19-9 levels can predict stage of disease or survival and whether a change in preoperative to postoperative CA19-9 or the postoperative CA19-9 predicts overall survival. Four hundred twenty-four consecutive patients with pancreatic adenocarcinoma underwent resection between January 1, 1985 and January 1, 2004. Of the patients with a bilirubin less than 2 mg/dL, 176 had preoperative CA19-9 values, and 111 had pre- and postoperative CA19-9 values. Survival was measured from the first postoperative CA19-9 level measured (median, 39 days) until death or last follow-up. A multivariate failure time model was fit using clinical, operative, pathologic, and adjuvant treatment characteristics, and a categorization was defined by the values and changes in CA19-9 before and after surgery. Of the 176 patients, 128 (73%) had T3 lesions, and 99 (56%) had N1 disease; 138 patients (78%) underwent pancreaticoduodenectomy. Median preoperative CA19-9 levels were lower in N0 patients compared with patients with positive nodes (nine v 164 U/mL, respectively; nonparametric P = .06) and in T1/T2 patients versus T3 patients (41 v 162 U/mL, respectively; P = .03). Median follow-up time (n = 111) was 1.8 years (range, 1 to 12.9 years), with overall actuarial 1-, 3-, and 5-year survival rates of 70%, 36%, and 30%, respectively. Significant predictors of survival on multivariate analysis included a decrease in CA19-9 (P = .0005), negative lymph nodes (P = .001), lower T stage (P = .0008), and postoperative CA19-9 less than 200 U/mL (P = .0007). In patients with pancreatic adenocarcinoma, preoperative CA19-9 correlates with stage of disease. Both a postoperative decrease in CA19-9 and a postoperative CA19-9 value of less than 200 U/mL are strong independent predictors of survival, even after adjusting for stage. CA19-9 levels should be included in a patient's perioperative care and should be considered for prognostic nomograms.
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              Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: a clinical example.

              To illustrate the effects of different methods for handling missing data--complete case analysis, missing-indicator method, single imputation of unconditional and conditional mean, and multiple imputation (MI)--in the context of multivariable diagnostic research aiming to identify potential predictors (test results) that independently contribute to the prediction of disease presence or absence. We used data from 398 subjects from a prospective study on the diagnosis of pulmonary embolism. Various diagnostic predictors or tests had (varying percentages of) missing values. Per method of handling these missing values, we fitted a diagnostic prediction model using multivariable logistic regression analysis. The receiver operating characteristic curve area for all diagnostic models was above 0.75. The predictors in the final models based on the complete case analysis, and after using the missing-indicator method, were very different compared to the other models. The models based on MI did not differ much from the models derived after using single conditional and unconditional mean imputation. In multivariable diagnostic research complete case analysis and the use of the missing-indicator method should be avoided, even when data are missing completely at random. MI methods are known to be superior to single imputation methods. For our example study, the single imputation methods performed equally well, but this was most likely because of the low overall number of missing values.
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                Author and article information

                Journal
                Cancer Inform
                101258149
                Cancer Informatics
                Libertas Academica
                1176-9351
                20 January 2010
                2009
                : 7
                : 281-291
                Affiliations
                [1 ]Department of Biomedical Informatics, Columbia University, 10032 New York, USA
                [2 ]Department of Computer Science, University of Tromsø, 9037 Tromsø, Norway
                [3 ]Department of Pathology, Yale University School of Medicine, 06511 New Haven, USA.
                Author notes
                Article
                cin-2009-281
                10.4137/cin.s3835
                2865167
                20508721
                c4ff1b84-82d5-48e0-b426-69fbc7933f3c
                © 2009 The authors.

                This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

                History
                Categories
                Original Research

                Oncology & Radiotherapy
                pancreatic adenocarcinomas,survival,prognosis,multivariable model
                Oncology & Radiotherapy
                pancreatic adenocarcinomas, survival, prognosis, multivariable model

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