Dataset Information

A Clinical Prediction Model to Assess Risk for Pancreatic Cancer Among Patients With New-Onset Diabetes.

ABSTRACT: BACKGROUND & AIMS:Approximately 50% of all patients with pancreatic ductal adenocarcinoma (PDA) develop diabetes mellitus before their cancer diagnosis. Screening individuals with new-onset diabetes might allow earlier diagnosis of PDA. We sought to develop and validate a PDA risk prediction model to identify high-risk individuals among those with new-onset diabetes. METHODS:We conducted a retrospective cohort study in a population representative database from the United Kingdom. Individuals with incident diabetes after the age of 35 years and 3 or more years of follow-up after diagnosis of diabetes were eligible for inclusion. Candidate predictors consisted of epidemiologic and clinical characteristics available at the time of diabetes diagnosis. Variables with P values <.25 in the univariable analyses were evaluated using backward stepwise approach. Model discrimination was assessed using receiver operating characteristic curve analysis. Calibration was evaluated using the Hosmer-Lemeshow test. Results were internally validated using a bootstrapping procedure. RESULTS:We analyzed data from 109,385 patients with new-onset diabetes. Among them, 390 (0.4%) were diagnosed with PDA within 3 years. The final model (area under the curve, 0.82; 95% confidence interval, 0.75-0.89) included age, body mass index, change in body mass index, smoking, use of proton pump inhibitors, and anti-diabetic medications, as well as levels of hemoglobin A1C, cholesterol, hemoglobin, creatinine, and alkaline phosphatase. Bootstrapping validation showed negligible optimism. If the predicted risk threshold for definitive PDA screening was set at 1% over 3 years, only 6.19% of the new-onset diabetes population would undergo definitive screening, which would identify patients with PDA with 44.7% sensitivity, 94.0% specificity, and a positive predictive value of 2.6%. CONCLUSIONS:We developed a risk model based on widely available clinical parameters to help identify patients with new-onset diabetes who might benefit from PDA screening.

SUBMITTER: Boursi B

PROVIDER: S-EPMC5337138 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Clinical Prediction Model to Assess Risk for Pancreatic Cancer Among Patients With New-Onset Diabetes.

Boursi Ben B Finkelman Brian B Giantonio Bruce J BJ Haynes Kevin K Rustgi Anil K AK Rhim Andrew D AD Mamtani Ronac R Yang Yu-Xiao YX

Gastroenterology 20161205 4

<h4>Background & aims</h4>Approximately 50% of all patients with pancreatic ductal adenocarcinoma (PDA) develop diabetes mellitus before their cancer diagnosis. Screening individuals with new-onset diabetes might allow earlier diagnosis of PDA. We sought to develop and validate a PDA risk prediction model to identify high-risk individuals among those with new-onset diabetes.<h4>Methods</h4>We conducted a retrospective cohort study in a population representative database from the United Kingdom. ...[more]

PMID: 27923728

Similar Datasets

Project description:BACKGROUND & AIMS:Of patients with new-onset diabetes (NOD; based on glycemic status) older than 50 years, approximately 1% are diagnosed with pancreatic cancer (PC) within 3 years. We aimed to develop and validate a model to determine risk of PC in patients with NOD. METHODS:We retrospectively collected data from 4 independent and nonoverlapping cohorts of patients (N = 1,561) with NOD (based on glycemic status; data collected at date of diagnosis and 12 months previously) in the Rochester Epidemiology Project from January 1, 2000 through December 31, 2015 to create our model. The model weighed scores for 3 factors identified in the discovery cohort to be most strongly associated with PC (64 patients with PC and 192 with type 2 diabetes): change in weight, change in blood glucose, and age at onset of diabetes. We called our model Enriching New-Onset Diabetes for Pancreatic Cancer (ENDPAC). We validated the locked-down model and cutoff score in an independent population-based cohort of 1,096 patients with diabetes; of these, 9 patients (82%) had PC within 3 years of meeting the criteria for NOD. RESULTS:In the discovery cohort, the END-PAC model identified patients who developed PC within 3 years of diabetes onset (area under receiver operating characteristic curve 0.87); a score of at least 3 identified patients who developed PC with 80% sensitivity and specificity. In the validation cohort, a score of at least 3 identified 7 of 9 patients with PC (78%) with 85% specificity; the prevalence of PC in patients with a score of at least 3 (3.6%) was 4.4-fold greater than in patients with NOD. A high END-PAC score in patients who did not have PC (false positives) was often due to such factors as recent steroid use or different malignancy. An ENDPAC score no higher than 0 (in 49% of patients) meant that patients had an extremely low risk for PC. An END-PAC score of at least 3 identified 75% of patients in the discovery cohort more than 6 months before a diagnosis of PC. CONCLUSIONS:Based on change in weight, change in blood glucose, and age at onset of diabetes, we developed and validated a model to determine risk of PC in patients with NOD based on glycemic status (END-PAC model). An independent prospective study is needed to further validate this model, which could contribute to early detection of PC.

Project description:Chemotherapy-related hospitalizations in patients with advanced cancer are common, distressing, and costly. Methods to identify patients at high risk of chemotherapy toxic effects will permit development of targeted strategies to prevent chemotherapy-related hospitalizations.To demonstrate the feasibility of using readily available clinical data to assess patient-specific risk of chemotherapy-related hospitalization.Nested case-control study conducted from January 2003 through December 2011 at the Mass General/North Shore Cancer Center, a community-based cancer center in northeastern Massachusetts. The parent cohort included 1579 consecutive patients with advanced solid-tumor cancer receiving palliative-intent chemotherapy. Case patients (n?=?146) included all patients from the parent cohort who experienced a chemotherapy-related hospitalization. Controls (n?=?292) were randomly selected from 1433 patients who did not experience a chemotherapy-related hospitalization.Putative risk factors for chemotherapy-related hospitalization-including patient characteristics, treatment characteristics, and pretreatment laboratory values-were abstracted from medical records. Multivariable logistic regression was used to model the patient-specific risk of chemotherapy-related hospitalization.Chemotherapy-related hospitalization, as adjudicated by the oncology clinical care team within a systematic quality-assessment program.A total of 146 (9.2%) of 1579 patients from the parent cohort experienced a chemotherapy-related hospitalization. In multivariate regression, 7 variables were significantly associated with chemotherapy-related hospitalization: age, Charlson comorbidity score, creatinine clearance, calcium level, below-normal white blood cell and/or platelet count, polychemotherapy (vs monotherapy), and receipt of camptothecin chemotherapy. The median predicted risk of chemotherapy-related hospitalization was 6.0% (interquartile range [IQR], 3.6%-11.4%) in control patients and 14.7% (IQR, 6.8%-22.5%) in case patients. The bootstrap-adjusted C statistic was 0.71 (95% CI, 0.66-0.75). At a risk threshold of 15%, the model exhibited a sensitivity of 49% (95% CI, 41%-57%) and a specificity of 85% (95% CI, 81%-89%) for predicting chemotherapy-related hospitalization.In patients initiating palliative chemotherapy for cancer, readily available clinical data were associated with the patient-specific risk of chemotherapy-related hospitalization. External validation and evaluation in the context of a clinical decision support tool are warranted.

Dataset Information

A Clinical Prediction Model to Assess Risk for Pancreatic Cancer Among Patients With New-Onset Diabetes.

Publications

A Clinical Prediction Model to Assess Risk for Pancreatic Cancer Among Patients With New-Onset Diabetes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets