A Clinical Prediction Model to Assess Risk for Pancreatic Cancer Among Patients With Acute Pancreatitis.
Pancreas
; 53(3): e254-e259, 2024 Mar 01.
Article
em En
| MEDLINE
| ID: mdl-38266222
ABSTRACT
OBJECTIVES:
We aimed to develop and validate a prediction model as the first step in a sequential screening strategy to identify acute pancreatitis (AP) individuals at risk for pancreatic cancer (PC). MATERIALS ANDMETHODS:
We performed a population-based retrospective cohort study among individuals 40 years or older with a hospitalization for AP in the US Veterans Health Administration. For variable selection, we used least absolute shrinkage and selection operator regression with 10-fold cross-validation to identify a parsimonious logistic regression model for predicting the outcome, PC diagnosed within 2 years after AP. We evaluated model discrimination and calibration.RESULTS:
Among 51,613 eligible study patients with AP, 801 individuals were diagnosed with PC within 2 years. The final model (area under the receiver operating curve, 0.70; 95% confidence interval, 0.67-0.73) included histories of gallstones, pancreatic cyst, alcohol use, smoking, and levels of bilirubin, triglycerides, alkaline phosphatase, aspartate aminotransferase, alanine aminotransferase, and albumin. If the predicted risk threshold was set at 2% over 2 years, 20.3% of the AP population would undergo definitive screening, identifying nearly 50% of PC associated with AP.CONCLUSIONS:
We developed a prediction model using widely available clinical factors to identify high-risk patients with PC-associated AP, the first step in a sequential screening strategy.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Pancreáticas
/
Pancreatite
Tipo de estudo:
Etiology_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article