Published online Nov 28, 2018. doi: 10.3748/wjg.v24.i44.5034
Peer-review started: September 10, 2018
First decision: October 24, 2018
Revised: October 29, 2018
Accepted: November 9, 2018
Article in press: November 9, 2018
Published online: November 28, 2018
Chronic pancreatitis (CP) involves a persistent destructive, inflammatory process that eventually leads to an irreversible damage to the endocrine and exocrine functions of the pancreas. CP has complications and poor prognosis, with the mortality rate being approximately two-fold higher than that in the general population.Incidence rate of pancreatic cancer is as high as 26-fold in patients with CP, suggesting that the risk of pancreatic cancer is significantly higher in subjects with CP. Therefore, It is critical to predict CP in patients with acute pancreatitis (AP).
The treatment option of CP is limited. We considered that the prediction of CP in high risk population and the early intervention in high risk category may decrease the disease burden and avoid the subsequent malignant change.
AP, recurrent AP (RAP), and CP are a continuum of diseases. However, only a small proportion of patients with AP progress to CP. It is critical to predict CP in patients with AP since CP is an important risk factor of pancreatic duct adenocarcinoma (PDAC).
A total of 5971 patients with one or more episodes of AP (ICD-9-CM code 577.0) recorded in the inpatient claims data from 2000 to 2013 were identified from the database. A 4-year look-back period was applied from 1996 to 1999 to ensure that all cases in our cohort were newly diagnosed and to reduce false incident cases. Eventually, 3739 patients with nonobstructive, nonbiliary AP were included for subsequent analysis. Next, we developed a model to predict the progress to CP in randomly selected two-thirds of this cohort (derivation cohort) and validated the model in the remaining one-third of this cohort (validation cohort). Outcomes and comorbidities were identified based on ICD-9-CM codes. CP was defined using ICD-9-CM codes (ICD-9-CM code 577.1), and abdominal computed tomography (CT) or abdominal magnetic resonance imaging (MRI) performed within 3 mo before the diagnosis of CP was also considered as essential to diagnose clinical CP. The risk of CP in patients with nonobstructive, nonbiliary AP was estimated using the Cox proportional hazards model. The significant β coefficients from the Cox model with backward selection procedure were used to construct an integer-based risk score for stratifying the risk of progress to CP. The referent for each variable was assigned a value of 0, and the coefficients for the other variables were calculated by dividing by the smallest coefficient in the model and then rounding to the nearest integer. Individual scores were assigned by summing the individual risk factor scores, and the cumulative incidence rate of each risk score was calculated. For easy application in clinical practice, the total risk scores were classified into low-risk category, moderate-risk category, and high-risk category based on similar magnitudes of hazard.
The multivariate analysis revealed that the presence of RAP, alcoholism, smoking habit, and age of onset of < 55 years were the four important risk factors for CP. We developed a scoring system from the derivation cohort by classifying the patients into low-risk, moderate-risk, and high-risk categories based on similar magnitudes of hazard and validated the performance using another validation cohort. Using this score, we could predict the development of CP in high risk population and arrange further intervention in high risk category. However, For the lack of reliable biomarkers for predicting CP at present, we didn’t include biomarkers in our variables. In the future, if more sensitive biomarkers for CP could be identified, the biomarkers should be added into the prediction model to improve the predictive value.
The presence of RAP, along with alcohol consumption, age of onset, and smoking habit have a high prediction value of CP. We developed a novel prediction score model for CP with excellent discrimination and also successfully validated this model in our study. Using this scoring system, a clinician can predict the outcome of a patient with AP and arrange further examination such as endoscopic ultrasound for the high-risk category(incidence rate of about 31 per 1000 person-years in high risk group), in order to early diagnosis of CP and the subsequent pancreatic cancer. Furthemore, healthcare providers can use this scoring system for assessing patient education in terms of alcohol and smoking abstinence,because the treatment option of CP is extremely limited.
We confirmed that RAP, alcohol drinking, age of onset and smoking habit are important risk factors for CP in Chinese population. Using this score,we could predict the development of CP in high risk population and arrange further intervention in high risk category.For the lack of reliable biomarkers for predicting CP at present, we didn’t include biomarkers in our variables analysis. In the future, if more sensitive biomarkers for CP could be identified, the biomarkers should be added into the prediction model to improve the predictive value for CP in patients with AP episode.