Risk factors and prediction score for chron

INTRODUCTION

Acute pancreatitis (AP) and chronic pancreatitis (CP) are common diseases with a worldwide prevalence. These diseases have become an important public health issue in several countries because of the high mortality rates and a considerable burden being laid on the healthcare system. AP is an inflammatory condition of the pancreas that has been considered as a self-limiting disease,with an incidence ranging from 5 to 10 per to 70-80 per in western countries, which appears to have increased in recent years[1]. In contrast, CP involves a persistent destructive, inflammatory process that eventually leads to an irreversible damage to the endocrine and exocrine functions of the pancreas, and the subsequent development of diabetes mellitus and frequent hospitalizations have become one of the burdens of public health. CP has a poor prognosis, with the mor-tality rate being approximately two-fold higher than that in the general population. Furthermore, a worldwide epidemiological survey conducted in 1993 revealed that the standardized 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[2].

An emerging consensus is that AP and CP are a continuum of diseases, and the intermediate stage be-tween them is recurrent AP (RAP)[3]. Several studies have discussed about the natural course of pancreatitis as well as the risk factors and protective factors that contribute to the transition from AP to RAP and CP, although the majority of them have been conducted in western countries[3,4]. The major risk factors for CP include smo-king, in addition to alcohol consumption. Moreover, it has been reported that cigarette smoking accelerates the pro-gression of alcoholic CP[5,6]. Furthermore, a recent study has revealed that alcohol consumption of ＞ 13.5 g/d and cigarette use of ＞ 5.5 cigarettes/d are associated with the development of CP[7]. Because only a small pro-portion of patients with AP progress to CP, and CP has been proven to be an important risk factor of pancreatic duct adenocarcinoma (PDAC), it is critical to predict the development of CP in a patient with AP. However, till date,no prediction scores for CP have been addressed in the English literature, although there were some prognosis scores for CP and AP[8,9]. Therefore, in this population-based, large-scale cohort study, we developed and validated a scoring system for predicting CP using data from the National Health Insurance Research Database(NHIRD) in Taiwan.

MATERIALS AND METHODS

Data source

We obtained data from the Taiwan NHIRD. The NHIRD is one of the most comprehensive databases in the world and includes all claims data from the National Health Insurance program, such as demographic data, number of ambulatory cases, records of clinic visits, hospital admissions, dental services, prescriptions, and disease status. The National Health Insurance program, which was initiated by the government of Taiwan in March 1995, covers ＞ 99% of the total population or appro-ximately 23 million people. Diagnostic codes used in the NHIRD for identifying diseases are based on the Inter-national Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), which has been proven to be highly accurate and valid[10-12]. This study was exempted from full review and was approved by the Institutional Review Board of the Changhua Christian Hospital (ap-proval number: ).

Study population

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 with a previous diagnosis of AP during the look-back period were excluded. Patients who had CP before the index date, those aged ＜ 18 or ＞ 100 years, those with a follow-up duration of ＜ 1 year, and those with biliary pancreatitis or obstructive pancreatitis[13](such as pancreatic cancer and pancreas divisum) were also excluded because these patients rarely progress to , 3739 patients with nonobstructive, nonbiliary AP were identified 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).

Outcome measures and relevant variables

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).

To avoid over-estimation of CP by ICD-9-CM coding alone,we excluded all patients without abdominal computed tomography (CT) or abdominal magnetic reso-nance imaging (MRI) performed within 3 mo before the diagnosis of CP.

Patients were followed up from the index date (i.e.,the date of first AP diagnosis) to the date when they withdrew from the insurance program or to the end of 2013. Major comorbid diseases diagnosed before the index date were defined as baseline comorbidities based on claims data. These comorbidities included obesity, hypertension, hyperlipidemia, diabetes mellitus,alcoholism [alcohol use-related codes: ICD-9-CM codes 291, 303, 305.0, 357.5, 571.0, 571.1, 571.2, and 790.3(V11.3)], smoking habit (smoking-related codes: ICD-9-CM 305.1, V15.82, 491, 492, 493, and 496), and chronic kidney disease. If the patients with AP enrolled in our study have drinking-related coding or smoking-related coding during their follow-up period after the first AP episode, we considered them have drinking or smoking habit. To evaluate the effects of socioeconomic factors on disease development, monthly income, and place of residence of patients were recorded. To quantify baseline comorbidities, Charlson’s comorbidity index(CCI) score was used. The history of long-term use of medications that have been reported as possible risk factors for AP, including statins, angiotensin-converting enzyme inhibitors, prednisolone, hydrochlorothiazide, sex hormones, and metformin, was also evaluated.

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