Abstract
To develop and apply a natural language processing algorithm for characterization of patients diagnosed with chronic pancreatitis in a diverse integrated U.S. healthcare system. Retrospective cohort study including patients initially diagnosed with chronic pancreatitis (CP) within a regional integrated healthcare system between January 1, 2006 and December 31, 2015. Imaging reports from these patients were extracted from the electronic medical record system and split into training, validation and implementation datasets. A natural language processing (NLP) algorithm was first developed through the training dataset to identify specific features (atrophy, calcification, pseudocyst, cyst and main duct dilatation) from free-text radiology reports. The validation dataset was applied to validate the performance by comparing against the manual chart review. The developed algorithm was then applied to the implementation dataset. We classified patients with calcification(s) or ≥2 radiographic features as advanced CP. We compared etiology, comorbid conditions, treatment parameters as well as survival between advanced CP and others diagnosed during the study period. 6,346 patients were diagnosed with CP during the study period with 58,085 radiology studies performed. For individual features, NLP yielded sensitivity from 88.7% to 95.3%, specificity from 98.2% to 100.0%. A total of 3,672 patients met cohort inclusion criteria: 1,330 (36.2%) had evidence of advanced CP. Patients with advanced CP had increased frequency of smoking (57.8% vs. 43.0%), diabetes (47.6% vs. 35.9%) and underweight body mass index (6.6% vs. 3.6%), all p<0.001. Mortality from pancreatic cancer was higher in advanced CP (15.3/1,000 person-year vs. 2.8/1,000, p<0.001). Underweight BMI (HR 1.6, 95% CL 1.2, 2.1), smoking (HR 1.4, 95% CL 1.1, 1.7) and diabetes (HR 1.4, 95% CL 1.2, 1.6) were independent risk factors for mortality. Patients with advanced CP experienced increased disease-related complications and pancreatic cancer-related mortality. Excess all-cause mortality was driven primarily by potentially modifiable risk factors including malnutrition, smoking and diabetes.
Highlights
Chronic pancreatitis is a distinct pathologic and clinical entity along the spectrum of inflammatory conditions that involve the pancreas
Reliable real-world data relating to the natural history of chronic pancreatitis is limited based on the inability to accurately characterize patients diagnosed in the context of routine clinical practice
A systematic approach to accurately identifying these features from the free-text of existing radiology reports is a major step towards enhancing our ability to study the natural history of chronic pancreatitis (CP) on a large-scale
Summary
Chronic pancreatitis is a distinct pathologic and clinical entity along the spectrum of inflammatory conditions that involve the pancreas. A systematic approach to accurately identifying these features from the free-text of existing radiology reports is a major step towards enhancing our ability to study the natural history of chronic pancreatitis (CP) on a large-scale. By developing various methods for semantic processing and analysis of clinical texts, these methods can be applied to a variety of clinical applications [5,6,7,8,9,10,11]. To date, this technology has not been applied to characterize features specific to chronic pancreatitis
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