Development and Validation of a Chronic Pancreatitis Prognosis Score in 2 Independent Cohorts.

ABSTRACT

BACKGROUND & AIMS: The clinical course of chronic pancreatitis is unpredictable. There is no model to assess disease severity or progression or predict patient outcomes. METHODS: We performed a prospective study of 91 patients with chronic pancreatitis; data were collected from patients seen at academic centers in Europe from January 2011 through April 2014. We analyzed correlations between clinical, laboratory, and imaging data with number of hospital readmissions and in-hospital days over the next 12 months; the parameters with the highest degree of correlation were used to develop a 3-stage chronic pancreatitis prognosis score (COPPS). The predictive strength was validated in 129 independent subjects identified from 2 prospective databases. RESULTS: The mean number of hospital admissions was 1.9 (95% confidence interval [CI], 1.39-2.44) and 15.2 for hospital days (95% CI, 10.76-19.71) for the development cohort and 10.9 for the validation cohort (95% CI, 7.54-14.30) (P = .08). Based on bivariate correlations, pain (numeric rating scale), level of glycated hemoglobin A1c, level of C-reactive protein, body mass index, and platelet count were used to develop the COPPS system. The patients' median COPPS was 8.9 points (range, 5-14). The system accurately discriminated stages of disease severity (low to high) A (5-6 points), B (7-9), and C (10-15). In Pearson correlation analysis of the development cohort, the COPPS correlated with hospital admissions (0.39; P < .01) and number of hospital days (0.33; P < .01). The correlation was validated in the validation set (Pearson correlation values of 0.36 and 0.44; P < .01). COPPS did not correlate with results from the Cambridge classification system. CONCLUSIONS: We developed and validated an easy to use dynamic multivariate scoring system, similar to the Child-Pugh-Score for liver cirrhosis. The COPPS allows objective monitoring of patients with chronic pancreatitis, determining risk for readmission to hospital and potential length of hospital stay.