Prediction Model for Detection of Sporadic Pancreatic Cancer (PRO-TECT) in a Population-Based Cohort Using Machine Learning and Further Validation in a Prospective Study

OBJECTIVESThere is currently no widely accepted approach to screening for pancreatic cancer (PC). We aimed to develop and validate a risk prediction model for PC across two health systems using electronic health records (EHR). METHODSThis retrospective cohort study consisted of patients 50-84 years of age meeting utilization criteria in 2008-2017 at Kaiser Permanente Southern California (KPSC, model training, internal validation) and the Veterans Affairs (VA, external validation). Random survival forests models were built to identify the most relevant predictors from >500 variables and to predict PC within 18 months of cohort entry. A prospective study was then conducted in KPSC to assess feasibility of the model for real-time implementation. RESULTSThe KPSC cohort consisted of 1.8 million patients (mean age 61.6) with 1,792 PC cases. The estimated 18-month incidence rate of PC was 0.77 (95% CI 0.73-0.80)/1,000 person-years. The three models containing age, abdominal pain, weight change and two laboratory biomarkers (ALT change/HgA1c, rate of ALT change/HgA1c, or rate of ALT change/rate of HgA1c change) had comparable discrimination and calibration measures (c-index: mean=0.77, SD=0.01-0.02; calibration test: p-value 0.2-0.4, SD 0.2-0.3). The VA validation cohort consisted of 2.6 million patients (mean age 66.1) with an 18-month incidence rate of 1.27 (1.23-1.30). A total of 606 patients were screened in the prospective pilot study at KPSC with 9 patients (1.5%) diagnosed with a pancreatic or biliary cancer. CONCLUSIONSUsing widely available parameters in EHR, we developed a population-based parsimonious model for early detection of sporadic PC suitable for real-time application. Study HighlightsO_ST_ABSWhat Is KnownC_ST_ABSO_LIPatients with pancreatic cancer are often diagnosed at late stages. C_LIO_LIEarly detection is needed to impact the natural history of disease progression and improve patient survival. C_LI What Is New HereO_LIMachine-learning was used to develop a population-based model for early detection of pancreatic cancer. The model was internally and externally validated in cohorts of 1.8 million and 2.6 million individuals, respectively. C_LIO_LICalibration was excellent in prospective pilot testing for detection of pancreatic malignancy. C_LI