A pancreatic cancer risk prediction model (Prism) developed and validated on large-scale US clinical data.

BACKGROUND: Pancreatic Duct Adenocarcinoma (PDAC) screening can enable early-stage disease detection and long-term survival. Current guidelines use inherited predisposition, with about 10% of PDAC cases eligible for screening. Using Electronic Health Record (EHR) data from a multi-institutional federated network, we developed and validated a PDAC RISk Model (Prism) for the general US population to extend early PDAC detection. METHODS: Neural Network (PrismNN) and Logistic Regression (PrismLR) were developed using EHR data from 55 US Health Care Organisations (HCOs) to predict PDAC risk 6-18 months before diagnosis for patients 40 years or older. Model performance was assessed using Area Under the Curve (AUC) and calibration plots. Models were internal-externally validated by geographic location, race, and time. Simulated model deployment evaluated Standardised Incidence Ratio (SIR) and other metrics. FINDINGS: With 35,387 PDAC cases, 1,500,081 controls, and 87 features per patient, PrismNN obtained a test AUC of 0.826 (95% CI: 0.824-0.828) (PrismLR: 0.800 (95% CI: 0.798-0.802)). PrismNN's average internal-external validation AUCs were 0.740 for locations, 0.828 for races, and 0.789 (95% CI: 0.762-0.816) for time. At SIR = 5.10 (exceeding the current screening inclusion threshold) in simulated model deployment, PrismNN sensitivity was 35.9% (specificity 95.3%). INTERPRETATION: Prism models demonstrated good accuracy and generalizability across diverse populations. PrismNN could find 3.5 times more cases at comparable risk than current screening guidelines. The small number of features provided a basis for model interpretation. Integration with the federated network provided data from a large, heterogeneous patient population and a pathway to future clinical deployment. FUNDING: Prevent Cancer Foundation, TriNetX, Boeing, DARPA, NSF, and Aarno Labs.

Development and validation of a pancreatic cancer risk model for the general population using electronic health records: An observational study.

AIM: Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at a late, incurable stage. We sought to determine whether individuals at high risk of developing PDAC could be identified early using routinely collected data. METHODS: Electronic health record (EHR) databases from two independent hospitals in Boston, Massachusetts, providing inpatient, outpatient, and emergency care, from 1979 through 2017, were used with case-control matching. PDAC cases were selected using International Classification of Diseases 9/10 codes and validated with tumour registries. A data-driven feature selection approach was used to develop neural networks and L2-regularised logistic regression (LR) models on training data (594 cases, 100,787 controls) and compared with a published model based on hand-selected diagnoses ('baseline'). Model performance was validated on an external database (408 cases, 160,185 controls). Three prediction lead times (180, 270 and 365 days) were considered. RESULTS: The LR model had the best performance, with an area under the curve (AUC) of 0.71 (confidence interval [CI]: 0.67-0.76) for the training set, and AUC 0.68 (CI: 0.65-0.71) for the validation set, 365 days before diagnosis. Data-driven feature selection improved results over 'baseline' (AUC = 0.55; CI: 0.52-0.58). The LR model flags 2692 (CI 2592-2791) of 156,485 as high risk, 365 days in advance, identifying 25 (CI: 16-36) cancer patients. Risk stratification showed that the high-risk group presented a cancer rate 3 to 5 times the prevalence in our data set. CONCLUSION: A simple EHR model, based on diagnoses, can identify high-risk individuals for PDAC up to one year in advance. This inexpensive, systematic approach may serve as the first sieve for selection of individuals for PDAC screening programs.

Rapid haplotype inference for nuclear families.

Hapi is a new dynamic programming algorithm that ignores uninformative states and state transitions in order to efficiently compute minimum-recombinant and maximum likelihood haplotypes. When applied to a dataset containing 103 families, Hapi performs 3.8 and 320 times faster than state-of-the-art algorithms. Because Hapi infers both minimum-recombinant and maximum likelihood haplotypes and applies to related individuals, the haplotypes it infers are highly accurate over extended genomic distances.