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 Article Swipe
Wansu Chen
,
Yichen Zhou
,
Fagen Xie
,
Rebecca K. Butler
,
Christie Y. Jeon
,
Tiffany Luong
,
Yu‐Chen Lin
,
Eva Lustigova
,
Joseph R. Pisegna
,
Sungjin Kim
,
Bechien U. Wu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.02.14.22270946
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.02.14.22270946
OBJECTIVES There 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). METHODS This 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. RESULTS The 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. CONCLUSIONS Using widely available parameters in EHR, we developed a population-based parsimonious model for early detection of sporadic PC suitable for real-time application. Study Highlights What Is Known Patients with pancreatic cancer are often diagnosed at late stages. Early detection is needed to impact the natural history of disease progression and improve patient survival. What Is New Here Machine-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. Calibration was excellent in prospective pilot testing for detection of pancreatic malignancy.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2022.02.14.22270946
- https://www.medrxiv.org/content/medrxiv/early/2022/02/15/2022.02.14.22270946.full.pdf
- OA Status
- green
- Cited By
- 2
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4213155697
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4213155697Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2022.02.14.22270946Digital Object Identifier
- Title
-
Prediction Model for Detection of Sporadic Pancreatic Cancer (PRO-TECT) in a Population-Based Cohort Using Machine Learning and Further Validation in a Prospective StudyWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-15Full publication date if available
- Authors
-
Wansu Chen, Yichen Zhou, Fagen Xie, Rebecca K. Butler, Christie Y. Jeon, Tiffany Luong, Yu‐Chen Lin, Eva Lustigova, Joseph R. Pisegna, Sungjin Kim, Bechien U. WuList of authors in order
- Landing page
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https://doi.org/10.1101/2022.02.14.22270946Publisher landing page
- PDF URL
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https://www.medrxiv.org/content/medrxiv/early/2022/02/15/2022.02.14.22270946.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.medrxiv.org/content/medrxiv/early/2022/02/15/2022.02.14.22270946.full.pdfDirect OA link when available
- Concepts
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Medicine, Cohort, Prospective cohort study, Incidence (geometry), Population, Electronic health record, Internal medicine, Cohort study, Health care, Environmental health, Optics, Economic growth, Physics, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 1, 2023: 1Per-year citation counts (last 5 years)
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22Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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