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Cited by 31Pancreatic and Hepatic Oncology ResearchGlobal Cancer Incidence and ScreeningPancreatitis Pathology and Treatment

Do changes in health reveal the possibility of undiagnosed pancreatic cancer? Development of a risk-prediction model based on healthcare claims data

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Baecker, Aileen, Kim, Sungjin, Risch, Harvey A., Nuckols, Teryl K., Wu, Bechien U., Hendifar, Andrew E., Pandol, Stephen J., Pisegna, Joseph R. +1 more·2019·PLOS ONE
DOI 10.1371/journal.pone.0218580PMC PMC6592596
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Abstract

BACKGROUND AND OBJECTIVE: Early detection methods for pancreatic cancer are lacking. We aimed to develop a prediction model for pancreatic cancer based on changes in health captured by healthcare claims data. METHODS: We conducted a case-control study on 29,646 Medicare-enrolled patients aged 68 years and above with pancreatic ductal adenocarcinoma (PDAC) reported to the Surveillance Epidemiology an End Results (SEER) tumor registries program in 2004-2011 and 88,938 age and sex-matched controls. We developed a prediction model using multivariable logistic regression on Medicare claims for 16 risk factors and pre-diagnostic symptoms of PDAC present within 15 months prior to PDAC diagnosis. Claims within 3 months of PDAC diagnosis were excluded in sensitivity analyses. We evaluated the discriminatory power of the model with the area under the receiver operating curve (AUC) and performed cross-validation by bootstrapping. RESULTS: The prediction model on all cases and controls reached AUC of 0.68. Excluding the final 3 months of claims lowered the AUC to 0.58. Among new-onset diabetes patients, the prediction model reached AUC of 0.73, which decreased to 0.63 when claims from the final 3 months were excluded. Performance measures of the prediction models was confirmed by internal validation using the bootstrap method. CONCLUSION: Models based on healthcare claims for clinical risk factors, symptoms and signs of pancreatic cancer are limited in classifying those who go on to diagnosis of pancreatic cancer and those who do not, especially when excluding claims that immediately precede the diagnosis of PDAC.

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  • Several studies show that the probability PDAC development can be further increased by including additional factors such as weight change, ethnicity, and lifestyle factors that are commonly contained in electronic medical records.

    “Of note, several studies show that the probability PDAC development can be further increased by including additional factors such as weight change, ethnicity, and lifestyle factors that are commonly contained in electronic medical records.171–178”

    From Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review (2021) · cites this paper as [176]

    The Methods and Results include weight loss and race in analyses of PDAC risk. The Discussion says lifestyle data were unavailable in this claims-based study and that their availability would have improved its models; it does not demonstrate that adding those factors increased predictive performance or establish that they are commonly recorded in electronic medical records. Our copy provides no page numbers for these passages. (LLM, conf=0.84)

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  • A Medicare claims model for new-onset diabetes, applied to people aged 68 years or older, identified a 3.5% predicted risk of pancreatic ductal adenocarcinoma over 1 year (AUC 0.73).

    “This model has been applied to an older population (≥68 years) with new-onset diabetes, and identified people at 3·5% predicted risk of pancreatic ductal adenocarcinoma over 1 year (AUC 0·73).106”

    From Early detection of pancreatic cancer (2020) · cites this paper as [106]

  • Further insurance-claim models have been developed and have poor diagnostic performance (AUC 0.73).

    “Further models based on insurance claims have been developed, with poor diagnostic performance (AUC 0·73).106,107”

    From Early detection of pancreatic cancer (2020) · cites this paper as [106]

  • Parametric models have been developed to identify high risk of pancreatic ductal adenocarcinoma among people with new-onset diabetes or pre-diabetes, with varying predictive performance.

    “Parametric models that can identify high risk for pancreatic ductal adenocarcinoma among individuals with new-onset diabetes, or new-onset pre-diabetes, have been developed, with varying predictive performances.103–106”

    From Early detection of pancreatic cancer (2020) · cites this paper as [106]

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Research works printed but not linked: 3

  1. [24]Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010; 21(1): 128– 38. 10.1097/EDE.0b013e3181c30fb2 20010215 PMC3575184 · DOI · Paper page
  2. [25]Harrell FE. Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. New York: Springer; 2001 xxii, 568 p. p.
  3. [27]Steyerberg EW, Harrell FE Jr., Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001; 54(8): 774– 81.. 11470385

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Authors
Baecker, A., Kim, S., Risch, H. A., Nuckols, T. K., Wu, B. U., Hendifar, A. E., Pandol, S. J., Pisegna, J. R., & Jeon, C. Y.
Affiliations
  • University of California, Los Angeles (US)
  • Cedars-Sinai Medical Center (US)
  • Yale University (US)
  • Kaiser Permanente (US)
  • VA Greater Los Angeles Healthcare System (US)
Corresponding author
Jeon, Christie Y.
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2019-06-25
Volume
14
Issue
6
Pages
e0218580
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0218580
PMC
PMC6592596
OpenAlex
W2954988597
Cited by
31
References
32
Citations by year
2026: 2 · 2025: 4 · 2024: 4 · 2023: 7 · 2022: 5 · 2021: 4 · 2020: 5
FWCI
1.46
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eScholarship (California Digital Library) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • Figshare (submittedVersion, cc-by)
Funders
  • National Cancer Institute (US) (R21CA220073)
  • National Institute of Diabetes and Digestive and Kidney Diseases (U01DK108314)
Topics
  • Health Sciences › Medicine › Oncology › Pancreatic and Hepatic Oncology Research
  • Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
  • Health Sciences › Medicine › Surgery › Pancreatitis Pathology and Treatment
Keywords
pancreatic ductal adenocarcinoma, risk prediction model, health insurance claims data, Medicare claims data, pre-diagnostic symptoms, new-onset diabetes, older adults
MeSH terms
Administrative Claims, Healthcare, Undiagnosed Diseases, Adenocarcinoma, Aged, Aged, 80 and over, Female, Health Status, Humans, Male, Pancreatic Neoplasms, Models, Statistical

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