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Optimising insulinoma detection: Algorithm development and validation in a hospital discharge abstract database in

Jingya Zhou1,2, Qianqian Shao3, Qiang Xu3

  • 1Department of Medical Records, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, China.

Health Information Management : Journal of the Health Information Management Association of Australia
|July 23, 2025
PubMed
Summary

Developing accurate algorithms to identify insulinoma using hospital discharge data is crucial. Comprehensive case-finding strategies, including improved documentation and coding, enhance the identification of this rare tumor.

Keywords:
ICD-10classification algorithmsclinical codinghealth information managementinsulinomainternational classification of diseasesislet cell tumourmorphology codepancreatic neuroendocrine neoplasmvalidation study

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Area of Science:

  • Endocrinology and Metabolism
  • Oncology
  • Health Informatics

Background:

  • The accuracy of using diagnostic codes in hospital discharge abstract databases (DAD) for identifying insulinoma has not been validated.
  • Insulinoma, a rare tumor, presents diagnostic challenges due to complex and variable documentation.

Purpose of the Study:

  • To develop and evaluate case-finding algorithms using ICD-10 codes from hospital DAD for identifying insulinoma.
  • To investigate the reasons behind misidentification of insulinoma cases within the DAD.

Main Methods:

  • A 12-year retrospective dataset from a Chinese medical center was analyzed.
  • Four ICD-10 based algorithms were tested for identifying insulinoma patients.
  • Algorithm performance was assessed against electronic medical records using sensitivity, specificity, and predictive values.

Main Results:

  • The study included 4929 pancreatic tumor patients, with 610 confirmed insulinoma cases across 5760 hospitalizations.
  • Algorithm variant 1 (code M8151) showed 69% sensitivity and 99.7% specificity.
  • The most comprehensive algorithm (variant 4) achieved 84.8% sensitivity and 99.5% specificity, with higher accuracy in endocrinology admissions and cases lacking pathological confirmation.

Conclusions:

  • A single morphology code is insufficient for accurately identifying insulinoma in the hospital DAD due to documentation variability.
  • Enhanced and comprehensive algorithms demonstrate improved accuracy in identifying confirmed insulinoma cases.
  • Multidisciplinary approaches, including precise pathology reporting and hypoglycaemia documentation, are vital for enhancing data sensitivity in DAD for insulinoma identification.