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PheValuator: Development and evaluation of a phenotype algorithm evaluator.

Joel N Swerdel1, George Hripcsak2, Patrick B Ryan3

  • 1Janssen Research & Development, 920 Route 202, Raritan, NJ 08869, USA; OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), 622 West 168th Street, PH-20, New York, NY 10032, USA.

Journal of Biomedical Informatics
|August 2, 2019
PubMed
Summary
This summary is machine-generated.

PheValuator efficiently estimates phenotype algorithm performance in healthcare databases. This tool aids in evaluating diagnostic accuracy, crucial for observational studies.

Keywords:
Diagnostic predictive modelingPhenotype algorithmsValidation

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Area of Science:

  • Health Informatics
  • Observational Health Research
  • Data Science in Healthcare

Background:

  • Phenotype algorithms (PAs) define diseases in observational healthcare databases using clinical data.
  • Complete evaluation of PAs (sensitivity, specificity, PPV) is rarely conducted.
  • A novel tool, PheValuator, is proposed for efficient PA performance estimation.

Purpose of the Study:

  • To introduce and evaluate PheValuator, a tool for estimating phenotype algorithm performance.
  • To assess the sensitivity, specificity, and positive predictive value (PPV) of various PAs.
  • To provide a method for robust validation of disease definitions in large datasets.

Main Methods:

  • Utilized four administrative claims datasets (2000-2017).
  • Developed a diagnostic predictive model for phenotypes.
  • Applied PheValuator to estimate PA performance using a 'probabilistic gold standard'.

Main Results:

  • The 1-time occurrence (1X) PA demonstrated high sensitivity but low PPV.
  • The 1-time inpatient (1X-IP-1stPos) PA showed high PPV but low sensitivity.
  • Specificity was consistently high across all examined PAs and datasets.

Conclusions:

  • PheValuator shows promise for estimating phenotype algorithm performance characteristics.
  • The tool facilitates a more complete evaluation of PAs in observational healthcare data.
  • Findings highlight trade-offs between sensitivity and PPV for different PA designs.