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Area of Science:

  • Pulmonary Medicine
  • Genetics
  • Data Science

Background:

  • Despite guidelines, alpha-1 antitrypsin deficiency (AATD) testing is underutilized in COPD patients.
  • Approximately 90% of individuals with AATD in the U.S. remain undiagnosed, highlighting a critical gap in care.

Purpose of the Study:

  • To develop and validate a predictive model for identifying AATD-positive individuals within the general COPD population.
  • To leverage real-world data for improved early detection of AATD.

Main Methods:

  • An extreme gradient boosting model was developed using a large database (EVERSANA) containing medical claims, prescription data, AATD testing, and EHRs.
  • Over 500 variables were utilized, and more than 20 models were optimized to predict AATD status.
  • Patients were classified as AATD-positive based on diagnostic codes, EHR data, laboratory tests, or AATD-related medication use.

Main Results:

  • The model was trained and validated on a cohort of 13,585 AATD-positive and 7796 AATD-negative patients.
  • Non-AATD laboratory results (e.g., respiratory comorbidities, blood chemistry) were crucial for model performance.
  • The final predictive model demonstrated high accuracy, achieving an area under the receiver operating characteristic curve of 0.9.

Conclusions:

  • Predictive modeling with real-world data offers a robust method for assessing AATD risk in COPD patients.
  • This approach can effectively identify individuals who warrant confirmatory genetic testing for AATD.
  • External validation is recommended to confirm the generalizability of these findings.