Detection of pelvic inflammatory disease: development of an automated case-finding algorithm using administrative...

Area of Science:

• Epidemiology
• Public Health Surveillance
• Health Informatics

Background:

• International Classification of Diseases, Ninth Revision (ICD-9) codes are standard for identifying pelvic inflammatory disease (PID) in administrative data.
• Current methods using ICD-9 codes alone may misclassify non-PID cases, impacting surveillance accuracy.
• Refining PID case identification is crucial for accurate disease monitoring and intervention.

Purpose of the Study:

• To develop and validate a case-finding algorithm to improve the accuracy of PID identification from administrative data.
• To compare the performance of the new algorithm against traditional ICD-9 code usage.
• To assess the algorithm's utility as a practical alternative to manual medical record review.

Main Methods:

• A classification and regression tree analysis was employed to develop the algorithm using data from Group Health (GH).
• Potential PID cases were identified in women aged 15-44 years at GH and Kaiser Permanente Colorado (KPCO).
• Algorithm validation was performed at KPCO, with medical record review used for verification.

Main Results:

• The positive predictive value (PPV) of ICD-9 codes alone for identifying clinical PID was 79%.
• The developed algorithm identified PID-appropriate treatment and younger age (15-25 years) as key predictors.
• The algorithm demonstrated high sensitivity (GH: 96.4%, KPCO: 90.3%) and PPV (GH: 86.9%, KPCO: 84.5%), though specificity was low (GH: 45.9%, KPCO: 37.0%).

Conclusions:

• The case-finding algorithm significantly enhances PID identification accuracy compared to using ICD-9 codes alone.
• The algorithm provides a practical and effective alternative to extensive medical record review for PID surveillance.
• Further refinement may be needed to improve the specificity of the algorithm for clinical PID case identification.