An Algorithm Recommendation to Detect Specific Pathology of the Lacrimal Sac

Area of Science:

• Ophthalmology
• Pathology
• Medical Diagnostics

Background:

• Lacrimal sac (LS) pathologies require accurate diagnosis.
• Current diagnostic pathways may involve unnecessary procedures for conditions like chronic dacryocystitis.

Purpose of the Study:

• To develop and recommend an efficient algorithm for detecting specific lacrimal sac pathologies.
• To differentiate specific LS pathologies from chronic dacryocystitis, minimizing unnecessary investigations.

Main Methods:

• Retrospective review of 296 patients who underwent LS biopsy.
• Data collection included patient demographics, history, examination, radiological findings, and pathology results.
• Regression analysis was used to predict specific pathologies, followed by causality evaluation to create a risk-scoring algorithm.

Main Results:

• A specific LS pathology was identified in 3.7% of patients.
• The proposed algorithm could reduce the need for radiological examination by 36.4% and biopsy by 29.6% in suspected cases.
• It significantly decreases unnecessary frozen biopsies, recommending them in only 12.5% of cases without specific pathology, while maintaining detection rates.

Conclusions:

• The developed algorithm adequately detects specific lacrimal sac pathologies.
• Implementation can decrease unnecessary diagnostic examinations and procedures, particularly in cases of chronic dacryocystitis.
• This approach optimizes diagnostic efficiency and resource utilization in lacrimal sac evaluations.