CLINICAL PREDICTORS OF BLASTOCYSTOSIS TREATMENT EFFICACY

Area of Science:

• Medical diagnostics
• Parasitology
• Clinical medicine

Background:

• Improving diagnostics and etiotropic therapy for human blastocystosis is crucial.
• Clinical manifestations of blastocystosis are diverse and can influence treatment efficacy.

Purpose of the Study:

• To assess the predictive value of clinical symptoms for blastocystosis therapy outcomes.
• To develop a prognostic algorithm for predicting treatment effectiveness in blastocystosis patients.

Main Methods:

• Retrospective analysis of clinical symptoms in 300 blastocystosis patients.
• Utilized a heterogeneous sequential Wald-Henkin procedure for algorithm development.
• Validated the predictive algorithm on a training group of 300 patients.

Main Results:

• Liver enlargement, right subcostal heaviness, and tachycardia were highly informative predictors (J values > 1.76).
• Symptoms like abdominal pain, memory impairment, and skin manifestations showed no predictive value.
• The developed algorithm demonstrated high efficiency with 77% correct predictions and only 3% erroneous predictions.

Conclusions:

• A prognostic algorithm based on clinical symptoms can effectively predict blastocystosis therapy outcomes.
• Identifying key predictive symptoms allows for better patient stratification and therapeutic strategy selection.
• The algorithm achieved high accuracy (77%) and reliability (p<0.05) in predicting treatment success.