Understanding Primary Ciliary Dyskinesia: Experience From a Mediterranean Diagnostic Reference Centre

Primary ciliary dyskinesia (PCD) remains a challenging diagnosis due to the absence of a universally accepted gold standard test. While clinical suspicion is the initial step, diagnostic confirmation requires specialized reference centers with advanced equipment and trained personnel. This gap in streamlined diagnostics motivates the need for clearer clinical criteria to guide referrals. Prior research has shown that PCD is a rare condition with complex manifestations, often overlapping with other respiratory disorders. No prior work had resolved how to efficiently triage patients before referral. This uncertainty drove the current work to identify clinical markers that could improve diagnostic accuracy. Establishing a reliable diagnostic pathway is essential for reducing unnecessary referrals and ensuring timely care. The Mediterranean region has limited access to diagnostic centers, making efficient triage even more critical. Existing diagnostic methods remain costly and time-consuming, limiting their widespread use. This work aims to address these limitations by proposing a data-driven approach to clinical triage.

This study aimed to define clinical criteria that could reliably identify patients likely to have PCD before referral to diagnostic centers. The researchers focused on developing a predictive model using clinical variables to improve triage accuracy. By analyzing a large Mediterranean cohort, they sought to identify patterns distinguishing PCD from other respiratory conditions. The motivation stemmed from the high cost and limited availability of specialized diagnostics in the region. The study also aimed to reduce unnecessary referrals by establishing clear thresholds for PCD suspicion. Clinical variables were selected based on their relevance to PCD symptomatology. The researchers proposed that a data-driven model could enhance diagnostic efficiency. The ultimate goal was to streamline patient management and improve access to appropriate care.

The study collected 18 clinical variables from 476 Mediterranean patients suspected of having PCD. These variables included age of symptom onset, respiratory symptoms, and anatomical features. Ciliary function and ultrastructure were analyzed to confirm PCD diagnoses in 89 individuals. Logistic regression was used to assess the association between each variable and PCD. A step-wise logistic regression model was constructed to identify the most predictive variables. A classification and regression tree (CART) was developed to classify individuals as PCD or PCD-like. The model incorporated variables such as situs inversus, atelectasis, and chronic cough. The CART model aimed to provide a visual diagnostic aid for clinicians. The approach combined statistical modeling with clinical data to improve triage accuracy.

Logistic regression identified significant associations between PCD and variables like situs inversus, recurrent otitis, and chronic productive cough. The step-wise model selected seven variables with 82% sensitivity and 88% specificity. The area under the curve (AUC) for the model was 0.92, indicating strong predictive power. The CART model classified patients using six key variables, including pansinusitis and bronchiectasis. PCD was more likely in patients with situs inversus and chronic wet cough. Recurrent pneumonias and rhinorrea were also strong indicators of PCD. The model's high specificity suggests it can reduce false positives. These findings suggest the model could guide clinical triage effectively.

The authors propose that the identified clinical variables can improve diagnostic triage for PCD. The step-wise logistic regression model provides a reliable method for predicting PCD likelihood. The CART model offers a visual tool for clinicians to classify patients efficiently. The high sensitivity and specificity suggest the model's potential for clinical use. The authors suggest that these criteria could reduce unnecessary referrals to diagnostic centers. The model's performance indicates it could be a valuable addition to current diagnostic protocols. The authors propose that implementing these criteria could improve diagnostic efficiency in the Mediterranean region. The findings support the use of data-driven approaches to enhance PCD diagnostics.