Abstract Background Primary ciliary dyskinesia (PCD) is a rare genetic disorder that affects cilia. The clinical manifestations of PCD are heterogeneous and non-specific, making it challenging to identify patients with PCD. Moreover, there are no gold standard diagnostic tests for PCD. Consequently, many patients with PCD remain undiagnosed, leading to worse patient morbidity, and increased burden on the healthcare system. A data-driven PCD case ascertainment algorithm (PCD-CAA) is a cost-effective and efficient approach for identifying potential PCD patients that need diagnostic testing, increase the diagnostic yield and improve our understanding of PCD disease frequency. Objective To develop a population-based PCD-CAA and assess its performance (i.e., sensitivity, specificity, Youden’s index). Methods We performed a retrospective case-control study using population-based health administrative data housed at ICES in Ontario, Canada from 1996 to 2020. Inclusion criteria: 1) Diagnosis of PCD, cystic fibrosis (CF) or asthma, 2) age 18 years old during the study period. Pediatric PCD patients (cases) were identified from the SickKids PCD Registry and linked with the ICES health administrative databases. Pediatric patients with CF or asthma (controls) were abstracted from the health administrative databases. Numerous PCD-CAA were developed using different combinations of clinical manifestations (i.e., bronchiectasis, chronic bronchitis, pneumonia, nasal polyps, rhinosinusitis, otic suppurative disorders, neonatal respiratory distress, and laterality defects) and their corresponding health services codes. The performance of each PCD-CAA was assessed using the CombiROC R package. Results For this study, we identified 126 cases and 1,262,194 controls (CF: 1307, Asthma: 1,260,887). We assessed 1024 PCD-CAA. The most sensitive PCD-CAA was having at least 2 of 5 clinical manifestations; bronchiectasis, chronic bronchitis, neonatal respiratory disorders, rhinosinusitis, laterality defects (sensitivity: 95%, specificity: 97%, Youden’s index: 0.92). Discussions To our knowledge, this is the first study to develop a PCD-CAA using population-based health administrative data. The final PCD-CAA demonstrated high sensitivity and specificity, making it an effective tool for population screening and health policy planning. External validation studies are needed to confirm its robustness and generalizability when applied to other institutional PCD cohorts and provincial health administrative databases. Following validation, implementation of the PCD-CAA into clinical and administrative workflows will be the next step. This abstract is funded by: Royal College
Wee et al. (Fri,) studied this question.
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