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Updated: Jan 15, 2026
High-speed Video Microscopy Analysis for First-line Diagnosis of Primary Ciliary Dyskinesia
Published on: January 19, 2022
Feasibility of machine learning analysis for the identification of patients with possible primary ciliary dyskinesia
Gully Burns1, Carey Kauffman2, Michele Manion2
1Chan Zuckerberg Initiative, PO BOX 8040, Redwood City, CA, 94063, USA.
Insights
Machine learning (ML) can screen for primary ciliary dyskinesia (PCD) in children using health claims data. This approach aids early identification of rare diseases, improving patient outcomes.
Area of Science:
- Medical Informatics
- Rare Disease Research
- Pediatric Health
Background:
- Primary ciliary dyskinesia (PCD) is a rare genetic disorder often diagnosed late, impacting patient health outcomes.
- Scalable screening tools are needed for early identification of underdiagnosed conditions like PCD.
Purpose of the Study:
- To evaluate the feasibility of using machine learning (ML) to screen for PCD in pediatric patients.
- To assess ML model performance using electronic health records and claims data.
Main Methods:
- A random forest model was developed using data from the PCD Foundation Registry and a national claims database.
- Features were derived from diagnostic, procedural, and pharmaceutical codes associated with PCD in pediatric patients.
- Models were trained and validated using confirmed PCD cases, related diagnoses (Q34.8 + EM), and matched controls.
Main Results:
- The ML model showed variable but promising performance in identifying potential PCD cases.
- Expanding the dataset improved model performance, making it suitable for screening purposes.
- Application to a large pediatric cohort identified a number of potential cases consistent with estimated PCD prevalence.
Conclusions:
- Machine learning models can effectively screen for PCD in pediatric populations using readily available claims data.
- This approach demonstrates feasibility even without a specific ICD code for PCD.
- ML-based screening holds potential for earlier diagnosis and intervention in rare diseases.
Background:
Significant diagnostic delays are common in primary ciliary dyskinesia (PCD), a rare disease that is significantly underdiagnosed. Scalable screening methods could improve early identification and health outcomes.
Research Question:
Can machine learning (ML) be used to screen for PCD in pediatric patients?
Study Design And Methods:
We evaluated the feasibility of a random forest model to screen for PCD using data from the PCD Foundation Registry and a national claims database. We identified a cohort of pediatric patients (< 18 years of age) with diagnostic codes indicative of conditions potentially associated with PCD, and studied diagnostic, procedural, and pharmaceutical codes associated with PCD to develop ML features. Models were trained on composite claims data from confirmed patients with PCD, patients with Q34.8 (Specific Congenital Malformation of the Respiratory System) diagnosed within 6 months of an Electron Microscopy procedure (Q34.8 + EM), and a randomly-selected, matched control group. Model performance was tested through fivefold cross-validation.
Results:
Using 82 confirmed pediatric PCD cases and 4161 matched controls, the model demonstrated variable performance (positive predictive value 0.45-0.73, sensitivity 0.75-0.94). Synthetic data augmentation did not improve results (positive predictive value 0.45-0.67, sensitivity 0.71-1.00). Expanding the dataset to include 319 Q34.8 + EM patients and 8214 controls improved performance (positive predictive value 0.51-0.54, sensitivity 0.82-0.90), suitable for screening. In a cohort of 1.32 million pediatric patients, 7705 were classified as positive, consistent with the estimated prevalence of PCD (1:7554).
Interpretation:
This study demonstrates the feasibility of using ML to screen for PCD using claims data, even in the absence of a specific International Classification of Disease (ICD) code. While unvalidated, this work may serve as the basis for future ML efforts in rare disease detection. Such screening approaches may aid in the identification of individuals who may benefit from timely diagnostic testing and targeted interventions.
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