Diverse clinical features and diagnostic delay in monogenic inborn errors of immunity: A call for access to genetic testing
Bibliographic record
Abstract
BACKGROUND: Inborn errors of immunity (IEIs) are a group of conditions affecting immune system development and function. Due to their clinical heterogeneity and lack of provider awareness, patients suffer from long diagnostic delays that increase morbidity and mortality. Next-generation sequencing facilitates earlier diagnosis and treatment of IEIs, but too often patients are unable to see the benefit of this technology due to gaps in providers' knowledge regarding which patients to test and barriers to accessing sequencing. METHODS: Here, we provide detailed clinical phenotyping and describe the impact of genetic sequencing on a cohort of 43 patients with monogenic IEIs seen in a tertiary care center from 2014 to 2019. Data were abstracted from a chart review, and a panel of clinical immunologists were consulted on the impact of genetic sequencing on their patients. RESULTS: We found that our patients had significant diagnostic delays, averaging 3.3 years; had diverse manifestations of immune system dysfunction; and had demonstrated highly complex medical needs, with on average 7.9 subspecialties involved in their care and 4.9 hospitalizations prior to definitive treatment. Our results also demonstrate the benefits of genetic testing, as it provided the majority of our patients with a diagnosis, and positively impacted their treatment, follow-up, and prognosis. CONCLUSION: This paper expands the paucity of literature on genetically confirmed IEIs in North America and supports the expansion of access to genetic testing for patients with clinical features suggesting IEI, such as those presented in our cohort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".