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Accuracy of Nasal Nitric Oxide Measurement as a Diagnostic Test for Primary Ciliary Dyskinesia. A Systematic Review and Meta-analysis

2017· review· en· W2613152717 on OpenAlexaff
Adam J. Shapiro, Maureen B. Josephson, Margaret Rosenfeld, Özge Yılmaz, Stephanie D. Davis, Deepika Polineni, Elena Guadagno, Margaret W. Leigh, Valéry Lavergne

Bibliographic record

VenueAnnals of the American Thoracic Society · 2017
Typereview
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversité de MontréalHôpital du Sacré-Cœur de MontréalMcGill University Health CentreMontreal Children's Hospital
FundersNational Heart, Lung, and Blood Institute
KeywordsPrimary ciliary dyskinesiaMedicineMeta-analysisGold standard (test)Receiver operating characteristicGrading (engineering)Diagnostic accuracyPathologyGenetic testingInternal medicineBronchiectasisLung

Abstract

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RATIONALE: Primary ciliary dyskinesia (PCD) is a rare disorder causing chronic otosinopulmonary disease, generally diagnosed through evaluation of respiratory ciliary ultrastructure and/or genetic testing. Nasal nitric oxide (nNO) measurement is used as a PCD screening test because patients with PCD have low nNO levels, but its value as a diagnostic test remains unknown. OBJECTIVES: To perform a systematic review to assess the utility of nNO measurement (index test) as a diagnostic tool compared with the reference standard of electron microscopy (EM) evaluation of ciliary defects and/or detection of biallelic mutations in PCD genes. DATA SOURCES: Ten databases were searched for reference sources from database inception through July 29, 2016. DATA EXTRACTION: Study inclusion was limited to publications with rigorous nNO index testing, reference standard diagnostic testing with EM and/or genetics, and calculable diagnostic accuracy information for cooperative patients (generally >5 yr old) with high suspicion of PCD. SYNTHESIS: Meta-analysis provided a summary estimate for sensitivity and specificity and a hierarchical summary receiver operating characteristic curve. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess study quality, and Grading of Recommendations Assessment, Development, and Evaluation was used to assess the certainty of evidence. In 12 study populations (1,344 patients comprising 514 with PCD and 830 without PCD), using a reference standard of EM alone or EM and/or genetic testing, summary sensitivity was 97.6% (92.7-99.2) and specificity was 96.0% (87.9-98.7), with a positive likelihood ratio of 24.3 (7.6-76.9), a negative likelihood ratio of 0.03 (0.01-0.08), and a diagnostic odds ratio of 956.8 (141.2-6481.5) for nNO measurements. After studies using EM alone as the reference standard were excluded, the seven studies using an extended reference standard of EM and/or genetic testing showed a summary sensitivity of nNO measurements of 96.3% (88.7-98.9) and specificity of 96.4% (85.1-99.2), with a positive likelihood ratio of 26.5 (5.9-119.1), a negative likelihood ratio of 0.04 (0.01-0.12), and a diagnostic odds ratio of 699.3 (67.4-7256.0). Certainty of the evidence was graded as moderate. CONCLUSIONS: nNO is a sensitive and specific test for PCD in cooperative patients (generally >5 yr old) with high clinical suspicion for this disease. With a moderate level of evidence, this meta-analysis confirms that nNO testing using velum closure maneuvers has diagnostic accuracy similar to EM and/or genetic testing for PCD when cystic fibrosis is ruled out. Thus, low nNO values accompanied by an appropriate clinical phenotype could be used as a diagnostic PCD test, though EM and/or genetics will continue to provide confirmatory information.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.085
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.026
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.085
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0170.031
Bibliometrics0.0090.008
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.332
GPT teacher head0.509
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

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Citations59
Published2017
Admission routes1
Has abstractyes

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