Evidence map›Paper›PMID 26216252›Full record

ArticleThe journal of allergy and clinical immunology. In practice

Symptom-Based Clustering in Chronic Rhinosinusitis Relates to History of Aspirin Sensitivity and Postsurgical Outcomes.

Rohit Divekar, Neil Patel, Jay Jin, John Hagan, Matthew Rank, Devyani Lal, Hirohito Kita, Erin O'Brien

Abstract read
In one paragraph

Article in The journal of allergy and clinical immunology. In practice. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Alterations of gut microbiome in eosinophilic chronic rhinosinusitis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024
    Article
  3. Updates on the Natural History and Clinical Characteristics of NSAID-ERD.The journal of allergy and clinical immunology. In practice · 2024
    Review
  4. Review
  5. The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2023
    Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Longitudinal Evaluation of Chronic Rhinosinusitis Symptoms in a Population-Based Sample.The journal of allergy and clinical immunology. In practice
    Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Rohit DivekarDivision of Allergic Diseases, Department of Medicine, Mayo Clinic, Rochester, Minn. Electronic address: Divekar.Rohit@mayo.edu.
Neil PatelDepartment of Otorhinolaryngology, Mayo Clinic, Rochester, Minn.
Jay JinDivision of Allergic Diseases, Department of Medicine, Mayo Clinic, Rochester, Minn.
John HaganDivision of Allergic Diseases, Department of Medicine, Mayo Clinic, Rochester, Minn.
Matthew RankDivision of Allergy, Asthma, and Clinical Immunology, Mayo Clinic, Scottsdale, Ariz.
Devyani LalDepartment of Otolaryngology, Mayo Clinic, Phoenix, Ariz.
Hirohito KitaDivision of Allergic Diseases, Department of Medicine, Mayo Clinic, Rochester, Minn.
Erin O'BrienDepartment of Otorhinolaryngology, Mayo Clinic, Rochester, Minn.

Funding

The Pathogenesis of Chronic RhinosinusitisR56AI049235 · NIAID · MAYO CLINIC ROCHESTER · PI KITA, HIROHITO · 2012 to 2012
$278k
NIAID NIH HHS R56 AI049235NIAID NIH HHS R56 AI49235
6 · The paper itself

Abstract

backgroundSymptoms burden in chronic rhinosinusitis (CRS) may be assessed by interviews or by means of validated tools such as the 22-item SinoNasal Outcome Test (SNOT-22). However, when only the total SNOT-22 scores are used, the pattern of symptom distribution and heterogeneity in patient symptoms is lost.

objectivesTo use a standardized symptom assessment tool (SNOT-22) on preoperative symptoms to understand symptom heterogeneity in CRS and to aid in characterization of distinguishing clinical features between subgroups.

methodsThis was a retrospective review of 97 surgical patients with CRS. Symptom-based clusters were derived on the basis of presurgical SNOT-22 scores using unsupervised analysis and network graphs. Comparison between clusters was performed for clinical and demographic parameters, postsurgical symptom scores, and presence or absence of a history of aspirin sensitivity.

resultsUnsupervised analysis reveals coclustering of specific symptoms in the SNOT-22 tool. Using symptom-based clustering, patients with CRS were stratified into severe overall (mean total score, 90.8), severe sinonasal (score, 62), moderate sinonasal (score, 40), moderate nonsinonasal (score, 37) and mild sinonasal (score, 16) clusters. The last 2 clusters were associated with lack of history of aspirin sensitivity. The first cluster had a rapid relapse in symptoms postoperatively, and the last cluster demonstrated minimal symptomatic improvement after surgery.

conclusionSymptom-based clusters in CRS reveal a distinct grouping of symptom burden that may relate to aspirin sensitivity and treatment outcomes.

Indexed as

Surveys and QuestionnairesAnti-Inflammatory Agents, Non-SteroidalAspirinChronic DiseaseCluster AnalysisCost of IllnessDrug HypersensitivityFemaleHumansMaleMiddle AgedPostoperative ComplicationsPreoperative PeriodRetrospective StudiesRhinitisSeverity of Illness IndexAnti-Inflammatory Agents, Non-SteroidalAspirinAspirin exacerbated respiratory diseaseAspirin sensitivityChronic rhinosinusitisCRS symptomsSNOT-22

Identifiers

PMID26216252
PMCPMC4641837

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.