title: Decompression illness medically reported by hyperbaric treatment facilities: cluster analysis of 1929 cases.
abstract: BACKGROUND
  The term decompression illness (DCI) describes maladies resulting from inadequate decompression, but there is little consensus concerning clinically useful DCI subclasses. Our aim was to explore an objective DCI classification using multivariate statistics to assess naturally associated clusters of DCI manifestations. We also evaluated their mapping onto other DCI classifications and investigated the association with therapeutic outcome.
  
  
  METHODS
  We defined the optimal number of clusters using "two-step" cluster analysis and Bayesian information criterion with confirmation by hierarchical clustering with squared Euclidian distances and Ward's method. The data were 1929 DCI cases reported by hyperbaric chambers to the Divers Alert Network (DAN America) from 1999-2003.
  
  
  RESULTS
  Four robust and highly significant clusters of DCI manifestations were demonstrated containing 300, 741, 333, and 555 patients. Each cluster had characteristic manifestations. Cluster 1 was effectively pain only. For Cluster 2, characteristic manifestations included numbness, paresthesia, and decreased skin sensitivity; for Cluster 3, malaise, paralysis, muscular weakness, and bladder-bowel dysfunction; and for Cluster 4, hearing loss, localized skin swelling, tinnitus, skin rash and mottling, confusion, dyspnea/chokes, muscular problems, vision problems, altered consciousness, headache, vertigo, nausea, fatigue, dizziness, and abnormal sensations.
  
  
  CONCLUSIONS
  Internal reliability was confirmed by arbitrarily dividing the dataset into two parts and repeating the analysis. The clusters mapped poorly onto traditional DCI categories (AGE, Type I DCS, Type II DCS), but more specifically onto the Perceived Severity Index (PSI). All three classification methods (DCI, Cluster, PSI) predicted complete relief of manifestations equally well. We conclude that cluster analysis is an objective method for classifying DCI manifestations independent of clinical judgment.
authors.affiliation: Galatasaray University, Industrial Engineering Department, Istanbul, Turkey. tozyigit@gsu.edu.tr
authors.forename: Tamer
authors.initials: T
authors.lastname: Ozyigit
authors.forename: Salih Murat
authors.initials: SM
authors.lastname: Egi
authors.forename: Petar
authors.initials: P
authors.lastname: Denoble
authors.forename: Costantino
authors.initials: C
authors.lastname: Balestra
authors.forename: Salih
authors.initials: S
authors.lastname: Aydin
authors.forename: Richard
authors.initials: R
authors.lastname: Vann
authors.forename: Alessandro
authors.initials: A
authors.lastname: Marroni
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D016000:Cluster Analysis; D003665:Decompression Sickness / Q000145:classification* / Q000453:epidemiology* / Q000209:etiology / Q000628:therapy; D004242:Diving / Q000009:adverse effects*; D005260:Female; D006801:Humans; D006931:Hyperbaric Oxygenation / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012720:Severity of Illness Index; D055815:Young Adult