
pmid: 19456008
omegaWe review the terminology of decompression illness (DCI), investigations of residual symptoms of decompression sickness (DCS), and application of survival analysis for investigating DCI severity and resolution. The Type 1 and Type 2 DCS classifications were introduced in 1960 for compressed air workers and adapted for diving and altitude exposure with modifications based on clinical judgment concerning severity and therapy. In practice, these proved ambiguous, leading to recommendations that manifestations, not cases, be classified. A subsequent approach assigned individual scores to manifestations and correlated total case scores with the presence of residual symptoms after therapy. The next step used logistic regression to find the statistical association of manifestations to residual symptoms at a single point in time. Survival analysis, a common statistical method in clinical trials and longitudinal epidemiological studies, is a logical extension of logistic regression. The method applies to a continuum of resolution times, allows for time varying information, can manage cases lost to follow-up (censored), and has potential for investigating questions such as optimal therapy and DCI severity. There are operational implications as well. Appropriate definitions of mild and serious manifestations are essential for computing probabilistic decompression procedures where severity determines the DCS probability that is acceptable. Application of survival analysis to DCI data would require more specific case information than is commonly recorded.
Diving, Humans, Kaplan-Meier Estimate, Recovery of Function, Decompression Sickness, Severity of Illness Index
Diving, Humans, Kaplan-Meier Estimate, Recovery of Function, Decompression Sickness, Severity of Illness Index
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 21 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
