Analysis of Cerebrospinal Fluid Pressure Estimation Using Formulae Derived From Clinical Data.

Journal Article (Journal Article;Multicenter Study)

PURPOSE: To evaluate a frequently used regression model and a new, modified regression model to estimate cerebrospinal fluid pressure (CSFP). METHODS: Datasets from the Beijing iCOP study from Tongren Hospital, Beijing, China, and the Mayo Clinic, Rochester, Minnesota, were tested in this retrospective, case-control study. An often-used regression model derived from the Beijing iCOP dataset, but without radiographic data, was used to predict CSFP by using demographic and physiologic data. A regression model was created using the Mayo Clinic dataset and tested against a validation group. The Mayo Clinic-derived formula was also tested against the Beijing Eye Study population. Intraclass correlation was used to assess predicted versus actual CSFP. RESULTS: The Beijing-derived regression equation was reported to have an intraclass correlation coefficient (ICC) of 0.71, indicating strong correlation between predicted and actual CSFP in the study population. The Beijing iCOP regression model poorly predicted CSFP in the Mayo Clinic population with an ICC of 0.14. The Mayo Clinic-derived regression model similarly did not predict CSFP in its Mayo Clinic validation group (ICC 0.28 ± 0.04) nor in the Beijing Eye Study population (ICC 0.06). CONCLUSIONS: Formulae used to predict CSFP derived from clinical data fared poorly against a large retrospective dataset. This may be related to differences in lumbar puncture technique, in the populations tested, or the timing of collection of physiologic variables in the Mayo Clinic dataset. Caution should be used when interpreting results based on formulaic derivation of CSFP.

Full Text

Duke Authors

Cited Authors

  • Fleischman, D; Bicket, AK; Stinnett, SS; Berdahl, JP; Jonas, JB; Wang, NL; Fautsch, MP; Allingham, RR

Published Date

  • October 1, 2016

Published In

Volume / Issue

  • 57 / 13

Start / End Page

  • 5625 - 5630

PubMed ID

  • 27760263

Electronic International Standard Serial Number (EISSN)

  • 1552-5783

Digital Object Identifier (DOI)

  • 10.1167/iovs.16-20119


  • eng

Conference Location

  • United States