Model System:

Burn

Reference Type:

JA

Accession No.:

Journal:


Archives of Physical Medicine and Rehabilitation

Year, Volume, Issue, Page(s):

, 88, 12 Suppl 2, S7-17

Publication Website:

Abstract:

Objectives
To determine whether the Burn Model System (BMS) population is representative of the larger burn population and to investigate threats to internal and external validity in a multicenter longitudinal database of severe burns.

Design
Cohort data for the BMS project have been collected since 1994. Follow-up data have been collected at 6, 12, and 24 months postburn. The demographic and burn characteristics of the BMS population were compared with those of patients in the National Burn Registry (NBR).

Setting
The BMS, which collected data for these analyses from 5 regional burn centers in the United States, and the NBR dataset, which is a registry of information collected through the Trauma Registry of the American College of Surgeons and includes data from 70 hospitals in the United States and Canada.

Participants
BMS study participants were severely burned patients treated at 1 of the 5 participating burn centers. We compared the BMS population with that of the NBR both in total and filtered to include only patients with comparable injuries.

Interventions
Not applicable.

Main Outcome Measures
Comparable demographic and burn characteristics contained in both the NBR and the 5-center BMS longitudinal database and baseline and follow-up distributions of demographic variables and burn characteristics in the BMS database.

Results
Although minor deviations in demographic distributions were found between the BMS and NBR and between discharge and follow-up populations, our results show that the BMS population sample is internally and externally valid and is adequate for answering research questions.

Conclusions
Cohort studies examining long-term outcomes have the potential flaw of using a nonrepresentative study population. The BMS population was found to be sufficiently representative, but future analyses will require cautious and purposeful application of statistical adjustment strategies.

Author(s):


Lezotte, D., Hills, R., Heltshe, S., Holavanahalli, R., Klein, M., Fauerbach, J., Blakeney, P., Engrav, L.

Participating Centers: