Linked Life Data

9878028

Source:http://linkedlifedata.com/resource/pubmed/id/9878028

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
1999-2-18
pubmed:abstractText
Although most surgical site infections (SSIs) occur after hospital discharge, there is no efficient way to identify them. The utility of automated claims and electronic medical record data for this purpose was assessed in a cohort of 4086 nonobstetric procedures following which 96 postdischarge SSIs occurred. Coded diagnoses, tests, and treatments were assessed by use of recursive partitioning, with 10-fold cross-validation, and logistic regression with bootstrap resampling. Specific codes and combinations of codes identified a subset of 2% of all procedures among which 74% of SSIs had occurred. Accepting a specificity of 92% improved the sensitivity from 74% to 92%. Use of only hospital discharge diagnosis codes plus pharmacy dispensing data had sensitivity of 77% and specificity of 94%. All of these performance characteristics were better than questionnaire responses from patients or surgeons. Thus, information routinely collected by health care systems can be the basis of an efficient, largely passive, surveillance system for postdischarge SSIs.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
AIM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0022-1899
pubmed:author
pubmed:issnType
Print
pubmed:volume
179
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
434-41
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1999
pubmed:articleTitle
Efficient identification of postdischarge surgical site infections: use of automated pharmacy dispensing information, administrative data, and medical record information.
pubmed:affiliation
Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA. ksands@bidmc.harvard.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't