Linked Life Data

3057251

Source:http://linkedlifedata.com/resource/pubmed/id/3057251

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
24
pubmed:dateCreated
1989-1-10
pubmed:abstractText
We created a microcomputer-based system that uses characteristics of the patient at admission to predict death within 30 days of hospital admission for Medicare patients with stroke, pneumonia, myocardial infarction, and congestive heart failure. These conditions account for 13% of discharges and 31% of 30-day mortality for Medicare patients over 64 years of age. The system was calibrated on a stratified, random sample of 5888 discharges (about 1470 for each condition) from seven states, with stratification by hospital type to make the sample nationally representative. The predictors must be specially abstracted from the medical record. The cross-validated R2 for predictions is 0.14 to 0.25, which is better than the values for other systems for which we have data. Risk-adjusted predicted group mortality rates may be useful in interpreting information on unadjusted mortality rates, and patient-specific predictions may be useful in identifying unexpected deaths for clinical review.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
AIM
pubmed:status
MEDLINE
pubmed:issn
0098-7484
pubmed:author
pubmed:issnType
Print
pubmed:volume
260
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
3617-24
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:articleTitle
Predicting hospital-associated mortality for Medicare patients. A method for patients with stroke, pneumonia, acute myocardial infarction, and congestive heart failure.
pubmed:affiliation
Baxter Healthcare Corp., Health Data Institute, Lexington, Mass.
pubmed:publicationType
Journal Article