Linked Life Data

14527702

Source:http://linkedlifedata.com/resource/pubmed/id/14527702

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2003-10-6
pubmed:abstractText
We developed and validated a prediction rule for the occurrence of early postoperative severe pain in surgical inpatients, using predictors that can be easily documented in a preoperative setting. A cohort of surgical inpatients (n=1416) undergoing various procedures except cardiac surgery and intracranial neurosurgery in a University Hospital were studied. Preoperatively the following predictors were collected: age, gender, type of scheduled surgery, expected incision size, blood pressure, heart rate, Quetelet index, the presence and severity of preoperative pain, health-related quality of life the (SF-36), Spielberger's State-Trait Anxiety Inventory (STAI) and the Amsterdam Preoperative Anxiety and Information Scale (APAIS). The outcome was the presence of severe postoperative pain (defined as Numeric Rating Scale > or =8) within the first hour postoperatively. Multivariate logistic regression in combination with bootstrapping techniques (as a method for internal validation) was used to derive a stable prediction model. Independent predictors of severe postoperative pain were younger age, female gender, level of preoperative pain, incision size and type of surgery. The area under the receiver operator characteristic (ROC) curve was 0.71 (95% CI: 0.68-0.74). Adding APAIS scores (measures of preoperative anxiety and need for information), but not STAI, provided a slightly better model (ROC area 0.73). The reliability of this extended model was good (Hosmer and Lemeshow test p-value 0.78). We have demonstrated that severe postoperative pain early after awakening from general anesthesia can be predicted with a scoring rule, using a small set of variables that can be easily obtained from all patients at the preoperative visit. Before this internally validated preoperative prediction rule can be applied in clinical practice to support anticipatory pain management, external validation in other clinical settings is necessary.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0304-3959
pubmed:author
pubmed:issnType
Print
pubmed:volume
105
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
415-23
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
Preoperative prediction of severe postoperative pain.
pubmed:affiliation
Department of Anesthesiology, Division of Perioperative Care and Emergency Medicine, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands. c.j.kalkman@azu.nl
pubmed:publicationType
Journal Article, Comparative Study