In-hospital cardiopulmonary resuscitation - Prearrest morbidity and outcome

Citation
R. De Vos et al., In-hospital cardiopulmonary resuscitation - Prearrest morbidity and outcome, ARCH IN MED, 159(8), 1999, pp. 845-850
Citations number
27
Categorie Soggetti
General & Internal Medicine","Medical Research General Topics
Journal title
ARCHIVES OF INTERNAL MEDICINE
ISSN journal
00039926 → ACNP
Volume
159
Issue
8
Year of publication
1999
Pages
845 - 850
Database
ISI
SICI code
0003-9926(19990426)159:8<845:ICR-PM>2.0.ZU;2-D
Abstract
Background: Considerations about the application of cardiopulmonary resusci tation (CPR) should include the expected probability of survival. The survi val probability after CPR maybe more accurately estimated by the occurrence in time of the prearrest morbidity of patients. Objective: To identify risk factors for poor survival after CPR in relation to the dynamics of prearrest morbidity. Methods: Medical records of CPR patients were reviewed. Prearrest morbidity was established by categorizing the medical diagnoses according to 3 funct ional time frames: before hospital admission, on hospital admission, and du ring hospital admission. Indicators of poor survival after CPR were identif ied through a logistic regression model. Results: included in the study were 553 CPR patients with a median age of 6 8 years (age range, 18-98 years); 21.7%, survived to hospital discharge. In dependent indicators of poor outcome were an age of 70 years or older (odds ratio [OR] = 0.6, 95% confidence interval [CI] = 0.4-0.9),stroke (OR = 0.3 , 95% CI = 0.1-0.7) or renal failure (OR = 0.31. 95% CI = 0.1-0.8) before h ospital admission, and congestive heart failure during hospital admission ( OR = 0.4, 95% CI = 0.2-0.9). Indicators of good survival were angina pector is before hospital admission (OR = 2.1, 95% CI = 1.3-.3.3) or ventricular d ysrhythmia as the diagnosis on hospital admission (OR = 11.0, 95% CI = 4.1- 33.7). Based on a logistic regression model, 17.4% of our CPR patients (n = 96) were identified as having a high risk for a poor outcome (<10% surviva l). Conclusions: Time of prearrest morbidity has a prognostic value for surviva l after CPR. Patients at risk for poor survival can be identified on or dur ing hospital admission, but the reliability and validity of the model needs further research. Although decisions will not be made by the model, its in formation can be useful for physicians in discussions about patient prognos es and to make decisions about CPR with more confidence.