Clinical Prediction Rule for Patient Outcome after In-Hospital CPR | OMICS International | Abstract
ISSN: 2165-7386

Journal of Palliative Care & Medicine
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Research Article

Clinical Prediction Rule for Patient Outcome after In-Hospital CPR

Satyam Merja1, Ryan H. Lilien1 and Hilary F. Ryder2,3*

1Department of Computer Science, University of Toronto, Lebanon

2Department of Medicine and The Dartmouth Institute, Dartmouth Medical School, Lebanon

3Section of Hospital Medicine, Dartmouth-Hitchcock Medical Center, Lebanon

*Corresponding Author:
Hilary F. Ryder, M.D
Department of Medicine and The Dartmouth Institute
Dartmouth Medical School
One Medical Center Drive
NH, 03756, Lebanon
Tel: (603) 650-8380
Fax: (603) 653-6110
E-mail: [email protected]

Received date March 16, 2015; Accepted date March 25, 2015; Published date March 30, 2015

Citation: Merja S, Lilien RH, Ryder HF (2015) Clinical Prediction Rule for Patient Outcome after In-Hospital CPR. J Palliat Care Med 5:217. doi:10.4172/2165-7386.1000217

Copyright: © 2015 Merja S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Abstract Background: Physicians and patients frequently overestimate likelihood of survival after in-hospital cardiopulmonary resuscitation (CPR). Discussions and decisions around resuscitation after in-hospital cardiopulmonary arrest often take place without adequate or accurate information. Methods: We conducted a retrospective chart review of 470 instances of resuscitation after in-hospital cardiopulmonary arrest. Individuals were randomly assigned to a derivation cohort and a validation cohort. Logistic Regression and Linear Discriminant Analysis were used to perform multivariate analysis of the data. The resultant best performing rule was converted to a weighted integer tool and thresholds of survival and non-survival were determined with an attempt to optimize sensitivity and specificity for survival. Results: A 10-feature rule, using thresholds for survival and non-survival, was created; the sensitivity of the rule on the validation cohort was 42.7%, and specificity was 82.4%. Conclusions: Utilizing information easily obtainable on admission, our clinical prediction tool, the Dartmouth Score, provides physicians individualized information about their patients’ probability of survival after in-hospital cardiopulmonary arrest. The Dartmouth Score may become a useful addition to medical expertise and clinical judgment in evaluating and communicating an individual’s probability of survival after in-hospital cardiopulmonary arrest after it is validated by other cohorts. Methodologically, because LDA outperformed LR in the creation of this clinical prediction rule, it may be an approach for others to more frequently consider when performing similar analysis.