Abstract

Cancer patients admitted to the intensive care unit (ICU) may be experiencing complications of disease or treatment-related effects. While acute complications related to disease and/or its therapeutic management vary in severity, the approach to ICU-centered care is complicated by actual versus perceived risks of poor outcomes. Prognostic models that inform clinical judgment of nurses and physicians may prove helpful in this population. The Acute Physiology and Chronic Health Evaluation II (APACHE II), Simplified Acute Physiology Score II (SAPS II) and Sequential Organ Failure Assessment (SOFA) are ICU-based models predicting 30-day mortality among the general ICU population. Although studies have been published on use of each model, prognostic accuracy for predicting 30-day, all-cause ICU mortality in the cancer population has yielded mixed results.

The purpose of this study was to determine which prognostic model demonstrated greatest prognostic accuracy among oncology patients. Framed within a derived Prognostic Framework, a meta-analysis of prospective and retrospective cohort studies using literature searches of CINAHL, Cochrane, PubMed and Web of Science databases spanning 2000 to 2017 timeframe was performed. Meta-regression with a random-effects model was used to summarize area under the receiver-operating characteristic curves (AUCs) to estimate overall predictive accuracy for the APACHE II, SAPS II, and SOFA. After comparing performances, APACHE II demonstrated greatest predictive accuracy.

Date of publication

Summer 8-14-2017

Document Type

Dissertation

Language

english

Persistent identifier

http://hdl.handle.net/10950/590

Committee members

Barbara K. Haas, Ph.D, Denise Greer, Ph.D, Gloria Duke, Ph.D, & Yong Tai Wang, Ph.D

Degree

Doctor of Philosophy in Nursing

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