Nurses and Physicians in a Medical Admission Unit Can Accurately Predict Mortality of Acutely Admitted Patients: A Prospective Cohort Study

Mikkel Brabrand, Jesper Hallas and Torben Knudsen

PLOS ONE, 2014, vol. 9, issue 7, 1-7

Abstract: Background: There exist several risk stratification systems for predicting mortality of emergency patients. However, some are complex in clinical use and others have been developed using suboptimal methodology. The objective was to evaluate the capability of the staff at a medical admission unit (MAU) to use clinical intuition to predict in-hospital mortality of acutely admitted patients. Methods: This is an observational prospective cohort study of adult patients (15 years or older) admitted to a MAU at a regional teaching hospital. The nursing staff and physicians predicted in-hospital mortality upon the patients' arrival. We calculated discriminatory power as the area under the receiver-operating-characteristic curve (AUROC) and accuracy of prediction (calibration) by Hosmer-Lemeshow goodness-of-fit test. Results: We had a total of 2,848 admissions (2,463 patients). 89 (3.1%) died while admitted. The nursing staff assessed 2,404 admissions and predicted mortality in 1,820 (63.9%). AUROC was 0.823 (95% CI: 0.762–0.884) and calibration poor. Physicians assessed 738 admissions and predicted mortality in 734 (25.8% of all admissions). AUROC was 0.761 (95% CI: 0.657–0.864) and calibration poor. AUROC and calibration increased with experience. When nursing staff and physicians were in agreement (±5%), discriminatory power was very high, 0.898 (95% CI: 0.773–1.000), and calibration almost perfect. Combining an objective risk prediction score with staff predictions added very little. Conclusions: Using only clinical intuition, staff in a medical admission unit has a good ability to identify patients at increased risk of dying while admitted. When nursing staff and physicians agreed on their prediction, discriminatory power and calibration were excellent.

Date: 2014
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0101739 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 01739&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0101739

DOI: 10.1371/journal.pone.0101739

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().