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Use of risk-adjusted CUSUM charts to monitor 30-day mortality in Danish hospitals

Authors Rasmussen TB, Ulrichsen SP, Nørgaard M

Received 15 November 2017

Accepted for publication 21 February 2018

Published 18 April 2018 Volume 2018:10 Pages 445—456

DOI https://doi.org/10.2147/CLEP.S157162

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Lucy Goodman

Peer reviewer comments 2

Editor who approved publication: Professor Irene Petersen


Thomas Bøjer Rasmussen, Sinna Pilgaard Ulrichsen, Mette Nørgaard

Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus N, Denmark

Background: Monitoring hospital outcomes and clinical processes as a measure of clinical performance is an integral part of modern health care. The risk-adjusted cumulative sum (CUSUM) chart is a frequently used sequential analysis technique that can be implemented to monitor a wide range of different types of outcomes.
Objective: The aim of this study was to describe how risk-adjusted CUSUM charts based on population-based nationwide medical registers were used to monitor 30-day mortality in Danish hospitals and to give an example on how alarms of increased hospital mortality from the charts can guide further in-depth analyses.
Materials and methods: We used routinely collected administrative data from the Danish National Patient Registry and the Danish Civil Registration System to create risk-adjusted CUSUM charts. We monitored 30-day mortality after hospital admission with one of 77 selected diagnoses in 24 hospital units in Denmark in 2015. The charts were set to detect a 50% increase in 30-day mortality, and control limits were determined by simulations.
Results: Among 1,085,576 hospital admissions, 441,352 admissions had one of the 77 selected diagnoses as their primary diagnosis and were included in the risk-adjusted CUSUM charts. The charts yielded a total of eight alarms of increased mortality. The median of the hospitals’ estimated average time to detect a 50% increase in 30-day mortality was 50 days (interquartile interval, 43;54). In the selected example of an alarm, descriptive analyses indicated performance problems with 30-day mortality following hip fracture surgery and diagnosis of chronic obstructive pulmonary disease.
Conclusion: The presented implementation of risk-adjusted CUSUM charts can detect significant increases in 30-day mortality within 2 months, on average, in most Danish hospitals. Together with descriptive analyses, it was possible to use an alarm from a risk-adjusted CUSUM chart to identify potential performance problems.

Keywords: monitoring scheme, clinical performance, CUSUM, hospital mortality, clinical quality
 

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