Objectives: Increases in case-mix adjusted mortality may be indications of decreasing quality of care. Risk-adjusted control charts can be used for in-hospital mortality monitoring in intensive care units by issuing a warning signal when there are more deaths than expected. The aim of this study was to systematically assess and compare, by computer simulation, expected delay before a warning signal was given for an upward shift in mortality rate in intensive care mortality data by different risk-adjusted control charts.
Design: We compared the efficiency of the risk-adjusted P-chart, risk-adjusted Additive P-chart, risk-adjusted Multiplicative P-chart, monthly Standardized Mortality Ratio, risk-adjusted Cumulative Sum, risk-adjusted Resetting Sequential Probability Ratio Test, and risk-adjusted Exponentially Weighted Moving Average control chart to detect an upward shift in mortality rate in eight different scenarios that varied by mortality increase factor and monthly patient volume.
Setting: Adult intensive care units in The Netherlands.
Patients: Patients admitted to 73 intensive care units from the Dutch National Intensive Care Evaluation quality registry from the year 2009.
Measurements: We compared the performance of the different risk-adjusted control charts by the median time-to-signal and the 6-month detection rate.
Main results: In all eight scenarios, the risk-adjusted Exponentially Weighted Moving Average control chart had the shortest median time-to-signal, and in four, the highest 6-month detection rate. The median time-to-signal for an average volume intensive care unit (i.e., 50 admissions per month) with an increase in mortality rate of R = 1.50 on the odds scale was 9 months for the risk-adjusted Exponentially Weighted Moving Average control chart.
Conclusions: The risk-adjusted Exponentially Weighted Moving Average control chart signaled the fastest in most of the simulated scenarios and is therefore superior in detecting increases in in-hospital mortality of intensive care patients compared to the other types of risk-adjusted control charts.
From the Department of Medical Informatics (AK, NFdK, NP), Academic Medical Center, University of Amsterdam, The Netherlands; Department of Intensive Care (EdJ), Leiden University Medical Center, Leiden, The Netherlands; Intensive Care Unit (DAC), Princess Alexandra Hospital, Brisbane, Australia; Southern Clinical School (DAC), Faculty of Health Sciences, University of Queensland, Brisbane, Australia.
*See also p. 1976.
The authors have not disclosed any potential conflicts of interest.
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