Wittenberg P. Modeling the patient mix for risk-adjusted CUmulative SUM charts.
Stat Methods Med Res 2022;
31:779-800. [PMID:
35139722 PMCID:
PMC9014690 DOI:
10.1177/09622802211053205]
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Abstract
The improvement of surgical quality and the corresponding early detection of its
changes is of increasing importance. To this end, sequential monitoring
procedures such as the risk-adjusted CUmulative SUM chart are frequently
applied. The patient risk score population (patient mix), which considers the
patients’ perioperative risk, is a core component for this type of quality
control chart. Consequently, it is important to be able to adapt different
shapes of patient mixes and determine their impact on the monitoring scheme.
This article proposes a framework for modeling the patient mix by a discrete
beta-binomial and a continuous beta distribution for risk-adjusted CUSUM charts.
Since the model-based approach is not limited by data availability,
any patient mix can be analyzed. We examine the effects on
the control chart’s false alarm behavior for more than 100,000 different
scenarios for a cardiac surgery data set. Our study finds a negative
relationship between the average risk score and the number of false alarms. The
results indicate that a changing patient mix has a considerable impact and, in
some cases, almost doubles the number of expected false alarms.
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