Joint Monitoring Scheme for Clinical Failures and Predisposed Risks

Loke, Chok K.; Gan, F. F.

doi:10.1080/16843703.2012.11673274

Cited by 16 publications

(16 citation statements)

References 29 publications

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“…In the cardiac surgery examples shown in Steiner et al , the control limits for the proposed CUSUM charts were set at specified levels to give the relatively large ARL 0 values given the patient population and the fitted risk‐adjustment models. However, several researchers have brought up issues about the effect of different risk distributions on the performance of risk‐adjusted CUSUM charts with constant control limits (see references , ). The ARL 0 values of risk‐adjusted CUSUM charts with the same risk adjustment model and constant control limits can vary by a factor of 10 for the highest and lowest risk patient populations.…”

Section: Introductionmentioning

confidence: 99%

Dynamic probability control limits for risk‐adjusted CUSUM charts based on multiresponses

Zhang

Loda

Woodall

2017

Statistics in Medicine

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For a patient who has survived a surgery, there could be several levels of recovery. Thus, it is reasonable to consider more than two outcomes when monitoring surgical outcome quality. The risk-adjusted cumulative sum (CUSUM) chart based on multiresponses has been developed for monitoring a surgical process with three or more outcomes. However, there is a significant effect of varying risk distributions on the in-control performance of the chart when constant control limits are applied. To overcome this disadvantage, we apply the dynamic probability control limits to the risk-adjusted CUSUM charts for multiresponses. The simulation results demonstrate that the in-control performance of the charts with dynamic probability control limits can be controlled for different patient populations because these limits are determined for each specific sequence of patients. Thus, the use of dynamic probability control limits for risk-adjusted CUSUM charts based on multiresponses allows each chart to be designed for the corresponding patient sequence of a surgeon or a hospital and therefore does not require estimating or monitoring the patients' risk distribution. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Dynamic probability control limits for risk‐adjusted CUSUM charts based on multiresponses

Zhang

Loda

Woodall

2017

Statistics in Medicine

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“…Several researchers have indicated that a change in patient risk distribution, which is usually assumed to be constant over time, can affect the control chart’s performance to monitor surgical performance. 4 , 16 – 19 , Previous studies, which took into account the variation in patient mix, often examined only a small number of different scenarios, which can be explained by limitations to simple manual manipulations or reclassifications of the used empirical data. Steiner et al measured their RA approach’s sensitivity for the patient mix from two surgeons in their sample data with the most extreme patient mixes 1 and for lowest risk and highest risk patients only.…”

Section: Related Literaturementioning

confidence: 99%

Modeling the patient mix for risk-adjusted CUSUM charts

Wittenberg

2022

Stat Methods Med Res

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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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“…15,16 Furthermore, even when the RA-CUSUM chart has been adjusted for the patients' risks, it remains sensitive to changes in the underlying predisposed risk distribution and the baseline incidence rate. 17,18 We propose a risk-adjusted Bernoulli CUSUM (RA-B-CUSUM) chart, very easy to implement and whose setting does not require great computational effort; in addition, it is not sensitive to changes in the underlying predisposed risk distribution and in the baseline incidence rate.…”

Section: Introductionmentioning

confidence: 99%

A new risk-adjusted Bernoulli cumulative sum chart for monitoring binary health data

Rossi

Sarto

Marchi

2016

Stat Methods Med Res

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To monitor a health event in patients with a specific risk of developing the event, a risk-adjusted cumulative sum chart is needed. The risk-adjusted cumulative sum chart proposed in the literature has some limitations. Setting appropriate control limits is not straightforward, there is no simple formula for constructing them, and they remain sensitive to changes in the underlying risk distribution and the baseline incidence rate. To overcome these limits, we propose a new risk-adjusted Bernoulli cumulative sum chart as a simple and efficient solution. Analyses of simulated and real data sets illustrate the performance and usefulness of the proposed procedure.

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Joint Monitoring Scheme for Clinical Failures and Predisposed Risks

Cited by 16 publications

References 29 publications

Dynamic probability control limits for risk‐adjusted CUSUM charts based on multiresponses

Dynamic probability control limits for risk‐adjusted CUSUM charts based on multiresponses

Modeling the patient mix for risk-adjusted CUSUM charts

A new risk-adjusted Bernoulli cumulative sum chart for monitoring binary health data

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