The Monitoring and Improvement of Surgical-Outcome Quality

Woodall, William H.; Fogel, Sandy; Steiner, Stefan H.

doi:10.1080/00224065.2015.11918141

Cited by 59 publications

(44 citation statements)

References 115 publications

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“…It is not reasonable for a VLAD to have horizontal control limits for signaling an improved or a deteriorated performance because the variance of the cumulative sum plotted increases with the sample size. Many authors recommended running the VLAD and some risk‐adjusted cumulative sum (RA‐CUSUM) control chart (such as Steiner et al) simultaneously, one for interpretation and the other for signaling . This approach requires a user to monitor both charts and alarm points matched.…”

Section: Introductionmentioning

confidence: 99%

A simple signaling rule for variable life‐adjusted display derived from an equivalent risk‐adjusted CUSUM chart

Wittenberg

Gan

Knoth

2018

Statistics in Medicine

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The variable life-adjusted display (VLAD) is the first risk-adjusted graphical procedure proposed in the literature for monitoring the performance of a surgeon. It displays the cumulative sum of expected minus observed deaths. It has since become highly popular because the statistic plotted is easy to understand. But it is also easy to misinterpret a surgeon's performance by utilizing the VLAD, potentially leading to grave consequences. The problem of misinterpretation is essentially caused by the variance of the VLAD's statistic that increases with sample size. In order for the VLAD to be truly useful, a simple signaling rule is desperately needed. Various forms of signaling rules have been developed, but they are usually quite complicated. Without signaling rules, making inferences using the VLAD alone is difficult if not misleading. In this paper, we establish an equivalence between a VLAD with V-mask and a risk-adjusted cumulative sum (RA-CUSUM) chart based on the difference between the estimated probability of death and surgical outcome. Average run length analysis based on simulation shows that this particular RA-CUSUM chart has similar performance as compared to the established RA-CUSUM chart based on the log-likelihood ratio statistic obtained by testing the odds ratio of death. We provide a simple design procedure for determining the V-mask parameters based on a resampling approach. Resampling from a real data set ensures that these parameters can be estimated appropriately. Finally, we illustrate the monitoring of a real surgeon's performance using VLAD with V-mask.

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Section: Introductionmentioning

confidence: 99%

A simple signaling rule for variable life‐adjusted display derived from an equivalent risk‐adjusted CUSUM chart

Wittenberg

Gan

Knoth

2018

Statistics in Medicine

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“…We postulate that at some time τ , the process changes, so that the parameters are

{bold-italicβ}_{0} = ((), β_{0}^{1}, β_{0}^{1}, β_{0}^{2}, \dots, β_{0}^{p})

before the change, and

{bold-italicβ}_{1} = ((), β_{1}^{0}, β_{1}^{1}, β_{1}^{2}, \dots, β_{1}^{p})

after the change. Most previous work on risk‐adjusted monitoring, such as Grigg and Farewell, Grigg and Spiegelhalter, Steiner et al , Zeng and Zhou and Woodall et al , assumes that the intercept β 0 is the only parameter that changes. This is equivalent to a constant change in the log‐odds for all patients, regardless of their initial condition as expressed in the predictor variables x i .…”

Section: Overviewmentioning

confidence: 99%

Risk‐adjusted Monitoring of Healthcare Quality: Model Selection and Change‐point Estimation

Steward

Rigdon

2016

Quality & Reliability Eng

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Risk adjustment, which is used when healthcare outcomes are monitored, involves taking into account measures of the patient condition and how these measures are related to the outcomes. When the outcome is dichotomous, such as survival/death, the modeling involves logistic regression to assess the relationship between the predictor(s) and the outcome. Most risk‐adjusted control charts are designed to detect a change in the log‐odds of the adverse outcome, but there are a number of possible changes that could occur. For example, there could be an increase in the probability of adverse outcomes for low‐risk patients with no change for high‐risk patients. We address the problem of risk‐adjusted monitoring as a change‐point problem with several possible change‐point models. For p risk variables, there are 2p + 1 possible change‐point models, because each of the slope parameters or the intercept in the logistic regression model can change. Our approach generalizes previous risk‐adjusted charts in that we look for changes in any of the parameters. We take a Bayesian approach and find the posterior distribution for the model (i.e., which coefficients changed), the time of the change, and the values of the parameters for those that changed. All three tasks are accomplished in the context of a single model. We apply reversible jump MCMC to account for the variable size of the parameter space. Copyright © 2016 John Wiley & Sons, Ltd.

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“…(2) Sort the N CUSUM statistics in ascending order C t, (1) , C t, (2) , C t, (3) , … , C t,(N) and take the N ′ = ⌈N ⋅ (1 − )⌉ largest CUSUM statistic C t,(N ′ ) as the approximated DPCL h t ( ).…”

Section: Dynamic Probability Control Limitsmentioning

confidence: 99%

Dynamic probability control limits for risk-adjusted Bernoulli CUSUM charts

Zhang

Woodall

2015

Statist. Med.

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The risk-adjusted Bernoulli cumulative sum (CUSUM) chart developed by Steiner et al. (2000) is an increasingly popular tool for monitoring clinical and surgical performance. In practice, however, the use of a fixed control limit for the chart leads to a quite variable in-control average run length performance for patient populations with different risk score distributions. To overcome this problem, we determine simulation-based dynamic probability control limits (DPCLs) patient-by-patient for the risk-adjusted Bernoulli CUSUM charts. By maintaining the probability of a false alarm at a constant level conditional on no false alarm for previous observations, our risk-adjusted CUSUM charts with DPCLs have consistent in-control performance at the desired level with approximately geometrically distributed run lengths. Our simulation results demonstrate that our method does not rely on any information or assumptions about the patients' risk distributions. The use of DPCLs for risk-adjusted Bernoulli CUSUM charts allows each chart to be designed for the corresponding particular sequence of patients for a surgeon or hospital.

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The Monitoring and Improvement of Surgical-Outcome Quality

Cited by 59 publications

References 115 publications

A simple signaling rule for variable life‐adjusted display derived from an equivalent risk‐adjusted CUSUM chart

A simple signaling rule for variable life‐adjusted display derived from an equivalent risk‐adjusted CUSUM chart

Risk‐adjusted Monitoring of Healthcare Quality: Model Selection and Change‐point Estimation

Dynamic probability control limits for risk-adjusted Bernoulli CUSUM charts

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