The CUSUM chart method as a tool for continuous monitoring of clinical outcomes using routinely collected data

Sibanda, Thabani; Sibanda, Nokuthaba

doi:10.1186/1471-2288-7-46

Cited by 52 publications

(48 citation statements)

References 14 publications

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“…The AL and UCL calculations are based on Gan (1991) abacus, in function of ARL (Average Run Length) value for both situations and the reference value k. In this study a significance level The CUSUM charts has a higher and consistent sensibility to parameters values variation. (Sibanda & Sibanda, 2007) …”

Section: Phasementioning

confidence: 99%

Opportunistic maintenance based on CUSUM control charts

Lampreia¹,

Vairinhos²,

Lobo³

et al. 2016

Maritime Technology and Engineering III

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The use of a Ship Maintenance Management System is fundamental for the good performance of equipments and the entire platform. Over the systematic maintenance, the opportunistic maintenance is a concept that aims to minimize outages and costs preventing undesirable failures. To implement this kind of maintenance statistical methodologies must be used. The Cumulative Sum charts have a very good performance applied to processes control in quality control. We proposed the use of Modified Cumulative Sum control charts to equipment maintenance. The data under study are observations of cooling water and oil temperatures from a diesel generator. In the first phase, we will apply traditional control charts, and, in the second phase, the Cumulative charts with a certain Average Run Length will be used. Then we will compare the results and extract conclusions, presenting measures for improvement.

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Section: Phasementioning

confidence: 99%

Opportunistic maintenance based on CUSUM control charts

Lampreia¹,

Vairinhos²,

Lobo³

et al. 2016

Maritime Technology and Engineering III

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“…SPC methods, such as the CUSUM, have been applied many times in different fields of health care to monitor mortality or another outcome measure. [7][8][9][10][17][18][19][20][21][22] However, for most interventions, the outcome measure in itself is not sufficient to enable evaluation of results, and risk adjustment using risk factors is required. 2 This means changes in both the outcome as well as in patient severity (ie, the risk factors) influence the benchmarking results.…”

Section: Importance Of Surveillancementioning

confidence: 99%

“…In health care, some of these techniques have already been opted for the monitoring of outcomes, for example, cumulative sum (CUSUM) techniques in cardiac surgery. [7][8][9][10] However, to our knowledge, we are the first to describe the use of SPC techniques in their use to monitor risk factors in clinical databases.…”

mentioning

confidence: 99%

Statistical Methods to Monitor Risk Factors in a Clinical Database

Siregar

Roes

Straten

et al. 2013

Circ: Cardiovascular Quality and Outcomes

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Background-Comparison of outcomes requires adequate risk adjustment for differences in patient risk and the type of intervention performed. Both unintentional and intentional misclassification (also called gaming) of risk factors might lead to incorrect benchmark results. Therefore, misclassification of risk factors should be detected. We investigated the use of statistical process control techniques to monitor the frequency of risk factors in a clinical database.Methods and Results-A national population-based study was performed using simulation and statistical process control. All patients who underwent cardiac surgery between January 1, 2007, and December 31, 2009, in all 16 cardiothoracic surgery centers in the Netherlands were included. Data on 46 883 consecutive cardiac surgery interventions were extracted. The expected risk factor frequencies were based on 2007 and 2008 data. Monthly frequency rates of 18 risk factors in 2009 were monitored using a Shewhart control chart, exponentially weighted moving average chart, and cumulative sum chart. Upcoding (ie, gaming) in random patients was simulated and detected in 100% of the simulations. Subtle forms of gaming, involving specifically highrisk patients, were more difficult to identify (detection rate of 44%). However, the accompanying rise in mean logistic European system for cardiac operative risk evaluation (EuroSCORE) was detected in all simulations.Conclusions-Statistical process control in the form of a Shewhart control chart, exponentially weighted moving average, and cumulative sum charts provide a means to monitor changes in risk factor frequencies in a clinical database. Surveillance of the overall expected risk in addition to the separate risk factors ensures a high sensitivity to detect gaming. The use of statistical process control for risk factor surveillance is recommended. (Circ Cardiovasc Qual Outcomes. 2013;6:110-118.)

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“…However, a critical change-point is the point where a major shift in the trend is recognised. There are several approaches to performing a trend analyses and 'changepoint analyses' (Page, 1955;Chow, 1960;Page, 1957;Hinkley, 1971;Hinkley and Schechtman, 1987;Pettitt, 1980;Box and Luceño, 1997;Sibanda and Sibanda, 2007;Fujisaki et al 2008;Taylor, 2000a;Taylor, 2011 Here, the CUSUMs are not the CUSUMs of the values instead they are the CUSUMs of differences between the values and the average. "These differences sum up to zero, so the CUSUM always ends at zero (S43=0)" (Taylor, 2000a).…”

Section: Stages Of Socioeconomic Transitionmentioning

confidence: 99%

“…According to the results of this particular technique in our study, we have not found absolute convergence yet, in any of the indicators selected in this study. Reply: There are several approaches to perform a trend analyses and 'changepoint analyses' (Page, 1955;Chow, 1960;Page, 1957;Hinkley, 1971;Hinkley and Schechtman, 1987;Pettitt, 1980;Box and Luceño, 1997;Sibanda and Sibanda, 2007;Fujisaki et al 2008;Taylor, 2000a;Taylor, 2011). The Chow test is a statistical and econometric test of whether the coefficients in two linear regressions on different data sets are equal or The Chow test, commonly used in econometrics to test for structural breaks.…”

Section: Replymentioning

confidence: 99%

Demographic Convergence and its Linkage with Health Inequalities in India

Goli

2014

SSRN Journal

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The CUSUM chart method as a tool for continuous monitoring of clinical outcomes using routinely collected data

Cited by 52 publications

References 14 publications

Opportunistic maintenance based on CUSUM control charts

Opportunistic maintenance based on CUSUM control charts

Statistical Methods to Monitor Risk Factors in a Clinical Database

Demographic Convergence and its Linkage with Health Inequalities in India

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