Risk-adjusted survival time monitoring with an updating exponentially weighted moving average (EWMA) control chart

Risk adjusted EWMA control chart based on support vector machine with application to cardiac surgery data

In the current study, we demonstrate the use of a quality framework to review the process for improving the quality and safety of the patient in the health care department. The researchers paid attention to assessing the performance of the health care service, where the data is usually heterogeneous to patient's health conditions. In our study, the support vector machine (SVM) regression model is used to handle the challenge of adjusting the risk factors attached to the patients. Further, the design of exponentially weighted moving average (EWMA) control charts is proposed based on the residuals obtained through SVM regression model. Analyzing real cardiac surgery patient data, we employed the SVM method to gauge patient condition. The resulting SVM-EWMA chart, fashioned via SVM modeling, revealed superior shift detection capabilities and demonstrated enhanced efficacy compared to the risk-adjusted EWMA control chart.

CGR-CUSUM: a continuous time generalized rapid response cumulative sum chart

Rapidly detecting problems in the quality of care is of utmost importance for the well-being of patients. Without proper inspection schemes, such problems can go undetected for years. Cumulative sum (CUSUM) charts have proven to be useful for quality control, yet available methodology for survival outcomes is limited. The few available continuous time inspection charts usually require the researcher to specify an expected increase in the failure rate in advance, thereby requiring prior knowledge about the problem at hand. Misspecifying parameters can lead to false positive alerts and large detection delays. To solve this problem, we take a more general approach to derive the new Continuous time Generalized Rapid response CUSUM (CGR-CUSUM) chart. We find an expression for the approximate average run length (average time to detection) and illustrate the possible gain in detection speed by using the CGR-CUSUM over other commonly used monitoring schemes on a real-life data set from the Dutch Arthroplasty Register as well as in simulation studies. Besides the inspection of medical procedures, the CGR-CUSUM can also be used for other real-time inspection schemes such as industrial production lines and quality control of services.

Monitoring the performance of a dedicated weaning unit using risk-adjusted control charts for the weaning rate in prolonged mechanical ventilation

BACKGROUND

Weaning rate is an important quality indicator of care for patients with prolonged mechanical ventilation (PMV). However, diverse clinical characteristics often affect the measured rate. A risk-adjusted control chart may be beneficial for assessing the quality of care.

METHODS

We analyzed patients with PMV who were discharged between 2018 and 2020 from a dedicated weaning unit at a medical center. We generated a formula to estimate monthly weaning rates using multivariate logistic regression for the clinical, laboratory, and physiologic characteristics upon weaning unit admission in the first two years (Phase I). We then applied both multiplicative and additive models for adjusted p-charts, displayed in both non-segmented and segmented formats, to assess whether special cause variation existed.

RESULTS

A total of 737 patients were analyzed, including 503 in Phase I and 234 in Phase II, with average weaning rates of 59.4% and 60.3%, respectively. The p-chart of crude weaning rates did not show special cause variation. Ten variables from the regression analysis were selected for the formula to predict individual weaning probability and generate estimated weaning rates in Phases I and II. For risk-adjusted p-charts, both multiplicative and additive models showed similar findings and no special cause variation.

CONCLUSION

Risk-adjusted control charts generated using a combination of multivariate logistic regression and control chart-adjustment models may provide a feasible method to assess the quality of care in the setting of PMV with standard care protocols.

A New Nonparametric Multivariate Control Scheme for Simultaneous Monitoring Changes in Location and Scale

Real-time monitoring of the breast cancer index is becoming increasingly important. It can help create advances in the diagnosis and treatment of breast cancer. In today's modern medical processes, simultaneously monitoring changes in observations in terms of location and scale are convenient for the implementation of control schemes but can be challenging. In this paper, we consider a new nonparametric control scheme for monitoring location and scale parameters in multivariate processes. The proposed method is easy to implement, and the performance of the proposed control procedure is discussed. Then, we compare the proposed scheme with some competing methods. Simulation results show that the proposed scheme can efficiently detect a range of shifts. The proposed chart can trigger an alert and timely discover the change of the breast cancer index.

Modeling the patient mix for risk-adjusted CUmulative SUM charts

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.

Quicker detection risk-adjusted cumulative sum charting procedures

When a patient is operated on, the surgical outcome depends on two major factors: (i) the patient's health condition and (ii) the surgical process comprising the surgeon, the supporting staff, operating environment, and equipment. An outcome is usually represented by one if a patient dies within 30 days of an operation and zero otherwise. Another method of measuring the outcome is to use survival time with truncation on the 30th day for monitoring purposes. In order to monitor a surgical process effectively, the health condition of a patient must be taken into consideration. This is usually done using a log-likelihood ratio statistic based on an outcome, that is, risk adjusted according to the health condition of the patient. The 30-day wait results in delay in signaling when a deterioration occurs. The consequence of having to wait even though a death has occurred is the potential loss of lives because of delay in signaling. Regular updating of patients' information can improve the sensitivity of a charting procedure. The main objective of this article is to develop and study the class of risk-adjusted cumulative sum procedures that are updated on a regular basis based on patients' current conditions, without having to wait 30 days. Our study shows that these charts do in fact signal earlier and there are differences among the various updating techniques and monitoring statistics.

Medical device active surveillance of spontaneous reports: A literature review of signal detection methods

PURPOSE

The collection and analysis of real-world data for the active monitoring of medical device performance and safety has become increasingly important. Spontaneous reports, such as those in the Food & Drug Administration's (FDA's) Manufacturer and User Facility Device Experience (MAUDE), provide early warning of potential issues with marketed devices. This review synthesizes the current literature on medical device surveillance signal detection and provides a framework for application of methods to active surveillance of spontaneous reports.

METHODS

Ovid MEDLINE, Ovid Embase, Scopus, and PubMed databases were systematically searched up to January 2019. Additionally, five methods articles from pharmacovigilance were added that had potential applications to medical devices.

RESULTS

Among 105 articles included, the most common source of data (84%) was registries; median time between data collection and publication was 8 years. Surgical procedure outcome signal detection articles comprised 83% while 14% were on device outcome signal detection. The most common family of methods cited (70%) was Sequential Probability Ratio.

CONCLUSION

Application of any signal detection algorithm requires careful consideration of influential factors, data limitations, and algorithmic assumptions. We recommend approaches using disproportionality, statistical process control, and sequential probability tests and provide R packages to further development efforts. The small number of published examples suggest that further development of statistical methods and technological solutions to analyze large amounts of data for device safety and performance is needed. Fundamental differences in products, data infrastructure, and the regulatory landscape suggest that medical device vigilance requires its own body of research distinct from pharmacovigilance.

<p>Prediction and Monitoring Method for Breast Cancer: A Case Study for Data from the University Hospital Centre of Coimbra</p>

Breast cancer is the second most common cancer in women after skin cancer. Breast cancer can occur in both men and women, but it is far more common in women. Real-time monitoring of breast cancer indicators is becoming increasingly important. It can help create advances in the diagnosis and treatment of breast cancer. In this paper, we provide a nonparametric statistical method to predict and detect breast cancer occur. The exponentially weighted moving average (EWMA) control scheme is based on rank methods so that it is completely nonparametric. It is efficient in detecting the shifts for multivariate processes. A real example data from the University Hospital Centre of Coimbra is given to illustrate this method.

The STRAND Chart: A survival time control chart

The STRAND Chart (Survival Time, Risk‐Adjusted, N‐Division Chart) is a new tool for online risk‐adjusted (RA) monitoring of survival outcomes. The chart is drawn in continuous time, making it responsive to change in the process of interest, for example, performance over time of a surgical unit and the procedures that they employ. Though it is difficult to achieve with charts designed for the purpose described, we show that our suggested chart keeps patient ordering intact. We discuss the difficulties maintaining patient ordering poses, making reference to other charts in the literature. Our conclusion is that the best approach to preserving patient ordering on any chart of this nature involves compromising on the fullness of presentation of the recorded data. The chart is divided into N strands, each strand relating to a benchmark patient's survival information at t_n days following treatment, n = 1,2,…,N. We present a simple version of the chart where the strands consist of Bernoulli RA exponentially weighted moving averages, recording RA failure rates at t_n days. It can detect recent process change and historical change. We illustrate the STRAND Chart using a well‐known UK post cardiac surgery survival dataset, where the nature of a certain cluster in the data can be seen.

A new VLAD-based control chart for detecting surgical outcomes

The timely detection of surgical quality changes is becoming increasingly important. Variable life-adjusted display (VLAD) has a wide range of applications in the medical field. However, the control limits of VLAD are not defined; thus, the charts alone cannot reveal whether surgical quality underwent a significant change. This paper proposes a new risk-adjusted exponentially weighted moving average VLAD (RAEV) chart and provides a control limit that can be used with the VLAD. The RAEV chart is designed to detect shifts in the odds ratios of patients' surgical risk. Simulation is used to demonstrate that the proposed RAEV chart efficiently detects shifts of various magnitudes, and an example is provided to illustrate the implementation of the RAEV control limits in a VLAD chart. Copyright © 2017 John Wiley & Sons, Ltd.

Decreasing infection in neonatal intensive care units through quality improvement

OBJECTIVE

To decrease the incidence of bloodstream infection (BSI) for neonates <29 weeks gestation through quality improvement.

DESIGN

Commencing in September 2011, eight neonatal intensive care units (NICUs) in New South Wales and Australian Capital Territory, Australia participated in the Sepsis Prevention in NICUs Group project, a multicentre quality improvement initiative to reduce neonatal infection through implementation of potentially better practices and development of teaching resources. Data were collected for neonates <29 weeks gestation from D3 to 35, using point of care data entry, for BSI, central line-associated BSI (CLABSI) and antibiotic use. Exponentially weighted moving average data trend lines for rates of BSI, CLABSI and antibiotic use for each NICU were automatically generated and composite charts were provided each month to participating NICUs.

RESULTS

Between January 2012 and December 2014, data were collected from D3 to 35 for 1075 neonates <29 weeks gestation who survived >48 h, for a total of 33 933 bed days and 14 447 central line days. There was a significant decrease from 2012 to 2014 in BSI/1000 bed days (7.8±3.0 vs 3.8±1.1, p=0.000), CLABSI/1000 bed days (4.6±2.1 vs 2.1±0.8, p=0.003), CLABSI/1000 central line days (9.9±4.3 vs 5.4±1.7, p=0.012) and antibiotic days/100 bed days (31.1±4.3 vs 25.5±4.2, p=0.046).

CONCLUSIONS

This study demonstrates a >50% reduction in BSI in extremely premature neonates from D3 to 35 following a collaborative quality improvement project to reduce neonatal infection across an NICU network, supported by timely provision of data.

Change point estimation in monitoring survival time

Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the mean survival time of patients who underwent cardiac surgery. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time CUSUM control charts for different magnitude scenarios. The proposed estimator shows a better performance where a longer follow-up period, censoring time, is applied. In comparison with the alternative built-in CUSUM estimator, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.

Risk-adjusted number-between failures charting procedures for monitoring a patient care process for acute myocardial infarctions

Risk-adjusted charts for monitoring a surgical or patient care process have recently gained prominence in the literature. To monitor a patient care process, especially to detect deterioration is crucial in saving patients' lives. In this paper, a new charting procedure is developed for monitoring a patient care process for patients admitted to a hospital with acute myocardial infarctions. This procedure is based on the number-between failures by taking the patients' risks into account. When there is a change in the risk distribution of incoming patients, it is demonstrated that this procedure will behave properly while the non-risk-adjusted counterpart will signal incorrectly that the patient care process has changed. The setting up of this chart to monitor a patient care process for patients with acute myocardial infarctions is described in detail.