scholarly journals 93. Early Recognition and Response to Increases in Surgical Site Infections (SSI) using Optimized Statistical Process Control (SPC) Charts – the Early 2RIS Trial: A Multicenter Stepped Wedge Cluster Randomized Controlled Trial (RCT)

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S59-S60
Author(s):  
Arthur W Baker ◽  
Iulian Ilieş ◽  
James C Benneyan ◽  
Yuliya Lokhnygina ◽  
Katherine R Foy ◽  
...  
Keyword(s):  
Nursing Homes ◽  
Best Practices ◽  
Process Control ◽  
Statistical Process Control ◽  
Stepped Wedge ◽  
Statistical Process ◽  
Material Support ◽  
Cleaning Products ◽  
Traditional Surveillance ◽  
Observation Period

Abstract Background Traditional approaches for SSI surveillance have deficiencies that can delay detection of SSI outbreaks and other clinically important increases in SSI rates. Optimized SPC methods for SSI surveillance have not been prospectively evaluated. Methods We conducted a prospective multicenter stepped wedge cluster RCT to evaluate the performance of SSI surveillance and feedback performed with optimized SPC plus traditional surveillance methods compared to traditional surveillance alone. We divided 13 common surgical procedures into 6 clusters (Table 1). A cluster of procedures at a single hospital was the unit of randomization and analysis, and 105 total clusters across 29 community hospitals were randomized to 12 groups of 8-10 clusters (Figure 1). After a 12-month baseline observation period (3/2016-2/2017), the SPC surveillance intervention was serially implemented according to stepped wedge assignment over a 36-month intervention period (3/2017-2/2020) until all 12 groups of clusters had received the intervention. The primary outcome was the overall SSI prevalence rate (PR=SSIs/100 procedures), evaluated with a GEE model with Poisson distribution. Table 1 Figure 1 Schematic for stepped wedge design. The 12-month baseline observation period was followed by the 36-month intervention period, comprised of 12 3-month steps. Results Our trial involved prospective surveillance of 237,704 procedures that resulted in 1,952 SSIs (PR=0.82). The overall SSI PR did not differ significantly between clusters of procedures assigned to SPC surveillance (781 SSIs/89,339 procedures; PR=0.87) and those assigned to traditional surveillance (1,171 SSIs/148,365 procedures; PR=0.79; PR ratio=1.10 [95% CI, 0.94–1.30]; P=.25) (Table 2). SPC surveillance identified 104 SSI rate increases that required formal investigations, compared to only 25 investigations generated by traditional surveillance. Among 10 best practices for SSI prevention, 453 of 502 (90%) SSIs analyzed due to SPC detection of SSI rate increases had at least 2 deficiencies (Table 3). Table 2 Poisson regression models comparing surgical site infection (SSI) prevalence rates for procedure clusters receiving statistical process control surveillance to SSI rates for clusters receiving traditional control surveillance. Table 3 Compliance with 10 best practices for surgical site infection (SSI) prevention among 502 SSIs analyzed during SSI investigations generated by statistical process control surveillance. Conclusion SPC methods more frequently detected important SSI rate increases associated with deficiencies in SSI prevention best practices than traditional surveillance; however, feedback of this information did not lead to SSI rate reductions. Further study is indicated to determine the best application of SPC methods to improve adherence to SSI quality measures and prevent SSIs. Disclosures Arthur W. Baker, MD, MPH, Medincell (Advisor or Review Panel member) Susan S. Huang, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Xttrium (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)

scholarly journals Early recognition and response to increases in surgical site infections using optimized statistical process control charts—the Early 2RIS Trial: a multicenter cluster randomized controlled trial with stepped wedge design

Trials ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Deverick J. Anderson ◽  
Iulian Ilieş ◽  
Katherine Foy ◽  
Nicole Nehls ◽  
James C. Benneyan ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Process Control ◽  
Statistical Process Control ◽  
Controlled Trial ◽  
Surgical Site Infections ◽  
Stepped Wedge ◽  
Statistical Process ◽  
Randomized Controlled ◽  
Cluster Randomized ◽  
Traditional Surveillance

Abstract Background Surgical site infections (SSIs) cause significant patient suffering. Surveillance and feedback of SSI rates is an evidence-based strategy to reduce SSIs, but traditional surveillance methods are slow and prone to bias. The objective of this cluster randomized controlled trial (RCT) is to determine if using optimized statistical process control (SPC) charts for SSI surveillance and feedback lead to a reduction in SSI rates compared to traditional surveillance. Methods The Early 2RIS Trial is a prospective, multicenter cluster RCT using a stepped wedge design. The trial will be performed in 29 hospitals in the Duke Infection Control Outreach Network (DICON) and 105 clusters over 4 years, from March 2016 through February 2020; year one represents a baseline period; thereafter, 8–9 clusters will be randomized to intervention every 3 months over a 3-year period using a stepped wedge randomization design. All patients who undergo one of 13 targeted procedures at study hospitals will be included in the analysis; these procedures will be included in one of six clusters: cardiac, orthopedic, gastrointestinal, OB-GYN, vascular, and spinal. All clusters will undergo traditional surveillance for SSIs; once randomized to intervention, clusters will also undergo surveillance and feedback using optimized SPC charts. Feedback on surveillance data will be provided to all clusters, regardless of allocation or type of surveillance. The primary endpoint is the difference in rates of SSI between the SPC intervention compared to traditional surveillance and feedback alone. Discussion The traditional approach for SSI surveillance and feedback has several major deficiencies because SSIs are rare events. First, traditional statistical methods require aggregation of measurements over time, which delays analysis until enough data accumulate. Second, traditional statistical tests and resulting p values are difficult to interpret. Third, analyses based on average SSI rates during predefined time periods have limited ability to rapidly identify important, real-time trends. Thus, standard analytic methods that compare average SSI rates between arbitrarily designated time intervals may not identify an important SSI rate increase on time unless the “signal” is very strong. Therefore, novel strategies for early identification and investigation of SSI rate increases are needed to decrease SSI rates. While SPC charts are used throughout industry and healthcare to improve and optimize processes, including other types of healthcare-associated infections, they have not been evaluated as a tool for SSI surveillance and feedback in a randomized trial. Trial registration ClinicalTrials.govNCT03075813, Registered March 9, 2017.

scholarly journals 877. Use of Statistical Process Control Charts for Early Detection of Healthcare Facility-Associated Nontuberculous Mycobacterial Outbreaks

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S475-S476
Author(s):  
Arthur W Baker ◽  
Ahmed Maged ◽  
Salah Haridy ◽  
Jason E Stout ◽  
Jessica L Seidelman ◽  
...  
Keyword(s):  
Early Detection ◽  
Process Control ◽  
Statistical Process Control ◽  
Moving Average ◽  
Healthcare Facility ◽  
Mycobacterium Abscessus ◽  
Control Limit ◽  
Outbreak Detection ◽  
Statistical Process ◽  
Traditional Surveillance

Abstract Background Nontuberculous mycobacteria (NTM) are increasingly implicated in healthcare facility-associated (HCFA) outbreaks. However, systematic methods for NTM surveillance and outbreak detection are lacking and represent an emerging need. We examined how statistical process control (SPC) might perform in NTM outbreak detection. Methods SPC charts were optimized for surgical site infection surveillance and adapted to analyze 3 NTM outbreaks that occurred from 2013-2016 at a single hospital. The first 2 outbreaks occurred contemporaneously and consisted of pulmonary Mycobacterium abscessus complex (MABC) acquisition and cardiac surgery-associated extrapulmonary MABC infection, respectively. The third outbreak was a pseudo-outbreak of Mycobacterium avium complex (MAC) at a bronchoscopy suite. We retrospectively analyzed monthly rates of unique patients who had: 1) positive respiratory cultures for MABC obtained on hospital day 3 or later; 2) positive non-respiratory cultures for MABC; and 3) positive bronchoalveolar lavage (BAL) cultures for MAC collected at the bronchoscopy suite. For each outbreak, we used these rates to construct a standardized moving average (MA) SPC chart with MA span of 3 months. Rolling baselines were estimated from average rates for months 7 through 12 prior to each monthly data point. SPC detection was indicated by the first data point above the upper control limit (UCL) of 3 standard deviations. Traditional surveillance detection was defined as the time of outbreak detection by routine infection control procedures. Results SPC detection occurred 5, 4, and 9 months prior to traditional surveillance outbreak detection for the three outbreaks, respectively (Figures 1 and 2). Prospective NTM surveillance using the MA chart potentially would have prevented an estimated 19 cases of pulmonary MABC, 9 cases of extrapulmonary MABC, and 80 cases of BAL MAC isolation (Table). No data points exceeded the UCL during 95 cumulative months of post-outbreak surveillance, suggesting low burden of false positive SPC signals. Figure 1. Use of a moving average statistical process control (SPC) chart for early detection of hospital-associated outbreaks of pulmonary Mycobacterium abscessus complex (MABC) and cardiac surgery-associated extrapulmonary MABC infection. The pulmonary chart analyzes cases of hospital-onset respiratory isolation of MABC. The extrapulmonary chart analyzes cases of positive non-respiratory cultures for MABC. CL, center line; LCL, lower control limit; UCL, upper control limit. Figure 2. Use of a moving average statistical process control (SPC) chart for early detection of a pseudo-outbreak of Mycobacterium avium complex (MAC) that occurred at a bronchoscopy suite. The chart analyzes cases of MAC isolated from bronchoalveolar lavage cultures. CL, center line; LCL, lower control limit; UCL, upper control limit. Table. Estimated cases of hospital-associated nontuberculous mycobacteria that would have been prevented by prospective surveillance with a moving average statistical process control (SPC) chart. Conclusion A single MA SPC chart detected 3 HCFA NTM outbreaks an average of 6 months earlier than traditional surveillance. SPC has potential to improve NTM surveillance, promoting early cluster detection and prevention of HCFA NTM. Disclosures All Authors: No reported disclosures

scholarly journals 1232. Potential Health and Cost Outcomes of Optimized Statistical Process Control Use for Surgical Site Infection Surveillance

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S443-S444
Author(s):  
Nicole Nehls ◽  
Iulian Ilieş ◽  
James C Benneyan ◽  
Arthur W Baker ◽  
Deverick J Anderson
Keyword(s):  
United States ◽  
Pilot Study ◽  
Process Control ◽  
Statistical Process Control ◽  
Healthcare Costs ◽  
Cost Savings ◽  
The United States ◽  
Outbreak Detection ◽  
Statistical Process ◽  
Traditional Surveillance

Abstract Background Surgical site infections (SSIs) are common (160,000–300,000 per year in the United States) and costly ($6,000–$25,500 per event) healthcare-associated infections with potentially lethal outcomes (2.1%–6.7% mortality rate). A prior analysis by our group suggested that statistical process control (SPC) can detect SSI outbreaks earlier than traditional epidemiological surveillance methods. This study aimed to quantify the potential impact of SPC surveillance on patient outcomes (prevented SSIs and deaths) and healthcare costs. Methods We retrospectively analyzed 30 SSI outbreaks occurring over a period of 8 years in a network of 50 community hospitals from the Southeastern United States. We applied 24 control chart variations, including 2 optimized for SSI surveillance, 6 with expert-defined pre-outbreak baselines (used in our pilot study), 4 with lagged rolling baselines (idem), and 12 common practice ones (using rolling baselines with no lag or fixed baselines). The charts used procedure-specific data from either the outbreak hospital or the entire network to compute baseline SSI rates. We calculated the average SSI rates during, before and after the outbreaks, and the months elapsed between SPC and traditional detection. We then used these values to estimate the number of SSIs that could have been prevented by SPC, and corresponding deaths avoided and cost savings (Figure 1). Results Optimized charts detected 96% of the outbreaks earlier than traditional surveillance, while pilot study and common practice charts did so only 65% (58%) of the time (Figure 2). Optimized charts could potentially prevent 15.2 SSIs, 0.64 deaths, and save $226,000 in excess care costs per outbreak. Overall, charts using network baselines performed better than those relying on local hospital data. Commonly used variations were the least effective, but were still able to improve on traditional surveillance (Figure 3). Conclusion SPC methods provide a great opportunity to prevent infections and deaths and generate cost savings, ultimately improving patient safety and care quality. While common practice SPC charts can also speed up outbreak detection, optimized SPC methods have a significantly higher potential to prevent SSIs and reduce healthcare costs. Disclosures All authors: No reported disclosures.

scholarly journals Successful Reduction of Postoperative Chest Tube Duration and Length of Stay After Congenital Heart Surgery: A Multicenter Collaborative Improvement Project

2021 ◽  
Author(s):  
Katherine E. Bates ◽  
Chloe Connelly ◽  
Lara Khadr ◽  
Margaret Graupe ◽  
Anthony M. Hlavacek ◽  
...  
Keyword(s):  
Collaborative Learning ◽  
Length Of Stay ◽  
Best Practices ◽  
Process Control ◽  
Statistical Process Control ◽  
Chest Tube ◽  
Congenital Heart ◽  
Geometric Mean ◽  
Learning Methods ◽  
Statistical Process

Background Congenital heart disease practices and outcomes vary significantly across centers, including postoperative chest tube (CT) management, which may impact postoperative length of stay (LOS). We used collaborative learning methods to determine whether centers could adapt and safely implement best practices for CT management, resulting in reduced postoperative CT duration and LOS. Methods and Results Nine pediatric heart centers partnered together through 2 learning networks. Patients undergoing 1 of 9 benchmark congenital heart operations were included. Baseline data were collected from June 2017 to June 2018, and intervention‐phase data were collected from July 2018 to December 2019. Collaborative learning methods included review of best practices from a model center, regular data feedback, and quality improvement coaching. Center teams adapted CT removal practices (eg, timing, volume criteria) from the model center to their local resources, practices, and setting. Postoperative CT duration in hours and LOS in days were analyzed using statistical process control methodology. Overall, 2309 patients were included. Patient characteristics did not differ between the study and intervention phases. Statistical process control analysis showed an aggregate 15.6% decrease in geometric mean CT duration (72.6 hours at baseline to 61.3 hours during intervention) and a 9.8% reduction in geometric mean LOS (9.2 days at baseline to 8.3 days during intervention). Adverse events did not increase when comparing the baseline and intervention phases: CT replacement (1.8% versus 2.0%, P =0.56) and readmission for pleural effusion (0.4% versus 0.5%, P =0.29). Conclusions We successfully lowered postoperative CT duration and observed an associated reduction in LOS across 9 centers using collaborative learning methodology.

scholarly journals 85. Use of Dual Statistical Process Control Charts for Early Detection of Surgical Site Infection Outbreaks at a Community Hospital Network

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S4-S5
Author(s):  
Arthur W Baker ◽  
Nicole Nehls ◽  
Iulian Ilieş ◽  
James C Benneyan ◽  
Deverick J Anderson
Keyword(s):  
Early Detection ◽  
Process Control ◽  
Surgical Site Infection ◽  
Statistical Process Control ◽  
Window Size ◽  
Outbreak Detection ◽  
Site Infection ◽  
Community Hospitals ◽  
Statistical Process ◽  
Traditional Surveillance

Abstract Background We recently showed that the empirical use of a combination of 2 moving average (MA) statistical process control (SPC) charts was highly sensitive and specific for detecting potentially important increases in surgical site infection (SSI) rates. We performed this follow-up study to examine the performance of these same SPC charts when applied to known SSI outbreaks. Methods We retrospectively applied 2 MA SPC charts to all 30 SSI outbreaks investigated from 2007 to 2015 in a network of over 50 community hospitals. These outbreaks were detected via routine SSI surveillance activities that occurred in the network. We reviewed prior outbreak investigation documentation to determine the estimated time of outbreak onset and time of traditional surveillance outbreak detection. The first SPC chart utilized procedure-specific, composite SSI data from the hospital network for its baseline; the baseline for the second chart was calculated from SSI data from the outbreak hospital undergoing analysis. Both charts used rolling baseline windows but varied in baseline window size, rolling baseline lag, and MA window size. SPC chart outbreak detection occurred when either chart had a data point above the upper control limit of 1 standard deviation. Time of SPC detection was compared with both time of outbreak onset and time of traditional surveillance detection. Results With the dual chart approach, SPC detected all 30 outbreaks, including detection of 25 outbreaks (83%) prior to their estimated onset (Figure 1). SPC detection occurred a median of 16 months (interquartile range, 12–21 months) prior to the date of traditional outbreak detection, which never occurred prior to outbreak onset. Both individual SPC charts exhibited at least 90% sensitivity in outbreak detection, but the dual chart approach showed superior sensitivity and speed of detection (Figure 2). Conclusion A strategy that employed optimized, dual MA SPC charts retrospectively detected all SSI outbreaks that occurred over 9 years in a network of community hospitals. SPC outbreak detection occurred earlier than traditional surveillance detection. These optimized SPC charts merit prospective study to evaluate their ability to promote early detection of SSI clusters in real-world scenarios. Disclosures All Authors: No reported Disclosures.

APPLICATION OF SPC AND GROWTH MODELS ON THE ECONOMIC TRAJECTORY OF THE LEATHER AND FOOTWEAR INDUSTRY

2020 ◽  
Author(s):  
Mario Lesina ◽  
Lovorka Gotal Dmitrovic
Keyword(s):  
Human Capital ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Growth Models ◽  
Pearson Correlation ◽  
Small Companies ◽  
Statistical Process ◽  
Production Staff ◽  
Footwear Industry

The paper shows the relation among the number of small, medium and large companies in the leather and footwear industry in Croatia, as well as the relation among the number of their employees by means of the Spearman and Pearson correlation coefficient. The data were collected during 21 years. The warning zone and the risk zone were determined by means of the Statistical Process Control (SPC) for a certain number of small, medium and large companies in the leather and footwear industry in Croatia. Growth models, based on externalities, models based on research and development and the AK models were applied for the analysis of the obtained research results. The paper shows using the correlation coefficients that The relation between the number of large companies and their number of employees is the strongest, i.e. large companies have the best structured work places. The relation between the number of medium companies and the number of their employees is a bit weaker, while there is no relation in small companies. This is best described by growth models based on externalities, in which growth generates the increase in human capital, i.e. the growth of the level of knowledge and skills in the entire economy, but also deductively in companies on microeconomic level. These models also recognize the limit of accumulated knowledge after which growth may be expected. The absence of growth in small companies results from an insufficient level of human capital and failure to reach its limit level which could generate growth. According to Statistical Process Control (SPC), control charts, as well as regression models, it is clear that the most cost-effective investment is the investment into medium companies. The paper demonstrates the disadvantages in small, medium and large companies in the leather and footwear industry in Croatia. Small companies often emerge too quickly and disappear too easily owing to the employment of administrative staff instead of professional production staff. As the models emphasize, companies need to invest into their employees and employ good production staff. Investment and support to the medium companies not only strengthens the companies which have a well-arranged technological process and a good systematization of work places, but this also helps large companies, as there is a strong correlation between the number of medium and large companies.

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