Keeping It Simple in Sepsis Measures

Hospitals That Report Severe Sepsis and Septic Shock Bundle Compliance Have More Structured Sepsis Performance Improvement

PURPOSE

Sepsis is a common cause of death. The Centers for Medicare and Medicaid Services severe sepsis/septic shock (SEP-1) bundle is focused on improving sepsis outcomes, but it is unknown which quality improvement (QI) practices are associated with SEP-1 compliance and reduced sepsis mortality. The objectives of this study were to compare sepsis QI practices in SEP-1 reporting and nonreporting hospitals and to measure the association between sepsis QI processes, SEP-1 performance, and sepsis mortality.

MATERIALS AND METHODS

This study linked survey data on QI practices from Iowa hospitals to SEP-1 performance data and mortality. Characteristics of hospitals and sepsis QI practices were compared by SEP-1 reporting status. Univariable and multivariable logistic and linear regression estimated the association of QI practices with SEP-1 performance and observed-to-expected sepsis mortality ratios.

RESULTS

One hundred percent of Iowa's 118 hospitals completed the survey. SEP-1 reporting hospitals were more likely to have sepsis QI practices, including reporting sepsis quality to providers (64% versus 38%, P = 0.026) and using the case review process to develop sepsis care plans (87% versus 64%, P = 0.013). Sepsis QI practices were not associated with increased SEP-1 scores. A sepsis registry was associated with decreased odds of being in the bottom quartile of sepsis mortality (odds ratio, 0.37; 95% confidence interval, 0.14 to 0.96, P = 0.041), and presence of a sepsis committee was associated with lower hospital-specific mortality (observed-to-expected ratio, -0.11; 95% confidence interval, -0.20 to 0.01).

CONCLUSIONS

Hospitals reporting SEP-1 compliance conduct more sepsis QI practices. Most QI practices are not associated with increased SEP-1 performance or decreased sepsis mortality. Future work could explore how to implement these performance improvement practices in hospitals not reporting SEP-1 compliance.

Implementation approaches and barriers for rule-based and machine learning-based sepsis risk prediction tools: a qualitative study

Objective

Many options are currently available for sepsis surveillance clinical decision support (CDS) from electronic medical record (EMR) vendors, third party, and homegrown models drawing on rule-based (RB) and machine learning (ML) algorithms. This study explores sepsis CDS implementation from the perspective of implementation leads by describing the motivations, tool choices, and implementation experiences of a diverse group of implementers.

Materials and Methods

Semi-structured interviews were conducted with and a questionnaire was administered to 21 hospital leaders overseeing CDS implementation at 15 US medical centers. Participants were recruited via convenience sampling. Responses were coded by 2 coders with consensus approach and inductively analyzed for themes.

Results

Use of sepsis CDS is motivated in part by quality metrics for sepsis patients. Choice of tool is driven by ease of integration, customization capability, and perceived predictive potential. Implementation processes for these CDS tools are complex, time-consuming, interdisciplinary undertakings resulting in heterogeneous choice of tools and workflow integration. To improve clinician acceptance, implementers addressed both optimization of the alerts as well as clinician understanding and buy in. More distrust and confusion was reported for ML models, as compared to RB models. Respondents described a variety of approaches to overcome implementation barriers; these approaches related to alert firing, content, integration, and buy-in.

Discussion

While there are shared socio-technical challenges of implementing CDS for both RB and ML models, attention to user education, support, expectation management, and dissemination of effective practices may improve feasibility and effectiveness of ML models in quality improvement efforts.

Conclusion

Further implementation science research is needed to determine real world efficacy of these tools. Clinician acceptance is a significant barrier to sepsis CDS implementation. Successful implementation of less clinically intuitive ML models may require additional attention to user confusion and distrust.

Sepsis is a life-threatening illness. Improving sepsis care is a growing priority for many hospitals. Patients at risk of developing sepsis can be identified before they get very sick using tools that analyze data from computerized medical records systems. A variety of options are available from different sources. Some tools are programmed using established sepsis screening criteria used in clinical practice. Others rely on machine learning, where computer algorithms identify patterns in the available data without being pre-programmed by a human being. In this study, we interviewed 21 individuals at 15 US medical centers who oversaw hospital level implementations of these tools. Teams were motivated by wanting to improve quality of care for patients with sepsis. One major challenge was making the tools identify as many patients truly at risk for sepsis as possible while limiting false identification of patients not actually at risk. Many interviewees also described lack of trust in the tools from the nurses and doctors using the tools. There was more distrust and confusion reported by implementers of tools that relied on machine learning than tools that programmed human logic. Strategies emphasizing user education, user support, and expectation management were reported to be helpful.

Inpatient hospital performance is associated with post-discharge sepsis mortality

Background

Post-discharge deaths are common in patients hospitalized for sepsis, but the drivers of post-discharge deaths are unclear. The objective of this study was to test the hypothesis that hospitals with high risk-adjusted inpatient sepsis mortality also have high post-discharge mortality, readmissions, and discharge to nursing homes.

Methods

Retrospective cohort study of age-qualifying Medicare beneficiaries with sepsis hospitalization between January 2013 and December 2014. Hospital survivors were followed for 180-days post-discharge, and mortality, readmissions, and new admission to skilled nursing facility were measured. Inpatient hospital-specific sepsis risk-adjusted mortality ratio (observed: expected) was the primary exposure.

Results

A total of 830,721 patients in the cohort were hospitalized for sepsis, with inpatient mortality of 20% and 90-day mortality of 48%. Higher hospital-specific sepsis risk-adjusted mortality was associated with increased 90-day post-discharge mortality (aOR 1.03 per each 0.1 increase in hospital inpatient O:E ratio, 95% CI 1.03–1.04). Higher inpatient risk adjusted mortality was also associated with increased probability of being discharged to a nursing facility (aOR 1.03, 95% CI 1.02–1.03) and 90-day readmissions (aOR 1.03, 95% CI 1.02–1.03).

Conclusions

Hospitals with the highest risk-adjusted sepsis inpatient mortality also have higher post-discharge mortality and increased readmissions, suggesting that post-discharge complications are a modifiable risk that may be affected during inpatient care. Future work will seek to elucidate inpatient and healthcare practices that can reduce sepsis post-discharge complications.

Notice to comply: A systematic review of clinician compliance with guidelines surrounding acute hospital-based infection management

PURPOSE

To identify and characterize studies evaluating clinician compliance with infection-related guidelines, and to explore trends in guideline design and implementation strategies.

DATA SOURCES

PubMed database, April 2017. Followed the PRISMA Statement for systematic reviews.

STUDY SELECTION

Scope was limited to studies reporting compliance with guidelines pertaining to the prevention, detection, and/or treatment of acute hospital-based infections. Initial search (1,499 titles) was reduced to 49 selected articles.

DATA EXTRACTION

Extracted publication and guideline characteristics, outcome measures reported, and any results related to clinician compliance. Primary summary measures were frequencies and distributions of characteristics. Interventions that led to improved compliance results were analyzed to identify trends in guideline design and implementation.

RESULTS OF DATA SYNTHESIS

Of the 49 selected studies, 18 (37%), 13 (27%), and 10 (20%) focused on sepsis, pneumonia, and general infection, respectively. Six (12%), 17 (35%), and 26 (53%) studies assessed local, national, and international guidelines, respectively. Twenty studies (41%) reported 1-instance compliance results, 28 studies (57%) reported 2-instance compliance results (either before-and-after studies or control group studies), and 1 study (2%) described compliance qualitatively. Average absolute change in compliance for minimal, decision support, and multimodal interventions was 10%, 14%, and 25%, respectively. Twelve studies (24%) reported no patient outcome alongside compliance.

CONCLUSIONS

Multimodal interventions and quality improvement initiatives seem to produce the greatest improvement in compliance, but trends in other factors were inconsistent. Additional research is required to investigate these relationships and understand the implications behind various approaches to guideline design, communication, and implementation, in addition to effectiveness of protocol impact on relevant patient outcomes.

Driving blind: instituting SEP-1 without high quality outcomes data

In 2015, the Centers for Medicare and Medicaid Services (CMS) instituted an all-or-none sepsis performance measure bundle (SEP-1) to promote high-quality, cost-effective care. Systematic reviews demonstrated only low-quality evidence supporting most of SEP-1's interventions. CMS has removed some but not all of these unproven components. The current SEP-1 version requires patients with suspected sepsis have a lactate level, blood cultures, broad-spectrum antibiotics and, if hypotensive, a fixed 30 mL/kg fluid infusion within 3 hours, and a repeat lactate if initially elevated within 6 hours. Experts have continued to raise concerns that SEP-1 remains overly prescriptive, lacks a sound scientific basis and presents risks (overuse of antibiotics and inappropriate fluids not titrated to need). To incentivize compliance with SEP-1, CMS now publicly publishes how often hospitals complete all interventions in individual patients. However, compliance measured across hospitals (5 studies, 48-2,851 hospitals) or patients (three studies, 110-851 patients) has been low (approximately 50%) which is not surprising given SEP-1's lack of scientific basis. The largest observational study (1,738 patients) reporting survival rates employing SEP-1 found they were not significantly improved with the measure (P=0.53) as did the next largest study (851 patients, adjusted survival odds ratio 1.36, 95% CI, 0.85 to 2.18). Two smaller observational studies (158 and 450 patients) reported SEP-1 improved unadjusted survival (P≤0.05) but were confounded either by baseline imbalances or by simultaneous introduction of a code sepsis protocol to improve compliance. Regardless, retrospective studies have well known biases related to non-randomized designs, uncontrolled data collection and failure to adjust for unrecognized influential variables. Such low-quality science should not be the basis for a national mandate compelling care for a rapidly lethal disease with a high mortality rate. Instead, SEP-1 should be based on high quality reproducible evidence from randomized controlled trials (RCT) demonstrating its benefit and thereby safety. Otherwise we risk not only doing harm but standardizing it.