More Than We Bargained For: The "Dominating" Cost Effectiveness of Sepsis Quality Improvement?

Critical Care Medicine www.ccmjournal. org 1465 Sepsis is a common, deadly, and expensive syndrome in which early, appropriate antibiotics and hemodynamic resuscitation appear to improve patient outcomes (1). This combination of facts has led to mandates, most notably the Centers for Medicare and Medicaid Services Severe Sepsis/ Septic Shock (SEP-1) measure and the New York State Sepsis Mandate, requiring hospitals to adopt sepsis treatment protocols and publicly report adherence to care bundle metrics. Despite signals for benefit (2), skeptics argue that these mandates—like the notorious pneumonia quality measure abandoned in the 2000s—lack adequate evidence and are likely to have unintended adverse consequences, including overtreatment of patients without sepsis (3, 4). To date, however, few studies have actually quantified such bystander effects. Despite the ongoing debate, essentially every hospital and health system we know of is currently expending substantial effort to improve performance on the SEP-1 measure. This is no trivial undertaking, as sepsis quality improvement efforts are expensive to initiate and maintain. Multiple costs borne by the health system include fixed investments in new electronic health record (EHR) tools, multidisciplinary workflow redesign, and baseline clinician education. In addition, there are direct and opportunity costs for ongoing clinical reinforcement, data collection, and continuing process improvement. It is no surprise that many clinicians and health systems find themselves asking, “Are these investments worthwhile? ” In this issue of Critical Care Medicine, Afshar et al (5) seek to answer this question from their health system’s perspective. After implementing a multimodal performance improvement program motivated by SEP-1, they conducted a before/after study addressing the program’s targeted and spillover effects in terms of patient outcomes and cost effectiveness. Their sepsis program appears similar to many others launched to address SEP-1, including clinician education, a dedicated data abstractor and program coordinator, ongoing physician feedback, and an EHR-based early warning score for inpatient deterioration. Their outcomes for the targeted population? Among SEP-1 eligible patients, the intervention appeared successful: postintervention median time-to-antibiotics decreased by 0.7 hours and risk-adjusted inpatient mortality fell by 1.8% in this population. At a cost of just over $100,000 per 1% decrease in risk-adjusted hospital mortality, their calculations suggest the quality improvement program was a potentially good bargain. However, what makes this study unique is that the primary analysis focused not on patients meeting SEP-1 criteria, but on a much larger population, namely all patients with suspected infection as defined by the timed dyad of culture and antibiotic. In other words, the authors studied the impact of a SEP-1 program on the less sick bystanders who have been of particular concern. In this broader suspected infection population, the postimplementation effects were impressive: among over 13,800 patients, median time to antibiotics shortened from 4.1 to 3.3 hours, adjusted in-hospital mortality decreased by 0.9%, and risk-adjusted charges per patient actually fell by $3,050. These decreased per patient charges far outweighed the programmatic costs of implementation, leading to an overall health system cost savings of over $272,000 per 1% decrease in hospital mortality. In cost-effectiveness terminology, an intervention like this one that simultaneously improves outcomes while also lowering costs is said to “dominate” its comparator. The results of the study by Afshar et al (5) therefore make implementation a seeming no-brainer. Of course, it is essential to interpret findings from this uncontrolled, single-center study with caution. The analysis implies a causal relationship between the quality improvement intervention and the outcome differences measured after sepsis program implementation. However, these types of partially controlled before/after analyses are vulnerable to being influenced by other “secular” changes in care unrelated to the intervention (6). Several alternative study designs are available to strengthen causal inference from investigations of systemic interventions and could lend greater confidence to the estimates from future studies (7). Another critical issue is that the intervention itself almost certainly altered the study population, as the criteria used to *See also p. 1371.