BACKGROUND: Approximately half of the life-limiting events, such as cardiopulmonary arrests or cardiac arrhythmias occurring in hospitals, are considered preventable. These critical events are usually preceded by clinical deterioration. Rapid response teams (RRTs) were introduced to intervene early in the course of clinical deterioration and possibly prevent progression to an event. An RRT was introduced at the Cleveland Clinic in 2009 and transitioned to an anesthesiologist-led system in 2012. We evaluated the association between in-hospital mortality and: (1) the introduction of the RRT in 2009 (primary analysis), and (2) introduction of the anesthesiologist-led system in 2012 and other policy changes in 2014 (secondary analyses). METHODS: We conducted a single-center, retrospective analysis using the medical records of overnight hospitalizations from March 1, 2005, to December 31, 2018, at the Cleveland Clinic. We assessed the association between the introduction of the RRT in 2009 and in-hospital mortality using segmented regression in a generalized estimating equation model to account for within-subject correlation across repeated visits. Baseline potential confounders (demographic factors and surgery type) were controlled for using inverse probability of treatment weighting on the propensity score. We assessed whether in-hospital mortality changed at the start of the intervention and whether the temporal trend (slope) differed from before to after initiation. Analogous models were used for the secondary outcomes. RESULTS: Of 628,533 hospitalizations in our data set, 177,755 occurred before and 450,778 after introduction of our RRT program. Introduction of the RRT was associated with a slight initial increase in in-hospital mortality (odds ratio [95% confidence interval {CI}], 1.17 [1.09–1.25]; P < .001). However, while the pre-RRT slope in in-hospital mortality over time was flat (odds ratio [95% CI] per year, 1.01 [0.98–1.04]; P = .60), the post-RRT slope decreased over time, with an odds ratio per additional year of 0.961 (0.955–0.968). This represented a significant improvement (P < .001) from the pre-RRT slope. CONCLUSIONS: We found a gradual decrease in mortality over a 9-year period after introduction of an RRT program. Although mechanisms underlying this decrease are unclear, possibilities include optimization of RRT implementation, anesthesiology department leadership of the RRT program, and overall improvements in health care delivery over the study period. Our findings suggest that improvements in outcome after RRT introduction may take years to manifest. Further work is needed to better understand the effects of RRT implementation on in-hospital mortality.

BACKGROUND: Precision medicine aims to change treatment from a “one-size-fits-all” approach to customized therapies based on the individual patient. Applying a precision medicine approach to a heterogeneous condition, such as the cardiopulmonary bypass (CPB)–induced inflammatory response, first requires identification of homogeneous subgroups that correlate with biological markers and postoperative outcomes. As a first step, we derived clinical phenotypes of the CPB-induced inflammatory response by identifying patterns in perioperative clinical variables using machine learning and simulation tools. We then evaluated whether these phenotypes were associated with biological response variables and clinical outcomes. METHODS: This single-center, retrospective cohort study used Cleveland Clinic registry data from patients undergoing cardiac surgery with CPB from January 2010 to March 2020. Biomarker data from a subgroup of patients enrolled in a clinical trial were also included. Patients undergoing emergent surgery, off-pump surgery, transplantation, descending thoracoabdominal aortic surgery, and planned ventricular assist device placement were excluded. Preoperative and intraoperative variables of patient baseline characteristics (demographics, comorbidities, and laboratory data) and perioperative data (procedural data, CPB duration, and hemodynamics) were analyzed to derive clinical phenotypes using K-means–based consensus clustering analysis. Proportion of ambiguously clustered was used to assess cluster size and optimal cluster numbers. After clusters were formed, we summarized perioperative profiles, inflammatory biomarkers (eg, interleukin [IL]-6 and IL-8), kidney biomarkers (eg, urine neutrophil gelatinase–associated lipocalin [NGAL] and IL-18), and clinical outcomes (eg, mortality and hospital length of stay). Pairwise standardized difference was reported for all summarized variables. RESULTS: Of 36,865 eligible cardiac surgery cases, 25,613 met inclusion criteria. Cluster analysis derived 3 clinical phenotypes: α, β, and γ. Phenotype α (n = 6157 [24%]) included older patients with more comorbidities, including heart and kidney failure. Phenotype β (n = 10,572 [41%]) patients were younger and mostly male. Phenotype γ (n = 8884 [35%]) patients were 58% female and had lower body mass index (BMI). Phenotype α patients had worse outcomes, including longer hospital length of stay (mean = 9 days for α versus 6 for both β [absolute standardized difference {ASD} = 1.15] and γ [ASD = 1.08]), more kidney failure, and higher mortality. Inflammatory biomarkers (IL-6 and IL-8) and kidney injury biomarkers (urine NGAL and IL-18) were higher with the α phenotype compared to β and γ immediately after surgery. CONCLUSIONS: Deriving clinical phenotypes that correlate with response biomarkers and outcomes represents an initial step toward a precision medicine approach for the management of CPB-induced inflammatory response and lays the groundwork for future investigation, including an evaluation of the heterogeneity of ...