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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Hosp Med. Author manuscript; available in PMC 2014 January 1.
Published in final edited form as:
Published online 2012 September 28. doi:  10.1002/jhm.1977
PMCID: PMC3538927
NIHMSID: NIHMS418083

The impact of proactive rounding by a rapid response team on patient outcomes at an academic medical center

Abstract

Background

The impact of rapid response teams (RRT) on patient outcomes remains uncertain.

Objective

To examine the effect of proactive rounding by an RRT on outcomes of hospitalized adults discharged from intensive care.

Design

Retrospective, observational study.

Setting

Academic medical center.

Patients

All adult patients discharged alive from the intensive care unit (ICU) at the University of California San Francisco Medical Center between January 2006 and June 2009.

Intervention

Introduction of proactive rounding by an RRT.

Measurements

Outcomes included the ICU readmission rate, ICU average length of stay (LOS), and in-hospital mortality of patients discharged from the ICU. Data were obtained from administrative billing databases and analyzed using an interrupted time series (ITS) model.

Results

We analyzed 17 months of pre-intervention data and 25 months of post-intervention data. Introduction of proactive rounding by the RRT did not change the ICU readmission rate (6.7% before versus 7.3% after, p = 0.24), the ICU LOS (5.1 days versus 4.9 days, p = 0.24) or the in-hospital mortality of patients discharged from the ICU (6.0% versus 5.5%, p=0.24). ITS models testing the impact of proactive rounding on secular trends found no improvement in any of the three clinical outcomes relative to their pre-intervention trends.

Conclusions

Proactive rounding by an RRT did not improve patient outcomes, raising further questions about RRT benefits.

Keywords: Hospital rapid response team, intensive care unit, readmission, hospital mortality, length of stay, academic medical centers

Introduction

Rapid response teams (RRT) have been promoted by numerous patient safety organizations to reduce preventable in-hospital deaths.14 Initial studies of RRTs were promising,57 but recent literature,811 including systematic reviews and meta-analyses, has called these findings into question. Nevertheless, RRTs remain popular in academic and community hospitals worldwide, and many have expanded their roles beyond solely responding to the deteriorating patient.12

Some RRTs, for example, proactively round on seriously ill ward patients and patients recently discharged from the intensive care unit (ICU) in an effort to prevent transitions to higher levels of care. Priestley demonstrated that institution of such a team, referred to as a critical care outreach team (CCOT), decreased in-hospital mortality while possibly increasing hospital length of stay (LOS).13 Three additional single-center studies from the United Kingdom, where CCOTs are common, specifically examined proactive rounding by CCOTs on the ICU readmission rate: two observed no improvement,1415 while the third, limited by a small sample size, demonstrated a modest reduction in ICU readmissions.16

We sought to determine the impact of proactive rounding by an RRT on patients discharged from intensive care on: the ICU readmission rate, ICU LOS, and in-hospital mortality of patients discharged from the ICU. We hypothesized that proactive rounding by an RRT would decrease the ICU readmission rate, ICU LOS, and the in-hospital mortality of patients discharged from the ICU.

Materials and Methods

Site and Subjects

We carried out a retrospective, observational study of adult patients discharged from the ICU at UCSF Medical Center between January 2006 and June 2009. UCSF is a 790-bed quaternary care academic hospital that admits approximately 17,000 patients annually and has five adult ICUs, with 62 beds and 3500 to 4000 ICU admissions annually. Our study was approved by the UCSF Medical Center Committee on Human Research; need for informed consent was waived.

Description of the RRT before June 1, 2007

Throughout the study, the goal of the RRT was unchanged: to assess, triage, and institute early treatment in patients who experienced an acute decline in their clinical status. From November 2005 to October 2006, the RRT was staffed by an attending hospitalist and medicine resident during daytime and by a critical care fellow at nighttime and on weekends. The RRT could be activated by any concerned staff member in response to a set of predetermined vital sign abnormalities, decreased urine output, or altered mental status, or simply if the staff member was concerned about the patient’s clinical status. Despite extensive educational efforts, utilization of the team was low (2.7 calls per 1000 admissions), and accordingly, it was discontinued in October 2006. After this time, staff would contact the primary team caring for the patient should concerns regarding the patient’s condition arise.

Description of the RRT after June 1, 2007

In an effort to expand its scope and utility, the RRT was reinstated on June 1, 2007 with a new composition and increased responsibilities. After this date, physician roles were eliminated, and the team composition changed to a dedicated critical care nurse and respiratory therapist, available 24 hours a day. Criteria for calling the team remained unchanged. In addition to responding to acute deteriorations in patients’ clinical courses, the RRT began to proactively assess all patients within 12 hours of discharge from the ICU and would continue to round on these patients daily until it was felt that they were clinically stable. During these rounds, the RRT would provide consultation expertise to the bedside nurse and contact the patient’s clinicians if concern existed about a patient’s clinical trajectory; decisions to transfer a patient back to the ICU ultimately rested with the patient’s primary team. During this time period, the RRT received an average of 110.6 calls per 1000 admissions.

Data sources

Data collected included: demographics, clinical information (APR severity of illness, APR risk of mortality, and the presence of 29 co-morbidities), whether there was a readmission to the ICU, the total ICU LOS, and the vital status at the time of hospital discharge.

Outcomes

Outcomes included: readmission to the ICU, defined as two non-contiguous ICU stays during a single hospitalization; ICU LOS, defined as the total number of ICU days accrued during hospitalization; and in-hospital mortality of patients discharged from the ICU.

Adjustment variables

Patient age, gender, race, and ethnicity were available from administrative data. We used admission diagnosis code data to classify co-morbidities using the method of Elixhauser.17

Statistical Analysis

For each of the three study outcomes, we assessed the effects of the intervention using multivariable models adjusting for patient- and service-level factors, including a gamma model for ICU LOS and logistic models for ICU readmission and in-hospital mortality of patients discharged from the ICU. We first compared unadjusted outcome levels before and after implementation. We then used an interrupted time series (ITS) framework to assess the effects of the intervention in terms of five measures: 1) the secular trend in the mean of the outcome before the intervention; 2) the change in the mean at the start of the implementation, or immediate effects; 3) the secular trend in the mean after implementation; 4) the change in secular trend, reflecting cumulative intervention effects; and 5) the net effect of the intervention, estimated as the adjusted difference between the fitted mean at the end of the post-intervention period and the expected mean if the pre-intervention trend had continued without interruption or change.

Secondary analyses

Given the heterogeneity of the RRT in the pre-intervention period, we assessed potential changes in trend at October 2006, the month in which the RRT was discontinued. We also examined changes in trend midway through the post-implementation period to evaluate for increased efficacy of the RRT with time.

Selection of co-variates

Age, race, and admitting service were included in both the pre-post and ITS models by default for face validity. Additional co-variates were selected for each outcome using backwards deletion with a retention criterion of p < 0.05, based on models that allowed the outcome rate to vary freely month to month. Because these data were obtained from administrative billing datasets, and the presence of co-morbidities could not be definitively linked with time points during hospitalization, only those co-morbidities that were likely present prior at ICU discharge were included. For similar reasons, APR severity of illness and risk of mortality scores, which were calculated from billing diagnoses at the end of hospitalization, were excluded from the models.

Results

Patient Characteristics

11,687 patients were admitted to the ICU during the study period; 10,288 were discharged from the ICU alive and included in the analysis. 4,902 (41.9%) patients were admitted in the 17 months prior to the introduction of proactive rounding by the RRT and 6,785 (58.1%) patients during the 25 months afterwards. Patients admitted in the two time periods were similar, although there were clinically small but statistically significant differences in race, APR severity of illness, APR risk of mortality, and certain co-morbidities between the two groups (Table 1).

Table 1
Patient Characteristics

ICU Readmission Rate

Introduction of proactive rounding by the RRT was not associated with unadjusted differences in the ICU readmission rate (6.7% pre-intervention versus 7.3% post-intervention, p = 0.24; Table 1). In the adjusted ITS model, the intervention had no net effect on the odds of ICU readmission (adjusted odds ratio [AOR] for net intervention effect 0.98, 95% confidence interval [CI] 0.42, 2.28), with similar secular trends both pre-intervention (AOR 1.00 per year, 95% CI 0.97, 1.03), and afterwards (AOR 0.99 per year, 95% CI 0.98, 01.00), and a non-significant increase at implementation (Table 2). Figure 1 uses solid lines to show the fitted readmission rates, a hatched line to show the projection of the pre-intervention secular trend into the post-intervention period, and circles to represent adjusted monthly means. The lack of a net intervention effect is indicated by the convergence of the solid and hatched lines 24 months post-intervention.

Figure 1
Adjusted ICU readmission rate before and after implementation of proactive rounding by the RRT.
Table 2
Adjusted impact of proactive rounding by an RRT on clinical outcomes

ICU Average LOS

Introduction of proactive rounding by the RRT was not associated with unadjusted differences in ICU average LOS (5.1 days pre-intervention versus 4.9 days post-intervention, p = 0.24; Table 1). Trends in ICU LOS may have changed in October 2006 (p=0.07), decreasing in the first half of the study period (adjusted rate ratio [ARR] 0.98 per year, 95% CI 0.96–1.00), but did not change significantly thereafter. As with the ICU readmission rate, neither the change in estimated secular trend after implementation (ARR 0.98, 95% CI 0.96–1.01), nor the net effect of the intervention (ARR 0.62, 95% CI 0.32–1.22) was statistically significant (Table 2); these results are depicted graphically in Figure 2.

Figure 2
Adjusted ICU LOS before and after implementation of proactive rounding by the RRT.

In-hospital Mortality of Patients Discharged from the ICU

Introduction of proactive rounding by the RRT was not associated with unadjusted differences in the mortality of patients discharged from the ICU (6.0% pre-intervention versus 5.5% post-intervention, p = 0.24; Table 1). Similarly, in the adjusted ITS model, the intervention had no statistically significant net effect on the mortality outcome (Table 2 and Figure 3).

Figure 3
Adjusted in-hospital mortality for patients discharged from the ICU before and after implementation of proactive rounding by the RRT.

Secondary analyses

Apart from weak evidence for a change in trend in ICU LOS in October 2006, no other changes in trend were found within the pre- or post-intervention periods (data not shown). This suggests that the heterogeneity of the pre-intervention RRT had no significant impact on the three outcomes examined and that the RRT intervention failed to gain efficacy with time in the post-intervention period. Additionally, we saw no outcome benefit in sensitivity analyses among all ICU patients or in service-defined analyses (e.g., surgical services), where ability to control for illness severity was improved.

Discussion

In this single center study, introduction of an RRT that proactively rounded on patients discharged from the ICU did not reduce the ICU readmission rate, ICU LOS, or mortality of patients discharged from the ICU after accounting for secular trends using robust ITS methods and adjusting for patient level factors.

Our study is consistent with two smaller studies that assessed the impact of proactive rounding by a CCOT on ICU readmission rate. Leary and colleagues14 found that proactively rounding by a CCOT did not reduce ICU readmissions or shorten the ICU LOS, although this study was limited by a surprisingly low ICU readmission rate and short ICU LOS prior to the intervention. Another study15 also observed no change in the ICU readmission rate following introduction of a proactively rounding CCOT but noted small reductions in both ICU and hospital mortality. The sole study showing an effect16 observed a lower ICU readmission rate and increased survival to hospital discharge (after excluding DNR patients) with implementation of a CCOT, although some of their findings may be explained by their CCOT’s use of palliative care services, a function not featured in our model.

Our study adds to the meta-analyses and systematic reviews810 that have questioned the hypothesis that a trained and proactive team of caregivers should be able to prevent patients from returning to the ICU. Perhaps one reason why this is not true is that proactive rounding by RRTs may have minimal effect in systems where “step-down” beds are readily available. At UCSF, nearly every patient transferred out of the ICU is triaged to a step-down unit, where telemetry and pulse oximetry are continuously monitored. Despite this, however, our institution’s two step-down units generate more calls to our RRT than any other units in the hospital.

We were surprised to see that proactive rounding failed to shorten ICU LOS, hypothesizing that clinicians would be more comfortable discharging patients from the ICU knowing that the RRT would be closely monitoring them afterwards. Although we have no data to support this hypothesis, increased use of the RRT may have also increased step-down bed use, as patients on the general medical-surgical floors were transferred to a higher level of care upon recommendation of the RRT, thereby delaying transfers out of the ICU. Moreover, the opening of an additional 16-bed ICU in October 2008 might have encouraged clinicians to transfer patients back to the ICU simply because beds were more easily accessible than before.

Introduction of proactive rounding by the RRT was also not associated with differences in the mortality rate of patients discharged from the ICU. This finding conflicts with the results of Garcea et al,15 Ball et al,16 and Priestley et al13, all of which found that implementation of a CCOT led to small but statistically significant reductions in in-hospital mortality. All three of these studies, however, examined smaller patient populations (1380, 470, and 2903 patients, respectively), and both the Ball and Priestley studies had significantly shorter periods of data collection (24 months and 32 weeks, respectively). Our results are based on models with confidence intervals and p-values that account for variability in all three underlying effect estimates but assume a linear extrapolation of the pre-intervention trend. This approach allowed us to flexibly deal with changes related to the intervention, while relying on our large sample size to define time trends not dealt with adequately (or at all) in previous research.

The lack of improvement in outcomes cannot be attributed to immaturity of the RRT or failure of the clinical staff to use the RRT adequately. A pre-specified secondary data analysis midway through the post-intervention time period demonstrated that the RRT failed to gain efficacy with time with respect to all three outcomes. The post-intervention RRT was also utilized far more frequently than its predecessor (110.6 vs. 2.7 calls per 1000 admissions, respectively), and this degree of RRT utilization far surpasses the “dose” considered to be indicative of a mature RRT system.12

Our study has several limitations. First, we relied on administrative rather than chart-collected data to determine the reason for ICU admission and the APR severity of illness and risk of mortality scores. It seems unlikely, however, that coding deficiencies or biases affected the pre-intervention and post-intervention patient populations differently. Even though we adjusted for all available measures, it is possible that we were not able to account for time trends in all potential confounders. Second, we did not have detailed clinical information on reasons for ICU readmission and whether readmissions occurred before or after the RRT proactively rounded on the patient. Therefore, potential readmissions to the ICU that might have been planned or which would have happened regardless of the presence of the RRT, such as for antibiotic desensitization, could not be accounted for. Third, introduction of proactive rounding by the RRT in June 2007 was accompanied by a change in the RRT’s composition, from a physician-led model to a nurse-led model. Therefore, inherent differences in the way that physicians and nurses might assess and triage patients could not have been adjusted for. Lastly, this was a retrospective study conducted at a single academic medical center with a specific RRT model, and our results may not be directly applicable to non-teaching settings or to different RRT models.

Our findings raise further questions about the benefits of RRTs as they assume additional roles, such as proactive rounding on patients recently discharged from the ICU. The failure of our RRT to reduce the ICU readmission rate, the ICU average LOS, and the mortality of patients discharged from the ICU raises concerns that the benefits of our RRT are not commensurate with its cost. While defining the degree of impact and underlying mechanisms are worthy of prospective study, hospitals seeking to improve their RRT models should consider how to develop systems that achieve the RRT’s promise in measureable ways.

  • ICU readmission model adjusted for attending service, age, race/ethnicity, co-morbidities (chronic pulmonary disease, weight loss, anemia, neurological disorders, rheumatoid arthritis, and solid tumors without metastasis), and clustering at the attending physician level.
  • Length of stay model adjusted for attending service, age, race/ethnicity, co-morbidities (drug abuse, rheumatoid arthritis, anemia, weight loss, paralysis, pulmonary circulation disease, neurological disorders, hypothyroidism, peptic ulcer disease, and solid tumors without metastasis), and clustering at the attending physician level.
  • Mortality model adjusted for attending service, age, race/ethnicity, co-morbidities (weight loss, lymphoma, metastatic cancer, chronic pulmonary and pulmonary circulation disease, and paralysis), and clustering at the attending physician level.

Acknowledgments

The authors would like to acknowledge Heather Leicester, MSPH, Senior Performance Improvement Analyst for Patient Safety and Quality Services at the University of California San Francisco for her work in data acquisition.

Funding/Support: Dr. Auerbach was supported by 5K24HL098372-02 from the National Heart Lung and Blood Institute during the period of this study although not specifically for this study; they had no role in the design or conduct of the study; the collection, analysis, or interpretation of the data; or the preparation, review, or approval of the manuscript.

Footnotes

Competing Interests

The authors have no competing financial interests to declare.

This manuscript is not under review by any other publication at this time.

Financial conflicts of interest:

Dr. Vittinghoff received salary support from an NIH grant during the time of this work for statistical consulting. He receives textbook royalties from Springer Verlag. Theo other authors have no financial conflicts of interest.

Author Contributions

Dr. Butcher had full access to all of the study data and takes responsibility for the integrity of the data and the accuracy of the data analysis. All remaining authors have seen the manuscript and agree with its contents.Study concept and design: Butcher, Auerbach, Vittinghoff.

Acquisition of data: Butcher

Analysis and interpretation of data: Butcher, Maselli, Vittinghoff, Auerbach.

Drafting of the manuscript: Butcher, Maselli, Vittinghoff, Auerbach.

Critical revision of the manuscript for important intellectual content: Butcher, Maselli, Vittinghoff, Auerbach

Statistical analysis: Maselli, Vittinghoff

Obtained funding: Auerbach.

Administrative, technical, or material support: Butcher, Auerbach

Study supervision: Butcher, Auerbach.

All authors read and approved the final manuscript.

Contributor Information

Dr. Brad W. Butcher, University of California San Francisco, Department of Medicine, Division of Hospital Medicine.

Dr. Eric Vittinghoff, University of California San Francisco, Department of Epidemiology and Biostatistics, Division of Biostatistics.

Ms Judith Maselli, University of California San Francisco, Department of Medicine, Division of Hospital Medicine.

Dr. Andrew D. Auerbach, University of California San Francisco, Department of Medicine, Division of Hospital Medicine.

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