ICU bouncebacks likely result from a combination of processes that are patient-specific, disease-specific, and healthcare system-specific [32, 33]. Some bouncebacks are likely unpreventable and represent new clinical events or unavoidable progression of disease ; consequently, their worth as a quality care metric has been questioned [7, 22, 33]. However, many bouncebacks are likely preventable due to premature discharge from ICUs, insufficient care after transfer, or failures in communication during critical handoff periods [27, 34,35,36]. An optimal bounceback rate is unclear, but is likely not zero percent given the inevitability of new clinical events and that managing patients in an intensive care setting for longer would cause practical problems with bed flow, lead higher costs, and lead to suboptimal overall resource utilization.
The most common factors cited as leading to unplanned ICU readmissions are respiratory distress/hypoxia, cardiac arrhythmias, and sepsis [3,4,5, 10,11,12,13, 37, 38]. Other risk factors that are cited less commonly include recent fever, neurological diagnosis, upper gastrointestinal bleeding, and older age [7, 13, 39]; however, these risk factors are largely derived from medical, surgical, and combined ICUs instead of those with exclusively neuro-ICU patients cared for in dedicated units. In the study that focused on neurologic patients, similarly non-neuromuscular respiratory failure and sepsis were the leading causes of bouncebacks . Aside from clinical diagnoses, a variety of both single-item values, including hematocrit, respiratory rate, and PaCO2, have been reported to be associated with ICU readmission [7, 38, 39] and several composite scores of acuity and illness including APACHE II and APS have been examined as possible predictors [7, 14,15,16,17]. Overnight transfers have been thought to potentially relate to a higher risk of bounceback, but studies have been mixed [10, 18, 19] as have studies of ICU stress and acuity [11, 20].
Several strategies at improving care for patients around the time of transfer have been utilized. Checklists focusing on structured reporting of a standard set of information have been utilized in several studies and settings [12, 31, 40,41,42] but have come under fire for adding burden to providers with sometimes unclear utility [43, 44]. ICU outreach programs in some setting have been shown to aid in transitions both to and from the ICU, aiding in communication and in some studies, decreasing mortality [24,25,26,27, 45, 46]. Lastly, integrating of several services around the time of transfer is another potentially useful strategy [28, 29].
At our urban academic tertiary care center, the bounceback rate for neurology patients over a nearly 28-month period was 6.7% after removing patients meeting exclusionary criteria; this was similar to other ICUs in our health system and other studies [9, 12, 13, 21,22,23, 38]. While those patients had a myriad of primary neurologic diagnoses, the reasons for their bouncebacks were largely medical. Several of these issues were common sequelae of their primary neurologic diagnoses or frequently co-occur, which further serves to emphasize the necessity of multidisciplinary care for neurologic patients. Similar to previous studies, respiratory distress and hypoxia, sepsis/hypotension, and cardiac arrhythmias drove the majority of the bouncebacks rather than primary neurological events such as recurrent stroke or seizure. Our study did not show a difference in post-hospitalization disposition as has been demonstrated in prior studies, and we only saw a significant increase in hospital length of stay in bounceback cases during the pilot project. It is possible that the sample size was not large enough to detect a difference given the low number of bouncebacks that occurred in the retrospective portion of the study.
Based on the results of our retrospective analysis, as well as previous studies, we designed a simple checklist to help identify patients who may be at high risk of decompensation within 48 h of transfer. The checklist was easy to use, quick to complete, and served as a highly visible reminder of the patients’ risk factors. After extensive stakeholder analysis and contextual inquiry, we designed a multidisciplinary transfer process that included physicians, nurses, and respiratory therapists and utilized technology to help augment communication and evaluation of patients felt to be at “high risk” for bounceback. The intervention was implemented in stages, which allowed for familiarization and sequential workflow refinements prior to the addition of other care providers. Multiple rounds of provider education aided with initial familiarization and refinements to the checklist layout and workflow occurred throughout the intervention.
Our tool was able to identify patients who were at high risk for bounceback, and two factors, subjective aspiration risk and cardiac arrhythmia in the ICU, were both independent predictors of bouncebacks. Analysis indicated that this checklist performed would have performed optimally if the presence of two risk factors instead of one was used to define a transferring patient as high risk. This will be a consideration moving forward weighing more efficient resource utilization against decreased sensitivity of the tool. Interestingly during the pilot, stroke patients were more likely to bounceback. This may indicate that our intervention was more effective for patients with non-stroke diagnoses and also highlights that this population may benefit from targeted interventions in the future. While there was a decrease in the incidence of bouncebacks between our retrospective analysis and during the pilot, this did not reach statistical significance. Stage 3 of the intervention, when all departments and technological changes were in place, had the lowest occurrence of bouncebacks. Lack of significance is likely due to low power owing to a low overall event rate.
ICU length of stay was not increased during the pilot, indicating that providers did not keep patients in the ICU for a longer period of time based on the intervention. Overall, compliance with the intervention was high and providers felt that it improved patient care and should be continued. The most common suggestions for future improvements included streamlining the ICU physicians’ portion of the documentation and limiting respiratory therapists’ involvement to patients with exclusively respiratory-associated risk factors.
There are several limitations to this study. We did not include neurosurgical patients in this initial pilot because the risk factors associated with neurosurgical patients were likely to be significantly different from a neuromedical population. Furthermore, the workflow of the neurosurgical ICU teams at our institution differs significantly from the neuromedical teams which would have required significant alterations in the quality improvement pilot. However, after the results of this pilot, our neurosurgical department has recently adopted their own version of this intervention which is currently in use. In our retrospective analysis, we did not compare the rates of risk factors against patients who did not bounceback, but rather used the commonly cited reasons for bounceback in those patients who did as well as factors cited in previous studies [3,4,5, 9,10,11,12]. NeuroICU bouncebacks occurred at a low rate in both the retrospective and prospective portions of our studies. There was not a significantly decreased risk of bounceback during the intervention, and continued data collection is necessary. There was data loss that was attributable to the physical loss of bounceback sheets; while this loss did not affect analysis of risk factors or bounceback rates, it may have affected analysis of compliance with pilot procedures and time to assessment goals. However, we believe the missing data to be non-differentially lost at random. The significance of bouncebacks from a floor to a step-down unit is also unclear as this population has not been studied elsewhere. Lastly, it is not clear whether this intervention performed at a single center is amenable to similar approach in other neuro-ICUs. A cornerstone of quality improvement work is the use of contextual inquiry in local environments to help design procedures and initiatives. While this work may not be immediately generalizable, we believe that this approach, similarly applied, may lead to center-specific solutions that aid in improving care for this population during a vulnerable transition.