Abstract
Introduction:
Intensive care unit (ICU) readmissions are associated with increased morbidity and mortality rates, longer hospitalization, and increased health-care expenditures. This study sought to present a large cohort of trauma patients readmitted to the ICU, characterizing risk factors and providing quality improvement strategies to limit ICU readmission.
Methods:
A retrospective cohort analysis was conducted on adult trauma patients admitted to the ICU at a single level I trauma center from 2014 to 2021. Patients were split into readmission and no readmission groups. Patients experiencing readmission were compared to a similar group that was not readmitted using descriptive statistics and logistic regression.
Results:
In this study, 3632 patients were included and 278 (7.7%) were readmitted to the ICU. Significant differences were found in age, Elixhauser Comorbidity score, number of days on a ventilator, and number of patients requiring ventilator support. Furthermore, logistic regression showed that increasing age and the Elixhauser Comorbidity Score were associated with an increased likelihood of ICU readmission. Over the study period, the ICU readmission rate increased while the ICU length decreased.
Conclusions:
Age, Elixhauser Comorbidity score, and ventilator use were all significant risk factors for ICU readmission. During our study period, a concerning trend of increasing ICU readmissions and decreased ICU length of stay was found. By identifying this trend, our institution was able to employ mitigation strategies that have successfully reversed the trend in ICU readmissions, decreasing the rate below the national average.
Keywords: Critical care, Improvement, Intensive care, Quality, Readmission, Trauma
Introduction
Traumatic injury is a major cause of mortality worldwide.1 As a result, much attention has been focused on improving surgical techniques, perioperative management, and critical care practices. Despite advancements, intensive care unit (ICU) readmissions continue to present a dire situation for the injured patient. Such patients are at higher risk of morbidity, mortality, and future hospital readmission.2–4 The latter consequence highlights the connection between ICU readmissions and the Hospital Readmission Reduction Program, an effort by Medicare to emphasize the importance of quality improvement to prevent adverse outcomes for patients.5,6
Many studies have characterized risk factors associated with ICU readmission, specifically highlighting increasing age, higher injury severity score (ISS), and greater comorbidities.7–9 Several studies have attempted to propose solutions, including critical care transition programs and scores predicting the risk of ICU readmission.10,11 Despite the abundance of research on ICU readmissions, there are few studies focused trauma patients.12 Furthermore, much of this literature describes smaller trauma cohorts and focuses on risk factors and outcomes without addressing strategies for future improvement.
The objective of this study was to explore a large cohort of trauma patients unexpectedly readmitted to the ICU within a single level 1 trauma center in a rural state. In addition to characterizing risk factors for readmissions, we aim to provide insight into quality improvement strategies to limit ICU readmissions.
Methods
This study was a retrospective cohort analysis using electronic medical record data from the Arkansas Clinical Data Repository and Arkansas Trauma Registry from 2014 to 2021. The Institutional Review Board deemed this retrospective study exempt (IRB #261889). All trauma patients 18 y old or older who were admitted to the ICU at our adult level I trauma center were included in the query. All patients with in-hospital mortality were excluded to prevent survivor bias in the readmission analysis. The patients were divided into those with any ICU readmission, and those with no ICU readmission. Independent and control variables (demographic and clinical data) were collected, including age, gender, ISS, Elixhauser Comorbidity score, ventilator use, and time on ventilator. Days in the ICU were determined for the first ICU stay for those readmitted to the ICU. Individual comorbidities and mechanisms of injury were assessed with the three most common reported in each category. Max abbreviated injury scores were determined for each body region for each patient in the data set. Those patients with a score ≥3 were added together and found as a percentage of the total group population. Reasons for readmission were categorized from admitting physician notes on events leading to ICU readmission. The categories chosen were adapted from those previous used in the literature.13
Median and interquartile range were reported for continuous variables, with bivariate relationships assessed using the Wilcoxon rank-sum test. Chi-square tests were used to compare the relationship of categorical variables and ICU readmission. To identify significant associations between demographic and clinical factors and ICU readmission, we performed logistic regression utilizing ICU readmission as the dependent variable with independent and control variables as described in the preceding paragraph. Explanatory variables are presented as odds ratios, with P values and 95% confidence intervals. We assessed model fit using the Hosmer–Lemeshow test. We considered the results significant with a P value < 0.05. All statistical analyses were conducted using Stata 17 (College Station, TX: StataCorp LLC.).
Results
During the study period, 3632 trauma patients were admitted to the ICU at an adult American College of Surgeons level I trauma center in a rural state. Of these patients, 278 (7.7%) were readmitted to the ICU. Demographic data are displayed in Table 1. When comparing the readmission and noreadmission groups, the patients readmitted to the ICU were older, had higher Elixhauser Comorbidity Scores, were more likely to require ventilator support, and those who required ventilator support were on the ventilator longer. In addition, a significant difference was found between the two groups for ICU days on first admission and the top three comorbidities identified in the population. However, there was no significant difference in sex or ISS. Figure shows injury data, presenting the percentage of patients in the readmission or no readmission group with a Max abbreviated injury score ≥3 for each body region.
Table 1 –
Values are presented as median (Q1, Q3) unless indicated otherwise.
| Characteristics | No readmission (n = 3354) | Readmission (n = 278) | P value |
|---|---|---|---|
| Age | 46 (31, 62) | 59 (39, 72) | <0.001 |
| Female, n (%) | 955 (28.5) | 85 (30.6) | 0.46 |
| ISS | 17 (10, 27) | 18 (9, 27) | 0.883 |
| Elixhauser comorbidity score | 3 (2, 5) | 5 (3, 8) | <0.001 |
| Ventilator days | 6 (2, 12) | 12 (5, 19) | <0.001 |
| Ventilator use, n (%) | 1511 (45.1) | 148 (53.2) | 0.008 |
| ICU days | 2 (1, 5) | 3 (1, 6) | <0.001 |
| Top three comorbidities, n (%) | |||
| Neuro | 1710 (51.0) | 166 (59.7) | 0.005 |
| Chronic HTN | 1523 (45.4) | 175 (62.9) | <0.001 |
| Electrolytes | 1482 (44.2) | 190 (68.3) | <0.001 |
| Top three mechanisms, n (%) | |||
| MVC | 1004 (29.9) | 96 (34.5) | |
| Fall under 1 m | 543 (16.2) | 82 (29.5) | |
| Other blunt mechanism | 266 (7.9) | 21 (7.6) | |
Comorbidities determined by Elixhauser Comorbidity definitions.
MVC = motor vehicle collision.
Fig. –
Values are percentages of total No Readmission or Readmission population.
Table 2 shows the result of a logistic regression modeling the relationship between several characteristics and ICU readmission. Two relationships were found to be significant: increasing age (P = <0.001) and increasing Elixhauser Comorbidity score (P = <0.001) were both associated with increased likelihood of ICU readmission. The most common reasons for readmission were respiratory, cardiovascular, and neurologic (Table 3), and the proportion of reasons for readmission categories did not differ between the years included in the dataset.
Table 2 –
Factors associated with ICU readmission.
| Characteristic | Odds ratio | 95% CI | P value | |
|---|---|---|---|---|
| Age | 1.015 | 1.002 | 1.023 | <0.001 |
| Female | 0.897 | 0.678 | 1.186 | 0.445 |
| ISS | 1.009 | 0.998 | 1.019 | 0.112 |
| Elixhauser comorbidity score | 1.146 | 1.105 | 1.189 | <0.001 |
Hosmer–Lemeshow test = 7.7, P = 0.46.
CI = confidence interval.
Table 3 –
Reasons for readmission.
| Readmission reason | Patients |
|---|---|
| Respiratory | 95 (34.2%) |
| Cardiovascular | 45 (16.2%) |
| Neurologic | 28 (10.1%) |
| Metabolic | 14 (5.0%) |
| Postoperative observation | 12 (4.3%) |
| Infectious | 9 (3.2%) |
| Hemorrhage | 8 (2.9%) |
| Alcohol withdrawal | 7 (2.5%) |
| Surgical emergency | 6 (2.2%) |
| Other | 27 (9.7%) |
| None | 27 (9.7%) |
Values presented as number and percent of readmitted patient population.
Over the course of the study period, the proportion of ICU readmissions generally increased (P = < 0.001), with the lowest rate reported in 2014 (3.4% of patients) and the highest rate reported in 2020 (12.0% of patients). The data describing this trend are presented in Table 4. In addition, average ICU days generally decreased throughout the study period. The longest ICU stay was in 2015, with a median of 2.57 (1.17, 4.97), and the shortest ICU length of stay was in 2020, with a median of 1.83 (0.96, 4.60). An uptick in ICU length of stay occurred from 2020 to 2021. This trend is described by the data in Table 5.
Table 4 –
ICU readmissions over time.
| Year | Readmission rate |
|---|---|
| 2014 | 0.034 |
| 2015 | 0.041 |
| 2016 | 0.034 |
| 2017 | 0.078 |
| 2018 | 0.073 |
| 2019 | 0.104 |
| 2020 | 0.120 |
| 2021 | 0.107 |
| P value | <0.001 |
Values are presented as proportion of patients readmitted.
Table 5 –
Average ICU days over time.
| Year | Median ICU days |
|---|---|
| 2014 | 2.29 (1.05-7.81) |
| 2015 | 2.57 (1.17-4.97) |
| 2016 | 2.11 (1.04-5.39) |
| 2017 | 2.43 (1.26-5.32) |
| 2018 | 2.14 (1.28-4.45) |
| 2019 | 1.90 (1.06-3.71) |
| 2020 | 1.83 (0.96-4.60) |
| 2021 | 2.55 (1.38-5.38) |
Values are presented as median (Q1, Q3). ICU length of stay is determined from the first stay in the ICU for all patients.
Discussion
Our study found that 7.7% of patients admitted to the ICU for traumatic injuries had readmissions to the ICU during their hospitalization. Risk factors that showed a significant difference between readmitted and nonreadmitted groups included age, Elixhauser Comorbidity score, ventilator use, number of days on the ventilator, and longer length of index ICU admission. Differences were also seen in the most common comorbidities in the study groups. Logistic regression showed a likelihood of ICU readmission with increasing age and Elixhauser Comorbidity score. Finally, a trend of increasing ICU readmissions was found over the study period. These results reinforce several key risk factors presented in the literature and provide a platform for discussion about the trend in our data over time.
The overall rate of ICU readmission was found to range from 4 to 14% by Rosenberg et al. in 2000.14 Since then, several publications of trauma populations have reported anywhere from 2.8% to 9.2%.15–18 Our results fall on the upper end of this range, closer to that reported by Agoubi et al., who studied an elderly population (>65 y old).15 The difference of 20 y in the average age of our population points to the disproportionately high readmission rate seen in our study. This statistic is likely related to several factors associated with ICU readmission reporting that will be discussed further. The injury profile for patients in our population follow similar trends to those seen in Laytin et al. with head and thorax representing the largest portion of injuries. Our population differed most significantly with a much higher rate of lower extremity injuries.18 As expected, increasing age and greater Elixhauser Comorbidity scores were associated with an increased risk of ICU readmission, as seen in previously published data.7,16,18–20 Furthermore, Rubano et al. and Campbell et al. found the need for a ventilator and the number of days on a ventilator were risk factors for increased ICU readmissions, respectively.7,20 Our population showed these similar risk factors, reinforcing the findings in the current literature. Of note, in this study we focused on true readmission (patients who had a prior ICU stay during their hospitalization). Some studies and metrics include patients with “unplanned” ICU visits who unexpectedly need to be transferred from the regular floor to ICU care.9,20,21 In our institution the primary concern was readmissions, and thus this particular cohort was explored.
An important finding in our cohort was the increasing rate of ICU readmission over the study period. The literature has found that the rate of ICU readmission typically remains stable over time or decreases with increased awareness and implementation of reduction strategies.22–24 Furthermore, the increase noted in our institutional population was noteworthy, with an almost three-fold increase from 2014 to 2020. One possible explanation is an increased effort by the clinicians at our institution to progress patients toward discharge. In addition, the progressive floors at our institution improved their respiratory resources and staffing to care for more complicated patients. This improvement may have made clinicians feel comfortable discharging more complicated patients earlier. Finally, the COVID-19 pandemic led to increased demand for ICU beds, likely resulting in premature discharges for some trauma patients. Each of these explanations has a common feature of early or premature discharge. A trend toward early discharge can be seen in our data, corresponding to the increase in ICU readmissions.
Many strategies have been proposed for preventing ICU readmissions. Ranney et al. found that ICU discharges delayed more than 24 h were associated with decreased readmissions to the ICU.12 Although these delays were unintended, the principle of longer stays being related to decreased readmissions presents the need for better decision-making and support when deciding to discharge a patient from the ICU. Some research has shown the benefits of advanced practice provider coordination, but other studies have not demonstrated a benefit to critical care transition programs.11,25 For older trauma patients, Olufajo et al. found consulting geriatrics helped reduce adverse outcomes, including readmission to the ICU.26 Several tools, including the RISC Score, the National Early Warning Score, and the Modified Early Warning Score, have been used to predict patients at increased risk of ICU readmission.10,27 A final strategy used physiologic and medication data to model the risk of ICU readmission.28 As discussed above, there are multiple approaches to solving the problem of ICU readmission, but there remains a gap in establishing criteria for discharge from the ICU.
At our institution, the first step was simple awareness. By increasing awareness of the trend in decreasing ICU length of stay and increasing ICU readmissions, the clinicians at our institution were able to employ a more risk-averse strategy of maintaining patients in the ICU longer. Although other reasons including cardiovascular, neurologic, and metabolic were found in our population, the most common reason for ICU readmission at our institution was respiratory, which is similar to trends described in the literature.29,30 To combat this cause, our institution implemented increased respiratory resources to help prevent respiratory failure in susceptible patients. As an institution, we continue to monitor our readmission rate through quality improvement statistics such as those produced by the Trauma Quality Improvement Program of the American College of Surgeons. To validate the trend in our data and the subsequent improvement in ICU readmissions and ICU length of stay, we turned to the Trauma Quality Improvement Program–produced figures, which measure unplanned ICU admissions in a single institution compared to the national average. Based on the results, we are encouraged that the interventions we have employed so far have successfully decreased our ICU readmission rate from an odds ratio of 1.50 to an odds ratio of 0.70, significantly lower than the national average.
Several limitations to our research exist. Previous studies described a difference in outcomes among patients readmitted to the ICU within 72 h versus after 72 h21,31 These data were not collected for our population; thus, we cannot comment on the effects of readmission timing on outcomes for our patient population. Furthermore, data collection for our patient cohort was unable to differentiate in-hospital mortality during the index ICU stay or during readmission. For this reason, we cannot discern the direct relationship between ICU readmission and mortality. Finally, we intended to determine the risks of readmission and discuss strategies for quality improvement for our entire population. Therefore, we did not separate our population by age or mechanism of injury as has been done in previous research.18 Finally, our study is retrospective in nature with all inherent limitations of such research; we can demonstrate association but not causality.
Conclusion
Our study found an overall rate of ICU readmission among a trauma population of 7.7%. Age, Elixhauser Comorbidity score, and ventilator use were all significant risk factors for ICU readmission. In addition, logistic regression modeling showed increasing age and Elixhauser Comorbidity scores were associated with significantly increased likelihood of ICU readmission. During our study period, a concerning trend of increasing ICU readmissions and decreased ICU length of stay was found. By identifying this trend, our institution has employed mitigation strategies that have successfully reversed the trend in ICU readmissions, decreasing the rate below the national average.
Supplementary Material
Supplementary data related to this article can be found at doi: 10.1016/j.jss.2024.09.074.
Funding
He receives funding from the National Institute of Nursing Research, National Institute of General Medical Sciences, and the National Center For Advancing Translational Sciences of the National Institutes of Health under award numbers R21NR021063, R01GM111324, UL1TR003107, KL2TR003108, and TL1TR003109 and also from the United States Department of Commerce, Minority Business Development Agency as part of the Capital Readiness Program under award number GRTKT0126134345. He also receives funding from the Substance Abuse and Mental Health Services Administration under award number H79TI086020-01. The project described was made possible by the Translational Research Institute, grant UL1TR003107 through the National Center for Advancing Translational Sciences of the National Institutes of Health.
Footnotes
Disclosure
Dr Kyle J. Kalkwarf is the Trauma Medical Consultant for the Arkansas Trauma System. He is a trauma center site surveyor for the Arkansas Trauma System and a QSource consultant for the Arkansas Preventable Mortality Committee. He also teaches advanced trauma life support for the Arkansas Trauma Society and has been an expert on medical malpractice cases. Dr Kevin W. Sexton has equity in HoopCare, Inc; hDrop Technologies, Inc; Decisio Healthcare, Inc; and Datafy, LLC. He has licensed intellectual property owned by Vanderbilt University and the University of Arkansas for Medical Sciences. He also has equity in HoopCare, Inc; hDrop Technologies, Inc; and Decisio Healthcare, Inc. He has licensed intellectual property owned by Vanderbilt University and the University of Arkansas for Medical Sciences. The other authors have no conflicts to disclose.
CRediT authorship contribution statement
Benjamin Moore: Writing – review & editing, Writing – original draft. Kacee J. Daniels: Writing – review & editing, Data curation. Blake Martinez: Writing – review & editing. Kevin W. Sexton: Conceptualization. Kyle J. Kalkwarf: Writing – review & editing, Conceptualization. Matthew Roberts: Writing – review & editing. Stephen M. Bowman: Writing – review & editing, Formal analysis. Hanna K. Jensen: Writing – review & editing, Conceptualization.
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