Early hospital readmission in the trauma population: are the risk factors different?

Fragmentation of Care After Geriatric Trauma: A Nationwide Analysis of outcomes and Predictors

The health care system for the elderly is fragmented, that is worsened when readmission occurs to different hospitals. There is limited investigation into the impact of fragmentation on geriatric trauma patient outcomes. The aim of this study was to compare the outcomes following readmissions after geriatric trauma. The Nationwide Readmissions Database (2016-2017) was queried for elderly trauma patients (aged ≥65 years) readmitted due to any cause. Patients were divided into 2 groups according to readmission: index vs non-index hospital. Outcomes were 30 and 180-day complications, mortality, and the number of subsequent readmissions. Multivariable logistic regression was performed to analyze the independent predictors of fragmentation of care. A total of 36,176 trauma patients were readmitted, of which 3856 elderly patients (aged ≥65 years) were readmitted: index hospital (3420; 89%) vs non-index hospital (436; 11%). Following 1:2 propensity matching, elderly with non-index hospital readmission had higher rates of death and MI within 180 days (P = .01 and .02, respectively). They had statistically higher 30 and 180-day pneumonia (P < .01), CHF (P < .01), arrhythmias (P < .01), MI (P < .01), sepsis (P < .01), and UTI (P < .01). On multivariable binary logistic regression analysis, pneumonia (OR 1.70, P = .03), congestive heart failure (CHF) (OR 1.80, P = .03), female gender (OR .72, P = .04), and severe Head and Neck trauma (AIS≥3) (OR 1.50, P < .01) on index admission were independent predictors of fragmentation of care. While the increase in time to readmission (OR 1.01, P < .01) was also associated independently with non-index hospital admission. Fragmented care after geriatric trauma could be associated with higher mortality and complications.

Transitional care programs for trauma patients: A scoping review

BACKGROUND

Transitional care programs establish comprehensive outpatient care after hospitalization. This scoping review aimed to define participant characteristics and structure of transitional care programs for injured adults as well as associated readmission rates, cost of care, and follow-up adherence.

METHODS

We conducted a scoping review in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews standard. Information sources searched were Medline, the Cochrane Library, CINAHL, and Scopus Plus with Full Text. Eligibility criteria were systematic reviews, clinical trials, and observational studies of transitional care programs for injured adults in the United States, published in English since 2000. Two independent reviewers screened all full texts. A data charting process extracted patient characteristics, program structure, readmission rates, cost of care, and follow-up adherence for each study.

RESULTS

A total of 10 studies described 9 transitional care programs. Most programs (60%) were nurse/social-worker-led post-discharge phone call programs that provided follow-up reminders and inquired regarding patient concerns. The remaining 40% of programs were comprehensive interdisciplinary case-coordination transitional care programs. Readmissions were reduced by 5% and emergency department visits by 13% among participants of both types of programs compared to historic data. Both programs improved follow-up adherence by 75% compared to historic data.

CONCLUSION

Transitional care programs targeted at injured patients vary in structure and may reduce overall health care use.

Injury-related emergency department visits and unplanned readmissions are associated with worse long-term mental and physical health

BACKGROUND

The risk factors for unplanned emergency department (ED) visits and readmission after injury and the impact of these unplanned visits on long-term outcomes are not well understood. We aim to: 1) describe the incidence of and risk factors for injury-related ED visits and unplanned readmissions following injury and, 2) explore the relationship between these unplanned visits and mental and physical health outcomes 6-12 months post-injury.

METHODS

Trauma patients with moderate-to-severe injury admitted to one of three Level-I trauma centers were asked to complete a phone survey to assess mental and physical health outcomes at 6-12 months. Patient reported data on injury-related ED visits and readmissions was collected. Multivariable regression analyses were performed controlling for sociodemographic and clinical variables to compare subgroups.

RESULTS

Of 7,781 eligible patients, 4675 were contacted and 3,147 completed the survey and were included in the analysis. 194 (6.2%) reported an unplanned injury-related ED visit and 239 (7.6%) reported an injury-related readmission. Risk factors for injury-related ED visits included: younger age, Black race, a lower level of education, Medicaid insurance, baseline psychiatric or substance abuse disorder and penetrating mechanism. Risk factors for unplanned injury-related readmission included younger age, male sex, Medicaid insurance, substance abuse disorder, greater injury severity and penetrating mechanism of injury. Injury-related ED visits and readmissions were associated with significantly higher rates of PTSD, chronic pain and new injury-related functional limitations in addition to lower SF-12 mental and physical composite scores.

CONCLUSIONS

Injury-related ED visits and unplanned readmissions are common after hospital discharge following treatment of moderate-severe injury and are associated with worse mental and physical health outcomes.

A multicenter, citywide report on recurrent violent injury

INTRODUCTION

The incidence of and risk factors for recurrent violent trauma are not well known. This information is needed to focus violence prevention efforts on at-risk cohorts. The purpose of this study was to determine the incidence of and risk factors for recurrence following violent injury in a large urban setting. We hypothesize that the overall incidence of recurrent violent injury is low but there are specific at-risk cohorts.

METHODS

A retrospective, citywide study of patients who sustained blunt assault or penetrating trauma from 2013 to 2019 was performed. Patients were tracked across all trauma centers using their name and date of birth. The primary outcome was incidence of recurrent violent injury, which was calculated by dividing the number of readmitted patients by the number who survived previous admissions due to penetrating trauma or blunt assault. Associations between readmission and injury severity score, abbreviated injury score, age, sex, hospital, mechanism of injury (MOI), and disposition were determined. Kaplan-Meier curves were plotted to determine the incidence of recurrent injury over time. A multivariable Cox proportional hazard model was used to examine the relationships between characteristics at first admission and time-to-readmission.

RESULTS

The recurrent injury rate was 836 patients (6.33%) out of 13,211 injured patients. Male, age 14-45 years old, discharge to jail or left against medical advice, and moderate/severe head injury were associated with re-injury. There was no association between recurrence and mechanism of injury or overall injury severity. Discharge to home was associated with a lower re-injury rate.

CONCLUSION

The low recurrent injury rate despite high injury prevalence suggests injury prevention efforts should target this demographic and their non-injured peers.

Predictors of hospital readmission after trauma: A retrospective cohort study in New Zealand

INTRODUCTION

Understanding predictors of hospital readmission following major trauma is important as readmissions are costly and some are potentially avoidable. This study describes the incidence of, and sociodemographic, injury-related and treatment-related factors predictive of, hospital readmission related to: a) all-causes, b) the index trauma injury, and c) subsequent injury events in the 30 days and 12 months following discharge for major trauma patients nationally in New Zealand.

METHODS

Data from the New Zealand Trauma Registry (NZTR) was linked with Ministry of Health hospital discharge data. Hospital readmissions were examined for all patients entered into the NZTR for an injury event between 1 January and 31 December 2018. Readmissions were examined for the 12-months following the discharge date for participant's index trauma injury.

RESULTS

Of 1986 people, 42% had ≥1 readmission in the 12 months following discharge; 15% within 30 days. Seven percent had ≥1 readmission related to the index trauma within 30 days of discharge; readmission was 3.43 (95% CI 1.87, 6.29) times as likely if the index trauma was self-inflicted compared to unintentional, and 1.64 (95% CI 1.15, 2.34) times as likely if the index trauma involved intensive care unit admission. Those admitted to hospital for longer for their index trauma were less likely to be readmitted due to their index trauma injury within 30 days compared to those admitted for 0-1 day. Seventeen percent were readmitted for a subsequent injury event within 12 months, with readmission more likely for older people (>65 years), those with comorbidities, Māori compared with non-Māori and those with higher trauma injury severity.

CONCLUSION

A substantial proportion of people are readmitted after discharge for major trauma. Factors identified in this study will be useful to consider when developing interventions to reduce preventable readmissions including those related to the index trauma injury, readmissions from other causes and subsequent injury-related readmissions. Further research specifically examining planned and unplanned readmissions is warranted.

Prospective validation and application of the Trauma-Specific Frailty Index: Results of an American Association for the Surgery of Trauma multi-institutional observational trial

BACKGROUND

The frailty index is a known predictor of adverse outcomes in geriatric patients. Trauma-Specific Frailty Index (TSFI) was created and validated at a single center to accurately identify frailty and reliably predict worse outcomes among geriatric trauma patients. This study aims to prospectively validate the TSFI in a multi-institutional cohort of geriatric trauma patients.

METHODS

This is a prospective, observational, multi-institutional trial across 17 American College of Surgeons Levels I, II, and III trauma centers. All geriatric trauma patients (65 years and older) presenting during a 3-year period were included. Frailty status was measured within 24 hours of admission using the TSFI (15 variables), and patients were stratified into nonfrail (TSFI, ≤0.12), prefrail (TSFI, 0.13-0.25), and frail (TSFI, >0.25) groups. Outcome measures included index admission mortality, discharge to rehabilitation centers or skilled nursing facilities (rehab/SNFs), and 3-month postdischarge readmissions, fall recurrences, complications, and mortality among survivors of index admission.

RESULTS

A total of 1,321 geriatric trauma patients were identified and enrolled for validation of TSFI (nonfrail, 435 [33%]; prefrail, 392 [30%]; frail, 494 [37%]). The mean ± SD age was 77 ± 8 years; the median (interquartile range) Injury Severity Score was 9 (5-13). Overall, 179 patients (14%) had a major complication, 554 (42%) were discharged to rehab/SNFs, and 63 (5%) died during the index admission. Compared with nonfrail patients, frail patients had significantly higher odds of mortality (adjusted odds ratio [aOR], 1.93; p = 0.018), major complications (aOR, 3.55; p < 0.001), and discharge to rehab/SNFs (aOR, 1.98; p < 0.001). In addition, frailty was significantly associated with higher adjusted odds of mortality, major complications, readmissions, and fall recurrence at 3 months postdischarge ( p < 0.05).

CONCLUSION

External applicability of the TSFI (15 variables) was evident at a multicenter cohort of 17 American College of Surgeons trauma centers in geriatric trauma patients. The TSFI emerged as an independent predictor of worse outcomes, both in the short-term and 3-month postdischarge.

LEVEL OF EVIDENCE

Prognostic and Epidemiological; Level III.

Hospital readmission after blunt traumatic rib fractures

BACKGROUND

Hospital readmissions are resource intensive, associated with increased morbidity, and often used as hospital-level quality indicators. The factors that determine hospital readmission after blunt thoracic trauma have not been sufficiently defined. We sought to identify predictors of hospital readmission in patients with traumatic rib fractures.

METHODS

We performed an 8-year (2011-2019) retrospective chart review of patients with traumatic rib fractures who required unplanned readmission within 30 days of discharge at a Level 1 trauma center. Patient characteristics, injury severity, and hospital complications were examined using quantitative analysis to identify readmission risk factors.

RESULTS

There were 13,046 trauma admissions during the study period. The traumatic rib fracture cohort consisted of 3,720 patients. The cohort included 206 patients who were readmitted within 30 days of discharge. The mean age of the traumatic rib fracture cohort was 57 years, with a 6-day median length of stay. The 30-day mortality rate was 5%. Use of anticoagulation (11.0 vs. 5.4; p = 0.029), diagnosis of a psychiatric disorder (10.2 vs. 5.3; p = 0.01), active smoking (7.3 vs. 5.0; p = 0.008), associated hemothorax (8.3 vs. 5.2; p = 0.010), higher abdominal Abbreviated Injury Scale (33.3 vs. 8.4 vs. 6.5; p = 0.002), rapid response activation (8.9 vs. 5.2; p = 0.005), admission to intensive care unit (7.7 vs. 4.5; p = 0.001), and diagnosis of in-hospital pneumonia (10.1 vs. 5.4; p = 0.022) were predictors of hospital readmission. On multivariate analysis, prescribed anticoagulation (odds ratio [OR], 2.22; p = 0.033), active smoking (OR, 1.58; p = 0.004), higher abdominal Abbreviated Injury Scale (OR, 1.50; p = 0.054), and diagnosis of a psychiatric disorder (OR, 2.00; p = 0.016) predicted hospital readmission.

CONCLUSION

In patients with traumatic rib fractures, those with anticoagulant use, those who actively smoke, those with a psychiatric diagnosis, or those with associated abdominal injuries are at the highest risk of rehospitalization following discharge. Quality improvement should focus on strategies and protocols directed toward these groups to reduce nonelective readmissions.

LEVEL OF EVIDENCE

Prognostic and Epidemiological; Level IV.

Costs and 30-day readmission after lower limb fractures from motorcycle crashes in Queensland, Australia: A linked data analysis

BACKGROUND

Lower limb trauma is the most common injury sustained in motorcycle crashes. There are limited data describing this cohort in Australia and limited international data establishing costs due to lower limb trauma following motorcycle crashes.

METHODS

This retrospective cohort study utilised administrative hospitalisation data from Queensland, Australia from 2011-2017. Eligible participants included those admitted with a principal diagnosis coded as lower extremity or pelvic fracture following a motorcycle crash (defined as the index admission). Multiply injured motorcyclists where the lower limb injury was not coded as the primary diagnosis (i.e. principal diagnosis was rather coded as head injury, internal organ injures etc.) were not included in the study. Hospitalisation data were also linked to clinical costing data. Logistic regression was used to determine risk factors for 30-day readmission. Costing data were compared between those readmitted and those who weren't, using bootstrapped t-tests and ANVOA.

RESULTS

A total of 3342 patients met eligibility, with the most common lower limb fracture being tibia/fibula fractures (40.8%). 212 participants (6.3%) were readmitted within 30-days of discharge. The following were found to predict readmission: male sex (OR 1.84, 95% CI 1.01-1.94); chronic anaemia (OR 2.19, 95% CI 1.41-3.39); current/ex-smoker (OR 1.60, 95% CI 1.21-2.12); emergency admission (OR 2.77, 95% CI 1.35-5.70) and tibia/fibula fracture type (OR 1.46, 95% CI 1.10-1.94). The most common reasons for readmission were related to ongoing fracture care, infection or post-operative complications. The average hospitalisation cost for the index admission was AU$29,044 (95% CI $27,235-$30,853) with significant differences seen between fracture types. The total hospitalisation cost of readmissions was almost AU$2 million over the study period, with an average cost of $10,977 (95% CI $9,131- $13,059).

CONCLUSIONS

Unplanned readmissions occur in 6.3% of lower limb fractures sustained in motorcycle crashes. Independent predictors of readmission within 30 days of discharge included male sex, chronic anaemia, smoking status, fracture type and emergency admission. Index admission and readmission hospitalisation costs are substantial and should prompt health services to invest in ways to reduce readmission.

Financial and Safety Impact of Simulation-based Clinical Systems Testing on Pediatric Trauma Center Transitions

Simulation offers multiple tools that apply to medical settings, but little is known about the application of simulation to pediatric trauma workflow changes. Our institution recently underwent significant clinical changes in becoming an independent pediatric trauma center. We used a simulation-based clinical systems testing (SbCST) approach to manage change-associated risks. The purpose of this study was to describe our SbCST process, evaluate its impact on patient safety, and estimate financial costs and benefits.

Safety of Elderly Fallers: Identifying Associated Risk Factors for 30-Day Unplanned Readmissions Using a Clinical Data Warehouse

BACKGROUND

Hospital readmissions are a major problem in the older people as they are frequent, costly, and life-threatening. Falls among older adults are the leading cause of injury, deaths, and emergency department visits for trauma.

OBJECTIVE

The main objective was to determine risk factors associated with a 30-day readmission after index hospital admission for fall-related injuries.

METHODS

A retrospective nested case-control study was conducted. Data from elderly patients initially hospitalized for fall-related injuries in 2019, in 11 of the Greater Paris University Hospitals and discharged home, were retrieved from the clinical data warehouse. Cases were admission of elderly patients who subsequently experienced a readmission within 30 days after discharge from the index admission. Controls were admission of elderly patients who were not readmitted to hospital.

RESULTS

Among 670 eligible index admissions, 127 (18.9%) were followed by readmission within 30 days after discharge. After multivariate analysis, men sex (odds ratio [OR] = 2.29, 95% confidence interval [CI] = 1.45-3.61), abnormal concentration of C-reactive protein, and anemia (OR = 2.22, 95% CI = 1.28-3.85; OR = 1.85, 95% CI = 1.11-3.11, respectively) were associated with a higher risk of readmission. Oppositely, having a traumatic injury at index admission decreased this risk (OR = 0.47, 95% CI = 0.28-0.81).

CONCLUSIONS

Reducing early unplanned readmission is crucial, especially in elderly patients susceptible to falls. Our results indicate that the probability of unplanned readmission is higher for patients with specific characteristics that should be taken into consideration in interventions designed to reduce this burden.

Emergency department return visits and hospital admissions in trauma team assessed patients initially discharged from the emergency department: a population-based cohort study

BACKGROUND

Many injured patients are transported directly to trauma centers, found to be minimally injured, and discharged directly home from the emergency department (ED). Our objectives were to characterize the short-term outcomes in this discharged patient population and to identify patient factors predictive of ED return visits.

METHODS

We conducted a retrospective population-based cohort study using linked administrative datasets involving patients assessed at trauma centers in Ontario, Canada between April 1, 2009 and March 31, 2020. Patients who were assessed by a trauma team and discharged directly home from ED were included. The primary outcome was the percentage of patients with an ED return visit within 14-days. We used multivariate logistic regression analyses to identify patient characteristics predictive of at least one ED return visit.

RESULTS

There were 5550 patients included in the study. 1004 (18.1%) of patients had at least one ED return visit but only 100 patients (1.8%) were admitted to hospital following initial discharge. Common reasons for ED return visits included wound care concerns (17.2%), head injury complaints (15.6%), and substance misuse (6.8%). Rural residence (OR 1.83, 95% CI: 1.45 - 2.29), history of anxiety disorder (OR 2.05, 95% CI: 1.54 - 2.73), high baseline ED usage (OR 2.58, 95% CI: 2.03 - 3.28), penetrating injury (OR 1.42, 95% CI: 1.20 - 1.68), and extremity fracture (OR 1.52, 95% CI: 1.24 - 1.88) predicted return visits.

CONCLUSION

Patients discharged directly have high rates of ED return visits but low rates of hospital admission or delayed surgical intervention. Trauma services should expand quality assurance initiatives to capture return visits, understand any gaps in clinical service provision, and aim to minimize unnecessary ED return visits.

LEVEL OF EVIDENCE

Level VI prognostic study.

Lost in Follow-Up: Predictors of Patient No-Shows to Clinic Follow-Up After Abdominal Injury

BACKGROUND

The aim of this study is to evaluate risk factors for non-attendance to post-discharge, hospital follow-up appointments for traumatically injured patients who underwent exploratory laparotomy.

METHODS

This is a retrospective chart review of patients who underwent exploratory laparotomy for traumatic abdominal injury at an urban, Midwestern, level I trauma center with clinic follow-up scheduled after discharge. Clinically, relevant demographic characteristics, patients' distance from hospital, and the presence of staples, sutures, and drains requiring removal were collected. Descriptive statistics of categorical variables were calculated as totals and percentages and compared with a chi-squared test or Fisher's exact when appropriate.

RESULTS

The sample included 183 patients who were largely assaultive trauma survivors (68%), male (80%), and black (53%) with a mean age of 35.4 ± 14.9 years. Overall, 18.5% no-showed for their follow-up appointment. On multivariate analysis for clinic no-show; length of stay (odds ratio = 0.92 [0.84-0.99], P = 0.04) and the need for suture, staple, or drain removal were protective for clinic attendance (odds ratio = 5.59 [1.07-7.01], P = 0.04). Overall, 12 patients (6.4%) were readmitted. Forty patients (18.3%) had their follow-up in the emergency department (ED). On multivariate regression of risk factors for ED visits, the only statistically significant factors (P < 0.05) were clinic appointment no-show (OR = 2.81) and self-pay insurance (OR = 4.78).

CONCLUSIONS

Abdominal trauma patients are at high risk of no-show for follow-up appointments and no-show visits are associated with ED visits. Future work is needed evaluating interventions to improve follow-up.

Impact of Medicaid expansion on young adult firearm and motor vehicle crash trauma patients

BACKGROUND

The Affordable Care Act Medicaid expansion has increased insurance coverage and reduced some disparities in care and outcomes among trauma patients, but its impact on subsets of trauma patients with particular mechanisms of injury are unclear. This study evaluated the association of the Affordable Care Act Medicaid expansion with insurance coverage, trauma care, and outcomes among young adults hospitalized for firearm- or motor vehicle crash-related injuries.

MATERIALS AND METHODS

We used statewide hospital discharge data from 5 Medicaid expansion and 5 nonexpansion states to compare changes in insurance coverage and outcomes among firearm and motor vehicle crash trauma patients aged 19-44 from before (2011-2013) to after (2014-2017) Medicaid expansion. We examined difference in differences overall, by race/ethnicity, and by zip-code-level median income quartile.

RESULTS

Medicaid expansion was associated with a decrease in the proportion of young adult motor vehicle crash and firearm trauma patients who were uninsured (motor vehicle crash: difference in differences - 12.7 percentage points, P < .001; firearm: difference in differences - 30.7 percentage points, P < .001). Medicaid expansion was also associated with increases in the percentage of patients discharged to any rehabilitation (motor vehicle crash: difference in differences 1.78 percentage points, P = .001; firearm: difference in differences 2.07 percentage points, P = .02) and inpatient rehabilitation (motor vehicle crash: difference in differences 1.21 percentage points, P = .001; firearm: difference in differences 1.58 percentage points, P = .002). Among patients with firearm injuries, Medicaid expansion was associated with a reduction in in-hospital mortality (difference in differences - 1.55 percentage points, P = .002).

CONCLUSION

In its first 4 years, the Affordable Care Act Medicaid expansion increased insurance coverage and access to rehabilitation among young adults hospitalized for firearm- or motor vehicle crash-related injuries while reducing inpatient mortality among firearm trauma patients.

The Association Between Early Versus Late Physical Therapy Initiation and Outcomes of Trauma Patients With and Without Traumatic Brain Injuries

BACKGROUND

There is a lack of literature regarding the most effective timing to initiate physical therapy (PT) among traumatically injured patients. We aim to evaluate the association between early PT/mobilization versus delayed or late PT/mobilization and clinical outcomes of trauma patients.

METHODS

A retrospective cohort analysis of an urban level-I trauma center from 2014 to 2019 was performed. Univariate analyses and multivariable logistic regression were performed with significance defined as P < 0.05.

RESULTS

A total of 11,937 patients were analyzed. Among patients without a traumatic brain injury (TBI), late PT initiation times were associated with 60% lower odds of being discharged home without services (P < 0.05), significantly increased hospital and ICU length of stay (H-LOS, ICU-LOS) (P < 0.05), and significantly higher odds of complications (VTE, pneumonia, pressure ulcers, ARDS) (P < 0.001). Among patients with a TBI, late PT initiation time had 76% lower odds of being discharged home without services (P < 0.05) and significantly longer H-LOS and ICU-LOS (P < 0.05) however did not experience significantly higher odds of complications (P > 0.05).

CONCLUSIONS

Among traumatically injured patients, early PT is associated with decreased odds of complications, shorter H-LOS and ICU-LOS, and a favorable discharge disposition to home without services. Adoption of early PT initiation/mobilization protocols and establishment of prophylactic measures against complications associated with delayed PT is critical to maximize quality of care and trauma patient outcomes. Multi-center prospective studies are needed to ascertain the impact of PT initiation times in greater detail and to minimize trauma patient morbidity.

Association of the Affordable Care Act Medicaid Expansion with Trauma Outcomes and Access to Rehabilitation among Young Adults: Findings Overall, by Race and Ethnicity, and Community Income Level

BACKGROUND

Low-income young adults disproportionately experience traumatic injury and poor trauma outcomes. This study aimed to evaluate the effects of the Affordable Care Act's Medicaid expansion, in its first 4 years, on trauma care and outcomes in young adults, overall and by race, ethnicity, and ZIP code-level median income.

STUDY DESIGN

Statewide hospital discharge data from 5 states that did and 5 states that did not implement Medicaid expansion were used to perform difference-in-difference (DD) analyses. Changes in insurance coverage and outcomes from before (2011-2013) to after (2014-2017) Medicaid expansion and open enrollment were examined in trauma patients aged 19 to 44 years.

RESULTS

Medicaid expansion was associated with a decrease in the percentage of uninsured patients (DD -16.5 percentage points; 95% CI, -17.1 to -15.9 percentage points). This decrease was larger among Black patients but smaller among Hispanic patients than White patients. It was also larger among patients from lower-income ZIP codes (p < 0.05 for all). Medicaid expansion was associated with an increase in discharge to inpatient rehabilitation (DD 0.6 percentage points; 95% CI, 0.2 to 0.9 percentage points). This increase was larger among patients from the lowest-compared with highest-income ZIP codes (p < 0.05). Medicaid expansion was not associated with changes in in-hospital mortality or readmission or return ED visit rates overall, but was associated with decreased in-hospital mortality among Black patients (DD -0.4 percentage points; 95% CI, -0.8 to -0.1 percentage points).

CONCLUSIONS

The Affordable Care Act Medicaid expansion, in its first 4 years, increased insurance coverage and access to rehabilitation among young adult trauma patients. It also reduced the socioeconomic disparity in inpatient rehabilitation access and the disparity in in-hospital mortality between Black and White patients.

Readmission for pleural space complications after chest wall injury: Who is at risk?

BACKGROUND

Little is known about patient characteristics predicting postdischarge pleural space complications (PDPSCs) after thoracic trauma. We sought to analyze the patient population who required unplanned hospital readmission for PDPSC.

METHODS

Retrospective review of adult patients admitted to a Level I Trauma Center with a chest Abbreviated Injury Scale (AIS) score of 2 or greater between January 2015 and August 2020. Those readmitted within 30 days of index hospitalization discharge for PDPSC were compared with those not readmitted. Demographics, injury characteristics, surgical procedures, imaging, and readmission data were retrieved.

RESULTS

Out of 17,192 trauma evaluations, 3,412 (19.8%) suffered a chest AIS score of 2 or greater injury and 155 experienced an unplanned 30-day hospital readmission. Of those, 49 (1.4%) were readmitted for the management of PDPSC (readmit PDPSC) and were compared with patients who were not readmitted (no readmit, n = 3,257). The readmit PDPSC group was significantly older age, heavier, comprised of fewer men, and suffered a higher mean chest AIS score. The readmit PDPSC group had a significantly higher incidence of rib fractures, flail chest, pneumothorax, hemothorax, scapula fractures, and a higher rate of tube thoracostomy placement during index admission. The discharge chest X-ray in the readmit PDPSC group demonstrated a pleural space abnormality in 36 (73%) of patients. Mean time to readmission was 10.2 (7.2) days, and hospital length of stay on readmission was 5.8 (3.7) days. Pleural effusion was the most common readmission diagnosis (44 [90%]), and 42 (86%) required tube thoracostomy.

CONCLUSION

We describe the subset of chest wall injury patients who require hospital readmission for PDPSC. Characteristics from index hospitalization associated with PDPSC include older age, female sex, heavier weight, presence of rib fractures, pleural space abnormality, scapular fracture, and chest tube placement. Further studies are needed to characterize this at-risk chest wall injury population, and to determine what interventions can facilitate outpatient management of postdischarge pleural space complications and mitigate readmission risk.

LEVEL OF EVIDENCE

Prognostic and epidemiologic, Level IV; Care management, Level V.

Readmission following surgical stabilization of rib fractures: Analysis of incidence, cost, and risk factors using the Nationwide Readmissions Database

BACKGROUND

Surgical stabilization of rib fractures (SSRF) has become increasingly common for the treatment of traumatic rib fractures; however, little is known about related postoperative readmissions. The aims of this study were to determine the rate and cost of readmissions and to identify patient, hospital, and injury characteristics that are associated with risk of readmission in patients who underwent SSRF. The null hypotheses were that readmissions following rib fixation were rare and unrelated to the SSRF complications.

METHODS

This is a retrospective analysis of the 2015 to 2017 Nationwide Readmission Database. Adult patients with rib fractures treated by SSRF were included. Univariate and multivariate analyses were used to compare patients readmitted within 30 days with those who were not, based on demographics, comorbidities, and hospital characteristics. Financial information examined included average visit costs and national extrapolations.

RESULTS

A total of 2,522 patients who underwent SSRF were included, of whom 276 (10.9%) were readmitted within 30 days. In 36.2% of patients, the reasons for readmissions were related to complications of rib fractures or SSRF. The rest of the patients (63.8%) were readmitted because of mostly nontrauma reasons (32.2%) and new traumatic injuries (21.1%) among other reasons. Multivariate analysis demonstrated that ventilator use, discharge other than home, hospital size, and medical comorbidities were significantly associated with risk of readmission. Nationally, an estimated 2,498 patients undergo SSRF each year, with costs of US $176 million for initial admissions and US $5.9 million for readmissions.

CONCLUSION

Readmissions after SSRF are rare and mostly attributed to the reasons not directly related to sequelae of rib fractures or SSRF complications. Interventions aimed at optimizing patients' preexisting medical conditions before discharge should be further investigated as a potential way to decrease rates of readmission after SSRF.

LEVEL OF EVIDENCE

Epidemiological study, level III.

Readmission Following Hospitalization for Traumatic Brain Injury: A Nationwide Study

OBJECTIVE

To determine whether sociodemographic and clinical factors were associated with nonelective readmission within 30 days of hospitalization for traumatic brain injury (TBI). Secondary objectives were to examine the effects of TBI severity on readmission and characterize primary reasons for readmission.

SETTING

Hospitalized patients in the United States, using the 2014 Nationwide Readmission Database.

PARTICIPANTS

All patients hospitalized with a primary diagnosis of TBI between January 1, 2014, and November 30, 2014. We excluded patients (1) with a missing or invalid length of stay or admission date, (2) who were nonresidents, and 3) who died during their index hospitalization.

DESIGN

Observational study; cohort study.

MAIN MEASURES

Survey weighting was used to compute national estimates of TBI hospitalization and nonelective 30-day readmission. Associations between sociodemographic and clinical factors with readmission were assessed using unconditional logistic regression with and without adjustment for suspected confounders.

RESULTS

There were 135 542 individuals who were hospitalized for TBI; 8.9% of patients were readmitted within 30 days of discharge. Age (strongest association for 65-74 years vs 18-24 years: adjusted odds ratio [AOR], 2.57; 95% CI: 2.02-3.27), documentation of a fall (AOR, 1.24; 95% CI: 1.13-1.35), and intentional self-injury (AOR, 3.13; 95% CI: 1.88-5.21) at the index admission were positively associated with readmission. Conversely, history of a motor vehicle (AOR, 0.69; 95% CI: 0.62-0.78) or cycling (AOR, 0.56; 95% CI: 0.40-0.77) accident was negatively associated with readmission. Females were also less likely to be readmitted following hospitalization for a TBI (AOR, 0.87; 95% CI: 0.82-0.92).

CONCLUSIONS

Many sociodemographic and clinical factors were found to be associated with acute readmission following hospitalizations for TBI. Future studies are needed to determine the extent to which readmissions following TBI hospitalizations are preventable.

High Cost and Resource Utilization of Frostbite Readmissions in the United States

BACKGROUND

Frostbite is a high morbidity, high-cost injury that can lead to digit or limb necrosis requiring amputation. Our primary aim is to describe the rate of readmission following frostbite injury. Our secondary aims are to describe the overall burden of care, cost, and characteristics of repeat hospitalizations of frostbite-injured people.

METHODS

Hospitalizations following frostbite injury (index and readmissions) were identified in the 2016 and 2017 Nationwide Readmission Database. Multivariable logistic regression was clustered by hospital and additionally adjusted for severe frostbite injury, gender, year, payor group, severity, and comorbidity index. Population estimates were calculated and adjusted for by using survey weight, sampling clusters, and stratum.

RESULTS

In the two-year cohort, 1,065 index hospitalizations resulted in 1,907 total hospitalizations following frostbite injury. Most patients were male (80.3%), lived in metropolitan/urban areas (82.3%), and nearly half were insured with Medicaid (46.4%). Of the 842 readmissions, 53.7% were associated with complications typically associated with frostbite injury. Overall, 29% of frostbite injuries resulted in at least one amputation. The average total cost and total LOS of readmissions was $236,872 and 34.7 days. Drug or alcohol abuse, homelessness, Medicaid insurance, and discharge AMA were independent predictors of unplanned readmission. Factors associated with multiple readmissions include discharge AMA and Medicare Insurance, but not drug or alcohol abuse or homelessness. The population-based estimated unplanned readmission rate following frostbite injury was 35.4% (95% CI 32.2 - 38.6%).

CONCLUSIONS

This is the first study examining readmissions following frostbite injury on a national level. Drug or alcohol abuse, homelessness, Medicaid insurance, and discharge AMA were independent predictors of unplanned readmission, while only AMA discharge and Medicare insurance were associated with multiple readmissions. Supportive resources (community and hospital-based) may reduce unplanned readmissions of frostbite injured patients with those additional risk factors.

Association of Psychosocial Factors and Hospital Complications with Risk for Readmission After Trauma

BACKGROUND

Unplanned hospital readmissions are associated with morbidity and high cost. Existing literature on readmission after trauma has focused on how injury characteristics are associated with readmission. We aimed to evaluate how psychosocial determinants of health and complications of hospitalization combined with injury characteristics affect risk of readmission after trauma.

MATERIALS AND METHODS

We conducted a retrospective cohort study of adult trauma admissions from July 2015 to September 2017 to Harborview Medical Center in Seattle, Washington. We assessed patient, injury, and hospitalization characteristics and estimated associations between risk factors and unplanned 30-d readmission using multivariable generalized linear Poisson regression models.

RESULTS

Of 8916 discharged trauma patients, 330 (3.7%) had an unplanned 30-d readmission. Patients were most commonly readmitted with infection (41.5%). Independent risk factors for readmission among postoperative patients included public insurance (adjusted Relative Risk (aRR) 1.34, 95% CI 1.02-1.76), mental illness (aRR 1.39, 1.04-1.85), and chronic renal failure (aRR 2.17, 1.39-3.39); undergoing abdominal, thoracic, or neurosurgical procedures; experiencing an index hospitalization surgical site infection (aRR 4.74, 3.00-7.50), pulmonary embolism (aRR 3.38, 2.04-5.60), or unplanned ICU readmission (aRR 1.74, 1.16-2.62); shorter hospital stay (aRR 0.98/d, 0.97-0.99), and discharge to jail (aRR 4.68, 2.63-8.35) or a shelter (aRR 4.32, 2.58-7.21). Risk factors varied by reason for readmission. Injury severity, trauma mechanism, and body region were not independently associated with readmission risk.

CONCLUSIONS

Psychosocial factors and hospital complications were more strongly associated with readmission after trauma than injury characteristics. Improved social support and follow-up after discharge for high-risk patients may facilitate earlier identification of postdischarge complications.

Australian Injury Comorbidity Indices (AICIs) to predict burden and readmission among hospital-admitted injury patients

BACKGROUND

Existing comorbidity measures predict mortality among general patient populations. Due to the lack of outcome specific and patient-group specific measures, the existing indices are also applied to non-mortality outcomes in injury epidemiology. This study derived indices to capture the association between comorbidity, and burden and readmission outcomes for injury populations.

METHODS

Injury-related hospital admissions data from July 2012 to June 2014 (161,334 patients) for the state of Victoria, Australia were analyzed. Various multivariable regression models were run and results used to derive both binary and weighted indices that quantify the association between comorbidities and length of stay (LOS), hospital costs and readmissions. The new and existing indices were validated internally among patient subgroups, and externally using data from the states of New South Wales and Western Australia.

RESULTS

Twenty-four comorbidities were significantly associated with overnight stay, twenty-seven with LOS, twenty-eight with costs, ten with all-cause and eleven with non-planned 30-day readmissions. The number of and types of comorbidities, and their relative impact were different to the associations established with the existing Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Measure (ECM). The new indices performed equally well to the long-listed ECM and in certain instances outperformed the CCI.

CONCLUSIONS

The more parsimonious, up to date, outcome and patient-specific indices presented in this study are better suited for use in present injury epidemiology. Their use can be trialed by hospital administrations in resource allocation models and patient classification models in clinical settings.

Trauma patients returning to the emergency department after discharge

BACKGROUND

While readmission rates of trauma patients are well described, little has been reported on rates of re-presentation to the emergency department (ED) after discharge. This study aimed to determine rates and contributing factors of re-presentation of trauma patients to the ED.

METHODS

One-year retrospective analysis of discharged adult trauma patients at a county-funded safety-net level one trauma center.

RESULTS

Of 1416 trauma patients, 195 (13.8%) re-presented to the ED within 30 days. Of those that re-presented, 47 (24.1%) were re-admitted (3.3% overall). The most common reasons for re-presentation were pain control and wound complications. Patients with Medicare (AOR 2.6, 95% CI 1.3 to 5.2) or other government insurance (AOR 2.5, 95% CI 1.6 to 4.1) were more likely to re-present than patients with private insurance.

CONCLUSION

A considerable number of trauma patients re-presented to the ED after discharge for reasons that did not require hospitalization. Discharge planning for certain vulnerable groups should emphasize wound care, pain control and scheduled follow-up to decrease the reliance on the ED.

Readmission rates and associated factors following rib cage injury

BACKGROUND

There remains a lack of knowledge about readmission characteristics after sustaining rib fractures. We aimed to determine rates, characteristics, and predictive/protective factors associated with unexpected reevaluation and readmission after rib cage injury.

METHODS

A retrospective review was performed based on trauma patients evaluated at an urban Level I trauma center from January 2014 to December 2016. Adult patients sustaining blunt trauma with more than one rib fracture or a sternomanubrial fracture were defined as having moderate to severe rib cage injury. Exclusion criteria included penetrating injury, death during initial hospitalization, and only one rib fracture. Reevaluation was defined as presenting at a hospital within 90 days of discharge urgently or emergently. Demographics, injury characteristics, comorbidities, complications, imaging, and readmission data were collected. Univariate and multivariate analysis was performed with a significance of p less than 0.05.

RESULTS

During the study period, 11,667 patients underwent trauma evaluation, of which 1,717 patients were found to have a moderate to severe rib cage injury. Within 90 days, 397 (23.1%) of patients underwent reevaluation, while 177 (10.3%) required readmission. One hundred forty-two (8.3%) patients were reevaluated specifically for chest-related complaints, and 55 (3.2%) required readmission. On univariate analysis, Injury Severity Score greater than 15, hospital length of stay longer than 7 days, intensive care unit (ICU) length of stay longer than 3 days, a worsened chest x-ray at discharge, a psychiatric comorbidity, a smoking comorbidity, deep vein thrombosis, unplanned readmission to the ICU, and unplanned intubation were higher in the overall reevaluation cohort. On multivariate analysis, age of 15 years to 35 years, Risk Assessment Profile score greater than 8, hypertension, psychiatric comorbidity, current smoker, and unplanned return to the ICU on index admission were predictive of reevaluation of overall reevaluation.

CONCLUSION

Moderate to severe rib cage injury is associated with high rates of reevaluation and readmission. Younger patients who smoke and required a return to the ICU are at greater risk for readmission.

LEVEL OF EVIDENCE

Level IV, Prognostic and Epidemiologic.

Readmissions after nonoperative trauma: Increased mortality and costs with delayed intervention

BACKGROUND

We sought to examine patterns of readmission after nonoperative trauma, including rates of delayed operative intervention and mortality.

METHODS

The Nationwide Readmissions Database (2013-2014) was queried for all adult trauma admissions and 30-day readmissions. Index admissions were classified as operative (OI) or nonoperative (NOI), and readmissions examined for major operative intervention (MOR). Multivariable regression modeling was used to evaluate risk for readmission requiring MOR and in-hospital mortality.

RESULTS

Of 2,244,570 trauma admissions, there were 59,573 readmissions: 66% after NOI, and 35% after OI. Readmission rate was higher after NOI compared with OI (3.6% vs. 1.7% p < 0.001). Readmitted NOI patients were older, with a higher proportion of Injury Severity Score ≥15 and were readmitted earlier (NOI median 8 days vs. OI 11 days). Thirty-one percent of readmitted NOI patients required MOR and experienced higher overall mortality compared with OI patients with operative readmission (NOI 2.9% vs. OI 2%, p = 0.02). Intracranial hemorrhage was an independent risk factor for NOI readmission requiring MOR in both the overall (hazard ratio, 1.11; 95% confidence interval [CI], 1.01-1.22) and Injury Severity Score of 15 or greater cohorts (hazard ratio, 1.46; 95% CI, 1.24-1.7), with a predominance of nonspine neurosurgical procedures (20.3% and 55.1%, respectively). Operative readmission after NOI cost a median of $17,364 (interquartile range, US $11,481 to US $27,816) and carried a total annual cost of US $147 million (95% CI, US $141 million to $154 million).

CONCLUSIONS

Nonoperative trauma patients have a higher readmission rate than operative index patients and nearly one third require operative intervention during readmission. Operative readmission carries a higher overall mortality rate in NOI patients and together accounts for nearly US $150 million in annual costs.

LEVEL OF EVIDENCE

Epidemiological, level III.

Unplanned readmission after traumatic injury: A long-term nationwide analysis

BACKGROUND

Long-term outcomes after trauma admissions remain understudied. We analyzed the characteristics of inpatient readmissions within 6 months of an index hospitalization for traumatic injury.

METHODS

Using the 2010 to 2015 Nationwide Readmissions Database, which captures data from up to 27 US states, we identified patients at least 15 years old admitted to a hospital through an emergency department for blunt trauma, penetrating trauma, or burns. Exclusion criteria included hospital transfers, patients who died during their index hospitalizations, and hospitals with fewer than 100 trauma patients annually. After calculating the incidences of all-cause, unplanned inpatient readmissions within 1 month, 3 months, and 6 months, we used multivariable logistic regression models to identify predictors of readmissions. Analyses adjusted for patient, clinical, and hospital factors.

RESULTS

Among 2,763,890 trauma patients, the majority had blunt injuries (92.5%), followed by penetrating injuries (6.2%) and burns (1.5%). Overall, rates of inpatient readmissions were 11.1% within 1 month, 21.6% within 6 months, and 29.8% within 6 months, with limited variability by year. After adjustment, the following were associated with all-cause 6 months inpatient readmissions: male sex (adjusted odds ratio [aOR], 1.10; 95% confidence interval [95% CI], 1.09-1.10), comorbidities (aOR, 1.21; 95% CI, 1.21-1.22), low-income quartiles (first and second) (aOR, 1.08; 95% CI, 1.07-1.10 and aOR, 1.04; 95% CI, 1.03-1.06, respectively), Medicare (aOR, 1.65; 95% CI, 1.62-1.69), Medicaid (aOR, 1.51; 95% CI, 1.48-1.53), being treated at private, investor-owned hospitals (aOR, 1.15; 95% CI, 1.12-1.18), longer hospital length of stay (aOR, 1.01; 95% CI, 1.01-1.01) and patient disposition to short-term hospital (aOR, 1.55; 95% CI, 1.49-1.62), skilled nursing facility (aOR, 1.43; 95% CI, 1.42-1.45), home health care (aOR, 1.27; 95% CI, 1.25-1.28), or leaving against medical advice (aOR, 1.85; 95% CI, 1.78-1.92).

CONCLUSION

Unplanned readmission after trauma is high and remains this way 6 months after discharge. Understanding the factors that increase the odds of readmissions within 1 month, 3 months, and 6 months offer a focus for quality improvement and have important implications for hospital benchmarking.

LEVEL OF EVIDENCE

Epidemiological study, level III.

[Analysis of causes and factors associated with hospital readmission in mild and moderate polythraumatism: An observational study]

BACKGROUND

Early readmissions (ER) occur during the 30 days after discharge, ER are common and expensive, associated with a decrease in the quality of care. The rate of ER in polytraumatic patients (PTP) is estimated between 4.3-15%. Our objective was to identify those factors associated with ER and its characteristics after suffering mild-moderate trauma in our area.

MATERIAL AND METHOD

This is a retrospective observational study, including data of patients with (PTP) mild or moderate admitted between July 2012 and June 2017 in our institution and their ER in public hospitals and/or outpatient centers. Demographic variables, diagnoses, procedures and characteristics of readmissions were collected. After a bivariant analysis was done, a Logistic regression had benn performed to determine risk factors for ER.

RESULTS

1013 patients were included, with median age of 38 years, ISS of 3 points and initial hospital stay of 1 day. 185 patients were readmitted (18.3%). Independent factors associated with ER were: injury mechanism, especially bicycle accident (OR 2.26), comorbidities highlighting HBP (OR 1.83) and COPD (OR 1.98), fracture immobilization (OR 1.99) and hospital admission in the initial care (OR 0.56). The causes of ER were: pain 61.6%, wound infection 15.1%, scheduled cures and deferred interventions 12.97%, medical 6.4% and psychiatric decompensation. 2.7% CONCLUSION: The ERs in mild-moderate PTP are multifactorial, our results show an association between factors such as injury mechanism, the presence of comorbidities and the procedures performed in the sentinel episode and the rate of ER. The implementation of simple interventions at discharge could reduce its incidence clearly.

Back so soon? Characterizing emergency department use after trauma

BACKGROUND

Trauma readmissions have been well studied but little data exists regarding Emergency Department (ED) utilization following an injury. This study was performed to determine the factors associated with a return to the ED after trauma.

METHODS

A retrospective review of all adult trauma patients evaluated between January and December of 2014 was performed. Demographics, follow-up plan, and characteristics of ED visits within 30 days of discharge were recorded. Predictive factors of ED utilization were identified using univariate analysis and multi-logistic regression.

RESULTS

Fourteen percent of 1836 consecutive patients returned to the ED within 30 days of initial trauma. On multi-logistic regression, penetrating trauma (OR 2.15 p = 0.001), and scheduled follow-up (OR 1.81 p = 0.046) remained significant predictors.

CONCLUSIONS

Penetrating trauma victims are at increased risk of returning to the ED, most often because of wound or pain issues. Recognizing these factors allows for targeted interventions to decrease ED resource utilization.

The role of sociodemographics in the occurrence of orthopaedic trauma

INTRODUCTION

We sought to determine the effects of sociodemographic factors on the occurrence of orthopaedic injuries in an adult population presenting to a level 1 trauma center.

MATERIALS AND METHODS

We conducted a retrospective chart review of patients who received orthopaedic trauma care at a level 1 academic trauma center.

RESULTS

20,919 orthopaedic trauma injury cases were treated at an academic level 1 trauma center between 01 January 1993 and 27 August 2017. Following application of inclusion/exclusion criteria, a total of 14,654 patients were retrieved for analysis. Out of 14,654 patients, 4602 (31.4%) belonged to low socioeconomic status (SES), 4961 (32.0%) to middle SES and 5361 (36.6%) to high SES. Following adjustment for age, sex, race, insurance status and injury severity score (ISS), patients belonging to middle SES vs. low SES (OR 0.77 [95% CI 0.63-0.94]; p = 0.009) or high SES vs. low SES (OR 0.77 [95% CI 0.62-0.95]; p = 0.016) had lower odds of receiving a penetrating injury as compared to a blunt injury.

CONCLUSION

The results from this study indicate that a link exists between sociodemographic factors and the occurrence of orthopaedic injuries presenting to a level 1 trauma center. The most common cause of injury varied within age groups, by sex, and within the different socioeconomic groups.

Complications, burden and in-hospital death among hospital treated injury patients in Victoria, Australia: a data linkage study

Background

A wide range of outcome measures can be calculated for hospital-treated injury patients. These include mortality, use of critical care services, complications, length of stay, treatment costs, readmission and nursing care after discharge. Each address different aspects and phases of injury recovery and can yield vastly different results. This study aims to: (1) measure and report this range of outcomes in hospital-treated injury patients in a defined population; and (2) describe the associations between injury characteristics, socio-demographics and comorbidities and the various outcomes.

Methods

A retrospective analysis was conducted of injury-related hospital admissions from July 2012 to June 2014 (152,835 patients) in Victoria, Australia. The admission records were linked within the dataset, enabling follow-up, to assess the outcomes of in-hospital death, burden, complications and 30-day readmissions. Associations between factors and outcomes were determined using univariate regression analysis.

Results

The proportion of patients who died in hospital was 0.9%, while 26.8% needed post-discharge care. On average patients had 2.4 complications (confidence interval (CI) 2.4–2.5) related to their initial injury, the mean cost of treating a patient was Australian dollars 7013 (CI 6929–7096) and the median length of stay was one day (inter quartile range 1–3). Intensive-care-unit-stay was recorded in 3% of the patients. All-cause 30-day readmissions occurred in 12.3%, non-planned 30-day readmissions in 7.9%, while potentially avoidable 30-day readmissions were observed in 3.2% of the patients. Increasing age was associated with all outcomes. The need for care post-discharge from hospital was highest among children and the oldest age group (85 years and over). Injury severity was associated with all adverse outcomes. Increasing number of comorbidities increased the likelihood of all outcomes. Overall, outcomes are shown to differ by age, gender, comorbidities, body region injured, injury type and injury severity, and to a lesser extent by socio-economic areas.

Conclusions

Outcomes and risk factors differ depending on the outcome measured, and the method used for measuring the outcome. Similar outcomes measured in different ways produces varying results. Data linkage has provided a valuable platform for a comprehensive overview of outcomes, which can help design and target secondary and tertiary preventive measures.

Electronic supplementary material

The online version of this article (10.1186/s12889-019-7080-y) contains supplementary material, which is available to authorized users.

Using clinical analysis to classify 30-day trauma readmissions

BACKGROUND

Unplanned hospital readmissions are common across specialties. Descriptive readmission studies commonly query large administrative databases, which some speculate lack clinical granularity. This article provides the framework for a process improvement initiative aimed at identifying clinically meaningful reasons for trauma readmission. Our study hypothesizes an expected difference between the clinically abstracted reasons for readmission and those documented by the data processing staff in the trauma registry and that those differences will be the starting point to target performance improvement.

METHODS

This is a retrospective, cohort study from 2014 to 2016 involving 18,998 trauma evaluations at a Level I trauma center. The systematic categorization of trauma readmissions was completed via clinical chart review. Readmissions were categorized following an organizational flowchart. The chart reviews ultimately resulted in two readmission categories: primary and secondary reasons for 30-day trauma readmission.

RESULTS

There were 413 readmissions, an overall readmission rate of 2.7%. The highest rate of readmission, by mechanism of injury, was gunshot wounds (11%). Secondary reasons for readmission predominated (76.1%). Complications led (41%), followed by observation (8.8%) and pain (8.6%). Following readmission chart review and categorization, the trauma registry data were queried and categorized via the same method. When the two methods of data collection were compared, there was a significant difference (p < 0.0001).

CONCLUSIONS

The granular dissection of readmission charts proved to assist in isolating clinically significant readmission variables, providing clarity into the reasons behind trauma readmission. If determined solely by the trauma registry data, our performance and quality improvement initiatives would be misguided. We recommend clinical oversight of databases, with clinical review of key areas in order to guide performance improvement.

Trauma transitional care coordination: A mature system at work

BACKGROUND

We have previously demonstrated effectiveness of a Trauma Transitional Care Coordination (TTCC) Program in reducing 30-day readmission rates for trauma patients most at risk. With program maturation, we achieved improved readmission rates for specific patient populations.

METHODS

TTCC is a nursing driven program that supports patients at high risk for 30-day readmission. The TTCC interventions include calls to patients within 72 hours of discharge, complete medication reconciliation, coordination of medical appointments, and individualized problem solving. Account IDs were used to link TTCC patients with the Health Services Cost Review Commission database to collect data on statewide unplanned 30-day readmissions.

RESULTS

Four hundred seventy-five patients were enrolled in the TTCC program from January 2014 to September 2016. Only 10.5% (n = 50) of TTCC enrollees were privately insured, 54.5% had Medicaid (n = 259), and 13.5% had Medicare (n = 64). Seventy-three percent had Health Services Cost Review Commission severity of injury ratings of 3 or 4 (maximum severity of injury = 4). The most common All Patient Refined Diagnosis Related Groups for participants were: lower-extremity procedures (n = 67, 14%); extensive abdominal/thoracic procedures (n = 40, 8.4%); musculoskeletal procedures (n = 37, 7.8%); complicated tracheostomy and upper extremity procedures (n = 29 each, 6.1%); infectious disease complications (n = 14, 2.9%); major chest/respiratory trauma, major small and large bowel procedures and vascular procedures (n = 13 each, 2.7%). The TTCC participants with lower-extremity injury, complicated tracheostomy, and bowel procedures had 6-point reduction (10% vs. 16%, p = 0.05), 11-point reduction (13% vs. 24%, p = 0.05), and 16-point reduction (11% vs. 27%, p = 0.05) in 30-day readmission rates, respectively, compared to those without TTCC.

CONCLUSION

Targeted outpatient support for high-risk patients can decrease 30-day readmission rates. As our TTCC program matured, we reduced 30-day readmission in patients with lower-extremity injury, complicated tracheostomy and bowel procedures. This represents over one million-dollar savings for the hospital per year through quality-based reimbursement.

LEVEL OF EVIDENCE

Therapeutic/care management, level III.

Variation in readmission rates among hospitals following admission for traumatic injury

INTRODUCTION

Readmission following hospital discharge is both common and costly. The Hospital Readmission Reduction Program (HRRP) financially penalizes hospitals for readmission following admission for some conditions, but this approach may not be appropriate for all conditions. We wished to determine if hospitals differed in their adjusted readmission rates following an index hospital admission for traumatic injury.

PATIENTS AND METHODS

We extracted from the AHRQ National Readmission Dataset (NRD) all non-elderly adult patients hospitalized following traumatic injury in 2014. We estimated hierarchal logistic regression models to predicted readmission within 30 days. Models included either patient level predictors, hospital level predictors, or both. We quantified the extent of hospital variability in readmissions using the median odds ratio. Additionally, we computed hospital specific risk-adjusted rates of readmission and number of excess readmissions.

RESULTS

Of the 177,322 patients admitted for traumatic injury 11,940 (6.7%) were readmitted within 30 days. Unadjusted hospital readmission rates for the 637 hospitals in our study varied from 0% to 20%. After controlling for sources of variability the range for hospital readmission rates was between 5.5% and 8.5%. Only 2% of hospitals had a random intercept coefficient significantly different from zero, suggesting that their readmission rates differed from the mean level of all hospitals. We also estimated that in 2014 only 11% of hospitals had more than 2 excess readmissions. Our multilevel model discriminated patients who were readmitted from those not readmitted at an acceptable level (C = 0.74).

CONCLUSIONS

We found little evidence that hospitals differ in their readmission rates following an index admission for traumatic injury. There is little justification for penalizing hospitals based on readmissions after traumatic injury.

Risk Factors for Bloodstream Infections Among an Urban Population with Skin and Soft Tissue Infections: A Retrospective Unmatched Case-Control Study

Introduction

The prevalence of acute bacterial skin and skin structure infections (ABSSSIs) continues to increase. Bloodstream infection (BSI) is a severe secondary complication of ABSSSI. The objective of this study was to determine clinical and sociodemographic risk factors for BSI in patients with acute bacterial skin and skin structure infections (ABSSSIs) and to determine if sociodemographic factors impact severity at presentation.

Methods

This was a retrospective unmatched (1:1) case-control study. Predictors of BSI and severe infection were sought through multivariable logistic regression analyses. Cases and controls were collected from two major medical centers located in downtown Detroit, Michigan: the Detroit Medical Center and the Henry Ford Health System. The population of interest included adult patients with community-onset (CO) ABSSSI treated at a participating hospital between January 2010 and December 2015. Cases were defined as those developing BSI within 48 h of admission with CO-ABSSSI as the primary source, while controls were those with CO-ABSSSI without BSI.

Results

A total of 392 patients (196 cases, 196 controls) were included. Independent predictors of BSI were male gender (aOR 1.85: 95% CI 1.11, 3.66), acute renal failure (aOR 2.08: 95% CI 1.18, 3.66), intravenous drug use (aOR 4.38, 95% CI 2.22, 8.62), and prior hospitalization (aOR 2.41, 95% CI 1.24, 4.93). African American race (aOR 2.18, 95% CI 1.38, 3.4), leukocytosis (aOR 2.24, 95% CI 1.41, 3.55), and prior hospitalization (aOR 2.07, 95% CI 1.19, 3.00) were significantly associated with infection severity.

Conclusion

Both clinical and sociodemographic factors were associated with BSI and severe infection underscoring the importance of social determinants of health in outcomes among underserved populations.

Readmissions after Firearm Injury Requiring Vascular Repair

BACKGROUND

Firearm injuries can be morbid and potentially have high resource utilization. Historically, trauma and vascular surgery patients are at higher risk for readmissions. Our goal was to assess the risk for readmission among patients undergoing vascular repair after a firearm injury.

METHODS

The National Readmission Database was queried from 2011 to 2014. All firearm injuries with or without vascular repair were analyzed. Multivariable analysis was conducted to assess the effect of concurrent vascular repair on readmissions at 30, 90, and 180 days.

RESULTS

There were 42,184 firearm injury admissions identified, where 93.3% did not undergo vascular repair and 6.7% required vascular repair. The overall in-hospital death rate was 8.2%. Average age was 29.9 ± 0.2 years, and 89.2% were male. Intent was most frequently assault (61.2%) followed by unintentional injury (26.5%), suicide (5.2%), and legal intervention (3.1%). Patients with vascular repair compared to those without vascular repair were more frequently admitted at teaching hospitals (85.2% vs. 81.8%, P = 0.042), had higher Agency for Healthcare Research and Quality (AHRQ) extreme severity of illness, AHRQ risk of mortality, New Injury Severity Score (NISS), and had more diagnoses and procedures (P < 0.0001). Patients with vascular repair compared to those without vascular repair also more frequently sustained abdominal/pelvis injury (40.4% vs. 23.4%, P < 0.0001) and were more likely to have anemia (5.9% vs. 3.6%, P = 0.009). Patients undergoing vascular repair had a higher rate for 30-day (8.9% vs. 5.5%, P = 0.0001), 90-day (18.1% vs 9.5%, P < 0.0001), and 180-day (22.3% vs. 13%, P < 0.0001) readmission. Kaplan-Meier analysis of unadjusted data showed a higher readmission rate over time with vascular repair. Multivariable analysis demonstrated that vascular repair was not associated with higher 30-day readmission (odds ratio [OR] 1.26, 95% confidence interval [CI] 0.92-1.72, P = 0.14) but was for 90-day (OR 1.38, 95% CI 1.14-1.68, P = 0.001) and 180-day readmission (OR 1.24, 95% CI 1.06-1.45, P = 0.009). Additional factors associated with 30-day readmission were higher NISS, discharge to a care facility, and Elixhauser score. Other factors associated with 90-day readmission were unintentional intent of injury, NISS, discharge to a care facility, and Elixhauser score. Factors also associated with 180-day readmission were insurance type, unintentional intent of injury, NISS, care facility discharge, and Elixhauser score.

CONCLUSIONS

Firearm injury resulting in vascular injury was associated with increased readmissions at 90 and 180 days. This study establishes baseline rates for readmission after vascular repair for firearm traumas and allows opportunity for improvement through targeted interventions for these patients. Vascular surgeons can have a more active role in managing this high-profile public health issue.

Association of Medicaid Expansion Under the Affordable Care Act With Outcomes and Access to Rehabilitation in Young Adult Trauma Patients

Importance

Trauma is the leading cause of death and disability among young adults in the United States. Young adults are also the age group most likely to be uninsured. Implementation of Medicaid expansion through the Affordable Care Act (ACA) has increased insurance coverage, but its associations with trauma care and outcomes among young adults nationwide remain unknown. We examined whether Medicaid expansion, in its first year, was associated with changes in insurance coverage and improved outcomes in young adults hospitalized for traumatic injury.

Objective

To assess the associations of ACA Medicaid expansion with insurance coverage, in-hospital mortality, failure to rescue, access to rehabilitation, and unplanned readmissions among hospitalized young adult trauma patients across many US states.

Design, Setting, and Participants

We used the Healthcare Cost and Utilization Project State Inpatient Databases to examine changes in insurance coverage and risk adjusted outcomes among young adults (age 19 to 44 years) who were hospitalized for injuries before and after Medicaid expansion and open enrollment occurred (2012-2013 vs 2014) in 11 US states that expanded Medicaid through the ACA. We also performed difference-in-difference analyses to compare these changes between 3 expansion states and 3 non-expansion states within the same geographic region.

Results

Of the 141 187 trauma patients hospitalized across 11 Medicaid expansion states, 43 871 (31.1%) were women, and the mean (SD) age was 31.4 (7.6) years. Medicaid expansion was associated with an increase in Medicaid coverage from 16 229 individuals (16.7%) to 15 358 individuals (34.9%) (difference: 18.2% [95% CI, 16.5%-20.0%]; P < .001), a decrease in lack of insurance from 27 016 individuals (27.8%) to 5589 individuals (12.7%) (difference: -15.1% [95% CI, -16.8% to -13.5%]; P < .001), and an increase in discharge to rehabilitation from 9220 individuals (11.4%) to 4736 individuals (12.6%) (difference: 1.16% [95% CI, 0.55%-1.77%]; P < .001). We found no significant reductions in in-hospital mortality, failure to rescue, or unplanned readmissions. Similar results were found when 3 of these states were compared with 3 geographically and demographically similar states that had not enacted Medicaid expansion.

Conclusions and Relevance

The first year of implementation of Medicaid expansion and open enrollment across 11 selected US states was associated with significant increases in Medicaid coverage, reductions in uninsured rates, and increased access to postdischarge rehabilitation among young adults hospitalized for injury. However, this study found no significant reductions in in-hospital mortality, failure to rescue, or unplanned readmissions.

Teenage Trauma Patients Are at Increased Risk for Readmission for Mental Diseases and Disorders

BACKGROUND

Most studies of readmission after trauma are limited to single institutions or single states. The purpose of this study was to determine the risk factors for readmission after trauma for mental illness including readmissions to different hospitals across the United States.

MATERIALS AND METHODS

The Nationwide Readmission Database for 2013 and 2014 was queried for all patients aged 13 to 64 y with a nonelective admission for trauma and a nonelective readmission within 30 d. Multivariable logistic regression was performed for readmission for mental diseases and disorders.

RESULTS

During the study period, 53,402 patients were readmitted within 30 d after trauma. The most common major diagnostic category on readmission was mental diseases and disorders (12.1%). The age group with the highest percentage of readmissions for mental diseases and disorders was 13 to 17 y (38%). On multivariable regression, the teenage group was also the most likely to be readmitted for mental diseases and disorders compared to 18-44 y (odds ratio [OR] 0.45, P < 0.01) and 45-64 y (OR 0.24, P < 0.01). Other high-risk comorbidities included HIV infection (OR 2.4, P < 0.01), psychosis (OR 2.2, P < 0.01), drug (OR 2.0, P < 0.01), and alcohol (OR 1.4, P < 0.01) abuse.

CONCLUSIONS

Teenage trauma patients are at increased risk for hospital readmission for mental illness. Efforts to reduce these admissions should be targeted toward individuals with high-risk comorbidities such as HIV infection, psychosis, and substance abuse.

US pediatric trauma patient unplanned 30-day readmissions

PURPOSE

We sought to determine readmission rates and risk factors for acutely injured pediatric trauma patients.

METHODS

We produced 30-day unplanned readmission rates for pediatric trauma patients using the 2013 National Readmission Database (NRD).

RESULTS

In US pediatric trauma patients, 1.7% had unplanned readmissions within 30days. The readmission rate for patients with index operating room procedures was no higher at 1.8%. Higher readmission rates were seen in patients with injury severity scores (ISS)=16-24 (3.4%) and ISS ≥25 (4.9%). Higher rates were also seen in patients with LOS beyond a week, severe abdominal and pelvic region injuries (3.0%), crushing (2.8%) and firearm injuries (4.5%), and in patients with fluid and electrolyte disorders (3.9%). The most common readmission principal diagnoses were injury, musculoskeletal/integumentary diagnoses and infection. Nearly 39% of readmitted patients required readmission operative procedures. Most common were operations on the musculoskeletal system (23.9% of all readmitted patients), the integumentary system (8.6%), the nervous system (6.6%), and digestive system (2.5%).

CONCLUSIONS

Overall, the readmission rate for pediatric trauma patients was low. Measures of injury severity, specifically length of stay, were most useful in identifying those who would benefit from targeted care coordination resources.

LEVEL OF EVIDENCE

This is a Level III retrospective comparative study.

Risk Factors Associated With Emergency Department Return Visits Following Trauma System Discharge

OBJECTIVES

Little evidence exists in the pediatric trauma literature regarding what factors are associated with re-presentation to the hospital for patients discharged from the emergency department (ED).

METHODS

This was a retrospective cohort study of trauma system activations at a pediatric trauma center from June 30, 2007, through June 30, 2013, who were subsequently discharged from the ED or after a brief inpatient stay. Returns within 30 days were reviewed. χ, Student t test, and univariate logistical regression were used to compare predictive factors for those returning and not.

RESULTS

One thousand eight hundred sixty-three patient encounters were included in the cohort. Seventy-two patients (3.9%) had at least 1 return visit that was related to the original trauma activation. Age, sex, language, race/ethnicity, ED length of stay, arrival mode, level of trauma activation, and transfer from an outside hospital did not vary significantly between the groups. Patients with public insurance were almost 2 times more likely to return compared with those with private insurance (odds ratio, 1.92; 95% confidence interval, 1.11-3.35). Income by zip code was associated with the risk of a return visit, with patients in neighborhoods at less than the 50th percentile income twice as likely to return to the ED (odds ratio, 2.15; 95% confidence interval, 1.30-3.54).

CONCLUSIONS

Patients with public insurance and those from low-income neighborhoods were significantly more likely to return to the ED after trauma system activation. These data can be used to target interventions to decrease returns in high-risk trauma patients.

Outcomes and Risk Factors for Polymicrobial Posttraumatic Osteomyelitis

Background: We hypothesized that polymicrobial posttraumatic osteomyelitis (PTO) may be associated with worse outcomes when compared to monomicrobial PTO. We therefore attempted to show the outcomes and predisposing factors associated with polymicrobial PTO.

Methods: A single-center case-control study was carried out from 2007 to 2012. The outcome variables analyzed were: the need for additional surgical and antibiotic treatments, rates of amputation, and mortality associated with the infection. Univariate and multivariable analyses using multiple logistic regression were performed to identify risk factors associated with polymicrobial PTO, and p < 0.05 was considered significant.

Results: Among the 193 patients identified, polymicrobial PTO was diagnosed in 37.8%, and was significantly associated with supplementary surgical debridement (56.1% vs. 31%; p < 0.01), a higher consumption of antibiotics, and more amputations (6.5% vs 1.3%; p < 0.01). Factors associated with polymicrobial PTO in the multivariable analysis were older age (odds ratio [OR] = 1.02, 95% confidence interval [CI] = 1.01 to 1.03, p = 0.04), working in agriculture (OR = 2.86, 95% CI = 1.05 to 7.79, p = 0.04), open fracture Gustilo type III (OR = 2.38, 95% CI = 1.02 to 5.56, p = 0.04), need for blood transfusion (OR = 2.15, 95% CI = 1.07 to 4.32, p = 0.03), and need for supplementary debridement (OR = 2.58, 95% CI = 1.29 to 5.16, p = 0.01).

Conclusions: PTO is polymicrobial in more than one-third of patients, associated with extra surgical and clinical treatment, and worse outcomes including higher rates of amputation.

Trauma Transitional Care Coordination: protecting the most vulnerable trauma patients from hospital readmission

Background

Unplanned hospital readmissions increase healthcare costs and patient morbidity. We hypothesized that a program designed to reduce trauma readmissions would be effective.

Methods

A Trauma Transitional Care Coordination (TTCC) program was created to support patients at high risk for readmission. TTCC interventions included call to patient (or caregiver) within 72 hours of discharge to identify barriers to care, complete medication reconciliation, coordination of appointments, and individualized problem solving. Information on all 30-day readmissions was collected. 30-day readmission rates were compared with center-specific readmission rates and population-based, risk-adjusted rates of readmission using published benchmarks.

Results

260 patients were enrolled in the TTCC program from January 2014 to September 2015. 30.8% (n=80) of enrollees were uninsured, 41.9% (n=109) reported current substance abuse, and 26.9% (n=70) had a current psychiatric diagnosis. 74.2% (n=193) attended outpatient trauma appointments within 14 days of discharge. 96.3% were successfully followed. Only 6.6% (n=16) of patients were readmitted in the first 30 days after discharge. This was significantly lower than both center-specific readmission rates before start of the program (6.6% vs. 11.3%, P=0.02) and recently published population-based trauma readmission rates (6.6% vs. 27%, P<0.001).

Discussion

A nursing-led TTCC program successfully followed patients and was associated with a significant decrease in 30-day readmission rates for patients with high-risk trauma. Targeted outpatient support for these most vulnerable patients can lead to better utilization of outpatient resources, increased patient satisfaction, and more consistent attainment of preinjury level of functioning or better.

Level of evidence

Level IV.

Hospital Readmissions After Pediatric Trauma

OBJECTIVES

To determine the rate, etiology, and timing of unplanned and planned hospital readmissions and to identify risk factors for unplanned readmission in children who survive a hospitalization for trauma.

DESIGN

Multicenter retrospective cohort study of a probabilistically linked dataset from the National Trauma Data Bank and the Pediatric Health Information System database, 2007-2012.

SETTING

Twenty-nine U.S. children's hospitals.

PATIENTS

51,591 children (< 18 yr at admission) who survived more than or equal to a 2-day hospitalization for trauma.

MEASUREMENTS AND MAIN RESULTS

The primary outcome was unplanned readmission within 1 year of discharge from the injury hospitalization. Secondary outcomes included any readmission, reason for readmission, time to readmission, and number of readmissions within 1 year of discharge. The primary exposure groups were isolated traumatic brain injury, both traumatic brain injury and other injury, or nontraumatic brain injury only. We hypothesized a priori that any traumatic brain injury would be associated with both planned and unplanned hospital readmission. We used All Patient Refined Diagnosis Related Groups codes to categorize readmissions by etiology and planned or unplanned. Overall, 4,301/49,982 of the patients (8.6%) with more than or equal to 1 year of observation time were readmitted to the same hospital within 1 year. Many readmissions were unplanned: 2,704/49,982 (5.4%) experienced an unplanned readmission in the first year. The most common reason for unplanned readmission was infection (22%), primarily postoperative or posttraumatic infection (38% of readmissions for infection). Traumatic brain injury was associated with lower odds of unplanned readmission in multivariable analyses. Seizure or RBC transfusion during the index hospitalization were the strongest predictors of unplanned, earlier, and multiple readmissions.

CONCLUSIONS

Many survivors of pediatric trauma experience unplanned, and potentially preventable, hospital readmissions in the year after discharge. Identification of those at highest risk of readmission can guide targeted in-hospital or postdischarge interventions.

Polytrauma patients in the Netherlands and the USA: A bi-institutional comparison of processes and outcomes of care

BACKGROUND

Modern trauma systems differ worldwide, possibly leading to disparities in outcomes. We aim to compare characteristics and outcomes of blunt polytrauma patients admitted to two Level 1 Trauma Centers in the US (USTC) and the Netherlands (NTC).

METHODS

For this retrospective study the records of 1367 adult blunt trauma patients with an Injury Severity Score (ISS) ≥ 16 admitted between July 1, 2011 and December 31, 2013 (640 from NTC, 727 from USTC) were analysed.

RESULTS

The USTC group had a higher Charlson Comorbidity Index (mean [standard deviation] 1.15 [2.2] vs. 1.73 [2.8], p<0.0001) and Injury Severity Score (median [interquartile range, IQR] 25 [17-29] vs. 21 [17-26], p<0.0001). The in-hospital mortality was similar in both centers (11% in USTC vs. 10% NTC), also after correction for baseline differences in patient population in a multivariable analysis (adjusted odds ratio 0.95, 95% confidence interval 0.61-1.48, p=0.83). USTC patients had a longer Intensive Care Unit stay (median [IQR] 4 [2-11] vs. 2 [2-7] days, p=0.006) but had a shorter hospital stay (median [IQR] 6 [3-13] vs. 8 [4-16] days, p<0.0001). USTC patients were discharged more often to a rehabilitation center (47% vs 10%) and less often to home (46% vs. 66%, p<0.0001), and had a higher readmission rate (8% vs. 4%, p=0.01).

CONCLUSION

Although several outcome parameters differ in two urban area trauma centers in the USA and the Netherlands, the quality of care for trauma patients, measured as survival, is equal. Other outcomes varied between both trauma centers, suggesting that differences in local policies and processes do influence the care system, but not so much the quality of care as reflected by survival.

Universal Health Insurance and its association with long term outcomes in Pediatric Trauma Patients

BACKGROUND

Racial disparities in mortality exist among pediatric trauma patients; however, little is known about disparities in outcomes following discharge.

METHODS

We conducted a longitudinal cohort study of children admitted for moderate to severe trauma, covered by TRICARE from 2006 to 2014. Patients were followed up to 90days after discharge. All children <18 years with a primary trauma diagnosis, an Injury Severity Score >9 and 90days of follow-up after discharge were included. Complications, readmissions and utilization of healthcare services up to 90days after discharge were compared between Black and White patients.

RESULTS

Of the 5192 children included, majority were White (74.6%, n=3871), with 15.4% Black (n=800) and 10.0% Other (n=521). Most common injuries involved the extremities or the pelvic girdle followed by the head or neck. Complication and readmission rates were 3.6% and 8.9% within 30days of discharge respectively and 4.4% and 9.3% within 90days of discharge. 99.0% of children had at least one outpatient visit by 90days. After adjusting for patient and injury characteristics no significant differences were detected between Black and White children in outcomes after discharge.

CONCLUSIONS

Universal insurance may help mitigate disparities in post discharge care in pediatric trauma populations by increasing access to outpatient services overall and within each racial group. Further studies are required to determine the appropriate timing and frequency of follow up care in order to achieve maximum reduction in use of acute care services after discharge.

Preinjury ASA score as an independent predictor of readmission after major traumatic injury

Background

Patients with trauma have a high predisposition for readmission after discharge. Unplanned solicitation of medical services is a validated quality of care indicator and is associated with considerable economic costs. While the existing literature emphasizes the severity of the injury, there is heterogeneity in defining preinjury health status. We evaluate the validity of the American Society of Anesthesiologists (ASA) Physical Status score as an independent predictor of readmission and compare it to the Charlson Comorbidity Index (CCI).

Methods

This is a single center, retrospective cohort study based on adult patients (>18 years of age) with trauma admitted to the Ottawa Hospital from January 1, 2004 to November 1, 2014. A multivariate logistic regression model is used to control for confounding and assess individual predictors. Outcome is readmission to hospital within 30 days, 3 months and 6 months.

Results

A total of 4732 adult patients were included in this analysis. Readmission rates were 6.5%, 9.6% and 11.8% for 30 days, 3 months and 6 months, respectively. Higher preinjury ASA scores demonstrated significantly increased risk of readmission across all levels in a dose-dependent manner for all time frames. The effect of preinjury ASA scores on readmission is most striking at 30 days, with patients demonstrating a 2.81 (1.88–4.22, P<0.0001), 3.59 (2.43–5.32, P<0.0001) and 7.52 (4.72–11.99, P<0.0001) fold odds of readmission for ASA class 2, 3 and 4, respectively, as compared with healthy ASA class 1 patients. The ASA scores outperformed the CCI at 30 days and 3 months.

Conclusions

The preinjury ASA score is a strong independent predictor of readmission after traumatic injury. In comparison to the CCI, the preinjury ASA score was a better predictor of readmission at 3 and 6 months after a major traumatic injury.

Level of Evidence

Prognostic and Epidemiological Study, Level III.

Functional outcomes after inpatient rehabilitation for trauma-improved but unable to return home

BACKGROUND

Twenty-five percent of trauma patients are discharged to postacute care, indicating a loss of physical function and need for rehabilitation. The purpose of this study was to quantify the functional improvements in trauma patients discharged from inpatient rehabilitation facility (IRF) and identify predictors of improvement.

MATERIALS AND METHODS

A retrospective cohort study of trauma patients aged ≥ 18 years were admitted to an IRF after discharge from a level-1 trauma center. Data included demographics, injury characteristics, hospital, and IRF course. The functional independence measure (FIM) was used to measure change in physical and cognitive function.

RESULTS

There were 245 patients with a mean age of 55.8 years and mean injury severity score (ISS) of 14.7. Fall was the leading mechanism of injury (45.7%). On IRF admission, 50.7% of patients required moderate or greater assistance. On discharge, the mean intraindividual change in FIM score was 29.9; 85.4% of the patients improved by ≥1 level of functioning. Before injury, 99.6% of patients were living at home, but only 56.0% were discharged home from the IRF, despite 81.8% requiring minimal assistance at most (23.5% to skilled nursing; 19.7% readmitted). Increasing age and lower ISS were associated with less FIM improvement, and increasing ISS was associated with increased FIM improvement.

CONCLUSIONS

More than 80% of the trauma patients experienced meaningful functional improvements during IRF admission. However, only half were discharged home, and a quarter required further institutional care. Further research is needed to identify the additional impediments to return to preinjury functioning.

Risk factors and costs associated with nationwide nonelective readmission after trauma

BACKGROUND

Most prior studies of readmission after trauma have been limited to single institutions, whereas multi-institutional studies have been limited to single states and an inability to distinguish between elective and nonelective readmissions. The purpose of this study was to identify the risk factors and costs associated with nonelective readmission after trauma across the United States.

METHODS

The Nationwide Readmission Database was queried for all patients with nonelective admissions in 2013 and 2014 with a primary diagnosis of trauma. Univariate and multivariate logistic regression identified risk factors for 30-day nonelective same- and different-hospital readmission. The diagnosis groups on readmission were evaluated, and the total cost of readmissions was calculated.

RESULTS

There were 1,180,144 patients admitted for trauma, the 30-day readmission rate was 9.4%, and 26.4% of readmissions occurred at a different hospital. The median readmission cost for patients readmitted to the same hospital was $8,298 (interquartile range, $4,899-$14,911), whereas the median readmission cost for patients readmitted to a different hospital was $8,568 (interquartile range, $4,935-$16,078; p < 0.01). Multivariate regression revealed that patients discharged against medical advice were at increased risk of readmission (odds ratio, 2.79; p < 0.01) and readmission to a different facility (odds ratio, 1.58; p < 0.01). Home health care was associated with a decreased risk of readmission to a different hospital (odds ratio, 0.74; p < 0.01). Septicemia and disseminated infections were the most common diagnoses on readmission (8.4%) and readmission to a different hospital (8.6%).

CONCLUSIONS

A significant portion of US readmissions occur at different hospitals with implications for continuity of care, quality metrics, cost, and resource allocation. Home health care reduces the likelihood of nonelective readmission to a different hospital. Infection was the most common reason for readmission, with ramifications for outcomes research and quality improvement.

LEVEL OF EVIDENCE

Care management/epidimeological, level IV.

Early unplanned trauma readmissions in a safety net hospital are resource intensive but not due to resource limitations

BACKGROUND

In an era of decreasing reimbursements, the incentive to decrease readmissions has never been greater. It has been suggested that trauma readmission is an indicator of poor hospital care or fragmented discharge. Even though trauma readmissions are relatively low, readmissions add significant cost, tie up already limited resources and lead to worse outcomes, including mortality. The literature on trauma readmissions is sparse, and the reasons and risk factors for readmission are inconsistent across studies. If readmissions are to serve as useful indicators of quality of care, we must elucidate factors that may predict readmissions.

METHODS

We performed a retrospective review of all admissions to our urban Level I trauma center from July 1, 2012, to June 30, 2015. All patients aged 16 years or older who were discharged alive were included. We identified all unplanned readmissions that occurred within 30 days of discharge and performed an extensive chart review to determine the reasons for readmission. We performed univariate and multivariable analyses.

RESULTS

We identified 6,026 index trauma admissions, with 158 (2.6%) unplanned readmissions within 30 days of discharge. The most common reasons for readmission were disease/symptom progression (30.2%), wound complications (28.9%), and pain control (11.8%). On multivariate analysis, only Injury Severity Score (odds ratio [OR], 1.02; 95% confidence interval [CI], 1.00-1.05; p=0.016), penetrating injuries (OR, 1.9; 95% CI, 1.12-3.24; p=0.018), and smoking (OR, 1.73; 95% CI, 1.05-2.86; p=0.031) were found to be significant. Hospital length of stay, insurance status, and race were not significant.

CONCLUSION

In a resource-limited environment, we expected a lack of access to care would lead to increased trauma readmissions; however, we were still able to achieve similar readmission rates, irrespective of insurance status and race. Our trauma readmission rate is low and consistent with previously published studies. Our results at our Level I trauma center support previously published studies that found Injury Severity Score and penetrating injury to be risk factors for readmission; however, more ubiquitous risk factors, such as hospital length of stay and discharge destination, were not significant. With no consensus on the risk factors for unplanned early trauma readmission, individual trauma centers should evaluate their specific risk factors for readmission to improve patient outcomes and decrease hospital costs.

LEVEL OF EVIDENCE

Care management, level IV; Epidemiologic, level IV.