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Proc (Bayl Univ Med Cent). 2012 January; 25(1): 6–12.
PMCID: PMC3246844

Ground-level falls: 9-year cumulative experience in a regionalized trauma system

Abstract

Ground-level falls (GLFs) are the leading cause of nonfatal hospitalized injuries in the US. We hypothesized that risk-adjusted mortality would not vary between levels of trauma center verification if regional triage functioned appropriately. Data were collected from our regional trauma registry for the years 2001 through 2009. A multilevel mixed-effects logistic regression model was developed to compare risk-adjusted mortality rates by trauma center level and by year. GLF patients numbered 8202 over 9 years with 2.1% mortality. Mean age was 74.5 years and mean probability of death was 0.021 (95% confidence interval [CI], 0.020–0.021). The level I center–treated patients had the highest probability of death (0.033) compared to levels II and III/IV patients (0.023 and 0.018, respectively; P < 0.001), with the highest mortality (6.0%, 3.1%, and 1.1% for levels I, II, and III/IV; P < 0.001). The adjusted odds ratio of mortality was lowest at the level I center (0.71; 95% CI, 0.56–0.91), while no difference existed between level II (1.17; 95% CI, 0.90–1.51) and level III/IV centers (1.22; 95% CI, 0.90–1.66). The 95% CIs for risk-adjusted mortality by year overlapped the 1.0 reference line for each year from 2002 to 2009. In conclusion, regional risk-adjusted mortality for GLF has varied little since 2002. More study is warranted to understand the lower risk-adjusted GLF mortality at the level I center for this growing patient population.

Trauma systems exist to provide optimal acute care for injured patients of all ages. Hoyt and Coimbra suggested that many severely injured patients are never taken to trauma centers, and only 15% of trauma patients actually require the additional services offered by level I and II centers (1). Effective trauma systems must therefore be inclusive and function cohesively to provide the most appropriate care to patients. This is ensured by the leadership of surgeons in the community.

A clear classification of hospital roles based upon trauma response and capabilities was established by the American College of Surgeons Committee on Trauma in 1976. These tiers of care range from level I/II, providing comprehensive care, to levels III, IV and V, providing stabilization and transport to higher-level facilities (2). Further refinement in the definition of trauma systems led to the concept of inclusive versus exclusive systems. Inclusive trauma systems comprise all acute care facilities in a geographical region regardless of their level of verification or expertise. Exclusive systems rely on a few high-level centers to care for the most severely injured patients (3). Utter et al showed that although both types of systems similarly triage patients, severely injured patients treated within inclusive systems experience better survival rates (4). Tinkoff et al recently published a report of the inclusive statewide trauma system in Delaware. Their findings of decreasing mortality of the most critically injured patients over 10 years compared favorably to a cohort of injury severity–matched patients from the National Trauma Data Bank, thus reinforcing the benefits of inclusive trauma systems (5).

A standard measurement of the quality of care among trauma systems is hospital mortality, since this is the most accessible and obvious endpoint. Proper triage enhances trauma system effectiveness and positively influences patient survival. As such, if a trauma system is properly functioning, patients with the highest probability of death based on anatomic injury should be referred to a level I or II center.

We sought to characterize the cumulative experience of our region's trauma system in caring for one of the growing segments of the trauma patient population—those injured in ground-level falls (GLFs). Specifically, we sought to evaluate the referral practice whereby those patients with higher predicted mortality should be admitted to level I and II trauma centers where resources are available to provide definitive care for critically injured patients. Conversely, those patients with lower predicted mortality should comprise the group of patients admitted to level III and IV trauma centers. We hypothesized that if the triage and referral patterns in our trauma system were functioning appropriately, the risk-adjusted mortality should not vary significantly between level I, II, and III/IV centers within our region. Additionally, we evaluated the adjusted mortality of our region's trauma system over time as a means to identify any significant temporal variation in patient mortality. The overarching goal of this study was to demonstrate areas for quality improvement as well as the strengths of our regional trauma system.

MATERIALS AND METHODS

Data from the Regional Trauma Registry for Trauma Service Area G (TSA-G) were analyzed. TSA-G comprises 19 counties encompassing 13,609 square miles of northeastern Texas. The population served is currently estimated to be over 850,000 (8). The coordinated plan for trauma care in TSA-G encompasses emergency medical services, trauma centers designated at all four levels of American College of Surgeons (ACS) verification, acute rehabilitation hospitals, and skilled nursing facilities. The hospitals in this region include one ACS level I, two level II, six level III, and 11 level IV trauma centers providing immediate and definitive trauma care to TSA-G patients.

The data presented in this report were accumulated over 9 years (2001–2009). Individual medical records were reviewed by each center's trauma program manager and submitted to the registrar for TSA-G. Patients were analyzed according to the ACS level of verification of the facility where inpatient admission occurred. For example, patients initially evaluated at a level III or IV facility and transferred to a level I or II center for definitive care were analyzed in this report as level I or II patients. Patients who were not transferred but were admitted to the level III or IV facility were analyzed accordingly.

Patients were identified by external cause of injury codes 880.1, 884.2, 884.3, 884.4, 884.6, 885.9, 888.0, and 888.1. We excluded patients <14 years of age because there were no designated pediatric trauma centers in TSA-G, and regional protocols required transfer of these patients, once stabilized, to a pediatric trauma center outside of TSA-G. We analyzed data including demographics, comorbidities, acute physiology, injuries, lengths of stay in the intensive care unit and hospital, survival, disposition, and ACS trauma center level of admission. Diagnoses for the acute injuries and comorbid conditions were codified according to the lexicon of the International Classification of Diseases, 9th Edition, Clinical Modification (ICD-9). The comorbidities were enumerated and a comorbidity score was assigned using the algorithm outlined by Elixhauser et al (6). The ICD-9 codes for injury and poisoning (800-999) were also used to categorize injuries into anatomic regions using the Barell Injury Diagnosis Matrix (7). Enumeration of comorbidities for the Elixhauser score and categorization of injuries into the Barell Injury Diagnosis Matrix were performed using the ICDPIC module for Stata developed by Clark et al (8).

The relative incidence of GLF admissions as a proportion of total trauma admissions was estimated and presented with 95% confidence intervals (CIs) per year for the years 2001 to 2008. This time interval was chosen because GLF admissions were not consistently reported as trauma admissions by all centers prior to 2001. Additionally, the data for 2009 did not include all 12 months and were also excluded from this analysis. The difference between the relative incidence of GLF in 2001 and that in 2008 was estimated using a two-sample test of proportion.

Patients were grouped and comparisons were made by the ACS level of admission and by year of admission. The chi-square test was used to evaluate differences between groups for binary variables. The rank-sum test and the Kruskal-Wallis equality-of-populations rank test were used to measure the differences for continuous variables among two and three groups, respectively. The probability of death was estimated using the Trauma Mortality Prediction Model (9).

A multiple variable logistic regression model was developed with death as the outcome of interest. The ability of the logistic regression model to discriminate fatalities from survivors was evaluated using the area under the receiver operating characteristic curve. The Hosmer-Lemeshow statistic was used to measure the goodness of fit of the model to the dataset.

As a means to compare risk-adjusted mortality rates by ACS level of hospital admission and year of admission, a multilevel mixed-effects logistic regression model was employed. Differences between groups were considered significant if the P values were ≤0.05.

RESULTS

During the study period, 8202 patients ≥14 years of age were admitted for GLFs. In 2001 GLF represented 13.3% (95% CI, 12.4%–14.2%) of trauma admissions in TSA-G. This increased over the period of this study to 21.4% (95% CI, 20.3%–22.5%) (P < 0.001) (Figure (Figure11). The characteristics of the cohort are described in Table Table11. The various types of GLF are categorized by their E code. Four of the E code groups had significant univariate associations with death. These E codes were accidental fall from wheelchair (884.3), accidental fall from bed (884.4), slipping, tripping, or stumbling (885.9), and falls resulting in striking against another object (888.1). These mechanisms of GLF were associated with mortality rates of 4.51%, 4.44%, 1.86%, and 4.71%, respectively (P < 0.05 for each). The overall mortality rate from GLFs in our region was 2.1% over 9 years.

Figure 1
Relative proportion of ground-level falls to overall trauma admissions.
Table 1
Characteristics of 8202 ground-level fall patients

The characteristics of the cohort were compared according to the trauma center designation level of the facility where the patients were admitted and treated (Table (Table22). The patients treated at the ACS level I center were younger, had more comorbid conditions, and more often presented with metabolic acidosis. Both the probability of death and the unadjusted mortality rate were highest for the level I center and lowest for the level III/IV centers. However, once the mortality rates were risk adjusted for physiologic derangement on presentation, patient comorbid disease burden, age, gender, brain injury, and totality of physical injury, the adjusted odds ratio for death was lowest for the level I center. It is noteworthy that the 95% CI of the level I center overlapped that of the other trauma center levels. Additionally, the 95% CIs of the level II centers overlapped considerably with those of the level III/IV centers (Table (Table22). Thus, although the mean adjusted odds ratios for mortality differed among the levels of trauma center verification, the overlap of the 95% CIs demonstrated only modest variation in adjusted odds of mortality among trauma center verification levels in TSA-G.

Table 2
Summarized characteristics of cohort by level of trauma center verification

A multivariate logistic model was constructed to explore the associations of several covariates with mortality (Table (Table33). The strongest independent predictors of death were hypotension (odds ratio, 8.91; P < 0.001) and metabolic acidosis on arrival (odds ratio, 7.19; P = 0.001). Among the comorbid conditions, cancer increased the odds of death nearly fourfold (odds ratio, 3.83; P = 0.003). This model adequately discriminated fatalities from survivors (area under the receiver operating characteristic curve = 0.85). Calibration to the dataset was also acceptable (Hosmer-Lemeshow chi-square statistic = 4.98; P = 0.89).

Table 3
Multivariable logistic regression model for death among 8202 ground-level falls*

The mean adjusted mortality rate with 95% CIs was plotted for the years 2001 through 2009 (Figure (Figure22). With the exception of the year 2001, the 95% CIs of the adjusted odds ratios for mortality from GLF crossed the 1.0 level, indicating the adjusted odds ratio for mortality remained consistent over time.

Figure 2
Adjusted mortality rates for ground-level falls by year.

DISCUSSION

GLFs are an increasing public health concern with significant economic and social consequences. It is estimated that >30% of older adults fall yearly, with >1 out of 5 incidents requiring acute care, costing more than $20 billion a year in the US (1012). Unintentional falls are currently the number one cause of injury-related death in persons aged 65 and above (16,650 in 2006), and the leading cause of nonfatal injuries treated in US emergency departments in all age groups with the exception of persons aged 15 to 24 years. Even in this group, falls are a close second (13). Additionally, unintentional falls are estimated to contribute to 8 million emergency department visits annually. Given the sweeping demographic changes occurring in the US, it is reasonable to assume that the incidence and impact of GLFs will increase.

GLFs have appropriately drawn the attention of academic and institutional research, most of which has focused on developing strategic fall risk assessment and prevention strategies. These efforts, manifest by scoring systems such as the Morse Fall Scale or recommendations by authorities such as the ACS Subcommittee on Injury Prevention and Control, are being sporadically adopted, but with mixed results (12, 14, 15). Much less has been published regarding the treatment and outcome side of this complex medical and social issue, which highlights the need for the development of evidence-based strategies in the multidisciplinary management of GLF patients (16). A better understanding of injury patterns, predictors of morbidity and mortality, and considerations for special subgroups would clearly benefit clinicians in the triage, assessment, treatment, and disposition of these patients. While measuring the efficacy of our trauma system, our data also contribute to the identification of these factors and point to the significance of factors not yet identified.

Mortality is a multifactorial result that incorporates the variable contributions of injury, acute physiology, and the patients' physiological functional status or burden of comorbid disease. Preexisting medical conditions and older age are both well-established risk factors of mortality among trauma victims. Each becomes difficult to study independently given that the elderly are more likely to suffer from chronic medical conditions (17). Multiple studies have implicated four disease processes that increase mortality in trauma patients: cirrhosis, cardiovascular disease, respiratory disease, and diabetes. Even after controlling for age, the effect of these diseases on mortality is significant (18, 19). We demonstrated that several comorbidities are associated with mortality in the present study. Malignancy, renal disorders, and cardiac disorders (arrhythmia, valve dysfunction, and congestive failure) were each independent predictors of death among GLF trauma patients in our region.

Two other reports demonstrated the relationship between comorbid conditions and mortality in patients hospitalized with injuries from GLFs. Using data from the Scottish Trauma Audit Group, Kennedy et al found that the mortality rate increased as the number of comorbid conditions increased in patients admitted following low-impact falls (20). Similarly, Hannan et al concluded that “pre-existing conditions … are significantly related (inversely) to survival of patients with trauma from low falls.” Hannan also advocated that comorbidity and age be included in the physiologic and anatomic injury models currently used to predict survival in these patients (21).

It is important to note that hypotension and metabolic acidosis on presentation to the emergency department carried the greatest odds of death in our cohort. Importantly, the strong associations with these characteristics and death are not unique to GLF patients and have been well described. The model we developed for mortality prediction included variables for age, gender, acute physiology, severity of anatomic injury, and comorbidity. The ultimate purpose of developing such a model was to arrive at a risk-adjusted odds ratio for death for each level of trauma center designation in our region, thus enabling a comparative assessment of the performance of the three levels of trauma centers in our system. Additionally, these data can be observed over time to detect temporal variation in the overall adjusted odds ratios of mortality for our region.

An effective trauma system includes proper triage of patients to a trauma center based not only on the level of injury severity but also on host factors such as age and comorbidities. Such host factors can greatly influence survival and should be integral components in the initial patient assessment. Identifying patient characteristics that adversely affect outcome should ideally prompt transport to a higher trauma center level. We observed that patients in TSA-G with the highest predicted mortality, whether based on anatomic injury severity alone or on the multivariate model we developed, were admitted and treated at the level I and II centers. We also observed that the crude mortality rate was highest at the level I center and lowest among the level III/IV centers, which seems intuitively congruent. However, after risk adjustment, the lowest odds ratios for death occurred in the level I center.

We believe that there should be only minimal differences among outcomes of level I and level II trauma centers serving a common region given the similar capabilities of levels I and II. Culica et al reviewed discharge data from the Texas Health Care Information Council to assess the outcomes of care among the regionalized trauma systems in Texas. Similar to the present study, they observed small variations in survival when the level I and II trauma centers across the state were compared (22). The difference we observed in adjusted odds ratio for mortality between the level I center and the other trauma center levels may reflect variations in triage, transfer, referral patterns, or clinical practice.

Similarities have been observed between outcomes when case mix adjustment was applied in other studies. MacKenzie et al compared mortality rates between level I trauma centers and non-trauma centers using data from 19 states as part of the National Study on the Costs and Outcomes of Trauma. They found that the unadjusted mortality rate was higher among patients treated in trauma centers compared to non-trauma centers. After adjustment for case mix, however, the risk of death within 1 year of injury was significantly lower for patients receiving care at a trauma center (23). Khuri et al also found that risk adjustment had a significant impact on the rank ordering of Veterans Affairs hospitals after implementing a system for the prospective collection and comparative reporting of postoperative mortality rates after major noncardiac operations (24). O'Connor et al sought to improve the mortality rates associated with coronary artery bypass graft surgery in Northern New England. Adjusted mortality rates were used to compare the efficacy of a coordinated intervention among the 23 cardiothoracic surgeons practicing in Maine, New Hampshire, and Vermont.

Risk-adjusted outcomes have become a popular tool for comparing the quality of care between hospitals as part of the so-called quality report cards. Perhaps the best use of risk-adjusted outcomes, however, may be the identification of opportunities for improvement, such as the refinement of our patterns of referral to higher levels of care for groups of patients at higher-than-average risk for death. As this is our first regionwide comparative assessment endeavor, additional refinement over time of our data acquisition and modeling may yield other significant covariates not included in the present model. Nonetheless, the present study provides a useful benchmark for our region's performance in triaging and treating patients injured in GLFs.

When our region is considered in total, the overall mortality rate from GLF was 2.1%. This rate compares favorably to other reports focusing on this mechanism of injury. Kennedy et al reported a 2.8% rate in patients with a mean age of 61.6 years (20). Other authors have published GLF mortality rates ranging from 4.2% to as high as 8.9% among adult trauma patients (25, 26). Bergeron et al reported a 13.4% mortality rate among patients admitted to a regional trauma center in Quebec, Canada (26). Our cohort and the Canadian study group had similar median injury severity scores of 9. These figures represent unadjusted mortality data, though at present, no omnibus metric exists to enable meaningful case mix–adjusted comparative assessment of outcomes between entire regional trauma systems.

In 2006 the ACS embarked on the Trauma Quality Improvement Program (TQIP). A key component of TQIP will be the comparative assessment of observed-to-expected mortality rates based on risk adjustment for institutional case mix (27). A pilot study by Hemmila et al assessed the feasibility of utilizing the infrastructure of the National Trauma Data Bank to provide risk-adjusted benchmarking of trauma centers. They observed differences in the observed-to-expected mortality ratios across similarly verified trauma centers (28). It is possible that efforts such as our study may be supplanted in the future by TQIP as a means of comparing trauma center performance across our region.

In our study, the adjusted odds of mortality remained fairly consistent from year to year. With the exception of 2001, the yearly adjusted odds of mortality did not vary significantly, as the 95% CIs spanned the 1.0 reference line. The variation in unadjusted mortality rates for the years 2002 to 2009 ranged from 1.2% in 2004 to 2.7% in 2002, indicating that the observed mortality was decreased by more than half over 2 years. Yet in the context of the risk adjustment regression model, we can see that the difference in mortality between these outlier years was less dramatic. This is valuable to us because appraisal of our performance from year to year based only on mortality incidence could lead to unfounded concern when unadjusted mortality is trending up, as in 2002. Similarly, when unadjusted mortality incidence is trending down, as in 2004, the lack of appropriate risk adjustment may allow for the misinterpretation that our outcomes had improved.

The most significant limitations of these data are that they were collected over 9 years and from 20 hospitals. As such, it is likely that some variation in data collection and management practices may have occurred between centers and over time. Nonetheless, this analysis provides a basis for future comparison, both in terms of methodology and outcomes. A second limitation to this study is the potential confounding effect attributable to interfacility transfer of GLF patients within our region. No attempt was made to determine correlations between outcome and transfer patterns. The effect of interfacility transfer, if significant, may potentially contribute to the observed difference in adjusted odds ratios for mortality between the level I and level II centers. However, we advocate that patients who require definitive management of injuries be treated in facilities equipped to accommodate the need at hand. Given that there are one level I and two level II trauma centers in TSA-G, appropriate interfacility transfer is an important component of trauma care in our region.

From these observations, we can conclude that the risk-adjusted hospital mortality for patients hospitalized for injuries sustained from GLF in TSA-G has remained consistent since the year 2003. Additionally, these patients are triaged across our region to appropriate levels of care, as demonstrated by the modest variation in adjusted odds ratios for mortality between levels of trauma center verification. More study is warranted to better define the nature of the lower adjusted odds ratio for mortality observed in the level I center compared to the other centers in the region. Understanding the relative contributions of case mix, referral patterns, and clinical practices may allow for an overall improvement in outcomes in our region for this growing population of trauma patients.

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