Development of a Risk-Scoring Tool to Determine Appropriate Level of Care in Acute Bacterial Skin and Skin Structure Infections in an Acute Healthcare Setting

Acute bacterial skin and skin structure infections (ABSSSIs) represent a large burden to acute care hospitals. In the United States, these infections account for approximately 2.27 million emergency department (ED) visits, and over 800,000 hospital admissions annually [1‐3]. According to the Healthcare Cost and Utilization Project National Inpatient Sample, hospital encounters for ABSSSIs are on the rise, associated with a mean adjusted cost of $9388 USD per visit [4]. To ease the increased burden and costs of ABSSSIs, ED and observation units (OUs) are used to manage patients likely to be discharged home within 24 h of initial care [5, 6]. Currently, however, there is a lack of consensus to establish which patients benefit most from ED/OU stay versus inpatient admission [7‐11].

Several severity scoring systems have been developed to aid in the determination of patient acuity and proper level of care in the management of ABSSSIs: these include the Eron/CREST (Clinical Resource Efficacy Support Team) Classification, the modified CREST using the Standardized Early Warning Severity Score (SEWS), the Ki and Rosenstein Score, and the Wilson Score [12‐16]. The Eron consensus criteria are used primarily in the UK as part of the CREST Guidelines for The Management of Cellulitis in Adults and are based on expert opinion [13, 15]. This severity classification uses patient past medical history and systemic signs and symptoms of infection to aid in the decision of site of care and determination of most appropriate antibiotic therapy. Marwick et al. used a modified version of the CREST classification together with an early patient acuity score and SEWS and demonstrated that CREST often led to over-treated patients [12, 17]. These scoring systems, however, have not been robustly validated and none have been utilized to determine ED versus OU status.

Evidence-based guidance is needed regarding which patients should be considered for short stay in the ED/OU versus inpatient setting before discharge. There is a need to better define the most appropriate level of care and antibiotic therapy for patients with ABSSSIs in order to maximize clinical outcomes while minimizing cost burden. Previous studies with clinical decision rules and risk-scoring tools, such as the CREST Classification, have demonstrated that a combination of patient co-morbidities and presenting physiological parameters is likely key to aiding in the determination of optimal patient management. This study sought to derive a predictive model and severity scoring system to aid in assessing patient acuity and predicting most appropriate levels of care, locations and antibiotic therapy, in order to decrease unnecessary hospitalizations, decrease costs of care, and ultimately improve patient outcomes.

Although ABSSSIs are common in the United States, and the Infectious Disease Society of America guidelines have been recently updated, there are still significant gaps in our understanding of optimal management [13]. In 2003, Eron et al. created the consensus-based criteria that would later be utilized in the CREST guidelines for the management of cellulitis in adults in the United Kingdom [13, 15]. This classification scheme aimed to guide appropriate patient disposition and antibiotic therapy. Since then, several studies have demonstrated that the CREST Classification tends to over-estimate level of care. In an analysis of 533, 263 ED visits for skin infections in 2003 from the Premier Hospital Database by Sulham et al., 57.85% of CREST Class II patients and 22.65% of CREST Class III patients were successfully treated on an outpatient basis with oral antibiotics [25]. Marwick et al. attempted to refine the CREST Classification and decrease clinical ambiguity by adding the SEWS score. The SEWS score has been significantly linked to increased hospital length of stay, need for intensive care, and overall mortality and, as such, seems like an optimal addition to the CREST Classification [26]. In a retrospective analysis of 205 patients, Marwick et al. determine that approximately 26% of patients received appropriate care based on this modified CREST Classification; however, this did not negatively impact overall clinical outcomes [12]. They sought to further validate this tool through a prospective study, but were unable to do so secondary to limited sample size [17]. Recently, Hashem et al. conducted a single-center retrospective assessment of the CREST Classification in 200 patients in the United States [24]. The authors found, similar to Marwick et al., that the CREST Classification resulted in significant over-treatment, with 63% of CREST Class I patients being treated with intravenous (IV) antibiotics when oral options were recommended as sufficient or overly broad-spectrum antibiotic therapy was used. Through these studies, it has become clear that, although the CREST Classification is potentially flawed, using a clinical decision support tool does have the potential to optimize patient care in the management of ABSSSIs.

Over the years, OUs or clinical decision support units have gained popularity for the treatment of patients and conditions that do not necessarily need several days of inpatient management but cannot be successfully treated purely on an outpatient basis. OUs can been advantageous to healthcare systems through decreasing unnecessary admissions, improving efficiency and patient satisfaction, and decreasing costs of care [5, 19]. Cost avoidance, through outpatient management versus the fixed payment system used for inpatient management, is a main driver for changes in how ABSSSIs are managed [8, 27]. The American College of Emergency Physicians recommends evidence-based guidelines to establish patients best suited to have successful care in the OU [19]. In their policy resource and education paper, however, it is clear that this is easier said than done in the case of ABSSSIs. Recently, Lodise et al. examined the economic impact of potentially avoidable admissions in patients with ABSSSIs [28]. In a large database analysis of over 120,000 inpatient cases, those without life-threating conditions or systemic signs and symptoms represented the largest group (n = 100,267). Additionally, more than half of these patients presented with a Charlson Comorbidity Score of zero. The care of these patients was associated with a mean cost of $5851 (SD $6756) USD per patient, which represents a large and potentially avoidable economic burden.

Several studies have specifically aimed to determine which past medical history and clinical characteristics in patients presenting to the ED with ABSSSIs result in either the need to upgrade care to inpatient admission or lead to overall failure of ED/OU care. These studies fail to provide a strong consensus. In a retrospective analysis by Sabbaj et al., the researchers attempted to develop a clinical decision rule to determine the need for more than 24 h of acute care. Although they were unable to develop a high sensitivity rule, a temperature > 37.8° C was associated with an odds of 2.91 (95% CI 1.65–5.12) of requiring more than 24 h of care [9]. A study by Volz et al. also demonstrated that an elevation in body temperature at presentation was associated with requiring more than 24 h of care at an odds ratio of 2.5 (95% CI 1.1–5.5) [7]. Additionally, a retrospective study by Shrock et al. found that an elevation in WBC > 15,000 cells/m³ was associated with a four-fold odds (OR = 4.06, 95% CI 1.53–10.74) of requiring greater than 24 h of care [10]. The currently derived predictive model encompasses similar metrics in terms of physiological parameters, with the addition of several comorbid conditions. This highlights the importance of considering a combination of factors when decided on appropriate treatment for ABSSSI extending beyond past medical history or size of lesion. The clinical utility of these models outside of the patient populations they have been studied in has not been fully explored. External generalizability of these tools may prove to be lacking; however, they do provide a framework for which other sites can build off to develop their own risk-scoring tools and clinical pathways in order to decrease inappropriate admissions or the use of intravenous antibiotics in ABSSSIs.

Although the current study demonstrated a small improvement in predictive ability with the newly derived risk score (higher AUROC), there are several limitations that must be noted. With the retrospective, single-centered nature of the study, there is likely recording and selection bias associated with data collection as well as limitations related to external generalizability. Only metrics available in the electronic medical record could be used to derive the model. The size of the affected area or lesion was not available for the majority of patients, which may confound results. For instance, the site of ABSSSI on the leg remained in the predictive model; however, this may represent the size of the lesion as opposed to the actual site. Additionally, practice patterns for our ED/OU may be different compared with other sites and may be altered secondary to factors not related to metrics in the electronic medical record (i.e. bed availability). Also, 96-h ED revisit was used to determine which patients were potentially inappropriately treated instead of the traditional 7 days. The DMC is only one of several large academic medical centers in the Detroit metropolitan area. Using a 96-h ED revisit endpoint is meant to help capture those patients likely presenting with a failure of initial treatment, as opposed to a re-infection, but re-visits are likely missed. Importantly, there may be misclassification bias in the analysis as patients treated at a higher level of care may have improved clinical outcomes despite not necessarily needing this higher level of care. Additionally, there is no way to account for inter-patient variability in prescribing practices as there is no gold standard for ABSSSI management to reference. This has potential implications to using this scoring tool outside of the population derived, especially without validation or in populations that were not included in the initial study [29].

The current study highlights opportunities to improve resource utilization and overall clinical outcomes in patients with ABSSSIs, and represents an initial step towards a clinical decision support tool. The current model also reinforces previous literature wherein it appears that physiological parameters and comorbid conditions interplay in the determination of appropriate patient disposition. In the future, a similar analysis with a large multicenter database would be warranted to improve external validity of the current predictive model and derived scoring tool. Ideally, this model would then be validated in a prospective, multicenter study in order to improve external validity and overall accuracy, as any retrospective analysis will experience similar limitations to those encountered in this current study. This model can serve as a proof-in-concept blueprint for developing a single-center risk scoring tool and clinical pathway that can aid in decreasing inappropriate resource utilization (i.e. admissions, IV antibiotics) in the management of ABSSSIs. Certain components, along with previously published literature, can assist investigators in deciding upon potentially relevant risk factors. The methods used in this study can also be extrapolated to assist a healthcare site in developing a similar tool that may use different risk factors based on their different patient populations. The results of this study highlight the importance of evaluating the patients with ABSSSIs with respect to not just the immediate clinical picture but also to past medical history.