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A clinical decision rule predicting outcomes of emergency department patients with altered mental status

Abstract Study objective Approximately 5% of emergency department patients present with altered mental status (AMS). AMS is diagnostically challenging because of the wide range of causes and is associated with high mortality. We sought to develop a clinical decision rule predicting admission risk among emergency department (ED) patients with AMS. Methods Using retrospective chart review of ED encounters for AMS over a 2‐month period, we recorded causes of AMS and numerous clinical variables. Encounters were split into those admitted to the hospital (“cases”) and those discharged from the ED (“controls”). Using the first month's data, variables correlated with hospital admission were identified and narrowed using univariate and multivariate statistics, including recursive partitioning. These variables were then organized into a clinical decision rule and validated on the second month's data. The decision rule results were also compared to 1‐year mortality. Results We identified 351 encounters for AMS over a 2‐month period. Significant contributors to AMS included intoxication and chronic disorder decompensation. ED data predicting hospital admission included vital sign abnormalities, select lab studies, and psychiatric/intoxicant history. The decision rule sorted patients into low, moderate, or high risk of admission (11.1%, 44.3%, and 89.1% admitted, respectively) and was predictive of 1‐year mortality (low‐risk group 1.8%, high‐risk group 34.3%). Conclusions We catalogued common causes for AMS among patients presenting to the ED, and our data‐driven decision tool triaged these patients for risk of admission with good predictive accuracy. These methods for creating clinical decision rules might be further studied and improved to optimize ED patient care.


Background
Altered mental status (AMS) can be defined as an acute change in thought content or level of arousal in the absence of focal neurological symptoms and signs. Health care workers frequently use AMS interchangeably with confusion, delirium, encephalopathy, altered level of consciousness, "found down," or unresponsive. AMS is present in ≈5%-10% of adult emergency department patients 1 with a high prevalence in elderly individuals and those with comorbid conditions. 2,3

Importance
The high mortality associated with acute mental status changes warrants treatment as a medical emergency. 4 Timely and focused care avoids diagnostic delays and reduces mortality. 5 However, the diagnostic challenge of AMS lies in the myriad of potential etiologies originating from virtually any organ system. 1,4,6 The clinical evaluation of AMS patients is variable because of the broad differential and lack of universally accepted guidelines. Workup in the emergency department (ED) can be extensive, leading to unnecessary costs and significant amounts of time needed to integrate test results into triage decisions. 7,8 Previous attempts to structure the diagnostic approach for AMS have focused on a narrow subset of causes or excluded important underdiagnosed conditions, such as dementia and schizophrenia. 4,[9][10][11][12] An alternative approach is to identify the essential information needed to determine whether a patient with AMS is admitted or discharged from the ED.

Goals of this investigation
Our objectives were to identify the causes of AMS in patients presenting to the ED and to create a clinical decision rule predicting patients' risk for hospital admission and mortality.

The Bottom Line
Altered mental status is a common reason for presentation to the emergency department (ED). In this study the authors applied recursive partitioning to clinical data from an academic medical center to derive a clinical decision rule identifying ED patients with altered mental status who may require hospital admission or who may be at increased risk for 1-year death. Although meriting independent validation, this study illustrates a potential approach to organizing risk stratification in this population.
The study was an observational retrospective cohort study on patients presenting to the UCDMC ED with a chief complaint of AMS in July and August of 2015. The goal of the study was to develop a clinical decision rule predicting admission of patients based upon statistical and hierarchical analysis of real-world data.

Study population
We retrospectively analyzed adult patients ( Table 3). If present, data from urine drug screens, urinalyses, electrocardiograms, chest x-rays (CXRs), lumbar puncture, and head imaging also were recorded and classified (listed in Table 3). Prespecified rules for classification of select tests as "normal" versus "abnormal" were used. For radiologic studies, the final chart diagnosis by the reading radiologist was used for classification according to prespecified rules.
Data on mortality were assessed though manual chart review by determining if the AMS patient was documented in the EHR to have died within 1 year of their ED visit or entered hospice within 6 months of the ED visit.

Outcomes
The primary outcome was hospital admission. The secondary outcome was 1-year mortality.

2.5.1
Handling of missing data Absent clinical data were common. We deliberately excluded studies ordered more than 24 hours after ED arrival because they reflected inpatient evaluation rather than ED triage. Because data were missing not at random, excluding encounters that lacked a given diagnostic test would bias the sample population and multiple imputation was not thought to be valid. 13 Acknowledging   Initially, each recorded laboratory or demographic value was tested to determine if it was associated with hospital admission from ED encounters during the month of July 2015 using univariate analysis (Table 3). We used a Fisher's exact test for categorical variables and a Wilcoxon rank-sum test for continuous variables. For continuous variables where either a high or a low value may be associated with ill health (eg, hyponatremia or hypernatremia), we assessed the absolute value of the lab after subtracting the median lab reference value. However, we found that the list of potentially useful variables (P < 0.05) was still too long to organize into a concise clinical decision rule, and thus we turned to multivariate approaches to narrow the list of critical clinical variables.
We next used non-parametric stepwise multiple regression, wherein the strongest single predictor of admission was identified and used as covariate to reassess the significance of all other variables.
When two predictors are highly correlated (eg, systolic and diastolic blood pressure) using one as a covariate downplays the importance of the other. Thus, covariate analysis permits identification of the next-strongest unique predictor of admission. The process repeats, and, at each step, P values are recalculated to adjust for any non-zero correlation between predictors. After enough cycles, addition of another variable becomes redundant. Such a redundant variable does not improve the multiple regression model and weakens one or more of the initial unique predictors to the point that it falls above a preselected threshold to enter the model (P = 0.05). This method helped identify the most salient predictive variables associated with hospital admission.
Given that stepwise multiple regression approaches often produce inefficient decision rules, recursive partitioning analysis can be used to generate meaningful decision rules from large and redundant variable sets. 14 Therefore, in addition to the previously described analyses, we separately analyzed the data using the recursive partitioning packages rpart and ctree packages in R. 15,16 These analyses helped further refine the list of important clinical variables predictive of hospital admission.
The outcome of performing the univariate, stepwise regression, and recursive partitioning analysis was the identification of a list of highquality predictors for hospital admission that could then be arranged into a clinical decision rule.

Clinical decision rule development
Multivariate methods narrowed the list of predictors to the following: known history of a significant psychiatric disorder (eg, schizophrenia) and/or a history of recent intoxication, serum creatinine, serum albumin (sometimes alongside aspartate aminotransferase), diastolic pressure, oxygenation on room air, ED triage assigned level of acuity, low hemoglobin, and abnormal brain imaging. For the development of a clinical decision rule, these variables were organized by the order of availability during a typical encounter. Responses to initial therapies (eg, glucose for hypoglycemia) were placed at the beginning of the decision rule because they often happen before hospital arrival, are high yield, 1 and were predictive using both univariate (Table 3) and multivariate analyses. Brain imaging was placed at the end of the decision rule given the order of availability in a typical encounter but was dropped after further refinements based on (1) review of July cases and (2) a desire to avoid sorting seriously ill patients into a "low-risk" category. We also found triage level of acuity performed best when moved later in the clinical decision rule. Cutoff values were manually adjusted to best sort patients into hospital admission versus discharge from the ED. This final clinical decision rule was used to sort patients into low, moderate, or high risk of admission. As an internal check on the performance of the clinical decision rule, we tested for an association between rule-assigned risk of admission and whether a patient was admitted on the July 2015 data using a chi-square test. The decision rule was not altered based on this internal check.

Clinical decision rule validation
The developed clinical decision rule was validated by application to a novel set of ED cases with AMS (August 2015). We employed chisquare tests to determine if there was an association between the risk of admission determined with the clinical decision rule, and mortality risk within year of presentation. In all "moderate-risk" cases, and all cases for which the clinical decision rule was discrepant with clinical outcome, we performed a qualitative review.

Cohort characteristics
Summary statistics of the included encounters are presented in Table 1.
Approximately half of the 325 patients were male. Ages were normally distributed with no differences between July and August cohorts.
Excluding repeat visits occurring during the study period, 64% of patients had at least one prior ED visit in the past year.
Of the 351 encounters studied, 41.3% led to hospital admission (Table 1). Of those admitted, the mean (± SD) hospital length of stay was 9.0 ± 18.0 days ( Table 1). The 48 patients who presented with AMS and died within one year of ED presentation were significantly older than those surviving (for each month P < 0.001; Table 1).
Of the identified cause(s) for a patient's AMS, 58.1% of encounters had only one identifiable cause (represented in Figure 1). These encounters were most often for intoxication and chronic psychiatric conditions. Less-common causes included stroke/TIA (8 cases) and hypoglycemia (7 cases). The remainder of AMS cases were multifactorial and frequently involved combinations of infection, dehydration, and chronic medical conditions (

Development of the clinical decision rule
Using univariate analyses, we found that many variables were associated with hospital admission (Table 3). Stepwise regression analysis of July data then revealed that admission was more likely when there was no psychiatric history nor recent intoxication (P < 0.001), albumin was low (P < 0.001), brain MRI was abnormal (P < 0.001), creatinine was high (P = 0.001), the patient was not oxygenating well on room air (P = 0.001), and when aspartate aminotransferase was high (P = 0.002).
Recursive partitioning analysis using the rpart package in R prior-

Evaluation of the clinical decision rule
The clinical decision rule's ability to predict admission was validated on a novel dataset acquired from August ED encounters. The clinical decision rule demonstrated a robust association between the decision rule assigned risk and admission (P < 0.001). Specifically, admission occurred in 15% (15/102) of the low-risk group, 46% (13/28) in the moderate-risk group, and 87% (60/69) in the high-risk group.

High-risk for hospitalization
Summing data from both months, 110 encounters were stratified by the clinical decision rule as high risk. Of these, we identified 12 cases for which the clinical decision rule predicted admission, but the patient was discharged from the ED. We reviewed each of these discrepant cases individually. In four, the patient was ill enough to warrant admission, but admission was inconsistent with goals of care or illness severity was recognized later. Of the remaining eight cases, patients rapidly returned to their baseline without intervention.

Low-risk for hospitalization
Combining data from both months, 20 patients categorized as low risk were admitted. In six cases, the single highest-priority diagnostic test based on clinical history-such as liver function tests for a patient running out of lactulose-was abnormal enough to warrant admission. Another nine low-risk patients were admitted for profound F I G U R E 2 Clinical decision rule for admission after ED presentation for AMS. All grayscale elements of the flowsheet were developed using data from July 2015 and then validated against data from August 2015. Blood pressure cutoffs are listed in mmHg. Abbreviations: AMS, altered mental status; DBP, diastolic blood pressure; ED, emergency department; SBP, systolic blood pressure; SpO 2 , oxygen saturation by pulse oximetry intoxication. The remaining five cases were admitted because of inadequate home care.

Moderate-risk for hospitalization
The clinical decision rule assigned 61 cases as moderate-risk. Patients who rapidly responded to treatment for hypoglycemia and dehydration were identified as moderate-risk (n = 13) and sometimes admitted because of other clinical concerns (eg, syncope workup). Review of the remaining 48 moderate-risk encounters revealed several things.

Prediction of mortality
Mortality within one year of ED presentation was compared to the clinical decision rule's assigned risk of admission. As some encounters were repeat visits, only the first visit was considered in these analyses. For July, no low-risk patients died in the subsequent year compared to 30% of high-risk patients (Figure 3). The moderate-risk group had an intermediate mortality of 12.9% (Figure 3). This pattern was replicated in the August data ( Figure 3). For each month, the rule-assigned risk was a significant predictor of mortality within one year ( Figure 3). Combining the two months, roughly one-third of high-risk assigned patients died within a year, compared to 1.8% and 12.3% in the low-and moderaterisk groups, respectively ( Figure 3).

Study limitations
This study has several limitations. The dataset used in this study is relatively small but allowed for manual chart review. Manual chart review was deemed important for this initial analysis to (1)

DISCUSSION
AMS is common in the ED with many etiologies and an associated high mortality rate. 4  Our objective was to identify which data could most accurately and concisely predict the need for hospital admission. Using retrospective analysis, we identified a concise list of variables that accurately predicted admission of AMS patients. These factors guided our development of a clinical decision rule aiming for a data-driven, time-efficient approach to initial AMS workup by stratifying patients according to risk of admission. We also found that stratification of admission risk was predictive of 1-year mortality.
In our patient population, the most common etiologies of AMS included intoxication, chronic medical conditions, and psychiatric conditions ( Figure 1). Multifactorial cases were common (151/351) with contributions from dehydration, infection, and/or chronic medical conditions ( Figure 1; Table 2). Given the higher medical complexity and baseline disease burden in older patients, it is not surprising older age was associated with hospital admission and increased 1-year mortality.
Surprisingly, age had a minimal contribution in multivariate analyses.
Instead, the most critical information was obtained through clinical history, vital signs, and a few high-yield serum studies ( Figure 2). This hierarchy of information matches prior work emphasizing the importance of clinical history-taking. 1 In the present dataset, AMS most commonly represents neurological dysfunction secondary to the dysfunction of other organ systems.
Our analysis indicates that presenting to an ED with AMS should be viewed as a critical life event. Patients with AMS presenting with a sufficiently large abnormality in any vital sign or select serum markers ( Figure 2) should be viewed as having multiorgan dysfunction with a high likelihood of admission and 1-year mortality. The ∼30% 1-year mortality observed in high-risk cases reflects the severity of multisystem disease underlying AMS presentations. Further studies on this concept are warranted, particularly whether mortality can be improved in AMS patients with earlier identification and treatment.
Given the lack of universally accepted approaches to AMS, published diagnostic algorithms support a broad initial workup. 1,5,6 Our algorithm implies many diagnostic tests are not necessary to determine the need for admission. Therefore, this represents an opportunity to limit testing and speed ED triage. As an example, CT of the head was ordered for 37% (88/220 cases) of low-and moderate-risk patients and was abnormal in five cases. Four of those abnormalities would have been captured by restricting CT imaging to those with (1) prior intracranial malignancy, (2) new head trauma, or (3) focal neurological signs. The fifth case was an incidental subdural hematoma that did not change clinical management. Had the CT head been ordered for all the decision rule high-risk patients (n = 120) and restricted to low-risk cases with the aforementioned indications, CT orders could be reduced by nearly 50%. However, it is recognized that some ED imaging for low-risk cases is warranted to fully rule out potential serious processes. Although expanded laboratory testing and brain imaging are expected for high-risk AMS patients, some of these studies might be safely deferred until hospital admission.
In summary, AMS is an emergency clinical presentation representing underlying organ dysfunction of variable etiology and an associated high mortality. We have created a method by which real world clinical data were used to generate a clinical decision rule for patients presenting to the ED with AMS. This method could be easily implemented in any hospital setting with an EHR. Further refinement of this method and the resultant clinical decision rule could be aided by modern machine learning techniques. Additional validation and refinement of this method may also be used to assist providers in arriving at decision points along the diagnostic journey more quickly and effectively.

CONFLICT OF INTEREST
The authors report no conflict of interest.

PRESENTATIONS
A portion of this work was presented at the American Academy of Neurology Annual Meeting, Boston, MA, April 2017.

AUTHOR CONTRIBUTIONS
GM, FG, and AD devised the project. DB, TJS, GM, and NPKK collected the data and developed the triage algorithm. DB performed the statistical analysis. DB, TJS, and AD wrote the manuscript with input from all authors.