Decision Making in Emergency Critical Care

SECTION 15 - The ED-ICU Transfer of Care

Severity of Illness Scores and Prognostication

David M. Maslove


The ability to quickly and accurately assess a patient's clinical status is essential to effective triage. This is especially true for patients with critical illness or injury. Severity of illness (SOI) scores help estimate the likelihood of impending clinical deterioration, identify appropriate services for consultation and admission, and enable practitioners to determine which patients will require frequent reassessment; this, in turn, helps guide time management and resource allocation.

In addition to their role in clinical assessment, disease-specific diagnostic and treatment algorithms frequently make use of SOI scores. Likewise, research trials in critical care almost always involve SOI scoring, as a means of both stratifying patients and comparing the results of one trial to another. Finally, some semblance of prognosis, even when imprecise and tentative, can be helpful in addressing the anxiety experienced by patients and families facing the uncertainty of critical illness.

To be useful in a busy emergency department (ED), an SOI score must be easy to use and its parameters should be reliable, objective, unambiguous, limited in number, and available at the time of initial assessment. This poses a challenge; easily obtained clinical parameters like vital signs are prone to disagreement between observers especially in dynamic situations when these signs fluctuate, while more objective laboratory values require additional time and resources to collect and analyze.

The simplest scoring systems use binary variables that are designated a specific cutoff value and then marked as either “present” or “absent.” Points assigned to each variable are tallied into an overall integer score that corresponds to a risk category. Traditionally, the most useful SOIs employed a small number of easily remembered parameters, allowing for rapid calculation at the point of care. Increasing adoption of smart phones and other mobile devices in the hospital setting has lessened the importance of simplicity in the scoring system, and SOIs are evolving in response to this technology.

The clinical variables included in SOI scores are determined in numerous ways, ranging from expert opinion to logistic regression. Ideally, scoring systems are derived from data describing one cohort of patients and then validated in a second, independent cohort. Additional studies are often carried out to assess a score's validity under a range of circumstances, such as geographic location or model of health care delivery. In order to maintain score performance, updates are required as practice patterns and case mix evolve.1

A score's discrimination refers to its utility in distinguishing patients who experience the outcome of interest, from those who do not. Discrimination is often expressed in terms of sensitivity and specificity, or by a receiver operator characteristics (ROC) curve that relates these terms over a range of cutoff values. Scores are said to be well calibrated if they perform equally well across a range of conditions, including low- and high-risk disease, different diagnoses, and different geographical regions.2

Some SOI scores are intended for use with specific clinical presentations and diagnoses, while others are more general. In all cases, prognostic indices and SOI scores must be interpreted with caution; such tools are derived based on population averages and therefore provide only a probabilistic estimate for any given patient. For the most part, SOI scores are meant to help inform clinical decision making, which typically involves many more demographic, physiologic, and psychosocial parameters than can be distilled to a single number.



The pneumonia severity index (PSI) for community-acquired pneumonia (CAP) is one of the most familiar disease-specific SOI scores. Also known as the PORT score, (for Pneumonia Patient Outcomes Research Team, the cohort in which it was validated), this SOI was published in 1997 and subsequently validated in several independent studies.3 Created to standardize admission practices and to identify low-risk patients suitable for home treatment, the PSI generates a score using age and 19 clinical variables recorded as either “present” or “absent.” The score, in turn, corresponds to one of five categories predicting risk of death at 30 days (Table 62.1).

TABLE 62.1 Risk Categories in the Pneumonia Severity Index

aCategory I is assigned to patients <50 years of age, with none of the specified coexisting conditions or physical exam findings.

Fine MJ, Auble TE, Yealy DM, et al. A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med. 1997;336:243–250.

Age and comorbidities weigh heavily in the PSI, predisposing the score to overestimate severity in elderly patients with chronic illness and to underestimate severity in young and otherwise healthy patients.4 In one validation study, only 20% of patients in the highest-risk class (V) were admitted to the ICU, proving that PSI is less useful in prognosticating for ICU admission than for hospital admission.5 Patients with HIV were excluded from the initial PSI study, and the index was shown to markedly underestimate disease severity in patients with pandemic influenza A(H1N1) during the 2009 outbreak.6 In a meta-analysis involving 16,519 patients, the PSI was found to be sensitive (pooled sensitivity 90%), but lacked specificity (pooled specificity 53%).7

With 20 variables to account for, the PSI can be cumbersome to use. A simpler score developed by the British Thoracic Society known as CURB-65 uses only five clinical parameters: confusion, blood urea nitrogen (BUN) level, respiratory rate (RR), blood pressure, and age.8 One point is assigned for each variable, depending on whether it is present or absent according to a specified cutoff value (Table62.2). As in the PSI, the total score is then used to assign a risk category that predicts mortality at 30 days. The CURB-65 score is less sensitive than is the PSI (pooled sensitivity 62%), but is more specific (pooled specificity 79%).7 Other versions of the CURB-65 score include CURB, in which age is omitted, and CRB-65, which does not require the laboratory value of BUN. The exclusion of BUN leads to a decrement in sensitivity (pooled sensitivity 33%), but improves specificity (pooled specificity 92%).7 Importantly, the original CURB cohorts excluded nursing home residents as well as immunocompromised patients including those with malignancy, HIV, and tuberculosis.

TABLE 62.2 CURB-65 Score

aMental Test Score of 8 or less or new disorientation in person, place, or time.

Lim WS, Van der Eerden MM, Laing R, et al. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax. 2003;58:377–382.

Like the PSI, the CURB-65 score performs poorly in predicting the need for ICU admission. Because delayed ICU admission increases mortality risk in patients with severe CAP, the SMART-COP score was designed to address this issue specifically. This score combines eight clinical characteristics to estimate the risk of requiring intensive respiratory support (either invasive or noninvasive mechanical ventilation) or infusions of vasopressors and can therefore be useful in assigning patients to the appropriate level of care (Table 62.3).9 A SMART-COP score of ≥3 was found to be more sensitive for the need for ICU-level support than was PSI class IV, PSI class V, or CURB-65 risk category 3 (92.3% vs. 73.6% vs. 38.5%, respectively). ATS/IDSA guidelines on CAP management also offer ICU admission criteria, including the need for invasive mechanical ventilation, septic shock with the need for vasopressors, or any three of a set of minor criteria similar to those used in the aforementioned CAP scores.10

TABLE 62.3 Smart-Cop Score

Total score used to predict risk of needing intensive respiratory or vasopressor support.
0–2 points = Low risk.
3–4 points = Moderate risk (1 in 8).
5–6 points = High risk (1 in 3).
≥7 points = Very high risk (2 in 3).

Charles PGP, Wolfe R, Whitby M, et al. SMART-COP: a tool for predicting the need for intensive respiratory or vasopressor support in community-acquired pneumonia. Clin Infect Dis. 2008;47:375–384.


In critical neurologic conditions such as subarachnoid hemorrhage (SAH), ischemic stroke, and traumatic brain injury, SOI scores—based on both clinical and imaging characteristics—can be used to estimate prognosis and, in some cases, inform treatment decisions.


First published in the mid-1970s, the Glasgow Coma Scale (GCS) was initially developed to standardize descriptions of coma. Later, it was modified specifically to evaluate level of consciousness following traumatic brain injury.11 To calculate the score, points are added for the patient's eye, verbal, and motor responses. Scores range from 3 to 15, with lower scores indicating greater severity of injury (Table62.4).

TABLE 62.4 Glasgow Coma Scale

Sternbach GL. The Glasgow coma scale. J Emerg Med. 2000;19:67–71.

Although GCS can be reported as a single sum, this may be less informative than an explicit breakdown of the constituent parts.11 Common confounders include sedation, analgesia, neuromuscular blockade, delirium, orbital trauma, and intubation, each of which can make it impossible to calculate one or more of the subscores.12 In intubated patients, for example, the verbal score is often represented by the letter “T,” which provides information, but precludes calculation of a total score.13 Alternative scoring systems, such as the Full Outline of UnResponsiveness (FOUR) score, may be more appropriate in critically ill intubated patients.14

In the prehospital setting, GCS is predictive of both death and hospitalization. A GCS of ≤13 in the field is an indication for immediate transport to a specialized trauma center.15 GCS calculated at ED admission is an independent predictor of mortality16 as well as of functional status at 6 months.17 In some studies of the GCS the motor component alone has been shown to correlate with mortality.16

In the GCS system, traumatic brain injury is classified as mild (GCS 13 to 15), moderate (GCS 9 to 12), or severe (GCS < 9).12 A GCS score of 8 or less is often cited as an indication for intubation. Current guidelines from the Eastern Association for the Surgery of Trauma recommend endotracheal intubation for patients with GCS ≤ 8, but note that patients with altered mental status and a GCS > 8 often require intubation as well.18 Airway obstruction, persistent hypoxemia, and hypoventilation should trigger prompt intubation regardless of mental status.

The GCS is likely the most widely used mental status score in the ICU.11,19 It is easily calculated at the bedside and can be repeatedly measured as a means of tracking the progression of injury and recovery. Interrater agreement depends on provider type and level of experience and is highest when scores are high.11 The GCS has become integral to other more recently developed SOI scoring systems, including the Acute Physiology and Chronic Health Evaluation (APACHE) and Simplified Acute Physiology Score (SAPS) systems discussed below.

Subarachnoid Hemorrhage

Numerous SOI scores exist for SAH, although most are derived from expert opinion and have only been validated in small cohorts.20 The most frequently used are the Hunt and Hess scale and the World Federation of Neurological Surgeons (WFNS) scale, which are based on clinical parameters, as well as the Fisher scale, based on computerized tomography (CT) imaging (Table 62.5).

TABLE 62.5 Common Scales Used in SAH25

Ferro JM, Canhão P, Peralta R. Update on subarachnoid haemorrhage. J Neurol. 2008;255:465–479.

The Hunt and Hess grading system can be difficult to apply consistently; some of its terms are ambiguous, and clinical findings have the potential to span multiple categories. Interrater agreement in applying the score is moderate (κ = 0.48).21 The score defines five classes, with a sixth (Hunt and Hess 0) sometimes included for patients with unruptured aneurysms. The Hunt and Hess scale is poorly powered to predict distinct outcomes for each individual class, and as such, classes are sometimes aggregated: Patients are often grouped into low scores (classes 0 to III) versus high scores (classes IV and V) or to “alert” (classes I and II), “drowsy” (classes III and IV), and “comatose” (class V).20,22 The WFNS comprises a condensed version of the GCS and an additional binary measure for the presence or absence of a focal motor deficit. Its prognostic value is unclear; some studies suggest it correlates with outcome, while others do not.20,22 The Fisher grading system uses CT findings and was initially established to predict the risk of vasospasm; it also has been shown to correlate with outcomes at 1 year and beyond. Patients in Fisher class 3 and 4 have an increased risk of poor outcome or death (relative risk 3.2 to 14.8).23 Fisher class does not, however, accurately predict long term health-related quality of life.24 The GCS has also been shown to correlate with outcomes in SAH.20

Ischemic Stroke

The National Institutes of Health Stroke Scale (NIHSS) is an 11-part evaluation of neurologic signs and is used for triage and prognostication of ischemic stroke. It incorporates measures of level of consciousness, gaze, visual fields, motor function, ataxia, sensation, speech, language, and neglect. The NIHSS has been shown to correlate with survival, length of stay, discharge destination, and functional status at 1 year.26 It has been used to identify patients who are appropriate candidates for thrombolytic therapy, with both very high-scoring and very low-scoring patients deemed not suitable for treatment. Patients with profound deficits isolated to a single component of the scale, such as severe aphasia, may score low but should be considered for thrombolysis nonetheless.27


Devised in the 1970s to predict complications of acute pancreatitis, the Ranson score is an early example of a disease-specific SOI score.32 Its use has largely been supplanted by more generalized scoring systems such as APACHE and Sequential Organ Failure Score (SOFA), reflecting the propensity of severe pancreatitis to result in multiple organ dysfunction.33

Establishing risk in acute gastrointestinal bleeding can be useful in determining which patients require hospital admission and urgent endoscopy. The Rockall score incorporates age, comorbidities, and the presence of shock to stratify patients according to risk of rebleeding and death.34 The Glasgow-Blatchford score (GBS) incorporates features of the presentation (melena, syncope), along with heart rate, blood pressure, hemoglobin, BUN, and the presence of cardiac or hepatic disease to derive an integer score.35 The GBS is predictive of a composite endpoint that includes death; rebleeding; and the need for blood transfusion, endoscopy, or surgery. It has been shown to outperform the Rockall score in a number of prospective evaluations, with an area under the (ROC) of approximately 0.9.3537

In patients with acute liver failure (ALF), SOI scoring has been used to estimate the risk of death, so that referral for transplant can be initiated if indicated. The King's College criteria (Table 62.6), developed in the United Kingdom, distinguish between ALF resulting from acetaminophen toxicity and ALF resulting from other causes, many of which portend a worse prognosis.38 In general, the King's College criteria predict mortality with specificity of approximately 90%, but sensitivity of only approximately 60%.39,40 This limits the utility of the score somewhat, as many patients who do not meet criteria should still be considered for transplant.41 The Model for End-Stage Liver Disease (MELD) score is a mathematical combination of the serum bilirubin, creatinine, and INR and is used to evaluate 3-month mortality risk in chronic liver disease. MELD has also been applied to patients with AFL, with a recent prospective analysis showing it to be a better predictor of death than the King's College criteria.42 In particular, the MELD score improved upon the poor negative predictive value of the King's College criteria, as 20 of the 22 patients who survived without transplantation had a MELD score ≤ 30.

TABLE 62.6 King's College Criteria for Liver Transplantation in Acute Liver Failure

Gotthardt D, Riediger C, Weiss KH, et al. Fulminant hepatic failure: etiology and indications for liver transplantation. Nephrol Dial Transplant. 2007;22:viii5–viii8.


Trauma severity scores were initially established for field triage.28 They have since become important in research, quality of care improvement, and health care administration. Stratifying trauma patients according to severity of injury allows not only the comparison of large and diverse patient groups but also the analysis of trauma outcomes in different settings. Some SOIs are based on anatomical regions of injury or systemic signs of organ dysfunction, while others are designed for specific types of injury.

Anatomical reporting systems allocate points for injuries sustained in distinct body regions. The injury severity score (ISS), one of the first such scores, assigns points based on the Abbreviated Injury Scale (AIS) to each of the six distinct body regions. The ISS is then calculated by adding the squares of the highest AIS values in each of the three most severely injured body regions (Table 62.7). Scores range from 1 to 75, with an AIS of 6 in any single region resulting in an automatic maximal score.

TABLE 62.7 Injury Severity Scale

ISS = A2 + B2 + C2 where A, B, and C are the highest AIS scores in each of the three most severely injured body regions.

Kim Y-J. Injury severity scoring systems: a review of application to practice. Nurs Crit Care. 2012;17:138–150.

The full extent of injury in any given body region is often not known until diagnostic imaging or surgery is performed. The ISS is therefore less useful as a field triage tool than as a means for comparing trauma outcomes in retrospective analyses of clinical and administrative data. An ISS ≥ 16 has been correlated with a mortality risk of 10% and is used as a cutoff above which patients should be treated at a specialized trauma center.28 The ISS may underestimate severity in cases of multiple injuries to the same body region or when significant injuries are sustained in more than three regions.29 A modification of the ISS, the New Injury Severity Score (NISS) attempts to address this shortcoming by adding the squares of the 3 highest AIS scores, regardless of the body regions in which they occur.30

While the ISS and NISS represent injury in purely anatomical terms, other scores incorporate physiologic variables that measure the systemic sequelae of trauma. The Revised Trauma Score (RTS) is one of the most commonly used physiologic scores and is derived from the GCS, systolic blood pressure (SBP), and RR (Table 62.8).29 The raw score, which is the sum of the coded values of the three variables, can be easily calculated in the field and used for prehospital triage. Values range from 0 to 12, with scores <11 predicting a mortality rate of 12% or greater, suggesting the need for immediate transfer to a trauma center.28 A weighted version of the RTS can also be calculated, which increases the importance of the GCS to reflect the morbidity of isolated severe head injury.29 The RTS may be difficult to use in patients with unstable or fluctuating vital signs and may underestimate injury severity in patients who have been adequately resuscitated.

TABLE 62.8 Revised Trauma Score

RTSc = 0.7326 SBP + 0.2908 RR + 0.9368 GCS.

Kim Y-J. Injury severity scoring systems: a review of application to practice. Nurs Crit Care. 2012;17:138–150.

Trauma scores that combine anatomical and physiologic components may overcome the limitations of either approach used in isolation. The Trauma and Injury Severity Score (TRISS) is a statistical method of combining the ISS and the RTS to predict mortality risk in either blunt or penetrating trauma.28 Newer scores, such as the mechanism, GCS, age, and arterial pressure (MGAP) score, incorporate additional clinical and mechanistic features in order to predict mortality.31


Since the early 1980s, a number of multiparameter SOI scoring systems have been designed to estimate mortality in unselected populations of critically ill patients. General SOI scores have been used to enroll patients into clinical trials, to measure disease progression over the course of an ICU stay, and to generate standardized mortality ratios and other measures used in comparing outcomes between ICUs, hospitals, and geographic locales.43 They are intended to describe groups of patients, with scores predicting average outcomes for the cohort to which they are applied. In the case of a single patient, only a probabilistic estimate of survival can be inferred.1 Clinicians must therefore exercise caution in applying these scores to individual patients, considering that clinical decisions are informed by significantly more information than is used in SOI scoring, including psychosocial factors and patient preferences.

General SOI scoring systems include the APACHE, the SAPS, and the Mortality Probability Model (MPM), all of which have undergone numerous revisions and reinventions over the last few decades. Clinical variables initially were selected based on expert opinion, but more recently have been determined by logistic regression, applied in some cases to data sets of over 100,000 patients.44

The APACHE system is the most popular, with APACHE IV being the most widely used score in the United States and APACHE II the most commonly used worldwide.43,44 APACHE II incorporates age, operative status (emergency vs. elective), and the presence of severe chronic organ dysfunction or immune suppression, along with 12 physiologic variables. It produces an estimate of mortality based on a mathematical combination of weighted variables.45 APACHE III is broken down into 3 constituent subscores (age, acute physiology, and chronic health evaluation) and is designed to predict mortality for each of 78 distinct diagnostic categories as well as risk-adjusted ICU length of stay.2,43 The latest iteration, APACHE IV, uses 142 clinical variables, 115 of which are admission diagnoses, and is used in approximately 7% of the entire United States ICU population.2,44 Both APACHE III and APACHE IV rely on proprietary algorithms to generate a final score and are made available as commercial services. All APACHE scores are based on the most abnormal values collected during the first 24 hours of the ICU stay.

Some of the newer general SOI scores, such as SAPS 3 and MPM II, are based on values collected at the time of ICU admission, rather than during the first 24 hours. As such, they may be more applicable to the period of ED management prior to transfer to the ICU. SAPS 3 requires input for 20 parameters, including age, comorbidities, pre-ICU clinical status, reasons for ICU admission, and physiologic measures.46 It has been shown in one large study to overestimate mortality risk as compared to its predecessor, SAPS II.47 The MPM0 III (the subscript “0” refers to the time relative to ICU admission that the score is calculated) includes 3 physiologic parameters, along with 13 other features related to chronic conditions, acute conditions, and other demographic and clinical features. It is an update of the MPM0 II, based on a new retrospective analysis of 124,855 patients in 135 ICUs.48 The APACHE, SAPS, and MPM models all exhibit good discrimination for predicting mortality, with areas under the ROC curve of between 0.8 and 0.9.2,44 Calibration for severity levels tends to be worse at the extremes. Calibration for different diagnoses is better for scores such as APACHE IV that incorporate diagnosis explicitly,49 while calibration for geographic region can be improved by local customization.43

In addition to general SOI scores such as those described above, there are a number of scores designed to measure degrees of organ dysfunction in critical illness. These scores, which include the Logistic Organ Dysfunction Score (LODS), the Multiple Organ Dysfunction Score (MODS), and the SOFA, are intended to be more descriptive than predictive. Each uses a similar panel of clinical variables to categorize degrees of perturbation in the neurologic, cardiovascular, respiratory, renal, hematologic, and hepatic organ systems. Scores can be used to convey the extent of organ dysfunction and to track progression of illness. For example, an increasing SOFA score over the first 48 hours of ICU admission has been shown to portend a twofold increase in mortality risk, as compared to a decreasing score (50% vs. 27%).43


The modern complement of SOI scores includes those used for specific disease conditions, those designed to predict functional status and mortality, and those intended to provide standardized descriptors of disease severity and organ dysfunction. While some scores (APACHE, SAPS, MPM) are of limited use in individualized treatment decisions, they can help provide a framework in which to compare populations of critically ill patients. Importantly, all SOI scores provide a common language, so that clinicians can quickly and efficiently convey disease severity to consultants and colleagues, even across different facilities.

Newer SOI scores will focus on predicting key outcomes not only for the patient but also for the health care system in which they are treated. The PREEDICCT project proposes to develop decision support tools for triage in pandemic and mass casualty situations or in other situations in which resources are constrained.50 These new scores will reflect the importance of resource allocation in decision making and the need to establish standardized practices that can apply equally in all settings.

Increasingly SOI scores are based on modern statistical techniques and rely less on expert opinion. As electronic medical record coverage expands, new opportunities will emerge to apply real-time data mining algorithms to the derivation and application of SOI scoring. This transformation has immense potential to improve both the precision and calibration of scores, which could in theory be customized even at the level of the individual hospital. Larger data sets could enable outcome prediction for rare conditions that might not otherwise have been captured by existing scoring systems.51 Better prognostication stands to benefit not only a wider range of patients but also the health systems that care for them.



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