Page 116 - Hall et al (2015) Principles of Critical Care-McGraw-Hill

84 PART 1: An Overview of the Approach to and Organization of Critical Care

dysfunction over time to enhance sensitivity, specificity, positive and
TABLE 13-1 Potential Uses of Severity-of-Illness Scoring Systems
negative predictive capability to mortality prediction. 7-11
Uses of scoring systems in randomized controlled trials (RCTs) and clinical research Scoring systems have been developed using databases from patients
To compare different RCTs and clinical studies already admitted to ICUs and not from the pool of patients outside the
To determine sample size ICU (eg, emergency, in-patient wards, operating room, and recovery
room), where the triage decision to admit a patient to the ICU is made.
To do stratified randomization (to determine subgroup identification and stratification While severity-of-illness scoring systems in theory could be used to
for severity of illness)
increase the accuracy of triage decisions regarding appropriateness of
To assess success of randomization ICU admission, reformulation of the current scoring methods would be
To assess treatment effects in subgroups (posttreatment subgroup identification) necessary to reflect the patient population outside the ICU, where triage
occurs. Obviously, ICU resources should be focused on patients who
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To compare study patients to patients in clinicians’ practices
are most able to benefit from ICU care. However, to date there are no
Uses of scoring systems for administrative purposes reports regarding the use of scoring systems to assist in decisions regard-
To describe resource utilization of ICU ing appropriateness of ICU admission.
To describe acuity of illness
To relate resource utilization to acuity of care DEVELOPMENT OF SCORING SYSTEMS
To guide reimbursement and budget of ICU The major scoring systems that are the focus of this chapter were designed
specifically to predict outcome of critical illness. Initially, clinical and phys-
Uses of scoring systems to assess ICU performance
iologic variable selection was based on subjective judgment and consensus
Quality assurance of clinicians, supplemented by extensive review of the relevant critical care
To assess performance of an ICU in general or for a specific disease category trials and outcomes literature. Subsequently, logistic regression modeling
was used to select significant predictive variables from a (often very large)
To assess performance of an ICU over time derivation cohort. Ideal variables are simple, inexpensive, well-defined,
To compare individual intensivists’ performances reproducible, and widely available measurements collected routinely in the
To assess the performance of a therapeutic intervention course of patient care. The design and development of severity of illness
scoring systems required collection of a large number of clinical and physi-
Comparison of ICU performance in different categories of hospitals, countries, etc
ologic variables collected on a large sample size of critically ill patients, as
To assess performance for different ICU administrative characteristics (open/closed unit, well as survival status at ICU and hospital discharge. Multiple logistical
communication, ICU director task, etc) regression identifies the specific variables that significantly predicted sur-
Effectiveness vival and assigns relative weights to each variable. This set of variables is
then retested prospectively for accuracy of prediction in another sample of
Uses of scoring systems to assess individual patient prognosis and to guide care patients (termed a validation or replication cohort) to validate the selected
Triage of patients variables and appropriate weighting of such variables. 12
Decisions regarding intensity of care The sampling frequency and the time period of measurement of
Decisions to withhold and withdraw care physiologic variables are important additional methodologic consider-
ations in the development of severity of illness scoring systems. Most
scoring systems use the most abnormal measurement of a physiologic
variable in the 24 hours prior to ICU admission. More recently, scoring
care between different ICUs and within the same ICU over time. For systems have used the most abnormal value of a physiologic variable for
example, severity-of-illness scoring systems have been used to assess each successive 24-hour period while a patient is in the ICU, and then
the impact on patient outcomes of planned changes in the ICU, such correlated these physiologic variables with outcome. Therefore, predic-
6
as changes in bed number, staffing ratios, and medical coverage. The tion prognosis could be adjusted daily depending on the patient’s course
fourth purpose of these scoring systems is to assess the prognosis of (natural history) and the patient’s physiologic response to treatment. In
individual patients in order to assist families and caregivers in mak- essence, changes in organ dysfunction are used to improve accuracy of
ing decisions about ICU care. Novel propensity scoring systems and outcome prediction. 7-11 Studies have shown that the change in organ dys-
case:control matching strategies have been and are used to simulate function from day 0 to day 1, from day 0 to day 3, and indeed from day
9
clinical trials to assess efficacy and safety of therapeutics in critical care. to day, can be used to accurately predict outcome of the critically ill.
13
This approach supplements (but may not replace) the need for RCTs to Another important consideration in the development of severity-of-
assess therapeutics in critical care. illness scoring systems is the patient cohort used to derive the scoring
Finally, scoring systems are used to evaluate suitability of patients for
novel therapy (eg, APACHE II was used to assess suitability of patients system. For example, it is relevant to know whether scoring systems were
derived in medical, surgical, or medical-surgical ICUs, whether com-
for prescription of the now discontinued recombinant human activated munity or tertiary care teaching hospital ICUs were used, whether ICUs
protein C [drotrecogin alfa] in sepsis). were selected from one country or from many countries, and how many
The general hypothesis underlying the use of severity-of-illness scor-
ing systems is that clinical variables assessed on ICU admission predict different ICUs were used to establish the scoring system. Furthermore,
scoring systems derived from the sample of patients involved in a clini-
survival and other outcomes of critically ill patients. This hypothesis cal trial may be biased (because of unique and often strict inclusion and
is based on observations that increasing age, presence of underlying exclusion criteria) and so may not represent a general population of
chronic disease, and increasingly severe abnormalities of the physiol- critically ill patients (ie, generalizability is reduced).
ogy of critically ill patients are associated with increased mortality.
Accordingly, most severity of illness scoring systems combine relevant ■
acute and chronic clinical variables to predict risk of death. Early in METHODOLOGIC CONSIDERATIONS
this evolution, severity-of-illness scores calculated at ICU admission or Critical appraisal of severity-of-illness scoring systems assesses accuracy
in the 24 hours following ICU admission were used to predict hospital (calibration and discrimination), reliability, content validity, and meth-
mortality. More recently, scores have been calculated over the course odological rigor. 14
of the ICU stay to provide updated (and more accurate) prediction of Discrimination describes the ability of a model to distinguish between
hospital mortality. This dynamic approach uses change in acute organ a patient who will live and one who will die. If discrimination is perfect,

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