Page 129 - Hall et al (2015) Principles of Critical Care-McGraw-Hill

CHAPTER 13: Assessment of Severity of Illness 95

examined 8724 critically ill patients and reported that crude death are variables that influence a clinician’s decision to treat with corti-
rates in hospital varied more than twofold between ICUs in Britain and costeroids, such as dose of norepinephrine being used (because clini-
Ireland. Application of the APACHE II equation produced an ROC cians often use corticosteroids in patients who are “not responsive” to
value of 0.83 and failed to explain outcome in four ICUs. They con- norepinephrine) and APACHE II (because perhaps sicker patients are
cluded that the American APACHE II equation did not fit their data more likely to be given corticosteroids in practice). Thus, controls and
uniformly, and cited systematic differences in medical definitions and cases would be matched as closely as possible according to baseline
diagnostic labeling, diagnostic mix, measurement of physiologic vari- norepinephrine dose and APACHE II. The second step in matching is
ables, effectiveness of treatment, and differences in age-specific health to determine from the literature and consensus opinion which variables
status between the two countries. at baseline are associated with increased risk of death (such as increased
The performance of APACHE III has been assessed in several APACHE II score, age, number of organ systems failing, etc). One
countries including Brazil, the United Kingdom, Korea, and would want to match corticosteroid-treated cases to comparable non-
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Australia. In most countries, the observed hospital mortality was corticosteroid-treated controls so that the patients are matched closely
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significantly higher than the APACHE III predicted mortality rate. In enough (numerically) for these variables associated with increased
the Australian study, when the model was corrected for hospital char- risk of death. As a result, any differences found in hospital mortality
acteristics, the observed hospital mortality rate was not different. The between corticosteroid-treated and nontreated controls can be attrib-
area under the ROC curve was 0.92. The APACHE III mortality model, uted to treatment group and not to differences in baseline variables that
when adjusted for hospital characteristics, had good discrimination and predict risk of death. In interim summary, for such a study of patients
calibration in the Australian adult ICU population. who had septic shock, covariates that could be included in the match-
ing algorithm would be age, APACHE II score (or SAPS II score), the
■ PROPENSITY SCORING SYSTEMS AND CASE MATCHING presence or absence of specific organ dysfunctions (eg, cardiovascular,
TO SIMULATE RANDOMIZED CONTROLLED TRIALS respiratory, renal, and hematologic), norepinephrine dose, surgical
status, and the site of primary infection.
RCTs are the “gold standard” of level I evidence for assessing efficacy of To address the likelihood that a clinician would prescribe cortico-
new or controversial therapies. However, RCTs are expensive, often run steroids, a propensity score (the likelihood of having received corti-
over several years, have tight inclusion and exclusion criteria, and so costeroids given the key baseline characteristics) would be calculated
are sometimes less generalizable than observational studies. There are a using covariates. The matching methodology would ensure that the
limited number of patients available to participate in RCTs, which lim- three most relevant covariates, eg, age, APACHE II score (or SAPS
its the number of trials and hypotheses that can be tested. The science II score), and propensity score are tightly matched between cortico-
and practice of critical care has advanced to address these limitations steroid-treated patients and the matched controls. For example, one
of RCTs. could decide that the matched patients must be within 5 years of age,
In recent years in critical care, investigators have used observational within 2 points on the APACHE II score (4 points on SAPS II score),
cohorts and sophisticated case matching systems to simulate RCTs of and within 0.6 standard deviations on propensity score. To control
interventions and drugs. The concept is to use an observational cohort for potential changes over time in best treatment and supportive care
and control for differences between control and treatment groups in of patients with septic shock, patients could be matched according
the design as opposed to in the analyses. 121-127 Most often, differences to date of enrollment in the cohort (eg, within 24 months of each other’s
between control and treatment groups are addressed in the adjusted date of enrollment).
analyses, for example, by using logistic regression and including vari- Another critical aspect of case-matched studies is that the matching
ables that differ between groups as covariates. Then the adjusted analy- of controls and cases must be done while blinded to outcome (in this
ses determine whether there is still a statistically significant difference in case hospital mortality) to minimize unintended and intended bias.
the outcomes of interest between treatment groups while adjusting for A two-phase transfer of data from each center would be implemented
differences between groups in baseline characteristics. In case-matched to ensure that the selection of matched control patients is imple-
studies, there are adjustments in design to balance control and treatment mented in an unbiased manner. First, the database would be loaded
groups because a well-matched control group is obviously critical to the with the (1) baseline variables needed for determination of eligibility
validity of such a nonrandomized study. 121-127 (screening for inclusion), (2) variables for matching, and (3) treatment
Let us consider an observational cohort of patients in whom clinicians group. Then patients would be matched (controls matched to cor-
have treated patients with septic shock with low-dose corticosteroids or ticosteroid-treated patients) without knowledge of outcome. Once
have not treated with corticosteroids. An investigator wishes to deter- the matching has been completed and the control patients have been
mine whether the cohort could be used to examine whether cortico- identified and matched to each corticosteroid-treated case, then the
steroid treatment (compared to no corticosteroid treatment) decreases database would be “locked” (ie, the controls and cases are now inextri-
hospital mortality. cably linked together). Then, the hospital mortality outcome of each
The investigators would have to agree on a set of inclusion and exclu- patient is loaded into the database. Finally, the statistical analyses are
sion criteria (eg, presence of two of four systemic inflammatory response now done comparing control group to corticosteroid-treated group to
syndrome (SIRS) criteria, presence of infection, and presence of hypo- determine whether there is an association of corticosteroid treatment
tension despite adequate fluid balance). Patients would be assessed for with hospital mortality.
eligibility according to the inclusion criteria and only those that are There are strengths and weakness of this approach. Some of the
eligible would be included for the selection of matched patients. Of note, strengths are as follows. Even though patients would not be prospec-
both treated and nontreated patients need to pass this inclusion and tively recruited for such a study, the use of strict eligibility criteria would
exclusion screen (as in an RCT). After screening of patients according to ensure that the validation of the treatment hypothesis (ie, treatment
eligibility, the full matching algorithm would be implemented. effect of corticosteroids in septic shock) would be conducted in a well-
Within the screened cohort, control patients would be selected pro- defined and relevant population of patients treated in practice. The
grammatically to match the corticosteroid-treated patients using an biggest challenge in choosing appropriate patients for the control group
algorithm that matches on (1) baseline demographic and disease vari- is overcoming the known patient selection bias due to lack of random-
ables that may have influenced clinicians’ decision to give corticoste- ization to corticosteroid treatment. The very fact that corticosteroids
roids and (2) variables that are associated with risk of death (if mortality are not uniformly prescribed for patients who have septic shock allows
is the primary outcome of interest). In a design-based approach, the this type of matched-patients study to be conducted. If corticosteroids
first set of variables that must be matched between controls and cases were used most of the time in the eligible patients, then it would be very

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