5
In the critically ill patient, diabetes
predicts mortality independent of
statin therapy but is not associated
with acute lung injury: a cohort
study.
6
Gavin C.K.W. Koh, MRCP; Alexander P.J. Vlaar, MD; Jorrit J. Hofstra, MD;
7
H. Katrien de Jong, MD; Samuel van Nierop; Sharon J. Peacock, PhD;
8
W. Joost Wiersinga, MD; Marcus J. Schultz, MD; Nicole P. Juffermans, MD
1
2
3
4
9
10
Supplementary methods
11
Statistical analysis
12
The Fisher exact test was used to compare categorical variables.
13
Quantile-quantile plots were used to check normality of continuous data and
14
to select appropriate transforms. Age could not be transformed to normal and
15
therefore the Mann-Whitney U or Kruskall-Wallis test was used to compare
16
groups. Age, BMI and glucose were modeled as unordered categorical
17
variables, because they have a non-linear association with mortality. The cut-
18
offs for BMI were those used by Bercault et al. (1); age was analyzed in
19
decades from 40 to 70 years; category boundaries for glucose were (4.0, 5.6,
20
7.0 and 11.1 mM). Severity scores (APACHE II & SAPS II) were also analyzed
21
using non-parametric methods, as these data are not on an interval scale.
1
1
There are four sulfonylureas available in The Netherlands: glyburide and
2
tolbutamide are known to block ischemic preconditioning (cardiac SUs),
3
whereas gliclazide and glimepiride do not (noncardiac SUs) (26–30). In this
4
analysis, the sulphonylureas were therefore divided by cardiac risk profile.
5
In order to determine the minimum number of patients with an
6
exposure (diabetes or drug treatment) in order to draw valid conclusions
7
about the association of that drug with the primary outcome, mortality, we
8
performed a retrospective sample size calculation. This showed that two
9
groups of 313 patients (treated and untreated, or diabetic and non-diabetic)
10
were needed to demonstrate a mortality difference of 10% (power 80%, alpha
11
5%) if the mortality in the unexposed group was 30% (sampsi command,
12
Stata 11).
13
Cox regression models were fit by maximum likelihood estimation and
14
the proportional hazards assumption was checked both graphically using log-
15
log plots and by testing the scaled Schoenfeld residuals using the method of
16
Grambsch and Therneau (2). BMI violated the proportional hazards
17
assumption, but modeling BMI as a time-varying covariate did not materially
18
alter estimates for parameters of interest. The secondary outcome (categorized
19
as acute lung injury [ALI], cardiac overload [CO] and patients without
20
pulmonary edema [No ALI/No CO]), were analyzed using multinomial
21
logistic regression with No ALI/No CO patients as the comparator.
22
Independence of irrelevant alternatives was checked using the Hausman-
23
McFadden test separately for ALI and CO (3).
2
1
We looked for statistical evidence of interaction between the 7 drug
2
classes analyzed in the multivariable models (for mortality as well as for
3
ALI/CO), but found none. No sensitivity analysis or imputation was
4
performed for missing outcome data, because the missing patients
5
represented only 0.5% of the total cohort.
6
We sought to avoid over adjustment bias and unnecessary adjustment
7
(34). The first bias, over adjustment, occurs when the investigator adjusts for a
8
factor on the causal pathway between the parameter of interest and the
9
outcome of interest, resulting in the spurious disappearance of a true
10
association between parameter and outcome (4). The second, unnecessary
11
adjustment, occurs when the investigator includes irrelevant parameters in
12
the model. As more and more irrelevant factors are included in a model,
13
confidence intervals for all variables widen and eventually cross one, simply
14
due to loss of power (4, 5). Selecting confounders for inclusion on the basis of
15
p-values is a common (but incorrect) practice, because p-values only provide
16
information on random errors. Confounding causes systematic bias, so a p-
17
value cannot provide information about whether a particular parameter is
18
confounding the effect of interest (6). External knowledge must always be
19
applied to identify confounders: two epidemiological tools available are
20
conceptual hierarchies (7) and directed acyclic graphs (8).
21
Our conceptual model for this study is presented in Supplementary
22
Figure
1
(Supplemental
Digital
23
http://links.lww.com/CCM/A452). Briefly, for any one factor, potential
3
Content
2,
1
confounders lie above the factor, while those below the factor of interest
2
cannot be confounders and must not be treated as such.
3
Medications lie below diabetes in the conceptual hierarchy and is on the
4
causal pathway between diabetes and outcome (ALI and mortality).
5
Medication therefore cannot confound the effect of diabetes. An analysis for
6
diabetes which corrects for the effect of pre-admission medication risks
7
introducing bias (9) and should be considered exploratory. We performed the
8
analysis regardless, because medication can be modified independently of
9
diabetes and the analysis potentially provides a valuable elaboration of how
10
the effect of diabetes might be changed by medication (10).
11
It is not meaningful to adjust for SAPS II or APACHE II score, because
12
severity is an outcome of diabetes, is not a confounder (Supplementary Figure
13
1 [Supplemental Digital Content 2, http://links.lww.com/CCM/A452), and
14
cannot be independently manipulated. The investigator who adjusts for
15
severity as a confounder may conclude incorrectly that diabetes has no effect
16
on mortality (10).
17
References
18
1.
Bercault N, Boulain T, Kuteifan K, et al.: Obesity-related excess mortality
19
rate in an adult intensive care unit: A risk-adjusted matched cohort
20
study. Crit. Care Med 2004; 32:998-1003[cited 2011 Feb 14]
4
1
2.
Grambsch PM, Therneau TM: Proportional hazards tests and diagnostics
2
based on weighted residuals. Biometrika 1994; 81:515 -526[cited 2011 May
3
28]
4
3.
5
6
Hausman JA, McFadden DL: Specification tests in econometrics.
Econometrica 1984; 46:1251–1271
4.
Schisterman EF, Cole SR, Platt RW: Overadjustment bias and
7
unnecessary adjustment in epidemiologic studies. Epidemiology 2009;
8
20:488-495[cited 2011 Feb 16]
9
5.
Koh GCKW, Luong M-L: Early removal of central venous catheters and
10
outcomes from candidemia. Clin. Infect. Dis 2010; 51:1347; author reply
11
1348-1350[cited 2011 Jun 4]
12
6.
Pearl J: Chapter 6.2. Why there is no statistical test for confounding, Why
13
many think there is, and Why they are almost right. In: Causality:
14
Models, Reasoning and Inference. Cambridge, England: Cambridge
15
University Press; 2009. p. 182f.
16
7.
Victora CG, Huttly SR, Fuchs SC, et al.: The role of conceptual
17
frameworks in epidemiological analysis: a hierarchical approach. Int J
18
Epidemiol 1997; 26:224-227[cited 2009 Jul 11]
19
20
8.
Robins JM: Data, design, and background knowledge in etiologic
inference. Epidemiology 2001; 12:313-320[cited 2009 Jul 14]
5
1
9.
Cole SR, Platt RW, Schisterman EF, et al.: Illustrating bias due to
2
conditioning on a collider. Int J Epidemiol 2010; 39:417-420[cited 2011 Feb
3
16]
4
5
10. Hennekens CH, Buring JE: Epidemiology in medicine. Philadelphia,
Pennsylvania: Lippincott, Williams & Wilkins; 1987.
6
6
1
Supplementary figure legends
2
Supplementary Figure 1. Directed acyclic graph describing causal
3
relationship between potential predictors of acute lung injury/cardiac
4
overload.
5
Causation (and time) flow from the top of the graph to the bottom. The
6
graph has been laid out in a hierarchical manner, so that ultimate causes are
7
at the top (nodes with no in-edges), ultimate outcomes (nodes with no out-
8
edges) are at the bottom, and nodes in each layer are only dependent on
9
nodes in the layer above. Any one factor can be confounded only by factors in
10
the levels above. Therefore, diabetes has only three possible confounders
11
within this graph (age, sex and BMI). Medication has five possible
12
confounders (age, sex, BMI, MI and DM).
13
7