Online Data Supplement

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Online Data Supplement

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Item

No Recommendation Page

No Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title

or the abstract 0 - 1

(b) Provide in the abstract an informative and balanced summary of what was done and what was found

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation

being reported 2

Objectives 3 State specific objectives, including any prespecified hypotheses 2 Methods

Study design 4 Present key elements of study design early in the paper 3 - 4 Setting 5 Describe the setting, locations, and relevant dates, including periods

of recruitment, exposure, follow-up, and data collection 3 - 4 Participants 6 (a) Give the eligibility criteria, and the sources and methods of

selection of participants. Describe methods of follow-up

4 (b) For matched studies, give matching criteria and number of

exposed and unexposed

4 Variables 7 Clearly define all outcomes, exposures, predictors, potential

confounders, and effect modifiers. Give diagnostic criteria, if applicable

3 - 4

Data sources/

measurement 8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group

3 - 4

Bias 9 Describe any efforts to address potential sources of bias 3 - 4

Study size 10 Explain how the study size was arrived at 3 - 4

Quantitative

variables 11 Explain how quantitative variables were handled in the analyses. If

applicable, describe which groupings were chosen and why 3 - 4 Statistical methods 12 (a) Describe all statistical methods, including those used to control

for confounding 3 - 4

(b) Describe any methods used to examine subgroups and interactions (c) Explain how missing data were addressed

(d) If applicable, explain how loss to follow-up was addressed (e) Describe any sensitivity analyses

Results

Participants 13* (a) Report numbers of individuals at each stage of study—e.g.

numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed

4

(b) Give reasons for non-participation at each stage (c) Consider use of a flow diagram

Descriptive data 14* (a) Give characteristics of study participants (e.g. demographic, clinical, social) and information on exposures and potential confounders

4

(b) Indicate number of participants with missing data for each variable of interest

(c) Summarise follow-up time (e.g., average and total amount)

Outcome data 15* Report numbers of outcome events or summary measures over time 4- 7

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Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (e.g., 95% confidence interval). Make clear which

confounders were adjusted for and why they were included

4 – 7 (b) Report category boundaries when continuous variables were categorized NA (c) If relevant, consider translating estimates of relative risk into absolute risk

for a meaningful time period NA

Other analyses 17 Report other analyses done—e.g. analyses of subgroups and interactions, and

sensitivity analyses 4 -

7 Discussion

Key results 18 Summarise key results with reference to study objectives 7 Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or

imprecision. Discuss both direction and magnitude of any potential bias 8-9 Interpretation 20 Give a cautious overall interpretation of results considering objectives,

limitations, multiplicity of analyses, results from similar studies, and other relevant evidence

7-9

Generalisability 21 Discuss the generalisability (external validity) of the study results 7-9 Other information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based 0

*Give information separately for exposed and unexposed groups.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological

background and published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at http://www.strobe-statement.org.

Table S1: STROBE Statement—Checklist of items that should be included in reports of cohort studies

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Comorbidity

Total (n=2297)

Premorbid Beta Blocker Exposure (n = 860; 37.4%)

No Premorbid Beta Blocker Exposure (n = 1437; 62.6%) Coronary Artery Disease,

n(%)

358 (15.6) 229 (26.6) 129 (9.0)

Chronic Obstructive Pulmonary Disease, n(%)

398 (17.3) 174 (20.2) 224 (15.6)

Hypertension, n(%) 1316 (57.3) 630 (73.2) 686 (47.7)

Atrial Fibrillation, n(%) 552 (24.0) 298 (34.6) 254 (17.7)

Malignancy, n(%) 311 (13.5) 91 (10.6) 220 (15.3)

Table S2: Comorbidity data

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Variable Odds ratio (95% CI) p-value

Age 1 (1-1.01) 0.45

Gender 1.11 (0.92-1.33) 0.28

Admission weight 1 (1-1) 0.87

ICU type 1.37 (1.14-1.64) 0.001

Apache z score 2.24 (2.05-2.44) 0

Beta blocker 0.80 (0.66-0.97) 0.03

Table S3 Multivariable linear regression model details for beta blocker and ICU mortality

Age 1.01 (1-1.02) 0.001

Gender 1.08 (0.92-1.26) 0.37

ICU type 1.53 (1.31-1.79) 0

Apache z score 2.01 (1.86-2.18) 0

Beta blocker 0.84 (0.71-0.99) 0.03

Table S4 Multivariable linear regression model details for beta blocker and hospital mortality

Age 1 (0.99-1.01) 0.78

Gender 1.08 (0.89-1.31) 0.44

ICU type 1.38 (1.14-1.67) 0.001

Apache z score 2.2 (2.01-2.41) 0

Cardioselective 0.88 (0.72-1.08) 0.23

Table S5 Multivariable linear regression model details for cardioselective beta blockers and ICU mortality

Age 1.01 (1-1.01) 0.006

Gender 1.07 (0.91-1.27) 0.42

ICU type 1.49 (1.26-1.76) 0

Apache z score 2 (1.84-2.18) 0

Cardioselective 0.89 (0.75-1.07) 0.22

Table S6 Multivariable linear regression model details for cardioselective beta blockers and hospital mortality

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Age 1 (1-1.01) 0.27

Gender 1.12 (0.90-1.40) 0.31

ICU type 1.46 (1.17-1.81) 0.001

Apache z score 2.22 (2-2.46) 0

Non cardioselective 0.59 (0.41-0.85) 0.005

Table S7 Multivariable linear regression model details for non-cardioselective beta blockers and ICU mortality

Age 1.01 (1-1.02) 0.002

Gender 1.05 (0.87-1.28) 0.59

Admission weight 1 (0.99-1) 0.3

ICU type 1.63 (1.35-1.97) 0

Apache z score 2.01 (1.83-2.21) 0

Non cardioselective 0.68 (0.51-0.92) 0.013

Table S8 Multivariable linear regression model details for non-cardioselective beta blockers and hospital mortality

Age 1 (1-1.01) 0.48

Gender 1.07 (0.86-1.35) 0.53

Admission weight 1(1-1) 0.81

ICU type 1.45 (1.16-1.8) 0.001

Apache z score 2.18 (1.96-2.42) 0

Atenolol 0.84 (0.51-1.38) 0.49

Table S9 Multivariable linear regression model details for atenolol and ICU mortality

Age 1.01 (1-1.02) 0.005

Gender 1.04 (0.86-1.27) 0.67

ICU type 1.51 (1.24-1.83) 0

Apache z score 1.99 (1.81-2.2) 0

Atenolol 0.96 (0.63-1.45) 0.84

Table S10 Multivariable linear regression model details for atenolol and hospital mortality

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Age 1.(1-1.01) 0.39

Gender 1.12 (0.89-1.4) 0.33

ICU type 1.51 (1.21-1.88) 0

Apache z score 2.21 (1.98-2.45) 0

Carvedilol 0.46 (0.29-0.73) 0.001

Table S11 Multivariable linear regression model details for carvedilol and ICU mortality

Age 1.01 (1-1.02) 0.008

Gender 1.06 (0.87-1.28) 0.58

ICU type 1.68 (1.39-2.04) 0

Apache z score 2 (1.82-2.2) 0

Carvedilol 0.64 (0.46-0.91) 0.01

Table S12 Multivariable linear regression model details for carvedilol and hospital mortality

Age 1 (1-1.01) 0.65

Gender 1.06 (0.87-1.29) 0.59

ICU type 1.4 (1.15-1.7) 0.001

Apache z score 2.22 (2.02-2.44) 0

Metoprolol 0.81 (0.64-1.02) 0.08

Table S13 Multivariable linear regression model details for metoprolol and ICU mortality

Age 1.01 (1-1.01) 0.01

Gender 1.04 (0.87-1.24) 0.66

ICU type 1.53 (1.29-1.82) 0

Apache z score 2.06 (1.89-2.25) 0

Metoprolol 0.83 (0.68-1.02) 0.08

Table S14 Multivariable linear regression model details for metoprolol and hospital mortality

ICU Mortality Hospital Mortality

Variable

c- index

H-L test

AIC Nagelkerke pseudo-R²

c- index

H-L test

AIC Nagelkerke pseudo-R²

Beta blocker 0.72 0.07 309

5

0.16 0.7 0.1 387

9

0.15 Cardioselective beta

blocker

0.71 0.02 287 2

0.15 0.7 0.02 356

1

0.14 Non-cardioselective

beta blocker

0.73 0.25 219 1

0.17 0.72 0.54 272

9

0.16

Atenolol 0.72 0.40 208

3

0.16 0.71 0.33 256

9

0.15

Carvedilol 0.73 0.41 211

0

0.17 0.72 0.24 264

7

0.16

Metoprolol 0.71 0.01 262

0

0.6 0.71 0.01 324

1

0.15

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Table S15 Model performance parameters. C-index: Area under the receiver operator characteristic curve.

Hosmer-Lemeshow (H-L) test: Goodness of fit test. Values above 0.7 are indicative of acceptable model performance. AIC: Akaike information criterion

Age 0.98 (0.97 – 0.99) 0

Gender 0.87 (0.67 – 1.11) 0.26

Admission weight 0.99 (0.98 – 0.99) 0

ICU type 0.70 (0.54 – 0.90) 0.005

Apache z score 3.12 (2.75 – 3.54) 0

Beta blocker 0.83 (0.65 – 1.07) 0.15

Table S16 Multivariable linear regression model details for eICU cohort, ICD9 sepsis classficiation, and ICU mortality

Age 0.99 (0.98 – 0.99) 0

Gender 0.89 (0.73 – 1.1) 0.28

Admission weight 0.99 (0.98 – 0.99) 0

ICU type 0.78 (0.63 – 0.96) 0.02

Apache z score 2.66 (2.38 – 2.96) 0

Beta blocker 0.93 (0.76 – 1.14) 0.47

Table S17 Multivariable linear regression model details for eICU cohort, ICD9 sepsis classficiation, and hospital mortality

Age 1 (0.99 – 1.01) 0.98

Gender 0.69 (0.44 – 1.07) 0.1

Admission weight 0.99 (0.98 – 1) 0.17

Apache z score 1.52 (1.2 – 1.9) 0

Beta blocker 0.73 (0.46 – 1.2) 0.18

Table S18 Multivariable linear regression model details for Pilsen cohort and ICU mortality

Age 1 (0.99 – 1.02) 0.28

Gender 0.81 (0.53 – 1.24) 0.33

Apache z score 1.38 (1.11 – 1.71) 0.004

Beta blocker 0.66 (0.43 – 1.04) 0.08

Table S19 Multivariable linear regression model details for Pilsen cohort and hospital mortality

Age 0.99 (0.98 – 1) 0.16

Gender 0.82 (0.58 – 1.16) 0.26

Apache z score 1.67 (1.41 – 1.98) 0

Beta blocker 1.09 (0.78 – 1.53) 0.6

Table S20 Multivariable linear regression model details for Nepean cohort and ICU mortality

Age 1 (1- 1) 0.04

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Gender 0.73 (0.53 – 1) 0.051

Admission weight 0.99 (0.98 – 1) 0

Apache z score 1.56 (1.34 – 1.83) 0

Beta blocker 0.94 (0.7 – 1.27) 0.69

Table S21 Multivariable linear regression model details for Nepean cohort and hospital mortality

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Age 0.99 (0.99 – 1) 0.15

Gender 0.75 (0.57 – 0.99) 0.04

Admission weight 0.99 (0.98 – 0.99) 0.001

ICU type 1.27 (0.96 – 1.69) 0.09

Apache z score 1.61 (1.41 – 1.84) 0

Beta blocker 0.95 (0.72 – 1.25) 0.71

Table S22 Multivariable linear regression model details for Sepsis 3 classification and ICU mortality

Age 1 (1 – 1) 0.02

Gender 0.76 (0.59 – 0.98) 0.03

Admission weight 0.99 (0.98 – 1) 0

ICU type 0.86 (0.66 – 1.12) 0.26

Apache z score 1.49 (1.32 – 1.69) 0

Beta blocker 0.86 (0.67 – 1.1) 0.86

Table S23 Multivariable linear regression model details for Sepsis 3 classification and hospital mortality

Age 1 (0.99 – 1.01) 0.69

Gender 1.12 (0.91 – 1.38) 0.28

ICU type 1.27 (1.03 – 1.55) 0.02

Apache z score 2.3 (2.08 – 2.54) 0

Beta blocker 0.82 (0.67 – 1.02) 0.07

Table S24 Multivariable linear regression model details for patients without septic shock and ICU mortality

Age 1 (1 – 1.01) 0.01

Gender 1.08 (0.91 – 1.29) 0.39

ICU type 1.41 (1.19 – 1.68) 0

Apache z score 2.07 (1.89 – 2.26) 0

Beta blocker 0.82 (0.68 – 0.98) 0.03

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Table S25 Multivariable linear regression model details for patients without septic shock and hospital mortality

Age 1.01 (0.99 – 1.03) 0.22

Gender 0.99 (0.65 – 1.53) 0.98

Admission weight 1 (0.99 – 1.01) 0.79

ICU type 1.35 (0.88 – 2.09) 0.17

Apache z score 1.86 (1.52 – 2.28) 0

Beta blocker 0.86 (0.53 – 1.38) 0.52

Table S26 Multivariable linear regression model details for patients with septic shock and ICU mortality

Age 1.02 (1 – 1.03) 0.02

Gender 1.05 (0.7 – 1.56) 0.82

Admission weight 1 (0.99 – 1.01) 0.87

ICU type 1.57 (1.05 – 2.36) 0.03

Apache z score 1.71 (1.41 – 2.07) 0

Beta blocker 1.11 (0.72 – 1.71) 0.64

Table S27

Multivariable linear regression model details for patients with septic shock and hospital mortality

Table S28: Mortality outcomes before and after propensity score matching. Standard mean difference (SMD)

Unmatched cohort Matched cohort

VariablesTotal (n

= 4075)

Premorbi d Beta Blocker Exposure (n =1550;

38%)

No premorbi d beta blocker exposure (n = 2525;

62%) p value

SMD

Premorbi d Beta Blocker Exposure (n =1549;

50%)

No premorbi d beta blocker exposure (n = 1549;

50%) p value

SMD

Age,

years 66.4 (15.7)

70.3 (13.5)

63.9

(16.4) <0.001 0.42 70.3

(13.5) 69.6

(13.7) 0.11 0.05

Gender, male; n (%)

2222 (55.4)

854 (54.9) 1368 (54.1)

0.61 0.02 854 (54.9) 855 (55.0) 0.97 0.00

Admissio n weight (kg)

82.7

(29.9) 84.1

(26.8) 81.8

(31.6) 0.01 0.05 84 (26.6) 82.8

(28.7) 0.24 0.04

Septic shock, n (%)

501 (12.3) 152 (9.8) 349 (13.8) <0.001 - 152 (9.8) 220 (14.1) <0.001 -

ICU type (mixed; n,

%)

2361

(57.8) 877 (56.4) 1484

(58.7) 0.15 0.03 876 (56.3) 883 (56.8) 0.8 0.00

APACHE z score, median (IQR)

-0.13 (- 0.69 – 0.56)

-0.062 (- 0.68 – 0.56)

-0.17 (- 0.79 – 0.56)

0.001 0.12 -0.06 (- 0.58 – 0.55)

-0.06 (- 0.68 – 0.66)

0.97 0.01

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Figure S1 Calibration plots for all fitted models

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Figure S2 Propensity score matching balance diagnostics a) histogram of propensity score distribution b) jitter plot analysis of propensity scores c) love plot analysis of covariate mean differences.