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Conway et al. Vessel Plus 2020;4:25 I http://dx.doi.org/10.20517/2574-1209.2020.19 Page 3 of 11

ICD-10-CM. Data recorded in the database include the date of admission and discharge, the unique patient
identifier, sex, date of birth, treating physician, address, diagnosis (principal and up to nine secondary),
and procedures performed (principal and up to nine secondary). Physiological and laboratory results are
automatically cross-linked and added to the database using the hospitals other systems.

Troponin measurements
In January 2011, our institution introduced a hscTnT assay (Roche Diagnostics) to replace the previous 4th-
generation cardiac troponin assay. Analysis was performed using a Roche cobas® 8000 analyser. No changes
to the analyser or assay were made during the study period. The performance characteristics of the hscTnT
assay were such that upper limit of normal was represented by the 99th centile of 14 ng/L. However, as
non-ischaemic myocardial strain could result in an elevated hscTnT, a value of ≥ 53 ng/L was deemed to be
a strong predictor of significant myocardial injury. In the current study, we have defined troponin-negative
as hscTnT < 25 ng/L and troponin-positive as hscTnT ≥ 25 ng/L. We further divided patients into six groups
based on hscTnT results: (1) no troponin assay requested; (2) < 25 ng/L; (3) 25-49 ng/L; (4) 50-99 ng/L; (5)
100-1000 ng/L; and (6) > 1000 ng/L. These cut-offs were chosen on the previously defined rounded hscTnT
value of 50 ng/L being predictive of true myocardial injury.

Risk predictors
[29]
We have previously derived and applied an Acute Illness Severity Score (AISS) , predicting 30-day in-
[30]
hospital mortality from laboratory parameters recorded in the ED . This weighted age adjusted score
defines six risk groups (I-VI) with cut-points for 30-day in-hospital mortality set at 1, 2, 4, 8 and 16%. We
further adjusted for Comorbidity as described below. In addition Sepsis categories of (1) no blood culture
request (2) negative blood culture and (3) positive blood culture were identified and used as an adjustor in
the multivariable logistic regression model .
[31]

Comorbidity score
[32]
In this study, comorbidity was assessed by a Comorbidity Score which we have derived . The first
incarnation of this score was published in 2014 and an updated version has subsequently been published .
[3]
The Comorbidity Score was derived from searching the hospital system for ICD codes that captured
functionally limiting chronic physical or mental health disorders. These ICD codes were then grouped
into the following ten systems: (1) cardiovascular; (2) respiratory; (3) neurological; (4) gastrointestinal; (5)
diabetes; (6) renal; (7) neoplastic disease; (8) others (including rheumatological disabilities); (9) ventilatory
assistance required; and (10) transfusion requirement. We additionally searched other existing databases in
our instituion for evidence of diabetes (Diamond database) , impaired respiratory function (FEV1 < 2 L),
[33]
[34]
hscTnT ≥ 25 ng/L , albumin < 35 G/dL, haemoglobin < 10 G/dL, and chronic kidney disease - MDRD <
60 mL/min × 1.73 m . The components of the score were then appropriately weighted according to 30-day
2[35]
in-hospital mortality.

Deprivation index
The smallest unit for which the Republic of Ireland Census reports results is the Electoral Division.
There are approximately 3,440 of these small administrative areas; data from sparsely populated Electoral
Divisions are merged where required to maintain confidentiality. This process resulted in a final total of 3,409
Electoral Divisions having available statistics. The SAHRU investigators employed principle components
analysis to generate a Deprivation Index using a weighted combination of four indicators; unemployment,
[36]
social class, type of housing tenure and car ownership . Deprivation Index scores were then divided into
quintiles according to their ranked raw scores from Q1 (least deprived) to Q5 (most deprived). This data
was then joined to the small area polygon geometries based upon their relative geographic positions, using
[37]
the ArcGS Geographic Information System software implementation of the Point-in-Polygon algorithm .