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Diabetes and Metabolism

Admission and fasting plasma glucose for estimating

risk of death of diabetic and nondiabetic patients

with acute coronary syndrome: nonlinearity of

hazard ratios and time-dependent comparisonBelen Cid-Alvarez,a Francisco Gude, b Carmen Cadarso-Suarez,c Eva Gonzalez-Babarro,a

Maria Xose Rodriguez-Alvarez,c Jose Maria Garcia-Acuna,a and Jose Ramon Gonzalez-Juanatey,a

Santiago de Compostela, Spain

Background In patients with acute coronary syndrome (ACS), increased plasma glucose levels are associated withworse outcome. Our aim is to ascertain the values of admission and fasting glucose for prediction of death among patients with

ACS; and to compare their predictive capacities.Methods The relationships of mortality to plasma glucose levels among 811 consecutive patients hospitalized with ACSwere estimated using spline Cox models. Blood samples were obtained upon admission and after overnight fast. The predictivecapacities of fasting and admission glucose were compared using time-dependent receiver operating characteristic curves.

Results Fasting and admission glucose levels were higher among the 151 patients who died (18.6%) than amongsurvivors (P < .001). Among the 558 patients with no history of diabetes (68.8%) there was a J-shaped dependence of the all- time mortality hazard ratio on fasting glucose that persisted when adjusted for covariates: hazard was lowest at 110 mg/dL(6.1 mmol/L), and significantly greater at levels <90 mg/dL (5.0 mmol/L) or >117 mg/dL (6.5 mmol/L). Likewise among nondiabetic patients, the predictive capacities of admission and fasting glucose were similar for forecast times of up to about1 year, but for later times the area under the receiver operating characteristic curve was larger for fasting glucose thanadmission glucose (P < .05). Neither admission nor fasting glucose levels discriminated among diabetic patients in regard torisk of death.

Conclusions Both admission and fasting glucose may be used for triage of nondiabetic ACS patients; fastingglucose may additionally be useful for long-term management, for which the relationship with the all-time mortality hazard

ratio is J-shaped. (Am Heart J 2009;158:989-97.)

Alterations of glucose metabolism are common among

patients admitted to hospital with acute coronary

syndrome (ACS), regardless of whether diabetes mellitus

has been diagnosed previously.1,2 ACS patients may

present with hyperglycemia during the acute period

(admission hyperglycemia) and/or exhibit hyper- or

hypoglycemic fasting plasma glucose levels duringhospitalization. These alterations have variously been

associated with larger inf arct size,3 a greater incidence of

congestive heart failure,4 cardiogenic shock,5,6 and both

short- and long-term mortality.7-11

Admission hyperglycemia, which some studies have

found to be associated with less risk for patients with a

previous history of diabetes than for those without,9,12

has been reported to reflect or be associated with a stress- induced increase in insulin resistance and other stress

responses.13,14 Fasting glucose is more related to the

background metabolic state, depending not only on tissue

insulin sensitivity but also on the state of complex

hormonal and metabolic networks.15

Most previous comparisons of the prognostic values of

admission and fasting glucose have considered death and

survival rates at just one fixed time post-admission or by

the end of follow-up, without attempting to determine

how prognostic value may depend on forecast time.4,10,11

However, taking forecast time dependence into account

might be useful in clinical decision-making: in principle it

From the Departments of a Cardiology and b Clinical Epidemiology, Complejo Hospitalario

Universitario de Santiago, and c Department of Statistics and Operations Research,

University of Santiago, Instituto de Investigacion Sanitaria de Santiago, Santiago de

Compostela, Spain.

Submitted March 4, 2009; accepted October 9, 2009.

Reprintrequests: Belen Cid-Alvarez,Cardiology Department, Hospital Clinico Universitario

de Santiago, Travesia da Choupana, s/n, 15706 Santiago de Compostela, Spain.

E-mail: [email protected]

0002-8703/$ - see front matter

© 2009, Mosby, Inc. All rights reserved.

doi:10.1016/j.ahj.2009.10.004

mailto:[email protected]

http://dx.doi.org/10.1016/j.ahj.2009.10.004

http://dx.doi.org/10.1016/j.ahj.2009.10.004

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is possible that while the acute event (admission

hyperglycemia) may strongly predict short-term risk, the

parameter more closely related to background metabo-

lism (fasting glucose) may be a more sensitive predictor of

the maintenance of a high risk level over time.

In the present study we aimed to ascertain andcompare the abilities of admission and fasting glucose

to predict the death of ACS patients, distinguishing

between those with and those without a previous

diagnosis of diabetes mellitus. The nonlinear relationships

between glucose levels and risk of death were modeled

by means of cubic spline Cox analyses, and the predictive

values of admission and fasting glucose were character-

ized for forecast times of up to 4 years by means of time-

dependent receiver operating characteristics (ROCs), a

technique that as far as we know has not previously been

employed in this context.

MethodsPatients

The study group comprised the 811 patients admitted to our

coronary care unit between September 2003 and March 2007

with a tentative diagnosis of ACS that was confirmed in

accordance with the universal definition of myocardial infarc-

tion by the patient's presenting (i) a troponin I level >99th

percentile of our reference population (0.6 ng/mL) in at least 1

of ≥3 blood samples taken during the first 6 hours after

admission, (ii) electrocardiographic (ECG) changes indicative of

new ischemia (new ST-T changes, new left bundle branch block,

or the development of pathological Q waves), and/or iii)

imaging evidence of new loss of viable myocardium or new regional wall motion abnormality.

Glucose measurementsSerum glucose was measured by the glucose oxidase method,

using a Siemens Advia 2400 AutoAnalyzer, in blood samples

taken upon admission and after an overnight fast of >8 hours

within 24 hours of admission. Patients were stratified into tertile

groups defined by fasting plasma glucose.

Ethics All 811 patients agreed to participate in the study, which was

reviewed and approved by the regional ethics committee.

Outcome measures and definitionsPatients were classified as known diabetic patients if they had

been informed of this diagnosis by a physician before admission

or were on oral antihyperglycemic agents, insulin, or diet

therapy. Glycohemoglobin on admission was not used as a

criterion because it is not recommended for the diagnosis of

diabetes by recent guidelines.

The outcome studied was death from any cause before August

2007. Patients were followed up by the study team throughout

their hospital stay. After discharge, vital status information was

acquired by reviewing the Galician Health Registry, by contact-

ing patients or their families individually and, if the patient had

been rehospitalized, by reviewing the hospital records of major

clinical events.

Statistical analysisSingle-variable Cox models were used to evaluate association

in the whole study group between the all-time risk of death (i.e.,the risk of death at any time during the study) and each of the

baseline demographic and clinical variables, including admis-

sion and fasting glucose. For the latter, given the nonlinear

nature of the association, the models were constructed using

natural cubic splines, HR curves and their asy mptotic 95% CIs

being defined relative to minimum risk values16; for the other

variables, linear models were used.17

Multivariable Cox models of all-time risk of death were also

constructed that, together with fasting glucose (which in the

single-variable analyses proved to have greater predictive value

than admission glucose), included variables of known

prognostic value, and variables of unproven prognostic value

that nevertheless emerged as significant predictors of mortality

in 2-covariate analyses with fasting glucose as the other covariate. In view of the results obtained in the two-covariate

analysis with diabetes, independent multivariate Cox models

were constructed for diabetic and nondiabetic patients. In

both cases, the variables included because they proved

significant in the two-covariate analyses were age, heart

failure (Killip class ≥2), and ST-segment elevation myocardial

infarction (STEMI), as well as fasting glucose; in addition, the

inclusion of sex, previous coronary artery disease, hyperten-

sion, creatinine and anemia was forced. In all the analyses

described in this par agraph, splines were employed to model

glucose dependence.18

Taking data censoring into account, the log hazard ratios of

the unadjusted Cox models were used as criterion variables X to

construct time-dependent ROCs19 via the corresponding sensitivity and specificity functions: for given time t and criterion

threshold c ,

sensitivity c; tð Þ = P X NcjTVtð Þ;

specificity c; tð Þ = P X VcjTNtð Þ;

where T is survival time. For each time t , the area under the

time-dependent ROC [AUC( t )] was calculated, and 95% CIs for

the AUC( t ) curves and for the difference between the admission

and fasting glucose AUC( t ) curves were constructed using a non-

parametric small-sample bootstrap method in which the

underlying Cox models were recalculated for each resample.

All statistical analyses were carried out in R using the packagessurvival (for fitting parametric Cox models), splines (for fitting

nonparametric Cox models), and survival ROC (for time-

dependent ROC curves). These packages are freely available at

http://cran.r-project.org.

Sources of funding and authorshipThis work was supported by grants from the Carlos III Health

Institute, Spain (redINSCOR [RD06/0003/0016], redIAPP

[RD06/0018/0006]), and the Spanish Ministry of Science and

Technology (MTM2008-01603), Xunta de Galicia (INCITE08P-

XIB208113PR). F Gude was supported by a grant (BAE09/90052)

from the Instituto de Salud Carlos-III (Spanish Ministry of

Science and Technology). The authors are solely responsible for

990 Cid-Alvarez et al American Heart Journal

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the design and conduct of this study, all study analyses, the

drafting and editing of the manuscript, and its final contents.

Results

Baseline and generalThe 811 patients enrolled in the study were aged 67 ±

13 years (mean ± SD), 206 (25%) were women, and 253

(31%) had a history of diabetes. The median duration of

follow-up was 18 months (range, 0-48 months), and 151

patients (18.6%) died. Nonsurvivors were older on

admission than survivors (76 ± 10 vs 65 ± 13 years, P <

.001), and were more likely to have a history of

hypertension ( P = .014), diabetes ( P < .001) and previous

myocardial infarction ( P = .016), but there were no

significant differences between survivors and non-survi-

vors as regards sex, smoking habit or dyslipemia. The

nonsurvivor group had higher incidences of three-vessel

disease and atrial fibrillation, smaller left ventricular ejection fractions, higher serum creatinine levels at

admission, higher Killip classes, and were less likely to

receive coronary angiography and percutaneous inter-

vention ( P < .001 in all cases) ( Table I ).

Table II summarizes the characteristics of the patient

groups defined by fasting glucose tertiles. Among the

three patient groups defined by fasting glucose tertiles

there were significant differences in age ( P < .001),

creatinine on admission ( P = .052), peak troponin in

the first 6 hours ( P < .001), and prevalences of heart

failure, diabetes and 3-vessel disease ( P < .001 in all

three cases) (see Table II ).

Glucose levels, mortality, and diabetesFasting and admission glucose levels were both higher

among non-survivors than survivors: 135 (102-181) versus

114 (99-144) mg/dL for fasting glucose (median [inter-

quartile range]; 7.5 [5.6-10.0] versus 6.3 [5.5-8.0] mmol/

L), 148 (111-217) versus 124 (106-175) mg/dL (8.2 [6.1-

12.0] versus 6.8 [5.8-9.7] mmol/L) for admission glucose.

For fasting glucose, the two-covariate analysis with

diabetes showed a statistically significant diabetes ×

glucose interaction ( P = .02): among non-diabetic

patients, the single-variable analysis showed a clear J-

shaped relationship between fasting glucose and log

hazard ratio, which was least for a fasting glucose level

of 106 mg/dL (5.9 mmol/L) ( Figure 1, A ), whereas

among known diabetic patients no level of fasting

glucose was associated with a significantly higher risk

of death ( Figure 1, B ). For admission glucose, there

was no statistically significant diabetes × glucose

interaction. Nevertheless, whereas nondiabetic patients

with admission glucose levels >150 mg/dL (8.3 mmol/

L) were at significantly higher than minimum risk, which

corresponded to a level of 110 mg/dL (6.1 mmol/L)

( Figure 2, A ), for patients with known diabetes there wasagain no level that was associated with a significantly

higher risk of death ( Figure 2, B ).

Comparison of admission and fasting glucose for predicting short- and long-term mortality

Among patients without known diabetes, the AUC( t )

curves for both admission and fasting glucose showed

sharp initial peaks of around 0.8, as did the curve for

fasting glucose among known diabetic patients. Subse-

quently, the AUC( t ) values for the 2 glucose parameters

were similar among nondiabetic patients for forecast

times up to rather longer than 1 year and, in both cases,

Table I. Baselinecharacteristics of patientsaccording to vital status

Non-survivors Survivors P

No. of subjects (%) 151 (19) 660 (81)

Age (y) 76 ± 10 65 ± 13 <.001Male sex, n (%) 105 (69) 500 (76) .138Hypertension, n (%) 100 (66) 363 (55) .014Known diabetes, n (%) 69 (46) 184 (28) <.001Dyslipaemia, n (%) 77 (51) 325 (49) .916Smoking, n (%) 60 (40) 334 (51) .466Prior CAD, n (%) 60 (40) 176 (27) .003STEMI, n (%) 52 (34) 261 (39) .510Killip class ≥ 2, n (%) 79 (52) 565 (86) <.001LVEF < 50%, n (%) 78 (52) 161 (24) <.001 Atrial fibrillation, n (%) 32 (21) 72 (11) <.001Three-vessel disease, n (%)⁎ 56 (57) 163 (30) <.001 Anemia, n (%) 75 (50) 128 (19) <.001Troponin I, ng/dL 19 (3-45) 10 (2-40) .061Creatinine, mg/dL 1.2 (1.0-1.6) 1.0 (0.8-1.1) <.001 Admission glucose,

mg/dL (mmol/L)

148 (111-217) 124 (106-175) <.001

Fasting glucose,mg/dL (mmol/L)

135 (102-181) 114 (99-144) <.001

CAD , Coronary artery disease. LVEF , left ventricular ejection fraction. Values aremeans ± SDs, or medians with the corresponding interquartile range in parentheses,as appropriate.⁎Coronary angiography results were available for 645 patients.

Table II. Baseline characteristics classified by fasting glucose tertiles

Fasting glucose,mg/dL (mmol/L)

<106(<5.8)

106-135(5.8-7.5)

>135(>7.5) P

Age, y 65 ± 14 68 ± 13 70 ± 11 <.001Male sex, % 76 77 71 .240Hypertension, % 52 58 62 .080Diabetes, % 12 22 60 <.001Dyslipaemia, % 49 44 55 .062Smoking, % 45 42 38 .358Prior CAD, % 32 25 30 .193STEMI, % 29 25 30 .193Killip class ≥ 2, % 15 18 29 <.001 Atrial fibrillation, % 9 16 16 .057 Three-vessel disease, %⁎ 31 27 44 <.001 Anemia, % 25 24 26 .954Troponin I, ng/dL 5 (1-21) 15 (3-53) 17 (4-56) <.001Creatinine, mg/dL 1.09 ± 0.60 1.11 ± 0.57 1.21 ± 0.70 .052

Values are means ± SDs, or medians with the corresponding interquartile range in

parentheses, as appropriate. CAD: coronary artery disease.⁎Coronary angiography results were available for 645 patients.

Cid-Alvarez et al 991American Heart Journal

Volume 158, Number 6

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tended to fall gradually from nearly 0.7 to around 0.65

( Figure 3, A ); for longer forecast times, however, the AUC

for fasting glucose tended to rise again towards 0.7, whereas the AUC for admission glucose fell <0.6, the

difference being statistically significant ( P < .05) for times

longer than 2.5 years ( Figure 3, C ). Among known

diabetic patients, neither admission glucose nor fasting

glucose afforded, at any time after the first few days, an

ROC with an AUC larger than 0.6 ( Figure 3, B and D ).

Multivariate analysisThe J-shaped relationship between fasting glucose

levels and log hazard ratio among non-diabetic patients

persisted after controlling for confounding factors: least

hazard was associated with fasting glucose levels of

around 110 mg/dL (6.1 mmol/L), and significantly greater

hazard at levels <90 mg/dL (5.0 mmol/L) or >119 mg/dL

(6.6 mmol/L) ( Figure 4 ). The other significant independent predictors of mortality identified were age, smoking,

STEMI, anemia, and a Killip class ≥2. ( Table III ).

DiscussionThe above results show that in our population neither

admission nor fasting glucose is predictive of all-cause

mortality among ACS patients with known diabetes.

However, among ACS patients without a prior diagnosis

of diabetes unadjusted risk is significantly greater than

minimum for admission glucose levels >150 mg/dL

(8.3 mmol/L); and as fasting glucose deviates in either

Figure 1

Nonparametric estimates of the dependence of all-time risk of death on fasting glucose among ACS patients without ( A ) and with (B) a prior diagnosis of diabetes mellitus (log hazard ratio, with 95% confidence intervals; unadjusted analyses). Ref , reference values. To convert mg/dL of glucose to mmol/L, divide by 18.


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direction from its minimum-risk value (110 mg/dL,

6.1 mmol/L) risk increases significantly even when

other risk factors are taken into account. The predictive

utilities of fasting glucose and admission glucose, asevaluated by the areas under time-dependent ROCs, are

similar for forecast times of up to about 1 year, but fasting

glucose is significantly more valuable for forecast times

longer than about 2.5 years.

Numerous previous studies have investigated the

utility of blood glucose for prognosis among diabetic

and/or nondiabetic patients following acute coronary

events,1,3-15,20,21 and most of those that have included

both patients with and patients without known

diabetes have found, like the present study, that

blood glucose level has greater prognostic value for

the latter.4,9,10,12,15 For example, Aronson et al.

reported that among 462 diabetic and 1101 non-

diabetic patients followed up for a median 24 months,

fasting glucose was only associated with increased risk

of death in the non-diabetic group, in which itsprognostic value was additional to that of the G RACE

risk score and left ventricular ejection fraction.15

Almost all the studies cited above distinguished only

between normal glucose levels and ≥1 levels of hypergly-

cemia. However, Svensson et al11 reported that among

diabetic patients a hypoglycemic lowest inhospital glucose

value was also associated with worse adjusted all-cause 2-

year mortality risk, and Kosiborod et al10 that hypoglyce-

mic mean glucose was likewise associated with increased

inhospital mortality, while Pinto et al. obtained similar

results for admission glucose as regards 30-day mortality.20

The present study found that both hypoglycemic and

Figure 2

Nonparametric estimates of the dependence of all-time risk of death on admission glucose among ACS patients without ( A ) and with (B) a prior diagnosis of diabetes mellitus (log hazard ratio, with 95% confidence bands; unadjusted analyses).



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Figure 3

Forecast time dependence of the AUC for glucose-based prediction of all-cause mortality among ACS patients without ( A ) and with (B) a prior diagnosis of don admission glucose; dotted lines, based on fasting glucose. Panels C (for nondiabetic patients) and D (for diabetic patients) show the difference betweeglucose, with the corresponding 95% confidence bands.

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hyperglycemic values of fasting glucose were predictive of

all-time mortality among non-diabetic patients, and neither

among diabetic patients.

Whether hypo- and hyperglycemia are mediators or

merely markers of adverse outcomes remains unclear.

That hypoglycemia may precipitate or increase myocar-

dial ischemia in diabetic patients is suggested by the

results of two small studies: in one, ischemia-related ECG

changes were associated with insulin-induced hypogly-

cemia in the presence of coronary disease,22 and in the

other hypoglycemia detected by continuous glucose

monitoring correlated with the presence of angina and

ischemia-related changes in ambulatory ECG records.23

These possible effects of hypoglycemia may be due to its

being associated with raised catecholamine levels and

decreased serum potassium,22 both of which are

associated with adverse cardiac outcome in ACS

patients.24 In non-diabetic patients, hypoglycemia may

be just a marker of the severity of background disease

such as abnormal liver function, congestive heart failure,

shock, occult malignancies, renal failure, or other clinical

conditions that adversely influence outcomes.25 Howev-

er, as Table III shows, in this study hypoglycemic fasting

glucose levels continued to indicate significantly greater

risk than the reference level after adjustment for potential

confounding factors.Elevated glucose levels during ACS may reflect high

levels of catecholamines, cortisol, and other factors that

are associated with the severity of ischemic dis-

ease.14,26,27 Elevated glucose has also been associated

with higher free fatty acid levels,28 reduced insulin

sensitivity, and impaired myocardial glucose use resulting

in increased ox ygen consumption and potential worsen-

ing of ischemia29—there have been reports of hypergly-

cemia being associated with marked reduction in

coronary flow reserve.30,31

The observed influence of diabetes status on the

relationship between blood glucose and mortality may

Figure 4

Nonparametric estimates of the dependence of all-time risk of death on fasting glucose among ACS patients without a prior diagnosis of diabetesmellitus, after controlling for potential confounding variables (log hazard ratio, with 95% confidence band; adjusted analysis).

Table III. Cox adjusted hazard ratios for all-cause death duringthe 4-year duration of the study among patients with no priordiagnosis of diabetes

HR 95% CI P

Age, y 1.07 1.04-1.10 <.001

Female sex 1.28 0.70-2.36 .420Hypertension 0.90 0.53-1.50 .630Smoking 2.01 1.13-3.57 .018Prior CAD 1.51 0.90-2.53 .112STEMI 2.37 1.42-3.96 <.001Coronary angiography 0.65 0.40-1.06 .087 Killip ≥ 2 2.34 1.42-3.86 <.001Creatinine, mg/dL 1.23 0.82-1.84 .310 Anemia 1.76 1.03-2.53 .037 Fasting glucose, mg/dL (mmol/L) <.001

66 (3.6) 2.61 0.55-12.474 (4.1) 2.87 1.18-6.9890 (5.0) 1.79 1.00-3.20110 (6.1) (reference level) 1.00119 (6.6) 1.11 1.02-1.20

150 (8.3) 2.21 1.49-3.28175 (9.7) 3.52 1.97-6.31200 (11.1) 5.21 2.57-10.5

The final model included all variables listed above, risk from fasting glucose beingmodelled by means of natural splines (see Methods; by way of illustration, results arelisted here for eight fasting glucose values). CAD: coronary artery disease.



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derive from at least two factors. Firstly, a proportion of

hyperglycemic ACS patients with no previous diagnosis

of diabetes—25% to 70% according to some esti-

mates32,33—are in fact diabetic patients; because these

patients usually fail to receive insulin or other treatment

appropriate for diabetic ACS patients upon admission,they may constitute a group with especially high short-

term risk.34 Secondly, a given hyperglycemic blood

glucose level implies a greater surge in blood glucose in

a truly nondiabetic patient than in a diabetic patient,

which suggests a higher degree of stress and hence

greater risk of death.9

Relatively few previous studies have compared the

prognostic utility of admission glucose with that of the

first postadmission fasting glucose measur ement or some

kind of average fasting glucose measure.7,10,21 Most of

those that have, have found that fasting glucose is the

better predictor of inhospital or 30-day mortality, at least

among patients without previously diagnosed diabetes,

and this finding appears to be independent of the therapy

received in hospital.7,10,21 In the present study, the AUC

of fasting glucose among non-diabetic patients tended to

be larger than that of admission glucose during this early

postinfarct period, but the small number of deaths meant

that the difference was not statistically significant. In

terms of AUC, both glucose parameters performed much

better in predicting deaths within the first few days post-

infarct than for any longer forecast time, doubtless due to

the more immediate relationship between acute stress

and risk of death. As noted above, in this study, the

difference between the AUCs of fasting and admissionglucose was statistically significant, in favor of the former,

for forecast times longer than about 2.5 years. Thus,

admission glucose appears to be primarily a marker of

early risk, reflecting, at least in part, differences in stress-

induced insulin resistance, etc., due to differ ences in

infarct size and/or hemodynamic compromise.2,14 The

greater ability of fasting glucose to predict long-term risk

may be due to its being mor e related to the patient's

background metabolic state.15

Study limitations

This study concerned the risk of all-cause mortality conditional on serum glucose levels measured in a sample

taken at admission and a second sample obtained, within

24 hours of the first, after an overnight fast of >8 hours.

We were not in a position to determine how many

patients had persistent hyper- or hypoglycemia during

hospitalization, or whether deaths were the consequence

of ACS. The observational nature of the study also means

that it does not show whether hyper- and/or hypoglyce-

mia were causally linked to adverse outcome, were

simply markers of greater disease severity, or both.

Moreover, it does not allow the possibility of confounding

due to unmeasured factors to be ruled out. However, as

circumstances tending to minimize this possibility we

note that the sample was large (811 patients) and

excluded no ACS patients seen in our centre during the

study period; that data collection was prospective; and

that all the patients in the sample were followed up

successfully. Furthermore, the observed relationshipsbetween fasting glucose and all-time mortality persisted

after extensive adjustment for ACS severity and a number

of comorbidities.

Conclusions Among patients who, at admission for ACS, are not

known to suffer diabetes, admission, and fasting plasma

glucose are both strong predictors of mortality. Likewise,

among nondiabetic patients, the predictive values of

admission and fasting glucose are similar for forecast

times of up to about 1 year, but the betterpredictor of long- term mortality is fasting glucose, for which the relationship

with the log hazard ratio of all-time mortality is J-shaped.

Neither admission nor fasting plasma glucose are useful in

our population for discriminating among known diabetic

patients as regards the risk of death after ACS.

According to our results, fasting glucose concentrations

in nondiabetic patients hospitalized for ACS may serve as

a simple marker to help clinicians stratify risk for optimal

triage and long-term surveillance and management.

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