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https://doi.org/10.1177/1474515120952214
European Journal of Cardiovascular Nursing
1 –9
© The European Society of Cardiology 2020
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DOI: 10.1177/1474515120952214
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Introduction
Acute myocardial infarction (AMI) is an emergent condi-
tion of coronary artery disease mainly caused by coronary
plaque rupture.1 Mortality from AMI has decreased dra-
matically over the past few decades, partly because of
improvements in early AMI management in the emergency
department (ED).2,3 Current guidelines emphasise the
importance of early risk stratification for identifying
patients at higher mortality risk requiring more aggressive
Early risk stratification of acute
myocardial infarction using a
simple physiological prognostic
scoring system: insights from
the REACP study
Dongze Li1, Yisong Cheng2, Jing Yu3, Yu Jia1, Fanghui Li2,
Qin Zhang1, Xiaoli Chen1, Yongli Gao1, Jiang Wu1, Lei Ye1,
Zhi Wan1, Yu Cao1 and Rui Zeng2
Abstract
Background: A more accurate and simpler scoring systems for early risk stratification of acute myocardial infarction at
admission can accelerate and improve decision-making.
Aim: To develop and validate a simple physiological prognostic scoring system for early risk stratification in patients
with acute myocardial infarction.
Methods: Easily accessible physiological vital signs and demographic characteristics of patients with acute myocardial
infarction at the time of presentation in the multicentre Retrospective Evaluation of Acute Chest Pain study were
used to develop a multivariate logistic regression model predicting 12 and 24-month mortality. The study population
consisted of 2619 patients from seven hospitals and was divided into a 70% sample for model derivation and a 30%
sample for model validation. A nomogram was created to enable prospective risk stratification for clinical care.
Results: The simple physiological prognostic scoring system consisted of age, heart rate, body mass index and Killip class.
The area under the receiver operating characteristic curve of the simple physiological prognostic scoring system was
superior to that of several risk scoring systems in clinical use. Net reclassification improvement, integrated discrimination
improvement and decision curve analysis of the derivation setalso revealed superior performance to the Global Registry
of Acute Coronary Events score, and the Hosmer–Lemeshow test indicated good calibration for predicting mortality in
patient with acute myocardial infarction in the validation set (P=0.612).
Conclusion: This simple physiological prognostic scoring system may be a useful risk stratification tool for early
assessment of patients with acute myocardial infarction.
Keywords
Acute myocardial infarction, risk stratification, vital signs, scoring system
Date received: 8 April 2020; revised: 31 July 2020; accepted: 3 August 2020
1Department of Emergency Medicine, West China Hospital, Sichuan
University, China
2Department of Cardiology, West China Hospital, Sichuan University, China
3West China School of Nursing, West China Hospital, Sichuan
University, China
Corresponding author:
Rui Zeng, Department of Cardiology, West China Hospital, Sichuan
University, 37 Guoxue Road, Chengdu, Sichuan 610041, China.
Email: zengrui_0524@126.com
952214CNU0010.1177/1474515120952214European Journal of Cardiovascular NursingLi et al.
research-article2020
Original Paper
2 European Journal of Cardiovascular Nursing 00(0)
care and therapy, for selecting the optimal care site and for
matching therapeutic intensity with risk.4–9 Risk stratifica-
tion based on the Global Registry of Acute Coronary
Events (GRACE) score has been well implemented and
proved to be clinically beneficial to patients.10 However,
inhospital mortality from AMI varies substantially across
hospitals (1.5–7.5%), suggesting the need for more accu-
rate assessment and better treatment decision algorithms to
guide individual care.11–14
Although the GRACE score was proved to have excel-
lent risk stratification efficiency,15 its multiple variables can
cause its use to be relatively time-consuming. Guidelines
recommend that the GRACE score should be completed
within 24 hours and re-evaluated before discharge.16
However, nearly 38% of inhospital deaths occur in the first
24 hours in patients with AMI.17 Thus, this complex scoring
system may miss many critical patients as early as when
they enter the ED. Moreover, guidelines recommend that
hospitals capable of percutaneous coronary intervention
(PCI) should treat patients with ST-segment elevation myo-
cardial infarction (STEMI) within 90 minutes of their first
contact with the medical system.18 Hence, there is hardly
time for risk stratification based on the GRACE score to be
performed before PCI. In addition, the condition of patients
with AMI can change rapidly. With this in mind, the tradi-
tional scoring system cannot immediately assess patients
during hospitalisation and realise timely replacement of
their risk level. Therefore, a simple prognostic scoring sys-
tem that does not rely on detailed medical history or inva-
sive medical testing is needed to conduct early and dynamic
risk assessments in the ED and even before admission.
Nursing intervention often precedes treatment by doc-
tors, because it is nurses who mainly measure vital signs,
have closer contact with patients and more frequently
observe changes in their condition. Therefore, a simple
physiological prognostic scoring (SPPS) system consist-
ing of easily accessible physiological vital signs and
demographic factors may compensate for the lack of nurs-
ing assessment tools for patients with AMI.19 We con-
ducted this retrospective multicentre cohort study of
patients from the multicentre Retrospective Evaluation of
Acute Chest Pain (REACP) study to develop and validate
such an SPPS system to identify patients with high-risk
AMI at admission.
Methods
Study population
Our study population consisted of patients with AMI
enrolled in the REACP study, which included patients with
acute chest pain who were admitted to EDs of seven tertiary
hospitals in China between January 2017 and December
2018. The REACP study is registered at www.chictr.org.cn
(identifier: ChiCTR1900024657). The present study was
conducted in accordance with the Declaration of Helsinki
and was approved by the institutional review boards of
Sichuan University West China Hospital and other partici-
pating hospitals.
In this present study, we designed and validated a novel
SPPS system for very early risk stratification of patients
with high-risk AMI undergoing primary PCI in the ED.
The inclusion criteria were as follows: age greater than 18
years, first-time diagnosis of STEMI or non-ST-segment
elevation myocardial infarction (NSTEMI), less than 12
hours between the onset of symptoms and ED admission,
and treatment with coronary angiography and primary
PCI. The exclusion criteria were as follows: accompanied
by malignant tumours, chronic heart failure, end-stage
hepatopathy, or renal failure at admission.
Data collection
Patient demographic and clinical characteristics on ED
admission, including age, sex, vital signs, body mass index
(BMI), medical history, laboratory test results, electrocar-
diograms, echocardiographic findings, coronary angiogra-
phy findings, adverse outcomes in hospital and therapies
in hospital, were collected from the electronic medical
records of each participating hospital. All laboratory tests
were performed using the standard procedures of Sichuan
University West China Hospital.
Definitions
Patients with a BMI of more than 24 kg/m2 were recorded
as overweight or obese. Hypertension was defined as
blood pressure (BP) of greater than 140/90 mmHg on at
least two occasions, BP of more than 130/80 mmHg in
patients with diabetes or chronic kidney disease or those
requiring antihypertensive treatment. Diabetes was defined
as a history of diabetes or the need for antidiabetic agents.
Smoking was classified as at least one cigarette per day for
more than 6 months. Alcohol drinking was defined as
drinking any type of alcoholic beverage at least once a
week for at least 6 months. The GRACE score was deter-
mined using medical history, biochemical variables, mark-
ers of myocardial injury and electrocardiography results
obtained in the ED (available at http://www.outcomes-
umassmed.org/grace).20 The Gensini score, which indi-
cates the severity of coronary artery lesions, was calculated
from coronary angiography reports available from the
institutional cath lab.21
Outcome and follow-up
The primary outcome was all-cause mortality, which was
confirmed through a combination of hospital medical
records, telephone contacts with family members and death
registration at the Sichuan Provincial Center for Disease
Control and Prevention. All reported events were reviewed
and verified by an outcome assessment committee blinded
Li et al. 3
to treatment assignment. The median follow-up time was 12
months, which was calculated from the onset date of AMI to
the date of an event or last follow-up.
Statistical analysis
Patients were divided into a 70% sample for model deriva-
tion (derivation set) and a 30% sample for model valida-
tion (validation set). Parametric continuous variables are
reported as mean ± standard deviation and non-parametric
continuous variables as median (25–75th percentiles).
Categorical variables are reported as numbers and percent-
ages. Parametric patient characteristics were compared
using one-way analysis of variance and non-parametric
characteristics using Kruskal–Wallis H test. Categorical
variables were compared using the chi-square test or
Fisher’s exact test.
To establish a scoring system not requiring measure-
ment using advanced hospital equipment in the derivation
set, the simple and accessible clinical parameters at ED
admission were entered into the univariate logistic regres-
sion model, and all significant (P<0.05) parameters were
re-entered into multivariate logistic regression model. The
10-fold cross-validation method was adopted to avoid
model overfitting. Parameters with P<0.05 in the multi-
variate logistic regression model were chosen, and a pre-
diction nomogram was then constructed to establish the
SPPS system.
For assessing discriminative performance of the SPPS
system for all-cause mortality, receiver operating charac-
teristic (ROC) curve metrics, net reclassification index
(NRI) and integrated discrimination improvement (IDI)
were calculated for both derivation and validation sets.22
An area under the curve (AUC) of greater than 0.75 indi-
cates that the model has good discrimination. NRI and IDI
values of more than 0 indicate that the new model brings
additional improvement over the old model. A calibration
curve was generated from the validation set and evaluated
using the Hosmer–Lemeshow test.23 In addition, decision
curve analysis (DCA) was conducted to evaluate the net
benefits of the SPPS system at various threshold probabil-
ities in the derivation and validation sets.24 In the Hosmer–
Lemeshow test, P>0.5 indicated that the model has good
calibration.
A two-tailed P<0.05 was considered significant for all
tests. Statistical analyses were performed using SPSS ver-
sion 20.0 (IBM Corp, Armonk, NY, USA), Stata version
14.0 (Stata Corp, College Station, TX, USA) and R soft-
ware 3.3.0 (Vienna, Austria).
Results
Patient characteristics
A total of 2619 patients with AMI were included in this
study. To construct a simple scoring system for rapid early
risk stratification, we gathered clinical and demographic
parameters easily obtained on or before ED admission.
There were no significant differences in the demographic
and clinical characteristics between the derivation set
(n=1749) and validation set (n=870; Supplementary
Table 1). In the derivation set, 165 (9.4%) of 1749 patients
died within a median follow-up time of 12 months (range
7–18) lower GRACE scores on admission, were more
likely to be men and exhibited higher systolic BP, dias-
tolic BP, BMI, Killip class and platelet counts (Table 1).
The baseline characteristics of surviving and non-surviv-
ing patients are summarised in Supplementary Table 2.
Nomogram construction
In the derivation set, age, heart rate, BMI, Killip class, sys-
tolic BP, diastolic BP, sex and smoking were associated
with all-cause mortality by univariate logistic regression
(all P<0.05, Table 2). Of these parameters, age, heart rate,
BMI and Killip class were independently associated with
mortality in the multivariate logistic regression analysis
(Table 2). These four parameters were then included in the
multivariate logistic regression analysis to derive the SPPS
system. A nomogram was constructed that incorporated
these four significant risk factors to estimate the 12 and
24-month survival rates (Figure 1). The total score (0–240)
was calculated as the sum of risk scores for each incorpo-
rated parameter.
Reclassification and discrimination accuracy
of SPPS
We evaluated the incremental benefits of the SPPS system
for survival versus mortality discrimination by comparing
the AUC from ROC analysis using GRACE, thrombolysis
in myocardial infarction (TIMI) and Gensini scores. In the
derivation set, the AUC of the SPPS system for predicting
all-cause mortality (0.868, 95% confidence interval (CI)
0.836–0.900, P<0.001) was larger than that of GRACE
(0.815, 95% CI 0.780–0.850, P<0.001), TIMI (0.720,
95% CI 0.680–0.760, P<0.001) and Gensini (0.513, 95%
CI 0.457–0.570, P=0.602) scores. Similar results were
found using the validation set (Figure 2).
We further confirmed this improved discrimination of
the SPPS system by calculating the IDI value relative to
the GRACE score (0.069, P<0.001 in the derivation set;
0.100, P=0.002 in the validation set; Table 3). We also
assessed the potential for clinical reclassification by the
SPPS system instead of the GRACE score using the cate-
gory-based NRI and found a significant NRI improvement
for both the derivation set (0.155, P<0.001) and validation
set (0.265, P<0.001; Table 3). We then evaluated calibra-
tion using the Hosmer–Lemeshow test, which revealed
good calibration for predicting mortality in patients with
AMI in the validation set (2=6.314, P=0.612), and
4 European Journal of Cardiovascular Nursing 00(0)
expected deaths were in good agreement with observed
deaths (Figure 3). Finally, the DCA showed that the net
benefit of the SPPS system was higher than that of the
GRACE score for any threshold probability in both the
derivation set (Supplementary Figure 1) and the validation
set (Figure 4).
Table 1. Comparison between survivors and non-survivors in the derivation set.
Variables Survival (n=1584) Death (n=165) P value
Age, years 65.1 ± 13.0 73.5 ± 10.0 <0.001
Men, n (%) 1208 (76.3) 110 (66.7) 0.008
BMI, kg/m224.2 ± 3.2 22.1 ± 3.5 <0.001
Heart rate/min 79.3 ± 17.7 92.9 ± 22 <0.001
SBP, mmHg 129.2 ± 23.9 122.6 ± 27.3 0.003
DBP, mmHg 78.9 ± 15.9 74.8 ± 16.8 0.002
Killip class, n (%) <0.001
I 973 (61.4) 40 (24.2)
II 406 (25.6) 28 (17.0)
III 155 (9.8) 31 (18.8)
IV 50 (3.2) 66 (40.0)
Smoking, n (%) 894 (56.4) 78 (47.3) 0.038
Drinking, n (%) 566 (35.7) 49 (29.7) 0.167
Hypertension, n (%) 818 (51.6) 88 (53.3) 0.683
Diabetes, n (%) 381 (24.1) 41 (24.8) 0.848
WBC, 109/L 9.6 ± 3.7 9.4 ± 3.6 0.575
Haemoglobin count, g/L 133.2 ± 22.9 132.1 ± 21.3 0.560
Platelet count, 109/L 183.2 ± 73.4 170.5 ± 69.6 0.037
Total bilirubin, mol/L 13.1 ± 7.1 13.4 ± 8.7 0.652
Albumin, g/L 40.8 ± 4.5 40.2 ± 5.5 0.240
Creatinine, mol/L 79 (66.97) 80 (67.10) 0.484
Triglycerides, mmol/L 1.8 ± 1.6 1.7 ± 1.3 0.525
Total cholesterol, mmol/L 4.4 ± 1.2 4.3 ± 1.1 0.385
HDL, mmol/L 1.1 ± 0.3 1.2 ± 0.3 0.333
LDL, mmol/L 2.7 ± 1.1 2.6 ± 1 0.294
CTn T pg/mL 429 (52.19) 367 (57.18) 0.768
BNP, pg/ml 810 (182.27) 1072 (219.27) 0.311
LVEF, % 54.5 ± 11.3 51.1 ± 38.8 0.316
GRACE score 146.1 ± 37.3 198.2 ± 43.7 <0.001
TIMI score 4 (3.5) 5 (4.7) <0.001
Gensini score 58 (28.93) 68 (24.10) 0.602
BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; WBC: white blood cell count; HDL: high-density lipoprotein;
LDL: low-density lipoprotein; CTn T: cardiac troponin T; BNP: brain natriuretic peptide; LVEF: left ventricular ejection fraction; GRACE: Global
Registry of Acute Coronary Events; TIMI: thrombolysis in myocardial infarction.
Table 2. Logistic regression analysis for all-cause mortality according to early accessible variables.
Variables Univariate logistic regression Multivariate logistic regression
OR 95% CI P value OR 95% CI P value
Age 1.061 1.045–1.077 <0.001 1.043 1.024–1.062 <0.001
Heart rate 1.034 1.026–1.043 <0.001 1.026 1.017–1.035 <0.001
Body mass index 0.813 0.771–0.857 <0.001 2.507 2.105–2.987 <0.001
Killip class 3.106 2.643–3.651 <0.001 0.886 0.835–0.94 <0.001
Women vs. men 1.606 1.139–2.265 0.007 0.924 0.559–1.529 0.760
SBP 0.988 0.982–0.995 0.001 0.994 0.983–1.004 0.241
DBP 0.983 0.973–0.994 0.002 0.999 0.983–1.016 0.939
Smoking 1.296 1.026–1.509 0.034 1.056 0.664–1.681 0.818
SBP: systolic blood pressure; DBP: diastolic blood pressure; OR: odds ratio; CI: confidence interval.
Li et al. 5
Figure 1. Nomogram for assessing the death risk of patients with acute myocardial infarction.
0.40.60.81.0
0.40.60.81.0
Viriables
SPPS
GRACE score
TIMI score
Gensini score
SPPS
GRACE score
TIMI score
Gensini score
Viriables
AUC 95% CI P
AUC 95% CI P
0.8680.836-0.900 < 0.001
0.8150.780–0.850 < 0.001
0.7200.680–0.760 < 0.001
0.5130.457–0.570 0.602
0.808 0.737-0.879 < 0.001
0.7910.732–0.849 < 0.001
0.7120.639–0.785 < 0.001
0.5120.421–0.603 0.771
*
*
*
*
*
(a)
(b)
Figure 2. The area of receiver operating characteristic curve (AUC) for evaluating the discrimination performance of simple
physiological prognostic scoring (SPPS), Global registry of Acute Coronary Events (GRACE) score, thrombolysis in myocardial
infarction (TIMI) score and Gensini score in both the derivation set (a) and the validation set (b). *Compared with SPPS, the
difference was statistically significant.
6 European Journal of Cardiovascular Nursing 00(0)
Prognostic value of SPPS for different types
of AMI
We evaluated the application scope of the SPPS system by
conducting a subgroup analysis based on AMI type. The
AUC for predicting death in patients with STEMI was
0.866 (95% CI 0.784–0.947) in the derivation set and
0.895 (95% CI 0.855–0.935) in the validation set, both of
which were superior to those of the GRACE, TIMI and
Gensini scores (Table 4). For patients with NSTEMI, the
AUC of the SPPS system was also superior to that of the
GRACE, TIMI and Gensini scores (Table 4).
Discussion
Using a derivation–validation design, the present study
constructed a SPPS system composed of only four simple
parameters and demonstrated its utility for the very early
identification of patients with high-risk AMI on ED
admission. Furthermore, the calibration and discrimination
of this SPPS system were superior to that of the GRACE
scoring system recommended by current guidelines for pre-
dicting all-cause mortality. Therefore, the SPPS system
may be useful for prospective and dynamic risk stratifica-
tion on ED admission or even for assessment by first
responders before reaching hospital.
Current systems such as the GRACE, TIMI and Global
Use of Strategies to Open Occluded Arteries were devel-
oped in clinical trial populations with STEMI, NSTEMI, or
unstable angina using clinical signs, electrocardiogram and
cardiac and kidney injury biomarkers for prospective risk
stratification within the first 24 hours after AMI onset.20,25,26
However, as these systems include more complex test
results and dynamic biomarkers, they are poorly suited for
risk stratification during the very early phase (0–1 hour) of
treatment in the ED. Therefore, it is difficult to determine
Table 3. Reclassification results for all-cause mortality in the derivation set and validation set.
Derivation set Validation set
SPPS GRACE AUC P value SPPS GRACE AUC P value
AUC 0.868 0.815 0.053 0.004 0.808 0.791 0.017 0.003
Events
Upward 42 19
No 95 41
Downward 28 13
No events
Upward 195 45
No 1083 561
Downward 306 191
NRI (categorical) 0.155 <0.001 0.265 <0.001
IDI 0.069 0.0013 0.100 0.0022
SPPS: simple physiological prognostic scoring; GRACE: Global Registry of Acute Coronary Events; AUC: area under curve; NRI: net reclassification
index; IDI: integrated discrimination improvement.
Figure 3. Calibration curves for the simple physiological
prognostic scoring (SPPS) system in the validation set. Figure 4. Decision curve analysis for the simple physiological
prognostic scoring (SPPS) system in the validation set. GRACE:
Global registry of Acute Coronary Events.
Li et al. 7
the benefits or necessity of invasive strategies and adjunct
medical therapies immediately on presentation to the ED,
especially for NSTEMI or unstable angina, using these scor-
ing systems. As inhospital deaths occurring in the first 24
hours account for a high proportion of patients with AMI,17
these scoring systems hardly cover all high-risk patients.
Moreover, the prognostic value of biomarkers is influenced
by the duration of onset of symptoms; for example, the peak
expression of C-reactive protein performs much better than
early stage.27 Therefore, the effectiveness of early assess-
ment by these scoring systems may be insufficient.
To establish a simple and useful scoring system, we
included age, heart rate, BMI and Killip class based on
multivariate logistic regression. Both in the ED and out-
side of the hospital, these immediately accessible parame-
ters have been reported to be independent predictors of
mortality from AMI.20,25,26 On the other hand, previous
scoring systems included BP, which would seem to be a
good candidate in our scoring system. However, BP was
excluded from the SPPS system because it was not found
to be significant in the multivariate logistic regression.
That may be because Killip class IV reflects hypotension
and shock; BP, although measured by an objective instru-
ment, is affected by the patient’s position, medicine and
stress, as well as by staff interactions. Moreover, several
cohort studies have shown malnutrition, rather than obe-
sity, to be associated with an increased risk of mortality in
patients with AMI.28 Thus, BMI and weight loss should
also be considered during early assessment.
According to ROC, IDI, NRI and DCA analysis, the
novel SPPS system performed better calibration and dis-
crimination than the GRACE score. We also conducted a
subgroup analysis to assess the utility of this system for
different AMI types and found good prognostic efficacy
for both STEMI and NSTEMI compared with GRACE
scores. Although the SPPS system was more effective than
the GRACE score in risk stratification in our study, it
needs to be acknowledged that it is still not possible to
replace the GRACE scores. In fact, these consist of more
dimensional indicators and can supply some relevant
information when deciding on treatment strategy. Our pre-
vious studies indicated that multidimensional indicators
may provide additional information for more accurate
prognosis.29–31 Admittedly, the GRACE scores have been
shown to benefit patients with AMI by risk stratification
and clinical decision-making.10 Therefore, application of
the two-scoring system, according to different situations,
may achieve better outcomes for patients.
It seems that the SPPS system will be of particular ben-
efit to nurses19 because they are the first to come into con-
tact with patients with AMI. Early risk assessment in the
ED, and even prehospital, can assist nurses with arranging
treatment sites that match risk and therapeutic intensity,
implementing invasive nursing operations in advance, and
improving the time point of thrombolysis or PCI. Moreover,
when patients’ vital signs and physical examinations are
recorded regularly, nurses can dynamically assess and
adjust the risk level of patients and thus change their nurs-
ing plans. Therefore, the SPPS system may fill the gap in
nursing assessment tools for patients with AMI.
This study has several limitations. First, it is based on a
multicentre respective cohort study conducted at the chest
pain centres of tertiary medical centres, which limited further
subgroup analysis and may introduce selection bias. Second,
the training and validation cohorts were drawn from the
same population; therefore, this system should be validated
in an external prospective cohort. Third, we did not derive
SPPS system indices for follow-up. We also did not investi-
gate the association between death and specific changes to
the SPPS system. In addition, we could not identify major
cardiovascular adverse events (MACEs), so the predictive
value of the SPPS system for MACEs was not investigated.
Table 4. The receiver operator characteristics curve analysis of SPPS and other representative scoring systems for discriminating
death in different types of AMI.
Derivation set Validation set
AUC (95% CI) P value AUC (95% CI) P value
STEMI
Genisi 0.510 (0.433–0.588) 0.768 0.568 (0.441–0.695) 0.206
TIMI 0.801 (0.748–0.854) <0.001 0.802 (0.706–0.897) <0.001
GRACE 0.851 (0.808–0.895) <0.001 0.865 (0.804–0.927) <0.001
SPPS 0.895 (0.855–0.935) <0.001 0.866 (0.784–0.947) <0.001
NSTEMI
Genisi 0.523 (0.440–0.606) 0.567 0.460 (0.335–0.585) 0.500
TIMI 0.606 (0.539–0.673) 0.007 0.573 (0.467–0.679) 0.217
GRACE 0.707 (0.637–0.777) <0.001 0.689 (0.590–0.788) 0.001
SPPS 0.788 (0.719–0.857) <0.001 0.731 (0.612–0.850) <0.001
CI: confidence interval; SPPS: simple physiological prognostic scoring; TIMI: thrombolysis in myocardial infarction; GRACE: Global Registry of
Acute Coronary Events; AUC: area under curve; STEMI: ST-segment elevated myocardial infarction; NSTEMI: non-ST-segment elevated myocardial
infarction.
8 European Journal of Cardiovascular Nursing 00(0)
Conclusions
The SPPS system is a useful tool to evaluate mortality
from AMI, whether STEMI and NSTEMI, at a very early
stage of treatment in the ED. In fact, the prognostic accu-
racy of the SPPS system is superior to that of the GRACE,
TIMI and Gensini scores at ED admission. Therefore, the
SPPS system may be a useful tool for early initial risk
stratification of patients with AMI by nurses in the ED or
even before hospitalisation.
Implications for practice
Rapid evaluation of acute myocardial infarc-
tion mortality risk can improve outcome. The
simple physiological prognostic scoring sys-
tem composed of only four simple parameters
could identify high-risk acute myocardial
infarction patients at admission.
Calibration and discrimination of this simple
physiological prognostic scoring system are
superior to the Global Registry of Acute
Coronary Events scoring system recom-
mended by current guidelines for predicting
all-cause mortality.
The simple physiological prognostic scoring
system could provide rapidly additional infor-
mation for patient with acute myocardial
infarction to guide individual care at admis-
sion to hospital without any medical device.
Acknowledgements
The author(s) would like to thank Zhi Zeng and the research
nurses for their support of this project.
Author contribution
DL, JW, LY, ZW, YC and RZ developed the study concept. YS,
JY, YJ, FL, QZ, XC and YG collected the epidemiological and
clinical data. DL, DL, YC and YJ summarised and analysed all
the data. DL, YJ and JW drafted the manuscript. ZW, ZZ, YC
and RZ revised the final manuscript.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial sup-
port for the research, authorship, and/or publication of this arti-
cle: This work was supported financially by grants from
Sichuan Science and Technology Program (nos 2020YFS0154,
2020YFSY0014, 2019JDRC0105), Sichuan University West
China Hospital (nos 2018HXFH001, 2018HXFH027,
20HXFH050) and West China School of Nursing, Sichuan
University (no. HXHL19023).
Supplemental material
Supplemental material for this article is available online.
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