A Simple Risk Stratification Model for ST-Elevation Myocardial Infarction (STEMI) from the Combination of Blood Examination Variables: Acute Myocardial Infarction-Kyoto Multi-Center Risk Study Group.
BACKGROUND
Many mortality risk scoring tools exist among patients with ST-elevation Myocardial Infarction (STEMI). A risk stratification model that evaluates STEMI prognosis more simply and rapidly is preferred in clinical practice.


RESULTS
We developed a simple stratification model for blood examination by using the STEMI data of AMI-Kyoto registry in the derivation set (n = 1,060) and assessed its utility for mortality prediction in the validation set (n = 521). We selected five variables that significantly worsen in-hospital mortality: white blood cell count, hemoglobin, C-reactive protein, creatinine, and blood sugar levels at >10,000/μL, <10 g/dL, >1.0 mg/dL, >1.0 mg/dL, and >200 mg/dL, respectively. In the derivation set, each of the five variables significantly worsened in-hospital mortality (p < 0.01). We developed the risk stratification model by combining laboratory variables that were scored based on each beta coefficient obtained using multivariate analysis and divided three laboratory groups. We also found a significant trend in the in-hospital mortality rate for three laboratory groups. Therefore, we assessed the utility of this model in the validation set. The prognostic discriminatory capacity of our laboratory stratification model was comparable to that of the full multivariable model (c-statistic: derivation set vs validation set, 0.81 vs 0.74). In addition, we divided all cases (n = 1,581) into three thrombolysis in myocardial infarction (TIMI) risk index groups based on an In TIME II substudy; the cases were further subdivided based on this laboratory model. The high laboratory group had significantly high in-hospital mortality rate in each TIMI risk index group (trend of in-hospital mortality; p < 0.01).


CONCLUSIONS
This laboratory stratification model can predict in-hospital mortality of STEMI simply and rapidly and might be useful for predicting in-hospital mortality of STEMI by further subdividing the TIMI risk index.
Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Kenji
http://orcid.org/0000-0002-6158-9395
K
Yanishi
Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Takeshi
T
Nakamura
Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Naohiko
http://orcid.org/0000-0003-3845-3542
N
Nakanishi
Department of Biostatistics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Isao
I
Yokota
Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Kan
K
Zen
Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Tetsuhiro
T
Yamano
Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Hirokazu
H
Shiraishi
Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Takeshi
http://orcid.org/0000-0002-3060-5444
T
Shirayama
Department of Cardiology, Kyoto First Red Cross Hospital, Kyoto, Japan.
Jun
J
Shiraishi
Department of Cardiology, Kyoto First Red Cross Hospital, Kyoto, Japan.
Takahisa
T
Sawada
Department of Cardiology, Kyoto First Red Cross Hospital, Kyoto, Japan.
Yoshio
Y
Kohno
Department of Cardiology, Kyoto Second Red Cross Hospital, Kyoto, Japan.
Makoto
M
Kitamura
Department of Cardiology, Tanabe Central Hospital, Kyoto, Japan.
Keizo
K
Furukawa
Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Satoaki
S
Matoba
D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000378:metabolism; D002097:C-Reactive Protein / Q000378:metabolism; D003404:Creatinine / Q000097:blood; D005260:Female; D005343:Fibrinolytic Agents / Q000627:therapeutic use; D006454:Hemoglobins / Q000378:metabolism; D017052:Hospital Mortality; D006801:Humans; D007564:Japan; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D011446:Prospective Studies; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors; D000072657:ST Elevation Myocardial Infarction / Q000097:blood / Q000150:complications / Q000175:diagnosis* / Q000401:mortality; D016019:Survival Analysis; D013927:Thrombosis / Q000097:blood / Q000150:complications / Q000175:diagnosis* / Q000401:mortality
D015415:Biomarkers; D001786:Blood Glucose; D005343:Fibrinolytic Agents; D006454:Hemoglobins; D002097:C-Reactive Protein; D003404:Creatinine