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Simple point of care risk stratification in acute coronary syndromes: the AMIS model


Kurz, D J; Bernstein, A; Hunt, K; Radovanovic, D; Erne, P; Siudak, Z; Bertel, O (2009). Simple point of care risk stratification in acute coronary syndromes: the AMIS model. Heart, 95(8):662-668.

Abstract

Background: Early risk stratification is important in the management of patients with acute coronary syndromes (ACS).
Objective: To develop a rapidly available risk stratification tool for use in all ACS.
Design and methods: Application of modern data mining and machine learning algorithms to a derivation cohort of 7520 ACS patients included in the AMIS (Acute Myocardial Infarction in Switzerland)-Plus registry between 2001 and 2005; prospective model testing in two validation cohorts.
Results: The most accurate prediction of in-hospital mortality was achieved with the “Averaged One-Dependence Estimators” (AODE) algorithm, with input of 7 variables
available at first patient contact: Age, Killip class, systolic blood pressure, heart rate, pre-hospital cardio-pulmonary resuscitation, history of heart failure, history of cerebrovascular disease. The c-statistic for the derivation cohort (0.875) was essentially maintained in
important subgroups, and calibration over five risk categories, ranging from <1% to >30% predicted mortality, was accurate. Results were validated prospectively against an independent AMIS-Plus cohort (n=2854, c-statistic 0.868) and the Krakow-Region ACS Registry (n=2635, c-statistic 0.842). The AMIS model significantly outperformed established “point-of-care” risk prediction tools in both validation cohorts. In comparison to a logistic
regression-based model, the AODE-based model proved to be more robust when tested on the Krakow validation cohort (c-statistic 0.842 vs. 0.746). Accuracy of the AMIS model
prediction was maintained at 12-months follow-up in an independent cohort (n=1972, c-statistic 0.877).
Conclusions: The AMIS model is a reproducibly accurate point-of-care risk stratification tool for the complete range of ACS, based on variables available at first patient contact.

Abstract

Background: Early risk stratification is important in the management of patients with acute coronary syndromes (ACS).
Objective: To develop a rapidly available risk stratification tool for use in all ACS.
Design and methods: Application of modern data mining and machine learning algorithms to a derivation cohort of 7520 ACS patients included in the AMIS (Acute Myocardial Infarction in Switzerland)-Plus registry between 2001 and 2005; prospective model testing in two validation cohorts.
Results: The most accurate prediction of in-hospital mortality was achieved with the “Averaged One-Dependence Estimators” (AODE) algorithm, with input of 7 variables
available at first patient contact: Age, Killip class, systolic blood pressure, heart rate, pre-hospital cardio-pulmonary resuscitation, history of heart failure, history of cerebrovascular disease. The c-statistic for the derivation cohort (0.875) was essentially maintained in
important subgroups, and calibration over five risk categories, ranging from <1% to >30% predicted mortality, was accurate. Results were validated prospectively against an independent AMIS-Plus cohort (n=2854, c-statistic 0.868) and the Krakow-Region ACS Registry (n=2635, c-statistic 0.842). The AMIS model significantly outperformed established “point-of-care” risk prediction tools in both validation cohorts. In comparison to a logistic
regression-based model, the AODE-based model proved to be more robust when tested on the Krakow validation cohort (c-statistic 0.842 vs. 0.746). Accuracy of the AMIS model
prediction was maintained at 12-months follow-up in an independent cohort (n=1972, c-statistic 0.877).
Conclusions: The AMIS model is a reproducibly accurate point-of-care risk stratification tool for the complete range of ACS, based on variables available at first patient contact.

Citations

23 citations in Web of Science®
22 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
610 Medicine & health
Language:English
Date:April 2009
Deposited On:09 Jan 2009 11:26
Last Modified:05 Apr 2016 12:45
Publisher:BMJ Publishing Group
ISSN:1355-6037
Additional Information:Copyright: BMJ Publishing Group Ltd & British Cardiovascular Society
Publisher DOI:https://doi.org/10.1136/hrt.2008.145904
PubMed ID:19066189

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