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Simple point of care risk stratification in acute coronary syndromes: The AMIS model
  1. David J. Kurz (david.kurz{at}triemli.stzh.ch)
  1. Cardiology, Triemli Hospital, Switzerland
    1. Abraham Bernstein (bernstein{at}ifi.uzh.ch)
    1. Institute of Informatics, University of Zurich, Switzerland
      1. Katrin Hunt (katrin{at}hunt.ch)
      1. Institute of Informatics, University of Zurich, Switzerland
        1. Dragana Radovanovic (dragana{at}ifspm.uzh.ch)
        1. AMIS-PLUS Data Centre, Institute of Social and Preventive Medicine, University of Zurich, Switzerland
          1. Paul Erne (paul.erne{at}ksl.ch)
          1. Cardiology, Kantonsspital Luzern, Switzerland
            1. Zbigniew Siudak (zbigniew.siudak{at}gmail.com)
            1. Department of Interventional Cardiology, University Hospital, Krakow, Poland
              1. Osmund Bertel (osmund.bertel{at}gmail.com)
              1. Cardiology, Triemli Hospital, Switzerland

                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.

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