PhD Scientific Days 2020

Clinical Medicine II. Posters

Review of Mortality Prediction Algorithms for STEMI Patients Undergoing Primary Percutaneous Coronary Intervention

Előadó neve

Dr. Domokos, Dominika

Előadó munkahelye

Heart and Vascular Center, Semmelweis University, Budapest, Hungary

Előadó telefonszáma

+36309199565

Előadó e-mail címe

dominikadomokos@gmail.com

Az előadás címe

Review of Mortality Prediction Algorithms for STEMI Patients Undergoing Primary Percutaneous Coronary Intervention

Szerző(k) neve és munkahelye

Dominika Domokos1, András Szabó2, Dávid Becker1, Béla Merkely1, Zoltán Ruzsa1,3, István Hizoh1
1 Heart and Vascular Center, Semmelweis University, Budapest, Hungary
2 Department of Anesthesiology and Intensive Care, Semmelweis University, Budapest, Hungary
3 Department of Invasive Cardiology, Bacs-Kiskun County University Teaching Hospital, Kecskemet, Hungary

Szekció

Clinical Medicine II. Posters

Language of the presentation

English

Section, first choice

Clinical Medicine

Section, second choice

Theoretical and Translational Medicine

Összefoglaló szövege

Mortality risk of ST-segment elevation myocardial infarction (STEMI) patients shows high variability. In order to assess individual risk, several scoring systems have been developed and validated. Yet, as treatment approaches evolve with improving outcomes and as even older patients with complex disease patterns are treated invasively, new or updated risk prediction algorithms are needed to maintain or increase prognostic accuracy. One of the most relevant improvements of therapy is primary percutaneous coronary intervention (PCI), since, compared with fibrinolysis, it further reduces mortality. Prediction algorithms may provide useful information for patients and relatives, as well as help physicians to allocate hospital resources. In addition, they may contribute to an improved quality of care as they can be used for risk adjustment in inter-organizational comparisons of health care providers with different case mixes. Furthermore, risk models may be helpful in clinical trial design identifying patients with the needed risk profile thereby increasing statistical power and reducing sample size and costs.
In the present work, we overview the general and individual characteristics and discriminative performance of the most studied and some recently constructed mortality risk models that were validated in patients with STEMI who underwent primary percutaneous coronary intervention.
We found that though the extensively validated “Global Registry of Acute Coronary Events” (GRACE) model was not particularly derived from data of invasively treated STEMI patients, it also performs well in the era of transradial primary PCI. Similarly, the Zwolle, “Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications”(CADILLAC), “Assessment of Pexelizumab in Acute Myocardial Infarction” (APEX-AMI), and “Age, Life support, Pressure, Heart rate, Access site” (ALPHA) models, that were constructed using primary PCI data, all seem to have comparable discriminative abilities. In contrast, the admission model “Thrombolysis in Myocardial Infarction” (TIMI), which was developed in the fibrinolysis era, might have less predictive power. Finally, the primary PCI admission model “Primary Angioplasty in Myocardial Infarction” (PAMI) is likely the weakest among the comparatively studied risk models concerning discriminatory ability.

Additional Information

Supervisor: István Hizoh
E-mail address: ihizoh@web.de

Bemutatás módja

Poszter

Kind

Szabad

Status

elfogadva

Accepted presentation method

poszter

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

4687

Start

12:59

End

13:02

Authors (legacy)

Dominika Domokos1, András Szabó2, Dávid Becker1, Béla Merkely1, Zoltán Ruzsa1,3, István Hizoh1
1 Heart and Vascular Center, Semmelweis University, Budapest, Hungary
2 Department of Anesthesiology and Intensive Care, Semmelweis University, Budapest, Hungary
3 Department of Invasive Cardiology, Bacs-Kiskun County University Teaching Hospital, Kecskemet, Hungary