PhD Scientific Days 2024

Cardiovascular Medicine and Research II.

Factors predicting mortality during the early phase of targeted temperature management in the treatment of post-cardiac arrest syndrome – the RAPID score

Előadó neve

Dr. Nagy, Bettina

Neptun code

YOE5W0

Előadó munkahelye

Heart and Vascular Center, Semmelweis University

Előadó telefonszáma

06707038933

Előadó e-mail címe

nagy.betta@gmail.com

Az előadás címe

Factors predicting mortality during the early phase of targeted temperature management in the treatment of post-cardiac arrest syndrome – the RAPID score

Szerző(k) neve és munkahelye

Bettina Nagy¹, Boldizsár Kiss¹, Ádám Pál-Jakab¹

1: Heart and Vascular Center, Semmelweis University

Bemutatás módja

Szóbeli

Szekció

Cardiovascular Medicine and Research II.

Language of the presentation

Hungarian

Preferred session

Cardiovascular Medicine and Research

Összefoglaló szövege

Intoduction: Survival rates after out-of-hospital cardiac arrest (OHCA) remain low, and early prognostication is challenging, notably for patients undergoing prolonged resuscitation or remaining in coma after cardiopulmonary resuscitation. While numerous intensive care unit scoring systems exist, their utility in the early hours following hospital admission, specifically in the targeted temperature management (TTM) population, is questionable. TTM is an intervention aimed at controlling body temperature to improve neurological outcome after cardiac arrest.
Aims: Our aim was to create a score system that may accurately estimate outcome within the first 12 hours after admission in patients receiving targeted temperature management (TTM) based on simple and easily measurable parameters.
Methods: We analyzed data from 103 out-of-hospital cardiac arrest patients who subsequently underwent TTM between 2016 and 2022. Patient demographic data, prehospital characteristics, as well as clinical and laboratory parameters that were already available in the first 12 hours after admission were examined. Multiple statistical analyses, encompassing contingency and Mann-Whitney tests for different data types, single imputation for missing data, nonlinear regression with cubic splines to explore relationships, and predictor selection via Akaike's criterion and bootstrap resampling, were executed. Model performance was evaluated through ROC analysis, calibration measures, and internal validation using 10000 replicates of bootstrapping.
Results: Heart rate, age, pH, initial rhythm, and right ventricular end-diastolic diameter were associated with 30-day mortality and were used to build our predictive model. The area under the receiver-operating characteristics curve for the model was 0.84. The model achieved a C-statistic of 0.7974, with internal validation indicating good calibration (intercept: -0.0190, slope: 0.7772) and low error rates (mean absolute error: 0.040).
Conclusion: The model we have developed may be suitable for early risk assessment of patients receiving TTM as part of primary post-resuscitation care. This study may be a basis of further prognostication investigations.
Funding:The study was supported by grant EFOP-3.6.3-VEKOP-16-2017-00009 (“Semmelweis 250+ Excellence Scholarship”).

University

Semmelweis University

Supervisor

Prof. Endre Zima

Publication of my abstract

I do not give consent to the publication of my abstract on the website of the congress.

Kind

Szabad

Status

elfogadva

Accepted presentation method

szóbeli

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

6861

Start

14:30

End

14:40