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1.
Heliyon ; 10(5): e26773, 2024 Mar 15.
Article in English | MEDLINE | ID: mdl-38444470

ABSTRACT

Objective: Acute cardiogenic shock is a life-threatening condition with mortality rates of up to 50%. If conventional therapy fails, veno-arterial extracorporeal membrane oxygenation (VA-ECMO) therapy has emerged to a promising alternative for temporary cardiac and respiratory support in specialized centers. However, it is only a bridge to recovery, final decision, heart transplantation or the permanent implantation of a left ventricular assist device. Therefore, the identification of the optimum weaning time point is challenging, and standardized weaning protocols are rare. Methods: In this explorative pilot study, we evaluated the potential benefit of blood flow measurements in the aortic arch using an ultrasonic cardiac output monitor (USCOM) for the primary endpoint of successful VA-ECMO weaning. 12 patients under VA-ECMO therapy for acute cardiogenic shock and a hemodynamic condition which qualified for a stepwise weaning process were included in this study. Main exclusion criterion was the presence of additional venting therapy for left ventricular unloading, e.g. Impella. Statistical comparisons were performed using the Mann-Whitney test and corrected for multiple testing by the Holm-Sidak method. Results: Peak velocity of flow in the aortic arch showed a positive correlation with weaning success independent of ECMO flow (weaning success vs. failure: 0.75 vs. 0.35 m/s (low ECMO support), p = 0.049), whereas we identified only a trend for mean pressure gradient, minute distance and stroke volume index. Conclusion: We hypothesize, that USCOM might provide an additive benefit to conventional strategies in its ability to predict successful VA-ECMO weaning and prevent pulmonary congestion. Larger upcoming trials are required to address this relevant topic and provide standardized treatment protocols for optimized weaning in the future.

2.
Eur Heart J Acute Cardiovasc Care ; 13(4): 347-353, 2024 May 07.
Article in English | MEDLINE | ID: mdl-38306600

ABSTRACT

AIMS: Studies have shown a so-called off-hour effect for many different diseases, but data are scarce concerning cardiogenic shock. We therefore assessed the association of off-hour vs. on-hour intensive care unit admission with 30-day mortality in patients with cardiogenic shock. METHODS AND RESULTS: In total, 1720 cardiogenic shock patients (666 admitted during off-hours) from two large university hospitals in Germany were included in retrospect. An admission during off-hours was associated with increased 30-day mortality compared to an admission during on-hours [crude mortality 48% vs. 41%, HR 1.17 (1.03-1.33), P = 0.017]. This effect remained significant after propensity score matching (P = 0.023). Neither patients with a combined SCAI stage D and E (P = 0.088) or C (P = 0.548) nor those requiring cardiopulmonary resuscitation (P = 0.114) had a higher mortality at off-hour admission. In contrast, those without veno-arterial extracorporeal membrane oxygenation [HR 1.17 (1.00-1.36), P = 0.049], without acute myocardial infarction [HR 1.27 (1.02-1.56), P = 0.029] or a with combined SCAI stage A and B [HR 2.23 (1.08-4.57), P = 0.025] had an increased mortality at off-hour admission. CONCLUSION: Our study showed an increased mortality in patients with cardiogenic shock admitted during off-hours, especially in those with a milder onset of disease. This stresses the importance of a thorough workup of each patient, especially at times of limited resources, the menace of underestimating the severity of cardiogenic shock, and the need for an improved 24×7 available risk stratification.


Subject(s)
Hospital Mortality , Intensive Care Units , Shock, Cardiogenic , Humans , Shock, Cardiogenic/mortality , Shock, Cardiogenic/therapy , Male , Female , Retrospective Studies , Intensive Care Units/statistics & numerical data , Aged , Hospital Mortality/trends , Germany/epidemiology , Time Factors , Middle Aged , Patient Admission/statistics & numerical data , Survival Rate/trends , Propensity Score
3.
J Electrocardiol ; 82: 11-18, 2024.
Article in English | MEDLINE | ID: mdl-37995553

ABSTRACT

BACKGROUND: Periodic repolarization dynamics (PRD) is an electrocardiographic biomarker that quantifies low-frequency (LF) instabilities of repolarization. PRD is a strong predictor of mortality in patients with ischaemic and non-ischaemic cardiomyopathy. Until recently, two methods for calculating PRD have been proposed. The wavelet analysis has been widely tested and quantifies PRD in deg2 units by application of continuous wavelet transformation (PRDwavelet). The phase rectified signal averaging method (PRDPRSA) is an algebraic method, which quantifies PRD in deg. units. The correlation, as well as a conversion formula between the two methods remain unknown. METHOD: The first step for quantifying PRD is to calculate the beat-to-beat change in the direction of repolarization, called dT°. PRD is subsequently quantified by means of either wavelet or PRSA-analysis. We simulated 1.000.000 dT°-signals. For each simulated signal we calculated PRD using the wavelet and PRSA-method. We calculated the ratio between PRDwavelet and PRDPRSA for different values of dT° and RR-intervals and applied this ratio in a real-ECG validation cohort of 455 patients after myocardial infarction (MI). We finally calculated the correlation coefficient between real and calculated PRDwavelet. PRDwavelet was dichotomized at the established cut-off value of ≥5.75 deg2. RESULTS: The ratio between PRDwavelet and PRDPRSA increased with increasing heart-rate and mean dT°-values (p < 0.001 for both). The correlation coefficient between PRDwavelet and PRDPRSA in the validation cohort was 0.908 (95% CI 0.891-0.923), which significantly (p < 0.001) improved to 0.945 (95% CI 0.935-0.955) after applying the formula considering the ratio between PRDwavelet and PRDPRSA obtained from the simulation cohort. The calculated PRDwavelet correctly classified 98% of the patients as low-risk and 87% of the patients as high-risk and correctly identified 97% of high-risk patients, who died within the follow-up period. CONCLUSION: This is the first analytical investigation of the different methods used to calculate PRD using simulated and clinical data. In this article we propose a novel algorithm for converting PRDPRSA to the widely validated PRDwavelet, which could unify the calculation methods and cut-offs for PRD.


Subject(s)
Electrocardiography , Myocardial Infarction , Humans , Heart Rate , Signal Processing, Computer-Assisted
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