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A cardiac arrest on a spiked helmet electrocardiographic sign in anterior leads in a patient with a diagnosis of acute myocardial infarction: A case report.

ABSTRACT: In patients with critical heart disease, such as acute coronary syndrome, aortic dissection, and other diseases, ST-segment elevation is a relatively common finding on the electrocardiogram (ECG). There are various other well-known signs described in heart diseases, such as negative T waves and q waves of necrosis. The "spiked helmet sign" is a novel electrocardiographic sign described first in 2011, whose pathophysiology and clinical applicability remain uncertain at this time. Herein we report the case of a cardiac arrest in a patient who developed the "spiked helmet electrocardiographic sign" concomitantly with acute myocardial infarction, leading to the patient's death from ventricular fibrillation. This case report aims to discuss the "spiked helmet electrocardiographic sign", and to detail the prognostic and diagnostic interest of this sign, wich seems to be different from a standard ST segment elevation.

SUBMITTER: Belarbi Z

PROVIDER: S-EPMC9142612 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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A cardiac arrest on a spiked helmet electrocardiographic sign in anterior leads in a patient with a diagnosis of acute myocardial infarction: A case report.

Belarbi Zakariae Z Brem Falmata Laouan FL El Ouafi Noha N

Annals of medicine and surgery (2012) 20220415

In patients with critical heart disease, such as acute coronary syndrome, aortic dissection, and other diseases, ST-segment elevation is a relatively common finding on the electrocardiogram (ECG). There are various other well-known signs described in heart diseases, such as negative T waves and q waves of necrosis. The "spiked helmet sign" is a novel electrocardiographic sign described first in 2011, whose pathophysiology and clinical applicability remain uncertain at this time. Herein we report ...[more]

PMID: 35638003

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Project description:BackgroundIn suspected myocardial infarction (MI), guidelines recommend using high-sensitivity cardiac troponin (hs-cTn)-based approaches. These require fixed assay-specific thresholds and timepoints, without directly integrating clinical information. Using machine-learning techniques including hs-cTn and clinical routine variables, we aimed to build a digital tool to directly estimate the individual probability of MI, allowing for numerous hs-cTn assays.MethodsIn 2,575 patients presenting to the emergency department with suspected MI, two ensembles of machine-learning models using single or serial concentrations of six different hs-cTn assays were derived to estimate the individual MI probability (ARTEMIS model). Discriminative performance of the models was assessed using area under the receiver operating characteristic curve (AUC) and logLoss. Model performance was validated in an external cohort with 1688 patients and tested for global generalizability in 13 international cohorts with 23,411 patients.ResultsEleven routinely available variables including age, sex, cardiovascular risk factors, electrocardiography, and hs-cTn were included in the ARTEMIS models. In the validation and generalization cohorts, excellent discriminative performance was confirmed, superior to hs-cTn only. For the serial hs-cTn measurement model, AUC ranged from 0.92 to 0.98. Good calibration was observed. Using a single hs-cTn measurement, the ARTEMIS model allowed direct rule-out of MI with very high and similar safety but up to tripled efficiency compared to the guideline-recommended strategy.ConclusionWe developed and validated diagnostic models to accurately estimate the individual probability of MI, which allow for variable hs-cTn use and flexible timing of resampling. Their digital application may provide rapid, safe and efficient personalized patient care.Trial registration numbersData of following cohorts were used for this project: BACC ( www.Clinicaltrialsgov ; NCT02355457), stenoCardia ( www.Clinicaltrialsgov ; NCT03227159), ADAPT-BSN ( www.australianclinicaltrials.gov.au ; ACTRN12611001069943), IMPACT ( www.australianclinicaltrials.gov.au , ACTRN12611000206921), ADAPT-RCT ( www.anzctr.org.au ; ANZCTR12610000766011), EDACS-RCT ( www.anzctr.org.au ; ANZCTR12613000745741); DROP-ACS ( https://www.umin.ac.jp , UMIN000030668); High-STEACS ( www.Clinicaltrialsgov ; NCT01852123), LUND ( www.Clinicaltrialsgov ; NCT05484544), RAPID-CPU ( www.Clinicaltrialsgov ; NCT03111862), ROMI ( www.Clinicaltrialsgov ; NCT01994577), SAMIE ( https://anzctr.org.au ; ACTRN12621000053820), SEIGE and SAFETY ( www.Clinicaltrialsgov ; NCT04772157), STOP-CP ( www.Clinicaltrialsgov ; NCT02984436), UTROPIA ( www.Clinicaltrialsgov ; NCT02060760).

Dataset Information

A cardiac arrest on a spiked helmet electrocardiographic sign in anterior leads in a patient with a diagnosis of acute myocardial infarction: A case report.

Publications

A cardiac arrest on a spiked helmet electrocardiographic sign in anterior leads in a patient with a diagnosis of acute myocardial infarction: A case report.

Similar Datasets

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