Background: Multi-omics delivers more biological insight than targeted investigations. We applied multi-omics to patients with heart failure with reduced ejection fraction (HFrEF). Methods: 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography mass spectrometry (LC-MS/GC-MS) and solid-phase microextraction (SPME) volatilomics in plasma and urine. HFrEF was defined using left ventricular global longitudinal strain, ejection fraction and NTproBNP. A consumer breath acetone (BrACE) sensor validated results in n = 73. Results: 28 metabolites were identified by GCMS, 35 by LCMS and 4 volatiles by SPME in plasma and urine. Alanine, aspartate and glutamate, citric acid cycle, arginine biosynthesis, glyoxylate and dicarboxylate metabolism were altered in HFrEF. Plasma acetone correlated with NT-proBNP (r = 0.59, 95% CI 0.4 to 0.7), 2-oxovaleric and cis-aconitic acid, involved with ketone metabolism and mitochondrial energetics. BrACE > 1.5 ppm discriminated HF from other cardiac pathology (AUC 0.8, 95% CI 0.61 to 0.92, p < 0.0001). Conclusion: Breath acetone discriminated HFrEF from other cardiac pathology using a consumer sensor, but was not cardiac specific.
Introduction: Hyperosmotic therapy with mannitol is frequently used for treatment cerebral edema, and 320 mOsm/kg H2O has been recommended as a high limit for therapeutic plasma osmolality. However, plasma hyperosmolality may impair cardiac function, increasing the risk of cardiac events. The aim of this study was to analyze the relation between changes in plasma osmolality and electrocardiographic variables and cardiac arrhythmia in patients treated for isolated traumatic brain injury (iTBI). Methods: Adult iTBI patients requiring mannitol infusion following cerebral edema, and with a Glasgow Coma Score below 8, were included. Plasma osmolality was measured with Osmometr 800 CLG. Spatial QRS-T angle (spQRS-T), corrected QT interval (QTc) and STJ segment were calculated from digital resting 12-lead ECGs and analyzed in relation to four levels of plasma osmolality: (A) <280 mOsm/kg H2O; (B) 280–295 mOsm/kg H2O; (C) 295–310 mOsm/kg H2O; and (D) >310 mOsm/kg H2O. All parameters were measured during five consecutive days of treatment. Results: 94 patients aged 18-64 were studied. Increased plasma osmolality correlated with prolonged QTc (p < 0.001), intensified disorders in STJ and increased the risk for cardiac arrhythmia. Moreover, plasma osmolality >313 mOms/kg H2O significantly increased the risk of QTc prolongation >500 ms. Conclusion: In patients treated for iTBI, excessively increased plasma osmolality contributes to electrocardiographic disorders including prolonged QTc, while also correlating with increased risk for cardiac arrhythmias.
Background Deep neural network artificial intelligence (DNN-AI)-based Heart Age estimations have been presented and used to show that the difference between an ECG-estimated Heart Age and chronological age is associated with prognosis. An accurate ECG Heart Age, without DNNs, has been developed using explainable advanced ECG (A-ECG) methods. We aimed to evaluate the prognostic value of the explainable A-ECG Heart Age and compare its performance to a DNN-AI Heart Age. Methods Both A-ECG and DNN-AI Heart Age were applied to patients who had undergone clinical cardiovascular magnetic resonance imaging. The association between A-ECG or DNN-AI Heart Age Gap, and cardiovascular risk factors was evaluated using logistic regression. The association between Heart Age Gaps and death or heart failure (HF) hospitalization was evaluated using Cox regression adjusted for clinical covariates/comorbidities. Results Among patients (n = 731, 103 (14.1%) deaths, 52 (7.1%) HF hospitalizations, median [interquartile range] follow-up 5.7 [4.7–6.7] years), A-ECG Heart Age Gap was associated with risk factors, and outcomes (unadjusted hazard ratio (HR) [95% confidence interval] (5-year increments): 1.23 [1.13–1.34] and adjusted HR 1.11 [1.01–1.22]). DNN-AI Heart Age Gap was associated with risk factors and outcomes after adjustments (HR (5-year increments): 1.11 [1.01–1.21]), but not in unadjusted analyses (HR 1.00 [0.93–1.08]), making it less easily applicable in clinical practice. Conclusion A-ECG Heart Age Gap associated with cardiovascular risk factors and HF hospitalization or death. Explainable A-ECG Heart Age Gap has the potential for improving clinical adoption and prognostic performance compared to existing DNN-AI-type methods.
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