Spectral analysis of cardiovascular oscillations in the 7-day regimen of losartan administration with and without cold stress.

Spectral analysis of heart rate (HR) and blood pressure (BP) variabilities (BPV and HRV) is widely available and utilized in understanding the dynamic cardiovascular autonomic regulation in a variety of pathophysiological conditions. In conscious cold-stressed (CS) rats, we examined the effect of a 7-day regimen administration of losartan, a selective nonpeptide angiotensin AT1 receptor blockade, on BPV and HRV at three frequency components: very-low frequency (VLF), low frequency (LF), and high frequency (HF). Key findings in changes of systolic BP (SBP), HR, and spectral power densities for cardiopulmonary oscillations (HF), sympathetic oscillations (LF), cardiovascular myogenic oscillations (VLF), and overall autonomic activity total power (TP) showed: (I) In the resting PreCS trial, compared with the saline, losartan increased HFBPV, TPHRV, all three HRV frequency powers, and the occurrence of the dicrotic notch (DN). However, it decreased SBP, HR, and the LFBPV frequency power. (II) In the CS trial, losartan significantly decreased SBP and DN occurrence and HR and LF/HFHRV but significantly increased HFHRV, TPBPV, and all three BPV frequency powers. In addition, similar to the saline, losartan showed positively correlated LFBPV and VLFBPV. Conversely, losartan converted the original inverse correlations between LFHRV and LFBPV of CS to a positive correlation. (III) Compared with saline in PreCS and CS trials, losartan detached the corresponding sympathetic oscillations between LFBPV and LFHRV. The overall result indicates that endogenous angiotensin II, through stimulation of the AT1 receptor, augments sympathetic tone but attenuates sympathetic oscillations in rats, particularly under the stressful cooling impacts.

Artificial intelligence-enabled electrocardiography identifies severe dyscalcemias and has prognostic value.

CONTEXT: Abnormal serum calcium concentrations affect the heart and may alter the electrocardiogram (ECG), but the detection of hypocalcemia and hypercalcemia (collectively dyscalcemia) relies on blood laboratory tests requiring turnaround time. OBJECTIVE: The study aimed to develop a bloodless artificial intelligence (AI)-enabled (ECG) method to rapidly detect dyscalcemia and analyze its possible utility for outcome prediction. METHODS: This study collected 86,731 development, 15,611 tuning, 11,105 internal validation, and 8401 external validation ECGs from electronic medical records with at least 1 ECG associated with an albumin-adjusted calcium (aCa) value within 4 h. The main outcomes were to assess the accuracy of AI-ECG to predict aCa and follow up these patients for all-cause mortality, new-onset acute myocardial infraction (AMI), and new-onset heart failure (HF) to validate the ability of AI-ECG-aCa for previvor identification. RESULTS: ECG-aCa had mean absolute errors (MAE) of 0.78/0.98 mg/dL and achieved an area under receiver operating characteristic curves (AUCs) 0.9219/0.8447 and 0.8948/0.7723 to detect severe hypercalcemia and hypocalcemia in the internal/external validation sets, respectively. Although < 20 % variance of ECG-aCa could be explained by traditional ECG features, the ECG-aCa was found to be associated with more complications. Patients with ECG-hypercalcemia but initially normal aCa were found to have a higher risk of subsequent all-cause mortality [hazard ratio (HR): 2.05, 95 % conference interval (CI): 1.55-2.70], new-onset AMI (HR: 2.88, 95 % CI: 1.72-4.83), and new-onset HF (HR: 2.02, 95 % CI: 1.38-2.97) in the internal validation set, which were also seen in external validation. CONCLUSION: The AI-ECG-aCa may help detecting severe dyscalcemia for early diagnosis and ECG-hypercalcemia also has prognostic value for clinical outcomes (all-cause mortality and new-onset AMI and HF).

Point-of-care artificial intelligence-enabled ECG for dyskalemia: a retrospective cohort analysis for accuracy and outcome prediction.

Dyskalemias are common electrolyte disorders associated with high cardiovascular risk. Artificial intelligence (AI)-assisted electrocardiography (ECG) has been evaluated as an early-detection approach for dyskalemia. The aims of this study were to determine the clinical accuracy of AI-assisted ECG for dyskalemia and prognostic ability on clinical outcomes such as all-cause mortality, hospitalizations, and ED revisits. This retrospective cohort study was done at two hospitals within a health system from May 2019 to December 2020. In total, 26,499 patients with 34,803 emergency department (ED) visits to an academic medical center and 6492 ED visits from 4747 patients to a community hospital who had a 12-lead ECG to estimate ECG-K+ and serum laboratory potassium measurement (Lab-K+) within 1 h were included. ECG-K+ had mean absolute errors (MAEs) of ≤0.365 mmol/L. Area under receiver operating characteristic curves for ECG-K+ to predict moderate-to-severe hypokalemia (Lab-K+ ≤3 mmol/L) and moderate-to-severe hyperkalemia (Lab-K+ ≥ 6 mmol/L) were >0.85 and >0.95, respectively. The U-shaped relationships between K+ concentration and adverse outcomes were more prominent for ECG-K+ than for Lab-K+. ECG-K+ and Lab-K+ hyperkalemia were associated with high HRs for 30-day all-cause mortality. Compared to hypokalemic Lab-K+, patients with hypokalemic ECG-K+ had significantly higher risk for adverse outcomes after full confounder adjustment. In addition, patients with normal Lab-K+ but dyskalemic ECG-K+ (pseudo-positive) also exhibited more co-morbidities and had worse outcomes. Point-of-care bloodless AI ECG-K+ not only rapidly identified potentially severe hypo- and hyperkalemia, but also may serve as a biomarker for medical complexity and an independent predictor for adverse outcomes.

Middle-age Asian male with cerebral venous thrombosis after COVID-19 AstraZeneca vaccination.

Vaccine-associated cerebral venous thrombosis has become an issue following the extensive vaccination program of the Coronavirus Disease of 2019 (COVID-19) Vaccine AstraZeneca (ChAdOx1 vaccine). The importance of early diagnosis should be emphasized due to the high mortality rate without appropriate treatment. Young female populations in western countries have been reported to be at a greater risk of this vaccine related thrombotic event, but cases in East Asia are lacking. Herein, we present the first case of cerebral venous sinus thrombosis 10 days after ChAdOx1 vaccination in a middle-age Asian male in Taiwan.

Artificial Intelligence-Assisted Electrocardiography for Early Diagnosis of Thyrotoxic Periodic Paralysis.

CONTEXT: Thyrotoxic periodic paralysis (TPP) characterized by acute weakness, hypokalemia, and hyperthyroidism is a medical emergency with a great challenge in early diagnosis since most TPP patients do not have overt symptoms. OBJECTIVE: This work aims to assess artificial intelligence (AI)-assisted electrocardiography (ECG) combined with routine laboratory data in the early diagnosis of TPP. METHODS: A deep learning model (DLM) based on ECG12Net, an 82-layer convolutional neural network, was constructed to detect hypokalemia and hyperthyroidism. The development cohort consisted of 39 ECGs from patients with TPP and 502 ECGs of hypokalemic controls; the validation cohort consisted of 11 ECGs of TPP patients and 36 ECGs of non-TPP individuals with weakness. The AI-ECG-based TPP diagnostic process was then consecutively evaluated in 22 male patients with TTP-like features. RESULTS: In the validation cohort, the DLM-based ECG system detected all cases of hypokalemia in TPP patients with a mean absolute error of 0.26 mEq/L and diagnosed TPP with an area under curve (AUC) of approximately 80%, surpassing the best standard ECG parameter (AUC = 0.7285 for the QR interval). Combining the AI predictions with the estimated glomerular filtration rate and serum chloride boosted the diagnostic accuracy of the algorithm to AUC 0.986. In the prospective study, the integrated AI and routine laboratory diagnostic system had a PPV of 100% and F-measure of 87.5%. CONCLUSION: An AI-ECG system reliably identifies hypokalemia in patients with paralysis, and integration with routine blood chemistries provides valuable decision support for the early diagnosis of TPP.

Portal vein innervation underlying the pressor effect of water ingestion with and without cold stress.

Water-induced pressor response appears mediated through the activation of transient receptor potential channel TRPV4 on hepatic portal circulation in animals. We sought to elucidate the mechanism of portal vein signaling in this response. Forty-five rats were divided into four groups: control rats without water ingestion (WI), control rats with WI, portal vein denervation rats with WI (PVDWI), and TRPV4 antagonist-treated rats with WI (anti-TRPV4WI). Cardiovascular responses were monitored throughout the experiments. Data analysis was performed using descriptive methods and spectral and cross-spectral analysis of blood pressure variability (BPV) and heart rate variability (HRV). Key results showed that at baseline (PreCS) before cold stress trial (CS), WI elicited robust pressor and tachycardia responses accompanied by spectral power changes, in particular, increases of low-frequency BPV (LFBPV) and very-LFBPV (VLFBPV), but decrease of very-low-frequency HRV. PVDWI, likewise, elicited pressor and tachycardia responses accompanied by increases of high-frequency BPV, high-frequency HRV, LFBPV, low-frequency HRV, and VLFBPV. When compared with WI at PreCS, WI at CS elicited pressor and tachycardia responses accompanied by increases of high-frequency BPV, LFBPV, and VLFBPV, whereas in WI, the CS-evoked pressor response and the accompanied LFBPV and VLFBPV increases were all tended augmented by PVDWI. When compared with WI and PVDWI at both PreCS and CS, however, anti-TRPV4WI attenuated their pressor responses and attenuated their increased LFBPV, VLFBPV, and very-low-frequency HRV. The results indicate that the portal vein innervation is critical for a buffering mechanism in splanchnic sympathetic activation and water-induced pressor response.