ECG-surv: A deep learning-based model to predict time to 1-year mortality from 12-lead electrocardiogram.

BACKGROUND: Electrocardiogram (ECG) abnormalities have demonstrated potential as prognostic indicators of patient survival. However, the traditional statistical approach is constrained by structured data input, limiting its ability to fully leverage the predictive value of ECG data in prognostic modeling. METHODS: This study aims to introduce and evaluate a deep-learning model to simultaneously handle censored data and unstructured ECG data for survival analysis. We herein introduce a novel deep neural network called ECG-surv, which includes a feature extraction neural network and a time-to-event analysis neural network. The proposed model is specifically designed to predict the time to 1-year mortality by extracting and analyzing unique features from 12-lead ECG data. ECG-surv was evaluated using both an independent test set and an external set, which were collected using different ECG devices. RESULTS: The performance of ECG-surv surpassed that of the Cox proportional model, which included demographics and ECG waveform parameters, in predicting 1-year all-cause mortality, with a significantly higher concordance index (C-index) in ECG-surv than in the Cox model using both the independent test set (0.860 [95% CI: 0.859- 0.861] vs. 0.796 [95% CI: 0.791- 0.800]) and the external test set (0.813 [95% CI: 0.807- 0.814] vs. 0.764 [95% CI: 0.755- 0.770]). ECG-surv also demonstrated exceptional predictive ability for cardiovascular death (C-index of 0.891 [95% CI: 0.890- 0.893]), outperforming the Framingham risk Cox model (C-index of 0.734 [95% CI: 0.715-0.752]). CONCLUSION: ECG-surv effectively utilized unstructured ECG data in a survival analysis. It outperformed traditional statistical approaches in predicting 1-year all-cause mortality and cardiovascular death, which makes it a valuable tool for predicting patient survival.

Representativeness of the PIONEER-HF and PARAGLIDE-HF in patients hospitalized with acute heart failure.

AIMS: The PIONEER-HF and PARAGLIDE-HF trials aimed to determine the efficacy and safety of the in-hospital initiation of sacubitril/valsartan in patients hospitalized for AHF. However, whether the inclusion and exclusion criteria of the trials apply to patients encountered in real-world routine care is unclear. This study aimed to investigate the applicability of the PIONEER-HF and PARAGLIDE-HF trials to real-world AHF patients. METHODS AND RESULTS: We identified 28 293 AHF hospitalized patients between August 2008 to August 2017 from the Chang Gung Research Database and classified them into four groups based on left ventricular ejection fraction (LVEF) and trial criteria. Cox proportional hazards models were used to compare the risk of HF hospitalization and cardiovascular (CV) death. We defined PIONEER-HF eligible (n = 3683) and non-eligible (n = 3502) patients with an LVEF ≤40%, and PARAGLIDE-HF eligible (n = 5191) and non-eligible (n = 5832) patients with an LVEF >40%. Over a mean follow-up of 3.5 years, the PIONEER-HF non-eligible and eligible groups exhibited similar rates of HF hospitalization and CV death (41.1% vs. 41.8%, adjusted hazard ratio [aHR]: 0.95; 95% CI: 0.88-1.04). No significant difference was found in the composite outcome between PARAGLIDE-HF non-eligible and eligible groups (36.7% vs. 38.6%; aHR: 0.97; 95% CI: 0.90-1.04). CONCLUSIONS: Using trial criteria, only 31.3% of AHF patients were eligible for sacubitril-valsartan. Yet, non-eligible patients demonstrated similar outcomes to eligible patients, indicating a need for further evaluation of sacubitril-valsartan benefits in non-eligible AHF patients.

Vericiguat suppresses ventricular tachyarrhythmias inducibility in a rabbit myocardial infarction model.

BACKGROUND: The VICTORIA trial demonstrated a significant decrease in cardiovascular events through vericiguat therapy. This study aimed to assess the potential mechanisms responsible for the reduction of cardiovascular events with vericiguat therapy in a rabbit model of myocardial infarction (MI). METHODS: A chronic MI rabbit model was created through coronary artery ligation. Following 4 weeks, the hearts were harvested and Langendorff perfused. Subsequently, electrophysiological examinations and dual voltage-calcium optical mapping studies were conducted at baseline and after administration of vericiguat at a dose of 5 µmol/L. RESULTS: Acute vericiguat therapy demonstrated a significant reduction in premature ventricular beat burden and effectively suppressed ventricular arrhythmic inducibility. The electrophysiological influences of vericiguat therapy included an increased ventricular effective refractory period, prolonged action potential duration, and accelerated intracellular calcium (Cai) homeostasis, leading to the suppression of action potential and Cai alternans. The pacing-induced ventricular arrhythmias exhibited a reentrant pattern, attributed to fixed or functional conduction block in the peri-infarct zone. Vericiguat therapy effectively mitigated the formation of cardiac alternans as well as the development of reentrant impulses, providing additional anti-arrhythmic benefits. CONCLUSIONS: In the MI rabbit model, vericiguat therapy demonstrates anti-ventricular arrhythmia effects. The vericiguat therapy reduces ventricular ectopic beats, inhibiting the initiation of ventricular arrhythmias. Furthermore, the therapy successfully suppresses cardiac alternans, preventing conduction block and, consequently, the formation of reentry circuits.

Quasi-Seniors' Perception, Response, and Planning from the Perspective of Successful Aging.

With the coming of a rapidly aging society, individuals born in the baby boom era after World War II are now facing the challenges of aging. From late middle age to successful aging, what are the perceptions and responses of these quasi-seniors? With this in mind, referring to Phelan's successful aging scale, the researchers developed the 4P Strategies (Physical, Psychological, Prospect, and Place and Relationships) tailored for quasi-seniors. Based on grounded theory, the results of 12 sessions of focused interviews (involving a total of 93 interviewees between the ages of 55 and 75; 41 males and 52 females; 48 not retired and 45 retired) were matched with the 4P Strategies. The results were the following: (1) regarding the Physical factor, the interviewees were shocked by their physical decline, and they had begun to devise strategies for health preservation and exercise; (2) regarding the Psychological factor, in order to mentally adapt, the interviewees agreed that moderate stress relief was absolutely necessary; (3) regarding the Prospect factor: the interviewees felt that one should make financial plans early, contemplate the value of life, and more actively learn and realize one's dreams; and (4) regarding the Place and Relationships factor, the interviewees aimed to rebuild their close relationships with their spouses, family members, and old friends and had polarized views regarding where to live in their old age. On the whole, the most discussed issue among the interviewees was where to live in their old age. Many had their own views and plans and did not stick to traditional views; however, they took the opinions of their significant others into account. During the interviews, interviewees wished to understand the responses of their peers to serve as a reference for their own decisions, and they realized that successful aging also required learning. This study aimed to encourage quasi-seniors about to enter their old age and help them to learn how to positively respond to aging as well as work towards a happy life with successful aging. This study could fill in gaps in research involving individuals in this age group and provide a reference for relevant policies.

Comparing Artificial Intelligence-Enabled Electrocardiogram Models in Identifying Left Atrium Enlargement and Long-term Cardiovascular Risk.

BACKGROUND: The role of P-wave in identifying left atrial enlargement (LAE) with the use of artificial intelligence (AI)-enabled electrocardiography (ECG) models is unclear. It is also unknown if AI-enabled single-lead ECG could be used as a diagnostic tool for LAE surveillance. We aimed to build AI-enabled P-wave and single-lead ECG models to identify LAE using sinus rhythm (SR) and non-SR ECGs, and compare the prognostic ability of severe LAE, defined as left atrial diameter ≥ 50 mm, assessed by AI-enabled ECG models vs echocardiography. METHODS: This retrospective study used data from 382,594 consecutive adults with paired 12-lead ECG and echocardiography performed within 2 weeks of each other at Chang Gung Memorial Hospital. UNet++ was used for P-wave segmentation. ResNet-18 was used to develop deep convolutional neural network-enabled ECG models for discriminating LAE. External validation was performed with the use of data from 11,753 patients from another hospital. RESULTS: The AI-enabled 12-lead ECG model outperformed other ECG models for classifying LAE, but the single-lead ECG models also showed excellent performance at a left atrial diameter cutoff of 50 mm. AI-enabled ECG models had excellent and fair discrimination on LAE using the SR and the non-SR data set, respectively. Severe LAE identified by AI-enabled ECG models was more predictive of future cardiovascular disease than echocardiography; however, the cumulative incidence of new-onset atrial fibrillation and heart failure was higher in patients with echocardiography-severe LAE than with AI-enabled ECG-severe LAE. CONCLUSIONS: P-Wave plays a crucial role in discriminating LAE in AI-enabled ECG models. AI-enabled ECG models outperform echocardiography in predicting new-onset cardiovascular diseases associated with severe LAE.

Machine learning-based prediction of acute mortality in emergency department patients using twelve-lead electrocardiogram.

Background: The risk of mortality is relatively high among patients who visit the emergency department (ED), and stratifying patients at high risk can help improve medical care. This study aimed to create a machine-learning model that utilizes the standard 12-lead ECG to forecast acute mortality risk in ED patients. Methods: The database included patients who visited the EDs and underwent standard 12-lead ECG between October 2007 and December 2017. A convolutional neural network (CNN) ECG model was developed to classify survival and mortality using 12-lead ECG tracings acquired from 345,593 ED patients. For machine learning model development, the patients were randomly divided into training, validation and testing datasets. The performance of the mortality risk prediction in this model was evaluated for various causes of death. Results: Patients who visited the ED and underwent one or more ECG examinations experienced a high incidence of 30-day mortality [18,734 (5.42%)]. The developed CNN model demonstrated high accuracy in predicting acute mortality (hazard ratio 8.50, 95% confidence interval 8.20-8.80) with areas under the receiver operating characteristic (ROC) curve of 0.84 for the 30-day mortality risk prediction models. This CNN model also demonstrated good performance in predicting one-year mortality (hazard ratio 3.34, 95% confidence interval 3.30-3.39). This model exhibited good predictive performance for 30-day mortality not only for cardiovascular diseases but also across various diseases. Conclusions: The machine learning-based ECG model utilizing CNN screens the risks for 30-day mortality. This model can complement traditional early warning scoring indexes as a useful screening tool for mortality prediction.

Long-term efficacy of sodium-glucose cotransporter 2 inhibitor therapy in preventing atrial fibrillation recurrence after catheter ablation in type 2 diabetes mellitus patients.

Background: Sodium-glucose cotransporter 2 inhibitors (SGLT2i) reduce new-onset atrial fibrillation (AF) in patients with type 2 diabetes mellitus (T2DM). We aimed to determine the long-term effects of SGLT2i on atrial tachyarrhythmia recurrence after catheter ablation (CA) in T2DM patients. Methods: This retrospective study enrolled consecutive patients with T2DM undergoing CA for AF between January 2016 and December 2021. Patient baseline demographic characteristics and use of anti-diabetic and anti-arrhythmic medications were analyzed. Echocardiographic parameters were obtained one day and 6 months after CA. Results: Our study population comprised 122 patients (70% paroxysmal AF). The baseline patient characteristics were similar between the SGLT2i-treated group (n = 45) and the non-SGLT2i-treated group (n = 77) except for stroke. At 6-month follow-up, body-mass index (BMI) was significantly decreased and left ventricular ejection fraction (LVEF) was significantly increased only in the SGLT2i group. E/e' was decreased 6 months after CA in both groups. During a mean follow-up of 33.7 ± 21.6 months, 22 of 122 patients had atrial tachyarrhythmia recurrence. The long-term atrial tachyarrhythmia-free survival rate was significantly higher in the SGLT2i-treated patients, and multivariate analysis revealed that AF type and SGLT2i use were independently associated with atrial tachyarrhythmia recurrence after CA. Conclusion: The use of SGLT2i and AF type were independent risk factors associated with atrial tachyarrhythmia recurrence after CA in T2DM patients with AF. This result was at least partly due to the pleiotropic effects of SGLT2i on BMI reduction and left ventricular function improvement.

Artificial intelligence-enabled electrocardiographic screening for left ventricular systolic dysfunction and mortality risk prediction.

Background: Left ventricular systolic dysfunction (LVSD) characterized by a reduced left ventricular ejection fraction (LVEF) is associated with adverse patient outcomes. We aimed to build a deep neural network (DNN)-based model using standard 12-lead electrocardiogram (ECG) to screen for LVSD and stratify patient prognosis. Methods: This retrospective chart review study was conducted using data from consecutive adults who underwent ECG examinations at Chang Gung Memorial Hospital in Taiwan between October 2007 and December 2019. DNN models were developed to recognize LVSD, defined as LVEF <40%, using original ECG signals or transformed images from 190,359 patients with paired ECG and echocardiogram within 14 days. The 190,359 patients were divided into a training set of 133,225 and a validation set of 57,134. The accuracy of recognizing LVSD and subsequent mortality predictions were tested using ECGs from 190,316 patients with paired data. Of these 190,316 patients, we further selected 49,564 patients with multiple echocardiographic data to predict LVSD incidence. We additionally used data from 1,194,982 patients who underwent ECG only to assess mortality prognostication. External validation was performed using data of 91,425 patients from Tri-Service General Hospital, Taiwan. Results: The mean age of patients in the testing dataset was 63.7 ± 16.3 years (46.3% women), and 8,216 patients (4.3%) had LVSD. The median follow-up period was 3.9 years (interquartile range 1.5-7.9 years). The area under the receiver-operating characteristic curve (AUROC), sensitivity, and specificity of the signal-based DNN (DNN-signal) to identify LVSD were 0.95, 0.91, and 0.86, respectively. DNN signal-predicted LVSD was associated with age- and sex-adjusted hazard ratios (HRs) of 2.57 (95% confidence interval [CI], 2.53-2.62) for all-cause mortality and 6.09 (5.83-6.37) for cardiovascular mortality. In patients with multiple echocardiograms, a positive DNN prediction in patients with preserved LVEF was associated with an adjusted HR (95% CI) of 8.33 (7.71 to 9.00) for incident LVSD. Signal- and image-based DNNs performed equally well in the primary and additional datasets. Conclusion: Using DNNs, ECG becomes a low-cost, clinically feasible tool to screen LVSD and facilitate accurate prognostication.

Association of Echocardiographic Parameter E/e' With Cardiovascular Events in a Diverse Population of Inpatients and Outpatients With and Without Cardiac Diseases and Risk Factors.

BACKGROUND: The echocardiographic parameter E/e' has been associated with cardiovascular (CV) events. However, few studies have analyzed multiple associated CV outcomes using E/e' in a diverse population of both inpatients and outpatients with and without cardiac diseases and risk factors. METHODS: Medical records of 75,393 patients without atrial fibrillation (AF) with first available E/e' were retrieved from our hospital database. Patients with mitral valve disease were excluded, and the remainder were studied in protocol 1 (70,819 patients). Patients with hypertension, diabetes mellitus, hyperlipidemia, CV diseases, prior CV events, CV surgeries, and left ventricular ejection fraction <50% or missing left ventricular ejection fraction were further excluded, and the remaining patients were studied in protocol 2 (14,665 patients). The study outcomes are major adverse CV events (MACE), which included myocardial infarction (MI), AF, ischemic and hemorrhagic stroke (IHS), hospitalization for heart failure (HHF), and cardiac death. The primary outcomes were MACE and each of the MACE components. RESULTS: At the end of maximal 5-year follow-up (median 22.18 months with interquartile range 7.20-49.08 months for MACE in protocol 1 and 23.46 months with interquartile range 8.15-49.02 months for MACE in protocol 2), compared with an E/e' value of <8, an intermediate value of E/e' 8 to 15 and a high value of E/e' >15 were significantly associated with MACE, MI, AF, IHS, HHF, and cardiac death in protocol 1 (all P < .0001). In protocol 2, an intermediate E/e' value of 8 to 15 and a high value of E/e' >15 were significantly associated with MACE, MI, AF, IHS, HHF, and CV death (all P < .05), except an intermediate value E/e' 8 to 15 was not associated with AF. CONCLUSIONS: In a diverse population of inpatients and outpatients with and without cardiac diseases and risk factors, the echocardiographic parameter E/e' was associated with CV events and is a useful marker of risk.

The Efficacy and Safety of Short-Term Tolvaptan Usage in Patients with Acute Decompensated Heart Failure.

Background: Patients admitted with acute decompensated heart failure (ADHF) have a poor prognosis and poor quality of life due to dyspnea and edema. Tolvaptan, a vasopressin V2 receptor antagonist, is an effective water diuretic. This study aimed to evaluate the efficacy and safety of a short course of tolvaptan to treat volume overload in patients with ADHF. Methods: We conducted a phase III, multicenter, randomized, double-blind, placebo-controlled study to evaluate the efficacy and safety of a short course of tolvaptan (15 mg/day for 4 days) in hospitalized ADHF patients with volume overload despite the use of conventional diuretics. The primary end-point was the change in body weight after 4 days of treatment. The secondary end-points were the change in intake/output balance, change in serum sodium/potassium concentrations, physician/patient assessed signs and symptoms of heart failure after 4 days of treatment, and all-cause mortality in 1 month. Results: A total of 110 patients were screened, and 91 were randomized to receive 15 mg/day of tolvaptan for 4 days (n = 46) or matching placebo (n = 45). Compared to the placebo-treated patients, tolvaptan significantly reduced body weight (-1.36 ± 2.13 kg in the tolvaptan group vs. -0.59 ± 1.27 kg in the placebo group, p = 0.0394). The tolvaptan group also had a negative intake/urine volume balance compared to the placebo group (-509.3 ± 2788.2 ml vs. 975.5 ± 1903.1 ml, p = 0.0059). The safety profile of tolvaptan was acceptable. Conclusions: Tolvaptan significantly reduced volume overload in hospitalized ADHF patients with volume overload despite the use of conventional diuretics.

The Clinical Application of Machine Learning-Based Models for Early Prediction of Hemorrhage in Trauma Intensive Care Units.

Uncontrolled post-traumatic hemorrhage is an important cause of traumatic mortality that can be avoided. This study intends to use machine learning (ML) to build an algorithm based on data collected from an electronic health record (EHR) system to predict the risk of delayed bleeding in trauma patients in the ICU. We enrolled patients with torso trauma in the surgical ICU. Demographic features, clinical presentations, and laboratory data were collected from EHR. The algorithm was designed to predict hemoglobin dropping 6 h before it happened and evaluated the performance with 10-fold cross-validation. We collected 2218 cases from 2008 to 2018 in a trauma center. There were 1036 (46.7%) patients with positive hemorrhage events during their ICU stay. Two machine learning algorithms were used to predict ongoing hemorrhage events. The logistic model tree (LMT) and the random forest algorithm achieved an area under the curve (AUC) of 0.816 and 0.809, respectively. In this study, we presented the ML model using demographics, vital signs, and lab data, promising results in predicting delayed bleeding risk in torso trauma patients. Our study also showed the possibility of an early warning system alerting ICU staff that trauma patients need re-evaluation or further survey.

Tuning pro-survival effects of human induced pluripotent stem cell-derived exosomes using elastin-like polypeptides.

Exosome-based regenerative therapies are potentially easier to manufacture and safer to apply compared to cell-based therapies. However, many questions remain about how to bio-manufacture reproducible and potent exosomes using animal-free reagents. Here we evaluate the hypothesis that designer biomaterial substrates can be used to alter the potency of exosomes secreted by human induced pluripotent stem cells (iPSCs). Two animal-free designer matrices were fabricated based on recombinant elastin-like polypeptides (ELPs): one including a cell-adhesive RGD ligand and a second with a non-adhesive RDG peptide. While iPSCs cultured on these two substrates and Matrigel-coated controls had similar levels of proliferation, the RDG-ELP substrate significantly increased protein expression of stemness markers OCT4 and SOX2 and suppressed spontaneous differentiation compared to those on RGD-ELP. The pro-survival potency of iPSC-derived exosomes was evaluated using three distinct stress tests: serum starvation in murine fibroblasts, hypoxia in human endothelial cells, and hyperosmolarity in canine kidney cells. In all three cases, exosomes produced by iPSCs grown on RDG-ELP substrates had similar pro-survival effects to those produced using iPSCs grown on Matrigel, while use of RGD-ELP substrates led to significantly reduced exosome potency. These data demonstrate that recombinant substrates can be designed for the robust bio-manufacturing of iPSC-derived, pro-survival exosomes.

Atrial fibrillation detection using ambulatory smartwatch photoplethysmography and validation with simultaneous holter recording.

BACKGROUND: Atrial fibrillation (AF)-associated embolic stroke is preventable, and AF detection may help to prevent stroke in subjects with paroxysmal AF. We aimed to evaluate the AF detection performance of smartwatch photoplethysmography (PPG) and the feasibility of ambulatory monitoring for AF detection in the daily life. DESIGN AND METHODS: Consecutive subjects who underwent ambulatory Holter electrocardiogram (ECG) monitoring for AF detection or AF burden evaluation were enrolled. The participants underwent 24 hours of simultaneous Holter ECG monitoring and continuous PPG recording using a Garmin smartwatch. The PPG signals were processed for noise rejection, beat detection, beat labeling, and rhythm labeling for each 5-minute segment. The accuracy of the PPG AF detection was calculated using the corresponding simultaneous Holter ECG as the AF diagnostic standard. RESULTS: Among the 200 available participants, 112 participants (56%) developed AF (the AF group). The sensitivity, specificity, and positive predicted value of AF detection in participants were 97.3%, 88.6%, and 91.6%, respectively. The area under the receiver operating characteristic curve was 0.90. When the performance was analyzed in these 5-minute segments, the sensitivity, specificity, and positive predicted values of AF detection were 97.1%, 86.8%, and 89.7%, respectively. CONCLUSIONS: This study demonstrated the feasibility of ambulatory monitoring for AF detection using a commercial smartwatch in daily life. A smartwatch may be an alternative screening tool to standard ambulatory Holter monitoring.

Added Value of Computed Tomography Virtual Intravascular Endoscopy in the Evaluation of Coronary Arteries with Stents or Plaques.

Coronary computed tomography angiography (CCTA) is a widely used imaging modality for diagnosing coronary artery disease (CAD) but is limited by a high false positive rate when evaluating coronary arteries with stents and heavy calcifications. Virtual intravascular endoscopy (VIE) images generated from CCTA can be used to qualitatively assess the vascular lumen and might be helpful for overcoming this challenge. In this study, one hundred subjects with coronary stents underwent both CCTA and invasive coronary angiography (ICA). A total of 902 vessel segments were analyzed using CCTA and VIE. The vessel segments were first analyzed on CCTA alone. Then, using VIE, the segments were classified qualitatively as either negative or positive for in-stent restenosis (ISR) or CAD. These results were compared, using ICA as the reference, to determine the added diagnostic value of VIE. Of the 902 analyzed vessel segments, CCTA/VIE had sensitivity, specificity, accuracy, positive predictive value, and negative predictive value (shown in %) of 93.9/90.2, 96.2/98.2, 96.0/97.7, 70.0/83.1, and 99.4/99.0, respectively, in diagnosing ISR or CAD, with significantly improved specificity (p = 0.025), accuracy (p = 0.046), and positive predictive value (p = 0.047). VIE can be a helpful addition to CCTA when evaluating coronary arteries.

Serum microRNA panels predict bariatric surgery outcomes.

OBJECTIVE: The weight losses after bariatric surgery are modulated by multiple factors in people with obesity. MicroRNAs (miRNAs) have been reported to show significant regulatory roles in adipose tissue. However, a serum miRNA signature to serve as a biomarker of sustained weight losses following bariatric surgery has not yet been established. METHODS: MiRNA microarray was used to identify differentially expressed miRNAs in the serum of patients with an effective response after bariatric surgery compared with those without. Excess weight loss > 55% at 6 months after surgery was defined as an effective response. RESULTS: Three miRNAs were shown to have a significantly differential expression between patients with or without an effective response following bariatric surgery. The miR-31-5p was downregulated, whereas miR-328-3p and miR-181a-5p were upregulated in the patients with effective responses compared with those without effective responses. Panels of the serum ratios of miR-328-3p/miR-31-5p or miR-181a-5p/miR-31-5p and individual BMI value exhibited good performance in preoperative prediction of treatment effectiveness. Bioinformatic analysis depicted that predicted targets of these miRNAs were involved in the regulation of the AMP-activated protein kinase signaling pathway. CONCLUSIONS: A circulating miRNA signature with clinical variables (BMI) can be a clinical biomarker to predict effectiveness following bariatric surgery.

Elevated serum matrix metalloproteinase-2 levels in heart failure patients with reduced ejection fraction and Cheyne-Stokes respiration.

STUDY OBJECTIVES: Cheyne-Stokes respiration (CSR), a kind of central sleep apnea, is referred to as a poor prognostic factor in heart failure patients with reduced ejection fraction (HFrEF). Matrix metalloproteinase (MMP) and B-type natriuretic peptide (BNP) play important roles in HFrEF patients and are markers of poor prognosis. However, there is no literature mentioning the changes in MMP and BNP in HFrEF patients with CSR. METHODS: From June 2018 to June 2019, 41 adult patients with stable heart failure and left ventricular ejection fraction < 50% were enrolled from the cardiology clinic. After history-taking and medication review to exclude possible central nervous system- or medication-related central sleep apnea, an overnight polysomnography study was performed, and CSR was identified. The morning serum MMP-2, MMP-9, and BNP levels were determined using enzyme-linked immunosorbent assay and fluorescence immunoassay techniques. A positive airway pressure device was applied to 7 patients for 3 months. RESULTS: The serum MMP-2 and BNP levels were significantly higher in HFrEF patients with CSR than in patients without CSR. In addition, elevated serum MMP-2 levels correlated well with the severity of sleep apnea and intermittent hypoxia, which were represented as the apnea-hypopnea index and the oxygen desaturation index. No positive correlation was found between those markers and left ventricular ejection fraction. Finally, the treatment of sleep apnea with continuous positive airway pressure for 3 months tended to reduce the elevated serum MMP-2 levels. CONCLUSIONS: Higher serum MMP-2 and BNP levels were found in HFrEF patients with CSR. Elevated MMP-2 levels were correlated with the severity of sleep apnea and intermittent hypoxia. CITATION: Chuang L-P, Pang J-HS, Lin S-W, et al. Elevated serum matrix metalloproteinase-2 levels in heart failure patients with reduced ejection fraction and Cheyne-Stokes respiration. J Clin Sleep Med. 2022;18(5):1365-1373.

Rapid and label-free detection of the troponin in human serum by a TiN-based extended-gate field-effect transistor biosensor.

In this article, the TiN sensitive film as a sensing membrane was deposited onto n+-type Si substrate by a DC sputtering technique for extended-gate field-effect transistor (EGFET) pH sensors and detection of cardiac troponin-I (cTn-I) in the patient sera for the first time. The crystal structure, Raman spectrum, element profile, surface roughness, and surface morphology of the TiN sensitive film were characterized by X-ray diffraction, Raman spectroscopy, secondary ion mass spectroscopy, atomic force microscopy, and scanning electron microscopy, respectively. The sensing performance of the TiN sensitive film is correlated with its relative structural feature. A high sensitivity of 57.49 mV/pH, a small hysteresis voltage of ∼1 mV, and a low drift rate of 0.31 mV/h were obtained in the TiN sensitive film. In addition, the pH sensitivity of this TiN EGFET sensor was preserved approximately 57 mV/pH after operation time of 180 days. Subsequently, the cTn-I antibodies with carboxyl groups activated by 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide (EDC) along with N-hydroxysuccinimide (NHS) were immobilized on the TiN sensitive film functionalizing with 3-aminopropyl triethoxysilane (APTES). After obtaining the successful immobilization of cTn-I antibodies on the TiN EGFET biosensor, the cTn-I antigen specifically binds with its relative antibody. The cTn-I EGFET biosensor showed a high sensitivity of 21.88 mV/pCcTn-I in a wide dynamic range of 0.01-100 ng/mL. Furthermore, the concentrations of cTn-I in patient sera measured by our TiN EGFET biosensors are comparable to those determined by commercial enzyme-linked immuno-sorbent assay kits.

Risk of major bleeding associated with concomitant use of anticancer drugs and direct oral anticoagulant in patients with cancer and atrial fibrillation.

This study evaluated the risk of major bleeding associated with concomitant use of direct oral anticoagulant (DOAC) and anticancer drugs (ACDs), which share metabolic pathways, in patients with atrial fibrillation (AF) and cancer. We performed a retrospective cohort study using Taiwan's National Health Insurance database and included patients with AF and cancer who received DOAC prescriptions from 1 to 2012 to 31 December 2017. The incidence of major bleeding in person-quarters with concomitant use of DOAC and any of 15 ACDs with inhibitory or competitive effects of CYP3A4 or P-gp activity (docetaxel, vinorelbine, methotrexate, irinotecan, etoposide, doxorubicin, cyclophosphamide, imatinib, nilotinib, abiraterone, bicalutamide, tamoxifen, anastrozole, cyclosporine, tacrolimus) was compared with that in person-quarters with DOAC alone. Adjusted incidence-rate differences between DOAC use with and without concurrent ACDs were estimated using Poisson regression models weighted by the inverse probability of treatment. In 13,158 patients with AF and cancer (76.9 ± 8.9 years; male 60%), 1545 major bleeding events occurred during 90,540 DOAC-exposed person-quarters. Concurrent use of DOAC and any of 15 ACDs occurred in only 18% of patients. Compared with use of DOAC alone, concomitant use of DOAC and these ACDs was not associated with an increased risk of major bleeding. Co-medication with DOAC and ACDs with inhibitory or competitive effects on CYP3A4 or P-gp activity was not associated with a higher risk of major bleeding than DOAC alone. Our findings may provide clinicians with confidence regarding the safety of concurrent use of DOAC and ACDs in patients with AF and cancer.

Efficacy of portable sleep monitoring device in diagnosing central sleep apnea in patients with congestive heart failure.

Introduction: Central sleep apnea (CSA) is a common and serious comorbidity mainly occurring in patients with heart failure (HF), which tends to be underdiagnosed and has not been widely studied. Overnight polysomnography (PSG) is the gold standard for diagnosing CSA; however, the time and expense of the procedure limit its applicability. Portable monitoring (PM) devices are convenient and easy to use; however, they have not been widely studied as to their effectiveness in detecting CSA in patients with HF. In the current study, we examined the diagnostic value of PM as a screening tool to identify instances of CSA among patients with HF. Methods: A total of 22 patients under stable heart failure conditions with an ejection fraction of <50% were enrolled. All patients underwent PM and overnight PSG within a narrow time frame. The measurements of the apnea-hypopnea index (AHI), hypopnea index (HI), central apnea index (CAI), and obstructive apnea index (OAI) obtained from PSG, automatic scoring, and manual scoring of PM were recorded. The results obtained from PSG and those from PM (automatic and manual scoring) were compared to assess the accuracy of PM. Results: Among the patients, CSA in 11 patients was found by PSG. The AHI measurements performed using manual scoring of PM showed a significant correlation with those performed using PSG (r = 0.69; P = 0.01). Nonetheless, mean AHI measurements showed statistically significant differences between PSG and automatic scoring of PM (40.0 vs. 23.7 events/hour, respectively; P < 0.001), as well as between automatic and manual scoring of PM (23.7 vs. 29.5 events/hour; P < 0.001). Central sleep apnea was detected by PM; however, the results were easily misread as obstructive apnea, particularly in automatic scoring. Conclusion: PM devices could be used to identify instances of central sleep apnea among patients with HF. The results from PM were well-correlated with standard PSG results, and manual scoring was preferable to automated scoring.