Renal Biomarkers in Heart Failure: Systematic Review and Meta-Analysis.

Background: Cystatin C, neutrophil gelatinase-associated lipocalin (NGAL), and kidney injury molecule (KIM)-1 are renal biomarkers increasingly appreciated for their role in the risk stratification and prognostication of heart failure (HF) patients. However, very few have been adopted clinically, owing to the lack of consistency. Objectives: The authors aimed to study the association between cystatin C, NGAL, and KIM-1 and outcomes, mortality, hospitalizations, and worsening renal function (WRF) in patients with acute and chronic HF. Methods: We included peer-reviewed English-language articles from PubMed and EMBASE published up to December 2021. We analyzed the above associations using random-effects meta-analysis. Publication bias was assessed using funnel plots. Results: Among 2,631 articles, 100 articles, including 45,428 patients, met the inclusion criteria. Top-tertile of serum cystatin C, when compared to the bottom-tertile, carried a higher pooled hazard ratio (pHR) for mortality (pHR: 1.59, 95% CI: 1.42-1.77) and for the composite outcome of mortality and HF hospitalizations (pHR: 1.49, 95% CI: 1.23-1.75). Top-tertile of serum NGAL had a higher hazard for mortality (pHR: 2.91, 95% CI: 1.49-5.67) and composite outcome (HR: 4.11, 95% CI: 2.69-6.30). Serum and urine NGAL were significantly associated with WRF, with pHRs of 2.40 (95% CI: 1.48-3.90) and 2.01 (95% CI: 1.21-3.35). Urine KIM-1 was significantly associated with WRF (pHR: 1.60, 95% CI: 1.24-2.07) but not with other outcomes. High heterogeneity was noted between studies without an obvious explanation based on meta-regression. Conclusions: Serum cystatin C and serum NGAL are independent predictors of adverse outcomes in HF. Serum and urine NGAL are important predictors of WRF in HF.

Hidden Flaws Behind Expert-Level Accuracy of Multimodal GPT-4 Vision in Medicine.

Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily focused on the accuracy of multi-choice questions alone. Our study extends the current scope by conducting a comprehensive analysis of GPT-4V's rationales of image comprehension, recall of medical knowledge, and step-by-step multimodal reasoning when solving New England Journal of Medicine (NEJM) Image Challenges - an imaging quiz designed to test the knowledge and diagnostic capabilities of medical professionals. Evaluation results confirmed that GPT-4V performs comparatively to human physicians regarding multi-choice accuracy (81.6% vs. 77.8%). GPT-4V also performs well in cases where physicians incorrectly answer, with over 78% accuracy. However, we discovered that GPT-4V frequently presents flawed rationales in cases where it makes the correct final choices (35.5%), most prominent in image comprehension (27.2%). Regardless of GPT-4V's high accuracy in multi-choice questions, our findings emphasize the necessity for further in-depth evaluations of its rationales before integrating such multimodal AI models into clinical workflows.

Low-cost prototyping of nitinol wires/frames using polymeric cores and sacrificial fixtures with application in individualized frames anchoring through the atrial septum.

Self-expanding frames for minimally invasive implants are typically made from nitinol wires and are heat treated to maintain the desired shapes. In the process of heat treatment, nitinol structures are placed in a high-temperature oven, while they are confined by a fixture. During this process, nitinol exerts a high amount of force. Accordingly, a fixture requires high mechanical strength and temperature resistance; this is why fixtures are typically made from metals. The use of metal fixture also increases the turnaround time and cost. However, accelerating this process is beneficial in many applications, such as rapid development of medical implants that are patient-specific. Inspired by the use of sacrificial layers in microfabrication technology, here we propose a novel method for shape setting nitinol wires using a sacrificial metal fixture. In this process, the nitinol wires are first aligned inside copper hypotubes. Next, the forming process is done using hand-held tools to shape complex geometrical structures, annealing the nitinol reinforced by copper, and then selectively etching copper hypotubes in ammonium persulfate solutions. In this process, other sacrificial cores, which are 3D printed or cast from low-cost polymers, are also used. This combination of polymeric cores and minimal use of metals enables reducing the cost and the turnaround time. As a proof of concept, we showed that this process was capable of fabricating springs with mm or sub-mm diameters. The result showed a change of less than 5% in the intended diameter of the nitinol spring with diameters ranging from ~ 0.7 to 1.9 mm, which confirms copper as a suitable sacrificial fixture to obtain the desired complex geometry for nitinol. A metric, based on the elastic strain stored in copper is suggested to predict the possible variation of the intended dimensions in this process. Finally, to demonstrate the potential of this method, as proof of concept, we fabricated NiTi wire frames designed for anchoring through the atrial septum. These frames demonstrated septal defect occluders that were designed based on a patient's cardiac image available in the public domain. This low-cost rapid fabrication technique is highly beneficial for a variety of applications in engineering and medicine with specific applications in rapid prototyping of medical implants.

Application of deep neural networks for inferring pressure in polymeric acoustic transponders/sensors.

Passive sensor-transponders have raised interest for the last few decades, due to their capability of low-cost remote monitoring without the need for energy storage. Their operating principle includes receiving a signal from a source and then reflecting the signal. While well-established transponders operate through electromagnetic antennas, those with a fully acoustic design have advantages such as lower cost and simplicity. Therefore, detection of pressures using the ultrasound signal that is backscattered from an acoustic resonator has been of interest recently. In order to infer the pressure from the backscattered signal, the established approach has been based upon the principle of detection of the shift to the frequency of resonance. Nevertheless, regression of the pressure from the signal with a small error is challenging and has been subject to research. Here in this paper, we explore an approach that employs deep learning for inferring pressure from the ultrasound reflections of polymeric resonators. We assess if neural network regressors can efficiently infer pressure reflected from a fully acoustic transponder. For this purpose, we compare the performance of several regressors such as a convolutional neural network, a network inspired by the ResNet, and a fully connected neural network. We observe that deep neural networks are advantageous in inferring pressure information with a minimal need for analyzing the signal. Our work suggests that a deep learning approach has the potential to be integrated with or replace other traditional approaches for inferring pressure from an ultrasound signal reflected from fully acoustic transponders or passive sensors.

Machine Learning in Cardiovascular Risk Prediction and Precision Preventive Approaches.

PURPOSE OF REVIEW: In this review, we sought to provide an overview of ML and focus on the contemporary applications of ML in cardiovascular risk prediction and precision preventive approaches. We end the review by highlighting the limitations of ML while projecting on the potential of ML in assimilating these multifaceted aspects of CAD in order to improve patient-level outcomes and further population health. RECENT FINDINGS: Coronary artery disease (CAD) is estimated to affect 20.5 million adults across the USA, while also impacting a significant burden at the socio-economic level. While the knowledge of the mechanistic pathways that govern the onset and progression of clinical CAD has improved over the past decade, contemporary patient-level risk models lag in accuracy and utility. Recently, there has been renewed interest in combining advanced analytic techniques that utilize artificial intelligence (AI) with a big data approach in order to improve risk prediction within the realm of CAD. By virtue of being able to combine diverse amounts of multidimensional horizontal data, machine learning has been employed to build models for improved risk prediction and personalized patient care approaches. The use of ML-based algorithms has been used to leverage individualized patient-specific data and the associated metabolic/genomic profile to improve CAD risk assessment. While the tool can be visualized to shift the paradigm toward a patient-specific care, it is crucial to acknowledge and address several challenges inherent to ML and its integration into healthcare before it can be significantly incorporated in the daily clinical practice.

Mining multi-center heterogeneous medical data with distributed synthetic learning.

Overcoming barriers on the use of multi-center data for medical analytics is challenging due to privacy protection and data heterogeneity in the healthcare system. In this study, we propose the Distributed Synthetic Learning (DSL) architecture to learn across multiple medical centers and ensure the protection of sensitive personal information. DSL enables the building of a homogeneous dataset with entirely synthetic medical images via a form of GAN-based synthetic learning. The proposed DSL architecture has the following key functionalities: multi-modality learning, missing modality completion learning, and continual learning. We systematically evaluate the performance of DSL on different medical applications using cardiac computed tomography angiography (CTA), brain tumor MRI, and histopathology nuclei datasets. Extensive experiments demonstrate the superior performance of DSL as a high-quality synthetic medical image provider by the use of an ideal synthetic quality metric called Dist-FID. We show that DSL can be adapted to heterogeneous data and remarkably outperforms the real misaligned modalities segmentation model by 55% and the temporal datasets segmentation model by 8%.

The journal of cardiovascular computed tomography: A year in review: 2022.

This review aims to summarize key articles published in the Journal of Cardiovascular Computed Tomography (JCCT) in 2022, focusing on those that had the most scientific and educational impact. The JCCT continues to expand; the number of submissions, published manuscripts, cited articles, article downloads, social media presence, and impact factor continues to grow. The articles selected by the Editorial Board of the JCCT in this review highlight the role of cardiovascular computed tomography (CCT) to detect subclinical atherosclerosis, assess the functional relevance of stenoses, and plan invasive coronary and valve procedures. A section is dedicated to CCT in infants and other patients with congenital heart disease, in women, and to the importance of training in CT. In addition, we highlight key consensus documents and guidelines published in JCCT last year. The Journal values the tremendous work by authors, reviewers, and editors to accomplish these contributions.

Comparing the pooled cohort equations and coronary artery calcium scores in a symptomatic mixed Asian cohort.

Background: The value of pooled cohort equations (PCE) as a predictor of major adverse cardiovascular events (MACE) is poorly established among symptomatic patients. Coronary artery calcium (CAC) assessment further improves risk prediction, but non-Western studies are lacking. This study aims to compare PCE and CAC scores within a symptomatic mixed Asian cohort, and to evaluate the incremental value of CAC in predicting MACE, as well as in subgroups based on statin use. Methods: Consecutive patients with stable chest pain who underwent cardiac computed tomography were recruited. Logistic regression was performed to determine the association between risk factors and MACE. Cohort and statin-use subgroup comparisons were done for PCE against Agatston score in predicting MACE. Results: Of 501 patients included, mean (SD) age was 53.7 (10.8) years, mean follow-up period was 4.64 (0.66) years, 43.5% were female, 48.3% used statins, and 50.0% had no CAC. MI occurred in 8 subjects while 9 subjects underwent revascularization. In the general cohort, age, presence of CAC, and ln(Volume) (OR = 1.05, 7.95, and 1.44, respectively) as well as age and PCE score for the CAC = 0 subgroup (OR = 1.16 and 2.24, respectively), were significantly associated with MACE. None of the risk factors were significantly associated with MACE in the CAC > 0 subgroup. Overall, the PCE, Agatston, and their combination obtained an area under the receiver operating characteristic curve (AUC) of 0.501, 0.662, and 0.661, respectively. Separately, the AUC of PCE, Agatston, and their combination for statin non-users were 0.679, 0.753, and 0.734, while that for statin-users were 0.585, 0.615, and 0.631, respectively. Only the performance of PCE alone was statistically significant (p = 0.025) when compared between statin-users (0.507) and non-users (0.783). Conclusion: In a symptomatic mixed Asian cohort, age, presence of CAC, and ln(Volume) were independently associated with MACE for the overall subgroup, age and PCE score for the CAC = 0 subgroup, and no risk factor for the CAC > 0 subgroup. Whilst the PCE performance deteriorated in statin versus non-statin users, the Agatston score performed consistently in both groups.

Severely Calcified Pericardium Causing Constrictive Pericarditis.

Constrictive pericarditis (CP) is a type of diastolic heart failure caused by an inelastic pericardium that impairs cardiac filling. Diagnosing CP can be challenging, and a variety of imaging techniques may be necessary. We present a unique case of severely calcified pericardium leading to CP.

Gender Differences and Outcomes of Hypoattenuated Leaflet Thickening (HALT) Following Transcatheter Aortic Valve Replacement: A Meta-analysis of Randomized and Cohort Studies.

Subclinical leaflet thrombosis is characterized by hypoattenuated leaflet thickening (HALT) after transcatheter aortic valve replacement (TAVR) on computed tomography. However, given the low incidence of HALT after TAVR, the clinical significance of HALT is still being investigated. We sought to generate a more reliable estimate of the risk factors and adverse outcomes associated with HALT after TAVR by pooling data from randomized trials and cohort studies. PubMed/Medline database was systematically searched from inception until November 24, 2021, using the following terms: ("hypoattenuated leaflet thickening" and "transcatheter aortic valve replacement") and ("Subclinical leaflet thrombosis" and "transcatheter aortic valve replacement"). A random effects model meta-analysis was conducted using Mantel-Haenszel odds ratios (ORs) and the associated 95% confidence intervals (CIs), mean difference and the associated 95%. Ten studies with a total of 1462 patients were included, with follow-up ranging between 4 months and 3 years. HALT occurred in 14.4% of the patients undergoing TAVR. HALT was not associated with increased risk of stroke/TIA (OR 1.38; 95% CI [0.61-3.11]; I2=0%) or increased risk of all-cause mortality (OR 0.67; 95% CI [0.25-1.80]; I2=0). HALT was associated with a greater post-procedural mean aortic valve gradient (mean difference 2.31 mmHg; 95% CI [0.27, 4.35]; I2=71%). Interestingly, there was a trend of higher risk of HALT in men (OR 1.37; 95% CI [0.82-2.30]; I2=44%) while there was a trend towards lower risk of HALT in the presence of CKD (OR 0.76; 95% CI [0.49-1.19]; I2=0%); these trends did not reach statistical significance. This meta-analysis shows that the occurrence of HALT following TAVR is associated with a greater post-procedural mean aortic valve gradient but no excess risk of death or cerebrovascular events. The clinical significance of this higher post-procedural mean aortic valve gradient is uncertain and requires further investigations.

Cardiac magnetic resonance for prophylactic implantable-cardioverter defibrillator therapy international study: prognostic value of cardiac magnetic resonance-derived right ventricular parameters substudy.

AIMS: Right ventricular systolic dysfunction (RVSD) is an important determinant of outcomes in heart failure (HF) cohorts. While the quantitative assessment of RV function is challenging using 2D-echocardiography, cardiac magnetic resonance (CMR) is the gold standard with its high spatial resolution and precise anatomical definition. We sought to investigate the prognostic value of CMR-derived RV systolic function in a large cohort of HF with reduced ejection fraction (HFrEF). METHODS AND RESULTS: Study cohort comprised of patients enrolled in the CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DefibrillAtor ThErapy registry who had HFrEF and had simultaneous baseline CMR and echocardiography (n = 2449). RVSD was defined as RV ejection fraction (RVEF) <45%. Kaplan-Meier curves and cox regression were used to investigate the association between RVSD and all-cause mortality (ACM). Mean age was 59.8 ± 14.0 years, 42.0% were female, and mean left ventricular ejection fraction (LVEF) was 34.0 ± 10.8. Median follow-up was 959 days (interquartile range: 560-1590). RVSD was present in 936 (38.2%) and was an independent predictor of ACM (adjusted hazard ratio = 1.44; 95% CI [1.09-1.91]; P = 0.01). On subgroup analyses, the prognostic value of RVSD was more pronounced in NYHA I/II than in NYHA III/IV, in LVEF <35% than in LVEF ≥35%, and in patients with renal dysfunction when compared to those with normal renal function. CONCLUSION: RV systolic dysfunction is an independent predictor of ACM in HFrEF, with a more pronounced prognostic value in select subgroups, likely reflecting the importance of RVSD in the early stages of HF progression.

Prolactin Inhibition in Peripartum Cardiomyopathy: Systematic Review and Meta-analysis.

Heart failure (HF) is one of the leading causes of maternal mortality and morbidity in the United States. Peripartum cardiomyopathy (PPCM) constitutes up to 70% of all HF in pregnancy. Cardiac angiogenic imbalance caused by cleaved 16kDa prolactin has been hypothesized to contribute to the development of PPCM, fueling investigation of prolactin inhibitors for the management of PPCM. We conducted a systematic review and meta-analysis to assess the impact of prolactin inhibition on left ventricular (LV) function and mortality in patients with PPCM. We included English language articles from PubMed and EMBASE published upto March 2022. We pooled the mean difference (MD) for left ventricular ejection fraction (LVEF) at follow-up, odds ratio (OR) for LV recovery and risk ratio (RR) for all-cause mortality using random-effects meta-analysis. Among 548 studies screened, 10 studies (3 randomized control trials (RCTs), 2 retrospective and 5 prospective cohorts) were included in the systematic review. Patients in the Bromocriptine + standard guideline directed medical therapy (GDMT) group had higher LVEF% (pMD 12.56 (95% CI 5.84-19.28, I2=0%) from two cohorts and pMD 14.25 (95% CI 0.61-27.89, I2=88%) from two RCTs) at follow-up compared to standard GDMT alone group. Bromocriptine group also had higher odds of LV recovery (pOR 3.55 (95% CI 1.39-9.1, I2=62)). We did not find any difference in all-cause mortality between the groups. Our analysis demonstrates that the addition of Bromocriptine to standard GDMT was associated with a significant improvement in LVEF% and greater odds of LV recovery, without significant reduction in all-cause mortality.

Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications.

Substantial milestones have been attained in the field of heart failure (HF) diagnostics and therapeutics in the past several years that have translated into decreased mortality but a paradoxical increase in HF-related hospitalizations. With increasing data digitalization and access, remote monitoring via wearables and implantables have the potential to transform ambulatory care workflow, with a particular focus on reducing HF hospitalizations. Additionally, artificial intelligence and machine learning (AI/ML) have been increasingly employed at multiple stages of healthcare due to their power in assimilating and integrating multidimensional multimodal data and the creation of accurate prediction models. With the ever-increasing troves of data, the implementation of AI/ML algorithms could help improve workflow and outcomes of HF patients, especially time series data collected via remote monitoring. In this review, we sought to describe the basics of AI/ML algorithms with a focus on time series forecasting and the current state of AI/ML within the context of wearable technology in HF, followed by a discussion of the present limitations, including data integration, privacy, and challenges specific to AI/ML application within healthcare.

Influence of Iron Deficiency on Clinical and Haemodynamic Parameters in Pulmonary Arterial Hypertension Cohorts.

BACKGROUND: Iron deficiency (Fedef) has been shown to be common in patients with group 1 or pulmonary arterial hypertension (PAH). Several studies have shown a negative impact of Fedef on clinical and haemodynamic parameters of the disease, but data from individual studies have not been strong enough to lead to incorporation of the finding of Fedef into prognostic or therapeutic algorithms. The goal of this meta-analysis was to combine data from available studies to better define any associations between Fedef and established variables of prognostic importance in PAH. METHODS: A literature search identified nine studies with extractable data relevant to the study questions. The impact of Fedef upon the following parameters was evaluated: 6-minute walk distance (6MWD), WHO-functional class, N-terminal pro-brain natriuretic peptide (NT-proBNP) levels, echocardiography, and findings from right heart catheterisation (RHC). Pooled results were reported as mean difference or risk difference with 95% confidence intervals utilising a random effects modeling approach. RESULTS: Fedef in the PAH population was common (47% of cases) and was associated with cardiovascular dysfunction (lower tricuspid annular plane systolic excursion [TAPSE], elevated NT-proBNP, and lower mixed venous oxygen saturation) and with reduction in functional capacity (lower 6MWD and higher functional class). CONCLUSION: This meta-analysis strengthens the relationships between Fedef and several markers of poor outcome in PAH. Fedef in patients with PAH warrants further scrutiny and merits consideration as a cause of clinical deterioration. Even though causation and longitudinal relationships between Fedef and PAH could not be identified, effect of Fedef on factors that affect disease prognosis is noteworthy and worthy of more focussed studies.

Contemporary Applications of Machine Learning for Device Therapy in Heart Failure.

Despite a better understanding of the underlying pathogenesis of heart failure (HF), pharmacotherapy, surgical, and percutaneous interventions do not prevent disease progression in all patients, and a significant proportion of patients end up requiring advanced therapies. Machine learning (ML) is gaining wider acceptance in cardiovascular medicine because of its ability to incorporate large, complex, and multidimensional data and to potentially facilitate the creation of predictive models not constrained by many of the limitations of traditional statistical approaches. With the coexistence of "big data" and novel advanced analytic techniques using ML, there is ever-increasing research into applying ML in the context of HF with the goal of improving patient outcomes. Through this review, the authors describe the basics of ML and summarize the existing published reports regarding contemporary applications of ML in device therapy for HF while highlighting the limitations to widespread implementation and its future promises.

Early versus late acute coronary syndrome risk patterns of coronary atherosclerotic plaque.

AIMS: The temporal instability of coronary atherosclerotic plaque preceding an incident acute coronary syndrome (ACS) is not well defined. We sought to examine differences in the volume and composition of coronary atherosclerosis between patients experiencing an early (≤90 days) versus late ACS (>90 days) after baseline coronary computed tomography angiography (CCTA). METHODS AND RESULTS: From a multicenter study, we enrolled patients who underwent a clinically indicated baseline CCTA and experienced ACS during follow-up. Separate core laboratories performed blinded adjudication of ACS events and quantification of CCTA including compositional plaque volumes by Hounsfield units (HU): calcified plaque >350 HU, fibrous plaque 131-350 HU, fibrofatty plaque 31-130 HU and necrotic core <30 HU. In 234 patients (mean age 62 ± 12 years, 36% women), early and late ACS occurred in 129 and 105 patients after a mean of 395 ± 622 days, respectively. Patients with early ACS had a greater maximal diameter stenosis and maximal cross-sectional plaque burden as compared to patients with late ACS (P < 0.05). Larger total, fibrous, fibrofatty, and necrotic core volumes were observed in the early ACS group (P < 0.05). Findings for total, fibrous, fibrofatty, and necrotic core volumes were reproduced in an external validation cohort (P < 0.05). CONCLUSIONS: Volumetric differences in composition of coronary atherosclerosis exist between ACS patients according to their timing antecedent to the acute event. These data support that a large burden of non-calcified plaque on CCTA is strongly associated with near-term plaque instability and ACS risk.