Preoperative multidetector computed tomography for isolated surgical aortic valve replacement: Planning for future transcatheter options

BACKGROUND

Valve-in-valve (ViV) transcatheter aortic valve replacement (TAVR) has become a valuable option in patients with bioprosthetic failure. However, potential issues with ViV TAVR may occur in patients with high-risk anatomy for coronary obstruction and patients with baseline smaller bioprosthetic valves at risk for patient prosthesis mismatch. The purpose of this study was, therefore, to use preoperative electrocardiography-gated, multidetector computed tomography (MDCT) in patients undergoing isolated surgical aortic valve replacement (SAVR) to (1) identify which would be high risk for coronary occlusion with ViV TAVR, and (2) predict intraoperative SAVR sizing.

METHODS

Among 223 patients from our institutions' database that underwent SAVR for aortic insufficiency (AI) or aortic stenosis (AS) between January 2012 and January 2020, 48 patients had MDCT imaging before surgery (AI; n = 31, AS; n = 17). Of all patients, 67% (n = 32) were bicuspid morphology.

RESULTS

With the use of virtual valve implantation, all patients with AI and bicuspid AS had feasible anatomy for ViV TAVR, while 38% of patients with tricuspid AS were high risk for coronary obstruction. There was a strong correlation between actual valve size implanted and preoperative MDCT measurements using annulus average diameter, area, and/or perimeter.

CONCLUSION

Preoperative MDCT in patients undergoing SAVR is a useful tool for lifetime management, particularly in patients with tricuspid AS. Decisions for surgical management may change based on MDCT's ability to predict intraoperative SAVR size and determine which patients may be high-risk candidates for future ViV TAVR due to coronary artery obstruction.

Infective Endocarditis After Surgical and Transcatheter Aortic Valve Replacement: A State of the Art Review

Prosthetic valve endocarditis (PVE) after surgical aortic valve replacement and transcatheter aortic valve replacement (TAVR) carries significant morbidity/mortality. Our review aims to compare incidence, predisposing factors, microbiology, diagnosis, management, and outcomes of PVE in surgical aortic valve replacement/TAVR patients. We searched PubMed and Embase to identify published studies from January 1, 2015 to March 13, 2020. Key words were indexed for original reports, clinical studies, and reviews. Reports were evaluated by 2 authors against a priori inclusion/exclusion criteria. Studies were included if they reported incidence and outcomes related to surgical aortic valve replacement/TAVR PVE and excluded if they were published pre-2015 or included a small population. We followed the Cochrane methodology and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for all stages of the design and implementation. Study quality was based on the Newcastle-Ottawa Scale. Thirty-three studies with 311 to 41 025 patients contained relevant information. The majority found no significant difference in incidence of surgical aortic valve replacement/TAVR PVE (reported as 0.3%-1.2% per patient-year versus 0.6%-3.4%), but there were key differences in pathogenesis. TAVR has a specific set of infection risks related to entry site, procedure, and device, including nonstandardized protocols for infection control, valve crimping injury, paravalvular leak, neo-leaflet stress, intact/calcified native leaflets, and intracardiac hardware. With the expansion of TAVR to lower risk and younger patients, a better understanding of pathogenesis, patient presentation, and guideline-directed treatment is paramount. When operative intervention is necessary, mortality remains high at 20% to 30%. Unique TAVR infection risks present opportunities for PVE prevention, therefore, further investigation is imperative.

The Role of Artificial Intelligence in Echocardiography

PURPOSE OF REVIEW

Echocardiography is an indispensable tool in diagnostic cardiology and is fundamental to clinical care. Significant advances in cardiovascular imaging technology paralleled by rapid growth in electronic medical records, miniaturized devices, real-time monitoring, and wearable devices using body sensor network technology have led to the development of complex data.

RECENT FINDINGS

The intricate nature of these data can be overwhelming and exceed the capabilities of current statistical software. Machine learning (ML), a branch of artificial intelligence (AI), can help health care providers navigate through this complex labyrinth of information and unravel hidden discoveries. Furthermore, ML algorithms can help automate several tasks in echocardiography and clinical care. ML can serve as a valuable diagnostic tool for physicians in the field of echocardiography. In addition, it can help expand the capabilities of research and discover alternative pathways in medical management. In this review article, we describe the role of AI and ML in echocardiography.

Cardiac mechanics in heart failure with preserved ejection fraction

Heart failure with preserved ejection fraction (HFpEF) is a complex clinical entity associated with significant morbidity and mortality. Common comorbidities including hypertension, coronary artery disease, diabetes, chronic kidney disease, obesity, and increasing age predispose to preclinical diastolic dysfunction that often progresses to frank HFpEF. Clinical HFpEF is typically associated with some degree of diastolic dysfunction, but can occur in the absence of many conventional diastolic dysfunction indices. The exact biologic links between risk factors, structural changes, and clinical manifestations are not clearly apparent. Innovative approaches including deformation imaging have enabled deeper understanding of HFpEF cardiac mechanics beyond conventional metrics. Furthermore, predictive analytics through data-driven platforms have allowed for a deeper understanding of HFpEF phenotypes. This review focuses on the changes in cardiac mechanics that occur through preclinical myocardial dysfunction to clinically apparent HFpEF.

Transcatheter aortic valve-in-valve-in-valve implantation with three-dimensional printing guidance: A case report

BACKGROUND

Valve-in-valve implantation (ViV) has become a valid option for the treatment of bioprosthetic valve failure. We describe the first in-man transfemoral transcatheter aortic valve replacement (TAVR) or "turducken" in a patient with previous TAVR within surgical aortic valve replacement with preprocedural guidance utilizing three-dimensional (3D) printing and intraprocedural guidance with fusion imaging.

CASE SUMMARY

A 65-year-old male with a previous valve-in-valve with a transcatheter Melody valve presented with symptomatic severe valvular and paravalvular aortic incompetence. Using 3D printing for preprocedural guidance and computed tomography angiography-fluoroscopy fusion imaging for intraprocedural guidance we successfully placed a 29 mm self-expanding valve with minimal residual gradients. The patient continued to be asymptomatic at 1- and 5-year follow-up.

DISCUSSION

We describe the first in man aortic valve-in-valve-in-valve using preprocedural guidance with 3D printing and intraprocedural guidance with fusion imaging. The valve has continued to demonstrate good function up to 5 years postprocedure suggesting that transcatheter aortic valve-in-valve-in-valve may be feasible with the proper pre- and intraprocedural guidance.

Galactorrhea Following Minimally Invasive Reoperative Mitral Valve Replacement: An Unexpected Complication

Galactorrhea, or nonpuerperal lactation, is a rare complication that can occur after trauma to the chest wall. Although galactorrhea has been reported after thoracic surgery, it has not been previously noted as a potential outcome following cardiac surgery. We present a unique case of hyperprolactinemic galactorrhea experienced by a 39-year-old nongravid female patient after having undergone reoperative mitral valve replacement via a right minithoracotomy. To the best of our knowledge, this is the first reported case of spontaneous lactation occurring after cardiac surgery.

Acute Type A Aortic Dissection Repair After Hours: Does It Influence Outcomes?

BACKGROUND

Time of day has been associated with adverse outcomes in certain surgical pathologies. Because acute type A aortic dissection typically mandates immediate repair, relatively little attention has been paid to the potential impact of the day-night timing of the operation itself. We sought to determine whether patients with acute dissection treated during typical working hours demonstrated a difference in outcomes compared with those who required surgery after hours.

METHODS

We undertook a comprehensive review of our prospectively collected database from July 2014 to October 2018. A total of 164 consecutive patients underwent primary repair of an acute type A dissection. Based on the procedure start time, patients were divided into 2 groups: working hours (7 am to 4 pm, Monday to Friday; n = 60), and after hours (all other times, including weekends and holidays; n = 104). We propensity-matched 58 pairs of patients and analyzed perioperative data and short-term clinical outcomes.

RESULTS

Thirty-day mortality for all 164 patients was 10.4% (17 deaths), which was not significantly different between the matched groups (working-hours: 8 deaths [13.8%] versus after hours: 4 deaths [6.9%]; P = .36). Perfusion, cross-clamp, and circulatory arrest times did not differ between groups, nor did the types of aortic repairs performed. Postoperative complications were also comparable, including stroke, reoperation for bleeding, and new-onset renal failure requiring dialysis.

CONCLUSIONS

Thirty-day mortality and major morbidity after acute type A dissection repair are independent of when the operation is performed. Expeditious surgical intervention is recommended for all primary acute type A dissection, irrespective of time of day.

Artificial Intelligence in Cardiac Imaging

Machine learning (ML), a subset of artificial intelligence, is showing promising results in cardiology, especially in cardiac imaging. ML algorithms are allowing cardiologists to explore new opportunities and make discoveries not seen with conventional approaches. This offers new opportunities to enhance patient care and open new gateways in medical decision-making. This review highlights the role of ML in cardiac imaging for precision phenotyping and prognostication of cardiac disorders.

Cardiac magnetic resonance imaging: the future is bright

Over the last 15 years, cardiovascular magnetic resonance (CMR) imaging has progressively evolved to become an indispensable tool in cardiology. It is a non-invasive technique that enables objective and functional assessment of myocardial tissue. Recent innovations in magnetic resonance imaging scanner technology and parallel imaging techniques have facilitated the generation of T1 and T2 parametric mapping to explore tissue characteristics. The emergence of strain imaging has enabled cardiologists to evaluate cardiac function beyond conventional metrics. Significant progress in computer processing capabilities and cloud infrastructure has supported the growth of artificial intelligence in CMR imaging. In this review article, we describe recent advances in T1/T2 mapping, myocardial strain, and artificial intelligence in CMR imaging.

Application of mobile health, telemedicine and artificial intelligence to echocardiography

The intersection of global broadband technology and miniaturized high-capability computing devices has led to a revolution in the delivery of healthcare and the birth of telemedicine and mobile health (mHealth). Rapid advances in handheld imaging devices with other mHealth devices such as smartphone apps and wearable devices are making great strides in the field of cardiovascular imaging like never before. Although these technologies offer a bright promise in cardiovascular imaging, it is far from straightforward. The massive data influx from telemedicine and mHealth including cardiovascular imaging supersedes the existing capabilities of current healthcare system and statistical software. Artificial intelligence with machine learning is the one and only way to navigate through this complex maze of the data influx through various approaches. Deep learning techniques are further expanding their role by image recognition and automated measurements. Artificial intelligence provides limitless opportunity to rigorously analyze data. As we move forward, the futures of mHealth, telemedicine and artificial intelligence are increasingly becoming intertwined to give rise to precision medicine.

Artificial Intelligence in Cardiovascular Medicine

PURPOSE OF REVIEW

The ripples of artificial intelligence are being felt in various sectors of human life. Machine learning, a subset of artificial intelligence, extracts information from large databases of information and is gaining traction in various fields of cardiology. In this review, we highlight noteworthy examples of machine learning utilization in echocardiography, nuclear cardiology, computed tomography, and magnetic resonance imaging over the past year.

RECENT FINDINGS

In the past year, machine learning (ML) has expanded its boundaries in cardiology with several positive results. Some studies have integrated clinical and imaging information to further augment the accuracy of these ML algorithms. All the studies mentioned in this review have clearly demonstrated superior results of ML in relation to conventional approaches for identifying obstructions or predicting major adverse events in reference to conventional approaches. As the influx of data arriving from gradually evolving technologies in health care and wearable devices continues to be more complex, ML may serve as the bridge to transcend the gap between health care and patients in the future. In order to facilitate a seamless transition between both, a few issues must be resolved for a successful implementation of ML in health care.

Beamforming algorithms for endocardial border detection

BACKGROUND

Software-based beamforming which utilizes delay and standard beamforming is a signal processing technique that temporarily stores data from each probe element to improve specular reflections to improve the image resolution. We compared a software algorithm which uses delay and standard beamforming with delay and sum beamforming in standard, hardware to evaluate endocardial borders and need for echo contrast.

METHODS

In this prospective study, eligible participants were ≥18 years of age referred clinically for transthoracic echocardiograms. A limited study consisting of three views (apical 4, apical 3, and apical 2 chamber) was performed with the software-based beamforming and standard platform. Number and quality of segments visualized were evaluated using a 17-segment model. Quality of segments was graded as 0 = not visualized, 1 = incompletely visualized, or 2 = completely visualized. Overall quality score for each study (0 = poor, 1 = adequate, 2 = good) was reported. The need for contrast was determined by ASE guidelines.

RESULTS

A total of 101 patients (mean age 61 ± 16 years, males 52%) were enrolled. Mean number of segments visualized in apical 4- (6.28 vs 5.65, P < .001), apical 3- (6.27 vs 5.54, P < .001), and apical 2-chamber views (6.26 vs 5.72 P < .001) was higher with the software vs standard platform. The average overall score for image quality was significantly better for the software platform vs standard (1.4 vs 0.9, P =< .001). With the software platform, 23% were judged as requiring contrast as compared with 45% for the standard platform (P < .001).

CONCLUSIONS

Delay and standard beamforming in software platform identified more segments with better image quality when compared to the standard high-end platform, decreasing the need for contrast usage.

Minimally Invasive Direct Coronary Artery Bypass for the management of Anomalous Left Coronary Artery from the Right Coronary Sinus

OBJECTIVE

Anomalous left coronary artery from the right coronary sinus (ALCA) is a known cause of sudden death. Surgical correction is recommended for all patients with interarterial course. We describe two patients who underwent surgical correction through an off pump- minimally invasive direct coronary artery bypass (MIDCAB) approach with good short- and mid-term results.

Latent myopathy is more pronounced in patients with low flow versus normal flow aortic stenosis with normal left ventricular ejection fraction who are undergoing surgical aortic valve replacement: Multicenter study with a brief review of the literature

BACKGROUND

Midwall fibrosis and low stroke volume are independent predictors of mortality in severe aortic stenosis (AS) with preserved LV ejection fraction (LVEF). The role of speckle tracking echocardiography (STE) to identify latent myopathy pre- and post- aortic valve replacement (AVR) in high risk AS patients with normal LVEF is limited.

METHODS

Demographic, 2D echocardiographic, and STE data were analyzed in patients with severe AS and preserved LVEF who underwent tissue AVR. Velocity vector imaging (VVI) was used to assess regional and global peak systolic longitudinal strain (GLS). Low flow (LF) was defined as an indexed LV stroke volume <35 mL/m² .

RESULTS

Between December 2008 and May 2011, 37 patients (75 ± 9 years, 51% male) had both pre- and post-AVR echos within 6.6 ± 6.5 months (median = 4 months; range = 2.5-9.5) of surgery. Compared with pre-AVR, GLS (-6.9 ± 4.9% vs -11.1 ± 4.1%; P < .001) and strain rate (-0.72 ± 0.3s^-1 vs -0.87 ± 0.3s^-1 ; P = .01) improved post-AVR. Pre-AVR mid-segments showed a similar myopathy as the basal segments (-9.5 ± 4.3% vs -9.0 ± 4.2%;P = .3). The 16 (43%) LF patients in this study had lower pre- and post-AVR strain compared to NF patients (GLS Pre-AVR:LF vs NF: -5.1 ± 4.1% vs -8.4 ± 4.9% (P = .04) and GLS Post-AVR:LF vs NF: -9.2 ± 3.7% vs -12.5 ± 3.9% (P = .01)). However, there was no difference in absolute and %change improvement in GLS post-AVR (LF vs NF:∆ -4.2 ± 3.5% vs ∆-4.1 ± 5.3% (P = .90) and 193 ± 214% vs 143 ± 230% change (P = .5)). The lowest GLS was seen in LF/HG AS followed by LF/LG, NF/LG and NF/HG AS; P = .03.

CONCLUSIONS

Latent myopathy is more pronounced in LF AS both pre- and post-AVR. Our study provides evidence of improvement in myopathy in LF AS despite a persistent worse myopathy compared to NF patients post-AVR.