Endovascular release of an Adams-DeWeese clip and iliocaval reconstruction for debilitating lower extremity swelling.

More than 10 million cases of venous thromboembolisms are reported on an annual basis and are major contributors to morbidity and mortality. Studies have found that ≤90% of pulmonary embolisms originate from the abdominal and lower extremity veins. The mainstay of venous thromboembolism treatment has been, and still continues to be, anticoagulation. However, for patients for whom anticoagulation is contraindicated or has failed, physicians have turned to surgical innovations such as inferior vena cava (IVC) filters to create partial interruption of the IVC. Before the invention of IVC filters, the Adams-DeWeese clip was developed to create caval interruption, which allowed for venous return while preventing pulmonary emboli from distal veins. We report a case of endovascular release of a long-term Adams-DeWeese clip, which had caused IVC occlusion and debilitating bilateral lower extremity swelling.

Identification of high-risk imaging features in hypertrophic cardiomyopathy using electrocardiography: A deep-learning approach.

BACKGROUND: Patients with hypertrophic cardiomyopathy (HCM) are at risk of sudden death, and individuals with ≥1 major risk markers are considered for primary prevention implantable cardioverter-defibrillators. Guidelines recommend cardiac magnetic resonance (CMR) imaging to identify high-risk imaging features. However, CMR imaging is resource intensive and is not widely accessible worldwide. OBJECTIVE: The purpose of this study was to develop electrocardiogram (ECG) deep-learning (DL) models for the identification of patients with HCM and high-risk imaging features. METHODS: Patients with HCM evaluated at Tufts Medical Center (N = 1930; Boston, MA) were used to develop ECG-DL models for the prediction of high-risk imaging features: systolic dysfunction, massive hypertrophy (≥30 mm), apical aneurysm, and extensive late gadolinium enhancement. ECG-DL models were externally validated in a cohort of patients with HCM from the Amrita Hospital HCM Center (N = 233; Kochi, India). RESULTS: ECG-DL models reliably identified high-risk features (systolic dysfunction, massive hypertrophy, apical aneurysm, and extensive late gadolinium enhancement) during holdout testing (c-statistic 0.72, 0.83, 0.93, and 0.76) and external validation (c-statistic 0.71, 0.76, 0.91, and 0.68). A hypothetical screening strategy using echocardiography combined with ECG-DL-guided selective CMR use demonstrated a sensitivity of 97% for identifying patients with high-risk features while reducing the number of recommended CMRs by 61%. The negative predictive value with this screening strategy for the absence of high-risk features in patients without ECG-DL recommendation for CMR was 99.5%. CONCLUSION: In HCM, novel ECG-DL models reliably identified patients with high-risk imaging features while offering the potential to reduce CMR testing requirements in underresourced areas.

Development and Validation of a Novel Preoperative Risk Score to Identify Patients at Risk for Nonhome Discharge after Elective Endovascular Aortic Aneurysm Repair (EVAR).

BACKGROUND: Nonhome discharge (NHD) to a rehabilitation or skilled nursing facility after elective endovascular aortic repair (EVAR) is uncommon. However, NHD after surgery has an important impact on patient quality of life and postdischarge outcomes. Understanding factors that put patients undergoing EVAR at high risk for NHD is essential to providing adequate preoperative counseling and shared decision making. This study aimed to identify independent predictors of NHD following elective EVAR and to create a clinically useful preoperative risk score. METHODS: Elective EVAR cases were queried from the Society for Vascular Surgery Vascular Quality Initiative 2014-2018. A risk score was created by splitting the data set into two-thirds for development and one-third for validation. A parsimonious stepwise hierarchical multivariable logistic regression controlling for hospital level variation was performed in the development dataset, and the beta-coefficients were used to assign points for a risk score. The score was then validated, and model performance assessed. RESULTS: Overall, 24,426 patients were included and 932 (3.8%) required NHD. Multivariable analysis in the development group identified independent predictors of NHD, which were used to create a 20-point risk score. Patients were stratified into 3 groups based upon their risk score: low risk (0-7 points; n = 16,699) with an NHD rate of 1.8%, moderate risk (8-13 points; n = 7,315) with an NHD rate of 7.3%, and high risk (≥14 points; n = 412) with an NHD rate of 21.8%. The risk score had good predictive ability with c-statistic = 0.75 for model development and c-statistic = 0.73 in the validation dataset. CONCLUSIONS: This novel risk score can predict NHD following EVAR using characteristics that can be identified preoperatively. Utilization of this score may allow for improved risk assessment, preoperative counseling, and shared decision making.

Readability of Spanish-Language Online Patient Educational Materials for Peripheral Artery Disease Do Not Meet Recommended Standards and Represent a Literacy Barrier to Care.

BACKGROUND: Online resources are a valuable source of information for patients and have been reported to improve engagement and adherence to medical care. However, readability of online patient educational materials (OPEMs) is crucial for them to serve their intended purpose. The American Medical Association (AMA) recommends that OPEM be written at or below the sixth grade reading level. To avoid disparities in access to comprehensible health information on peripheral artery disease (PAD), it is imperative that the readability of PAD OPEM is appropriate for both English-speaking and Spanish-speaking patients. The aim of this study is to evaluate the readability of PAD OPEM in Spanish and compare to English-language OPEM. METHODS: We conducted a Google search in English and Spanish using "peripheral arterial disease" and "enfermedad arterial periferica", respectively, and the top 25 patient-accessible articles were collected for each. Articles were categorized by source type: hospital, professional society, or other. Readability of English-language OPEM was measured using the Flesch Reading Ease Readability Formula, Automated Readability Index, Coleman-Liau Index, Flesch-Kincaid Grade Level, Gunning Fog, Linsear Write Formula, and the Simple Measure of Gobbledygook Index. Readability of Spanish OPEM was measured using the Fernández-Huerta Index and Índice Flesch-Szigriszt Scale. Readability of the articles was compared to the AMA recommendation, between English- and Spanish-language, and across sources using statistical tests appropriate to the data. RESULTS: OPEM from professional societies represented the fewest number of English- (n = 7, 28%) and Spanish-language (n = 6, 24%) articles. Most English-speaking (n = 18, 72%) and Spanish-language (n = 20, 80%) OPEM were considered difficult as measured by the Flesch Reading Ease Readability Formula and Fernández-Huerta Index, respectively, but did not significantly differ between languages (P = 0.59). There were no significant differences in the average readability of all readability measurements across sources (hospital, professional society, or other). All the average readability grade levels for English-speaking and Spanish-language OPEM was significantly higher than the sixth grade reading level (P < 0.01). Only 3 (6%) OPEM met the AMA recommended reading level and there was no significant difference between English-language and Spanish-language OPEM (P = 1.0). CONCLUSIONS: Nearly all Spanish-language and English-language PAD OPEM assessed were written at a reading grade level higher than recommended by the AMA. There was no significant difference in the readability of materials from hospitals or professional societies. To prevent further widening of health disparities related to literacy, health content creators, particularly hospitals and professional societies, should prioritize, develop, and ensure that English-language and Spanish-language patient education materials are written at a level appropriate for the public.

Predicting ventricular tachycardia circuits in patients with arrhythmogenic right ventricular cardiomyopathy using genotype-specific heart digital twins.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiac disease that leads to ventricular tachycardia (VT), a life-threatening heart rhythm disorder. Treating ARVC remains challenging due to the complex underlying arrhythmogenic mechanisms, which involve structural and electrophysiological (EP) remodeling. Here, we developed a novel genotype-specific heart digital twin (Geno-DT) approach to investigate the role of pathophysiological remodeling in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of different genotypes. This approach integrates the patient's disease-induced structural remodeling reconstructed from contrast-enhanced magnetic-resonance imaging and genotype-specific cellular EP properties. In our retrospective study of 16 ARVC patients with two genotypes: plakophilin-2 (PKP2, n = 8) and gene-elusive (GE, n = 8), we found that Geno-DT accurately and non-invasively predicted the VT circuit locations for both genotypes (with 100%, 94%, 96% sensitivity, specificity, and accuracy for GE patient group, and 86%, 90%, 89% sensitivity, specificity, and accuracy for PKP2 patient group), when compared to VT circuit locations identified during clinical EP studies. Moreover, our results revealed that the underlying VT mechanisms differ among ARVC genotypes. We determined that in GE patients, fibrotic remodeling is the primary contributor to VT circuits, while in PKP2 patients, slowed conduction velocity and altered restitution properties of cardiac tissue, in addition to the structural substrate, are directly responsible for the formation of VT circuits. Our novel Geno-DT approach has the potential to augment therapeutic precision in the clinical setting and lead to more personalized treatment strategies in ARVC.

Effects of the multi-channeled oral irrigation (MCOI) unit in preventing dental plaque formation and gingivitis: A randomized controlled trial.

PURPOSE: To evaluate the efficacy of COMORAL a new multi-channeled oral irrigation (MCOI) unit with pulsating water jet, in plaque score reduction and gingivitis. METHODS: This was a single-blinded clinical randomized controlled trial (NCT05031260). Forty-two healthy subjects between 18 to 35 years old were initially recruited, and the control group (n = 20) and the intervention group (n = 17) were randomly assigned. Both groups were asked to brush their teeth one or two times a day without any supplementary oral hygiene products while the intervention group used COMORAL 3 times a day, 5 days a week. Clinical indices including gingival index (GI), plaque index (PI), bleeding on probing (BOP), pocket depth (PD), gingival recession (GR), and clinical attachment loss (CAL) were obtained at the baseline (D0), day 14 (D14), and day 28 (D28). Saliva was collected to examine the presence of periodontal pathogens. The repeated measures analysis of variance or generalized estimating equation was used to compare the interaction between groups and time points. The independent t-test or Mann-Whitney test were used for intergroup differences at each time point. RESULTS: At V0, PI, GI, BOP, and PD scores showed no differences between the two groups. At V1 and V2, these scores showed significant difference between two groups (P < 0.05) such that the intervention group showed gradual decreases while the control group showed no change. There were no differences in GR, CAL, and periodontal pathogens between the two groups. COMORAL showed improvement in reducing gingival inflammation and dental plaque formation adjuvant to routine toothbrushing in healthy adults. CLINICAL SIGNIFICANCE: The results of this study can be useful to clinicians when selecting oral hygiene devices that can help improve patients' routine oral hygiene practice and their overall oral health.

Association of epicardial and intramyocardial fat with ventricular arrhythmias.

BACKGROUND: Among patients with ischemic cardiomyopathy (ICM) and nonischemic cardiomyopathy (NICM), myocardial fibrosis is associated with an increased risk for ventricular arrhythmia (VA). Growing evidence suggests that myocardial fat contributes to ventricular arrhythmogenesis. However, little is known about the volume and distribution of epicardial adipose tissue and intramyocardial fat and their relationship with VAs. OBJECTIVE: The purpose of this study was to assess the association of contrast-enhanced computed tomography (CE-CT)-derived left ventricular (LV) tissue heterogeneity, epicardial adipose tissue volume, and intramyocardial fat volume with the risk of VA in ICM and NICM patients. METHODS: Patients enrolled in the PROSE-ICD registry who underwent CE-CT were included. Intramyocardial fat volume (voxels between -180 and -5 Hounsfield units [HU]), epicardial adipose tissue volume (between -200 and -50 HU), and LV tissue heterogeneity were calculated. The primary endpoint was appropriate ICD shocks or sudden arrhythmic death. RESULTS: Among 98 patients (47 ICM, 51 NICM), LV tissue heterogeneity was associated with VA (odds ratio [OR] 1.10; P = .01), particularly in the ICM cohort. In the NICM subgroup, epicardial adipose tissue and intramyocardial fat volume were associated with VA (OR 1.11, P = .01; and OR = 1.21, P = .01, respectively) but not in the ICM patients (OR 0.92, P =.22; and OR = 0.96, P =.19, respectively). CONCLUSION: In ICM patients, increased fat distribution heterogeneity is associated with VA. In NICM patients, an increased volume of intramyocardial fat and epicardial adipose tissue is associated with a higher risk for VA. Our findings suggest that fat's contribution to VAs depends on the underlying substrate.

Predicting Ventricular Tachycardia Circuits in Patients with Arrhythmogenic Right Ventricular Cardiomyopathy using Genotype-specific Heart Digital Twins.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiac disease that leads to ventricular tachycardia (VT), a life-threatening heart rhythm disorder. Treating ARVC remains challenging due to the complex underlying arrhythmogenic mechanisms, which involve structural and electrophysiological (EP) remodeling. Here, we developed a novel genotype-specific heart digital twin (Geno-DT) approach to investigate the role of pathophysiological remodeling in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of different genotypes. This approach integrates the patient's disease-induced structural remodeling reconstructed from contrast-enhanced magnetic-resonance imaging and genotype-specific cellular EP properties. In our retrospective study of 16 ARVC patients with two genotypes: plakophilin-2 (PKP2, n = 8) and gene-elusive (GE, n = 8), we found that Geno-DT accurately and non-invasively predicted the VT circuit locations for both genotypes (with 100%, 94%, 96% sensitivity, specificity, and accuracy for GE patient group, and 86%, 90%, 89% sensitivity, specificity, and accuracy for PKP2 patient group), when compared to VT circuit locations identified during clinical EP studies. Moreover, our results revealed that the underlying VT mechanisms differ among ARVC genotypes. We determined that in GE patients, fibrotic remodeling is the primary contributor to VT circuits, while in PKP2 patients, slowed conduction velocity and altered restitution properties of cardiac tissue, in addition to the structural substrate, are directly responsible for the formation of VT circuits. Our novel Geno-DT approach has the potential to augment therapeutic precision in the clinical setting and lead to more personalized treatment strategies in ARVC.

Advanced breast cancer metastasized in the brain: treatment standards and innovations.

Breast cancer continues to be a difficult disease to treat due to high rates of metastasis. Metastasis to the brain presents a unique and often overlooked challenge. In this focused review, we discuss the epidemiology of breast cancer and which types frequently metastasize to the brain. Novel treatment approaches are highlighted with supporting scientific evidence. The role of the blood-brain barrier and how it may become altered with metastasis is addressed. We then highlight new innovations for Her2-positive and triple-negative breast cancer. Finally, recent directions for luminal breast cancer are discussed. This review serves to enhance understanding of pathophysiology, spark continued innovation, and provide a user-friendly resource through tables and easy-to-process figures.

Evaluation of a deep learning-enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias.

Personalized, image-based computational heart modelling is a powerful technology that can be used to improve patient-specific arrhythmia risk stratification and ventricular tachycardia (VT) ablation targeting. However, most state-of-the-art methods still require manual interactions by expert users. The goal of this study is to evaluate the feasibility of an automated, deep learning-based workflow for reconstructing personalized computational electrophysiological heart models to guide patient-specific treatment of VT. Contrast-enhanced computed tomography (CE-CT) images with expert ventricular myocardium segmentations were acquired from 111 patients across five cohorts from three different institutions. A deep convolutional neural network (CNN) for segmenting left ventricular myocardium from CE-CT was developed, trained and evaluated. From both CNN-based and expert segmentations in a subset of patients, personalized electrophysiological heart models were reconstructed and rapid pacing was used to induce VTs. CNN-based and expert segmentations were more concordant in the middle myocardium than in the heart's base or apex. Wavefront propagation during pacing was similar between CNN-based and original heart models. Between most sets of heart models, VT inducibility was the same, the number of induced VTs was strongly correlated, and VT circuits co-localized. Our results demonstrate that personalized computational heart models reconstructed from deep learning-based segmentations even with a small training set size can predict similar VT inducibility and circuit locations as those from expertly-derived heart models. Hence, a user-independent, automated framework for simulating arrhythmias in personalized heart models could feasibly be used in clinical settings to aid VT risk stratification and guide VT ablation therapy. KEY POINTS: Personalized electrophysiological heart modelling can aid in patient-specific ventricular tachycardia (VT) risk stratification and VT ablation targeting. Current state-of-the-art, image-based heart models for VT prediction require expert-dependent, manual interactions that may not be accessible across clinical settings. In this study, we develop an automated, deep learning-based workflow for reconstructing personalized heart models capable of simulating arrhythmias and compare its predictions with that of expert-generated heart models. The number and location of VTs was similar between heart models generated from the deep learning-based workflow and expert-generated heart models. These results demonstrate the feasibility of using an automated computational heart modelling workflow to aid in VT therapeutics and has implications for generalizing personalized computational heart technology to a broad range of clinical centres.

Hemodialysis Access Outcomes for Patients with Unstable Housing.

BACKGROUND: End-stage renal disease has traditionally been noted to disproportionately affect patients with fewer resources. Our goal was to assess practice patterns and outcomes in patients with unstable housing undergoing permanent hemodialysis access creation. STUDY DESIGN: A retrospective, single-center review of patients with unstable housing was conducted. Perioperative and long-term outcomes were assessed. Univariable and multivariable analysis as well as Kaplan-Meier analysis were performed. RESULTS: There were 144 patients (63% male) identified with a mean age of 57 years. Thirty-three percent of patients had no current permanent residence, and 81% had at least 3 addresses the year before operation. Access type included brachiocephalic (48%), brachiobasilic (19%), radiocephalic fistulas (11%), and prosthetic grafts (19%). Thirty-day readmission was seen in 27.8% of patients. Kaplan-Meier analysis showed that 1-year and 4-year patient survival were 94.4% and 80.6%, respectively. Multivariable analysis demonstrated that newly formed access (odds ratio [OR] 3.66, 95% CI 1.02 to 13.16, p = 0.05), absence of a permanent residence (OR 2.92, 95% CI 1.15 to 7.44, p = 0.03), and female gender (OR 2.86, 95% CI, 1.18 to 7.14, p = 0.02) were associated with 90-day readmission. Multivariable analysis of mortality revealed that previous stroke (hazard ratio [HR] 7.15, 95% CI 1.93 to 26.5, p = 0.003), history of alcohol use disorder (HR 4.55, 95% CI 1.22 to 16.99, p = 0.024), and age (HR 1.10, 95% CI, 1.02 to 1.18, p = 0.017) were associated with 4-year mortality; housing instability was not associated with decreased survival. Preoperative tunneled dialysis catheter (HR 1.63, 95% CI 1.02 to 2.61, p = 0.04) was associated with 4-year reintervention and frequent address change (HR 0.47, 95% CI 0.27 to 0.81, p = 0.01) was found to be a protective factor against long-term reintervention. CONCLUSION: Poor outcomes in patients with unstable housing were primarily driven by comorbidities. Lack of permanent residence was significantly associated with readmission.

Wavefront directionality and decremental stimuli synergistically improve identification of ventricular tachycardia substrate: insights from personalized computational heart models.

AIMS: Multiple wavefront pacing (MWP) and decremental pacing (DP) are two electroanatomic mapping (EAM) strategies that have emerged to better characterize the ventricular tachycardia (VT) substrate. The aim of this study was to assess how well MWP, DP, and their combination improve identification of electrophysiological abnormalities on EAM that reflect infarct remodelling and critical VT sites. METHODS AND RESULTS: Forty-eight personalized computational heart models were reconstructed using images from post-infarct patients undergoing VT ablation. Paced rhythms were simulated by delivering an initial (S1) and an extra-stimulus (S2) from one of 100 locations throughout each heart model. For each pacing, unipolar signals were computed along the myocardial surface to simulate substrate EAM. Six EAM features were extracted and compared with the infarct remodelling and critical VT sites. Concordance of S1 EAM features between different maps was lower in hearts with smaller amounts of remodelling. Incorporating S1 EAM features from multiple maps greatly improved the detection of remodelling, especially in hearts with less remodelling. Adding S2 EAM features from multiple maps decreased the number of maps required to achieve the same detection accuracy. S1 EAM features from multiple maps poorly identified critical VT sites. However, combining S1 and S2 EAM features from multiple maps paced near VT circuits greatly improved identification of critical VT sites. CONCLUSION: Electroanatomic mapping with MWP is more advantageous for characterization of substrate in hearts with less remodelling. During substrate EAM, MWP and DP should be combined and delivered from locations proximal to a suspected VT circuit to optimize identification of the critical VT site.

Proteinopathies and Neurotrauma: Update on Degenerative Cascades.

Neurotrauma, especially repetitive neurotrauma, is associated with the development of progressive neurodegeneration leading to chronic traumatic encephalopathy (CTE). Exposure to neurotrauma regularly occurs during sports and military service, often not requiring medical care. However, exposure to severe and/or repeated sub-clinical neurotrauma has been shown cause physical and psychological disability, leading to reduce life expectancy. Misfolding of proteins, or proteinopathy, is a pathological hallmark of CTE, in which chronic injury leads to local and diffuse protein aggregates. These aggregates are an overlapping feature of many neurodegenerative diseases such as CTE, Alzheimer's Disease, Parkinsons disease. Neurotrauma is also a significant risk factor for the development of these diseases, however the mechanism's underlying this association are not well understood. While phosphorylated tau aggregates are the primary feature of CTE, amyloid-beta, Transactive response DNA-binding protein 43 (TDP-43), and alpha-synuclein (αSyn) are also well documented. Aberrant misfolding of these proteins has been shown to disrupt brain homeostasis leading to neurodegeneration in a disease dependent manor. In CTE, the interaction between proteinopathies and their associated neurodegeneration is a current area of study. Here we provide an update on current literature surrounding the prevalence, characteristics, and pathogenesis of proteinopathies in CTE.

Ovarian Cancer Metastasis to the Central Nervous System: A Literature Review.

Ovarian cancer is one of the leading causes of cancer-related deaths among women in the United States. Metastasis to the central nervous system has become more frequent in the previous decades, however, treatment options remain limited. In this review, we discuss the pathophysiology of ovarian cancer and how metastasis to the central nervous system typically occurs. We then discuss cases of metastasis presented in the literature to evaluate current treatment regimens and protocols. Finally, we highlight emerging treatment options that are being utilized in clinics to provide personalized treatment therapy for a patient's unique diagnosis. This review aims to further the understanding of pathophysiology, stimulate further innovative treatments, and present accessible resources through tables and figures.

Enhancement of THz generation in LiNbO3 waveguides via multi-bounce velocity matching.

To realize the full promise of terahertz polaritonics (waveguide-based terahertz field generation, interaction, and readout) as a viable spectroscopy platform, much stronger terahertz fields are needed to enable nonlinear and even robust linear terahertz measurements. We use a novel geometric approach in which the optical pump is totally internally reflected to increase the distance over which optical rectification occurs. Velocity matching is achieved by tuning the angle of internal reflection. By doing this, we are able to enhance terahertz spectral amplitude by over 10x compared to conventional single-pass terahertz generation. An analysis of the depletion mechanisms reveals that 3-photon absorption and divergence of the pump beam are the primary limiters of further enhancement. This level of enhancement is promising for enabling routine spectroscopic measurements in an integrated fashion and is made more encouraging by the prospect of further enhancement by using longer pump wavelengths.

A retrospective chart review evaluating pre-operative dental extractions on patients with end-stage heart failure undergoing advanced surgical cardiac therapies.

OBJECTIVES: End-stage heart failure patients are functionally compromised by multiple physiologic mechanisms, placing them at increased risk of peri- and post-operative complications. This study aimed to evaluate if dental treatment performed before advanced cardiac interventions, including orthotopic heart transplant and mechanical circulatory support, increases the risk of adverse events. STUDY DESIGN: A retrospective chart review spanning January 2011 to December 2020 was performed. Inpatients with end-stage heart disease were evaluated by the hospital dentistry service at UCLA Ronald Reagan Medical Center. Three hundred and five consults met the inclusion criteria. The patients were divided into 2 groups: those who underwent dental treatment and those who did not require dental treatment. The wait time from dental consultation to cardiac intervention (days), dental complications, medical adverse events, and deaths were evaluated. RESULTS: Dental complications were only experienced in the form of intraoral bleeding. There was no significant difference in the number of medical adverse events or deaths between groups. CONCLUSIONS: The elimination of oral infection before advanced cardiac interventions does not increase the risk of morbidity or mortality.

Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death.

Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of "virtual hearts" using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.

Mechanism of bisphosphonate-related osteonecrosis of the jaw (BRONJ) revealed by targeted removal of legacy bisphosphonate from jawbone using competing inert hydroxymethylene diphosphonate.

Bisphosphonate-related osteonecrosis of the jaw (BRONJ) presents as a morbid jawbone lesion in patients exposed to a nitrogen-containing bisphosphonate (N-BP). Although it is rare, BRONJ has caused apprehension among patients and healthcare providers and decreased acceptance of this antiresorptive drug class to treat osteoporosis and metastatic osteolysis. We report here a novel method to elucidate the pathological mechanism of BRONJ by the selective removal of legacy N-BP from the jawbone using an intra-oral application of hydroxymethylene diphosphonate (HMDP) formulated in liposome-based deformable nanoscale vesicles (DNV). After maxillary tooth extraction, zoledronate-treated mice developed delayed gingival wound closure, delayed tooth extraction socket healing and increased jawbone osteonecrosis consistent with human BRONJ lesions. Single cell RNA sequencing of mouse gingival cells revealed oral barrier immune dysregulation and unresolved proinflammatory reaction. HMDP-DNV topical applications to nascent mouse BRONJ lesions resulted in accelerated gingival wound closure and bone socket healing as well as attenuation of osteonecrosis development. The gingival single cell RNA sequencing demonstrated resolution of chronic inflammation by increased anti-inflammatory signature gene expression of lymphocytes and myeloid-derived suppressor cells. This study suggests that BRONJ pathology is related to N-BP levels in jawbones and demonstrates the potential of HMDP-DNV as an effective BRONJ therapy.

Real-Time Prediction of Mortality, Cardiac Arrest, and Thromboembolic Complications in Hospitalized Patients With COVID-19.

Background: COVID-19 infection carries significant morbidity and mortality. Current risk prediction for complications in COVID-19 is limited, and existing approaches fail to account for the dynamic course of the disease. Objectives: The purpose of this study was to develop and validate the COVID-HEART predictor, a novel continuously updating risk-prediction technology to forecast adverse events in hospitalized patients with COVID-19. Methods: Retrospective registry data from patients with severe acute respiratory syndrome coronavirus 2 infection admitted to 5 hospitals were used to train COVID-HEART to predict all-cause mortality/cardiac arrest (AM/CA) and imaging-confirmed thromboembolic events (TEs) (n = 2,550 and n = 1,854, respectively). To assess COVID-HEART's performance in the face of rapidly changing clinical treatment guidelines, an additional 1,100 and 796 patients, admitted after the completion of development data collection, were used for testing. Leave-hospital-out validation was performed. Results: Over 20 iterations of temporally divided testing, the mean area under the receiver operating characteristic curve were 0.917 (95% confidence interval [CI]: 0.916-0.919) and 0.757 (95% CI: 0.751-0.763) for prediction of AM/CA and TE, respectively. The interquartile ranges of median early warning times were 14 to 21 hours for AM/CA and 12 to 60 hours for TE. The mean area under the receiver operating characteristic curve for the left-out hospitals were 0.956 (95% CI: 0.936-0.976) and 0.781 (95% CI: 0.642-0.919) for prediction of AM/CA and TE, respectively. Conclusions: The continuously updating, fully interpretable COVID-HEART predictor accurately predicts AM/CA and TE within multiple time windows in hospitalized COVID-19 patients. In its current implementation, the predictor can facilitate practical, meaningful changes in patient triage and resource allocation by providing real-time risk scores for these outcomes. The potential utility of the predictor extends to COVID-19 patients after hospitalization and beyond COVID-19.