The siRNA-mediated knockdown of AP-1 restores the function of the pulmonary artery and the right ventricle by reducing perivascular and interstitial fibrosis and key molecular players in cardiopulmonary disease

Lung Ultrasonography Provides Robust, Predictive Clinical Data About COVID-19

Lung ultrasound (US) parameters have high discriminative accuracy across a range of clinical settings for COVID-19, according to study results published in The Journal of Infectious Diseases.

Investigators from the RAISON and EPICC research groups conducted an international multisite study to assess the generalizability and utility of lung US for COVID-19 prognostication across various clinical settings. Between June 2020 and June 2023, adults with polymerase chain reaction (PCR)- or rapid antigen-confirmed COVID-19 were prospectively enrolled in 5 cohorts designated for validation at Johns Hopkins Hospital (n=75; 39.3%), Duke University Hospital (n=28; 14.7%), Walter Reed National Military Medical Center (n=42; 22.0%), and Madigan Army Medical Center (n=15; 7.9%) in the United States, as well as Fort Portal Regional Referral Hospital (n=33; 16.2%) in Uganda. Data from an additional 264 patients from Johns Hopkins Hospital were used as the derivation cohort. At enrollment, patients underwent 12-zone lung US and 6-second clips were read offsite. The primary outcome was progression to higher-level care on the basis of lung US results. Logistic regression models were used in the analyses.

The pooled validation vs derivation cohort was more predominately women (57.9%; P =.002) and younger (median age, 45.0; P <.001), and comorbid conditions such as hypertension (35.1%), chronic pulmonary disease (16.8%), and diabetes (15.7%) were less common. At baseline, 84 (44.9%) patients in the validation cohort were hospitalized. Progression in oxygen requirements, hospitalization, or death during the study were observed in 11 (5.7%) patients.

In the validation cohort, the top 5 predictors for future progression were percent of lung fields with B-lines (cross-validated area under the receiver operating characteristic curve [cvAUC], 0.88; 95% CI, 0.87-0.90), discrete B-lines (cvAUC, 0.87; 95% CI, 0.85-0.88), oxygen saturation (cvAUC, 0.82; 95% CI, 0.81-0.84), A-lines (cvAUC, 0.80; 95% CI, 0.78-0.81), and mean lung US score (cvAUC, 0.74; 95% CI, 0.72-0.76).

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Validation of ultrasound models within target populations is critical for determining the clinical utility prior to adoption.

The top 5 predictors in the derivation cohort were respiratory rate (cvAUC, 0.7; 95% CI, 0.68-0.72), percent of lung fields with A-lines (cvAUC, 0.66; 95% CI, 0.65-0.67), mean lung US score (cvAUC, 0.63; 95% CI, 0.61-0.66), B-lines (cvAUC, 0.63; 95% CI, 0.60-0.66), and confluent B-lines (cvAUC, 0.63; 95% CI, 0.61-0.65).

Using a specificity cutoff of 70%, observing A-lines in less than 1/4 of lung fields (78%; 95% CI, 51%-100%) or B-lines in 1/2 and more of lung fields (78%; 95% CI, 51%-100%) was sensitive for predicting progression in the validation cohort. B-line sensitivity was similar between the 2 cohort groups, while specificity with any B- or discrete B-lines was higher in the validation vs derivation cohort.

In a pooled population of both the validation and derivation cohorts (n=454), the best predictors for COVID-19 progression were percent of B-lines (cvAUC 0.73; 95% CI, 0.72-0.73), percent of A-lines (cvAUC 0.71; 95% CI, 0.71-0.72), respiratory rate (cvAUC 0.70; 95% CI, 0.69-0.71), percent of discrete B-lines (cvAUC 0.70; 95% CI, 0.69-0.70), and mean lung US score (cvAUC 0.69; 95% CI, 0.68-0.69).

The best performing composite model comprised the predictors percent of B-lines, respiratory rate, heart rate, and percent of A-lines (cvAUC, 0.78; 95% CI, 0.77-0.80).

Study limitations include the heterogenous study populations, small sample sizes, and the inability to obtain arterial gas measurements.

"Validation of ultrasound models within target populations is critical for determining the clinical utility prior to adoption," the investigators concluded.

Disclosure: One study author declared affiliations with biotech, pharmaceutical, and/or device companies. Please see the original reference for a full list of authors' disclosures.

AI Enhances Understanding Of Radiation-induced Cardiac Arrhythmia Risk In Lung Cancer Patients

Researchers from Brigham and Women's Hospital, a founding member of the Mass General Brigham healthcare system, have used artificial intelligence tools to accelerate the understanding of the risk of specific cardiac arrhythmias when various parts of the heart are exposed to different thresholds of radiation as part of a treatment plan for lung cancer. Their results are published in JACC: CardioOncology.

"Radiation exposure to the heart during lung cancer treatment can have very serious and immediate effects on a patient's cardiovascular health," said corresponding author Raymond Mak, MD, of the Department of Radiation Oncology at Brigham and Women's Hospital. "We are hoping to inform not only oncologists and cardiologists, but also patients receiving radiation treatment, about the risks to the heart when treating lung cancer tumors with radiation."

The emergence of artificial intelligence tools in health care has been groundbreaking and has the potential to positively reshape the continuum of care, including informing treatment plans for patients with cancer. Mass General Brigham, as one of the nation's top integrated academic health systems and largest innovation enterprises, is leading the way in conducting rigorous research on new and emerging technologies to inform the responsible incorporation of AI into care delivery. 

For patients receiving radiation therapy to treat non-small cell lung cancer (NSCLC), arrhythmias or irregular rhythms of the heart can be common. Because of the close proximity of the heart to the lungs and with NSCLC tumors being near or around the heart, the heart can receive collateral damage from radiation dose spillage meant to target the cancer tumors. Prior studies have found that this type of exposure to the heart is associated with general cardiac issues. However, this nuanced study demonstrated that the risk for different types of arrhythmias can vary significantly based on the pathophysiology and cardiac structures that are exposed to different levels of radiation.

In order to classify the types of arrhythmias that are associated with cardiac substructures receiving radiation, researchers conducted a retrospective analysis on 748 patients in Massachusetts, who were treated with radiation for locally advanced NSCLC. The arrhythmia subtypes cataloged included atrial fibrillation, atrial flutter, other supraventricular tachycardia, bradyarrhythmia, and ventricular tachyarrhythmia or asystole.

The team's statistical analyses indicated that about one out of every six patients experienced at least one grade 3 arrhythmia with a median time of 2.0 years until the first arrhythmia. Grade 3 classifications are considered serious events that likely need intervention or require hospitalization. They also found that almost one-third of patients who experienced arrhythmias also suffered from major adverse cardiac events.

The arrhythmia classes outlined in the study did not entirely encompass the range of heart rhythm issues that are possible, but the authors note that these observations still create a better understanding of the possible pathophysiology pathways and potential avenues for minimizing cardiac toxicity after receiving radiation treatment. Their work also offers a predictive model for dose exposure and the type of expected arrhythmia.

For the future, the researchers believe that radiation oncologists should collaborate with cardiology experts to better understand the mechanisms of heart injuries and their connection to radiation treatment. In addition, they should take advantage of modern radiation treatment to actively sculpt radiation exposure away from the specific cardiac regions that are at high risk for causing arrhythmias. According to Mak, this study, alongside previous research, will help with surveillance, screening, and informing radiation oncologists on which parts of the heart to limit radiation exposure to, and in turn, mitigate complications.

An interesting part of what we did was leverage artificial intelligence algorithms to segment structures like the pulmonary vein and parts of the conduction system to measure the radiation dose exposure in over 700 patients. This saved us many months of manual work. So, not only does this work have potential clinical impact, but it also opens the door for using AI in radiation oncology research to streamline discovery and create larger datasets."

Raymond Mak, MD, Department of Radiation Oncology, Brigham and Women's Hospital

Source:

Journal reference:

Atkins, K., et al. (2024) Cardiac Substructure Radiation Dose and Associations With Tachyarrhythmia and Bradyarrhythmia After Lung Cancer Radiotherapy. JACC: CardioOncology. Doi.Org/10.1016/j.Jaccao.2024.07.005.

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