Tailoring Risk Prediction Models to Local Populations.

Importance: Risk estimation is an integral part of cardiovascular care. Local recalibration of guideline-recommended models could address the limitations of existing tools.

Objective: To provide a machine learning (ML) approach to augment the performance of the American Heart Association's Predicting Risk of Cardiovascular Disease Events (AHA-PREVENT) equations when applied to a local population while preserving clinical interpretability.

Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs : A Risk Prediction Study.

Background: Guidelines for primary prevention of atherosclerotic cardiovascular disease (ASCVD) recommend a risk calculator (ASCVD risk score) to estimate 10-year risk for major adverse cardiovascular events (MACE). Because the necessary inputs are often missing, complementary approaches for opportunistic risk assessment are desirable.

Objective: To develop and test a deep-learning model (CXR CVD-Risk) that estimates 10-year risk for MACE from a routine chest radiograph (CXR) and compare its performance with that of the traditional ASCVD risk score for implications for statin eligibility.

Congenitally Fused Cervical Spine Is Associated With Adjacent-Level Degeneration in the Absence of Cervical Spine Surgery.

Background: Cervical fusion surgery is associated with adjacent-level degeneration, but surgical and technical factors are difficult to dissociate from the mechanical effects of the fusion itself.

Objective: To determine the effect of fusion on adjacent-level degeneration in unoperated patients using a cohort of patients with congenitally fused cervical vertebrae.

Methods: We identified 96 patients with incidental single-level cervical congenital fusion on computed tomography imaging.

Validation of a Deep Learning-Based Model to Predict Lung Cancer Risk Using Chest Radiographs and Electronic Medical Record Data.

Importance: Lung cancer screening with chest computed tomography (CT) prevents lung cancer death; however, fewer than 5% of eligible Americans are screened. CXR-LC, an open-source deep learning tool that estimates lung cancer risk from existing chest radiograph images and commonly available electronic medical record (EMR) data, may enable automated identification of high-risk patients as a step toward improving lung cancer screening participation.

Objective: To validate CXR-LC using EMR data to identify individuals at high-risk for lung cancer to complement 2022 US Centers for Medicare & Medicaid Services (CMS) lung cancer screening eligibility guidelines.

Outcomes of a Student-Led Telemedicine Clinic in Response to COVID-19.

In response to the coronavirus disease-2019 (COVID-19) pandemic, we developed and launched a student-led telemedicine program in Chelsea. From April to November 2020, over 200 student volunteers contacted over 1000 patients to assess COVID-19 symptoms, provide counseling, and triage patients. Through a retrospective cohort study, we determined that student triage decision was associated with patient outcomes, including hospitalization status, COVID-19 test administration, and COVID-19 test result.

Predicting Discharge Disposition Following Meningioma Resection Using a Multi-Institutional Natural Language Processing Model.

Background: Machine learning (ML)-based predictive models are increasingly common in neurosurgery, but typically require large databases of discrete variables for training. Natural language processing (NLP) can extract meaningful data from unstructured text.

Objective: To present an NLP model that predicts nonhome discharge and a point-of-care implementation.

Ezh2 does not mediate retinal ganglion cell homeostasis or their susceptibility to injury.

Epigenetic predisposition is thought to critically contribute to adult-onset disorders, such as retinal neurodegeneration. The histone methyltransferase, enhancer of zeste homolog 2 (Ezh2), is transiently expressed in the perinatal retina, particularly enriched in retinal ganglion cells (RGCs). We previously showed that embryonic deletion of Ezh2 from retinal progenitors led to progressive photoreceptor degeneration throughout life, demonstrating a role for embryonic predisposition of Ezh2-mediated repressive mark in maintaining the survival and function of photoreceptors in the adult.