Neuromuscular Blockade Reversal with Sugammadex Reduces Cardiac Complications and OR Time for Prone Lumbar Spinal Fusion Compared to Neostigmine.

Study Design: Retrospective cohort study.

Objective: To evaluate inpatient complication profiles of patients receiving neuromuscular blockade reversal via sugammadex versus neostigmine/glycopyrrolate.

Summary Of Background Data: Sugammadex is a neuromuscular blockade reversal agent that binds non-depolarizing muscle relaxants.

Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.

Background: Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems.

Objective: To assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score.

Machine learning identification of patient clusters for cutaneous melanoma.

Background: Cutaneous melanoma is a cancer arising in melanocyte skin cells and is the deadliest form of skin cancer worldwide. Although some risk factors are known, accurate prediction of disease progression and probability for metastasis are difficult to ascertain, given the complexity of the disease and the absence of reliable predictive markers. Since early detection and treatment are essential to enhance survival, this study utilizing machine learning (ML) aims to further delineate additional risk factors associated with cutaneous melanoma.