Factors Affecting Continuous Renal Replacement Therapy (CRRT) in Patients With Septic Shock: An Analysis of a National Inpatient Sample Database.

Background: Septic shock is defined as sepsis with hypotension requiring vasopressors to maintain a mean arterial pressure above 65 mmHg and having a serum lactate level of more than 2 mmol/L despite adequate volume resuscitation as per the Sepsis-3 criteria. Continuous renal replacement therapy (CRRT) is commonly utilized in septic shock patients for the treatment of acute kidney injury as well as for modulating immune response and maintaining hemodynamic stability.

Methods: We looked at the National Inpatient Sample database in 2019.

Individual and prescription level factors associated with overdose in opioid naïve older people.

Background: Opioid naïve older adults may be at risk of overdose after receiving an initial opioid prescription.

Methods: This population-based cohort study from a linked dataset of patients in Oregon, linking all payer claims data to other administrative datasets, aimed to assess the prescription- and patient-level characteristics associated with increased odds of opioid overdose after an initial opioid prescription. Included patients were ≥65 years old and received an index pain-formulation opioid prescription between 2016 and 2019.

Detection of late gadolinium enhancement in patients with hypertrophic cardiomyopathy using machine learning.

Background: Late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) in hypertrophic cardiomyopathy (HCM) typically represents myocardial fibrosis and may lead to fatal ventricular arrhythmias. However, CMR is resource-intensive and sometimes contraindicated. Thus, in patients with HCM, we aimed to detect LGE on CMR by applying machine learning (ML) algorithm to clinical parameters.

Increasing financial incentives can lower the cost of trial recruitment.

Monetary incentives are commonly used to help recruit trial participants. Some studies have found greater recruitment with larger incentives, while others have found smaller incentives more cost-effective in terms of cost per participant. As part of an implementation study, we compared the impact of four approaches to recruitment, three of which involved phone recruitment with varying financial incentives.

Prediction of new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy using machine learning.

Aims: Atrial fibrillation (AF) is the most common sustained arrhythmia among patients with hypertrophic cardiomyopathy (HCM), leading to increased symptom burden and risk of thromboembolism. The HCM-AF score was developed to predict new-onset AF in patients with HCM, though sensitivity and specificity of this conventional tool are limited. Thus, there is a need for more accurate tools to predict new-onset AF in HCM.