Prospective validation of clinical deterioration predictive models prior to intensive care unit transfer among patients admitted to acute care cardiology wards.

. Very few predictive models have been externally validated in a prospective cohort following the implementation of an artificial intelligence analytic system. This type of real-world validation is critically important due to the risk of data drift, or changes in data definitions or clinical practices over time, that could impact model performance in contemporaneous real-world cohorts.

Beyond prediction: Off-target uses of artificial intelligence-based predictive analytics in a learning health system.

Introduction: Artificial-intelligence (AI)-based predictive analytics provide new opportunities to leverage rich sources of continuous data to improve patient care through early warning of the risk of clinical deterioration and improved situational awareness.Part of the success of predictive analytic implementation relies on integration of the analytic within complex clinical workflows. Pharmaceutical interventions have off-target uses where a drug indication has not been formally studied for a different indication but has potential for clinical benefit.

Signatures of illness in children requiring unplanned intubation in the pediatric intensive care unit: A retrospective cohort machine-learning study.

Acute respiratory failure requiring the initiation of invasive mechanical ventilation remains commonplace in the pediatric intensive care unit (PICU). Early recognition of patients at risk for respiratory failure may provide clinicians with the opportunity to intervene and potentially improve outcomes. Through the development of a random forest model to identify patients at risk for requiring unplanned intubation, we tested the hypothesis that subtle signatures of illness are present in physiological and biochemical time series of PICU patients in the early stages of respiratory decompensation.

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring.

A new development in the practice of medicine is Artificial Intelligence-based predictive analytics that forewarn clinicians of future deterioration of their patients. This proactive opportunity, though, is different from the reactive stance that clinicians traditionally take. Implementing these tools requires new ideas about how to educate clinician users to facilitate trust and adoption and to promote sustained use.

Predictive Monitoring-Impact in Acute Care Cardiology Trial (PM-IMPACCT): Protocol for a Randomized Controlled Trial.

Background: Patients in acute care wards who deteriorate and are emergently transferred to intensive care units (ICUs) have poor outcomes. Early identification of patients who are decompensating might allow for earlier clinical intervention and reduced morbidity and mortality. Advances in bedside continuous predictive analytics monitoring (ie, artificial intelligence [AI]-based risk prediction) have made complex data easily available to health care providers and have provided early warning of potentially catastrophic clinical events.

Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond.

As the global response to COVID-19 continues, nurses will be tasked with appropriately triaging patients, responding to events of clinical deterioration, and developing family-centered plans of care within a healthcare system exceeding capacity. Predictive analytics monitoring, an artificial intelligence (AI)-based tool that translates streaming clinical data into a real-time visual estimation of patient risks, allows for evolving acuity assessments and detection of clinical deterioration while the patient is in pre-symptomatic states. While nurses are on the frontline for the COVID-19 pandemic, the use of AI-based predictive analytics monitoring may help cognitively complex clinical decision-making tasks and pave a pathway for early detection of patients at risk for decompensation.

Major complications and mortality within 30 days of an electrophysiological procedure at an academic medical center: implications for developing national standards.

Background: Despite growing attention to performance and quality measures, national standards for reporting of outcomes after all electrophysiology (EP) procedures have not yet been developed. We sought to characterize the incidence and timing of adverse events up to 30 days after EP procedures at a tertiary academic medical center.

Methods And Results: We prospectively followed all patients undergoing EP procedures between January 2010 and September 2012.

Cost of a recall of a single-center experience managing the Riata defibrillator lead.

Riata and Riata ST defibrillator leads (St. Jude Medical, Sylmar, California) were recalled in 2011 due to increased risk of insulation failure leading to externalized cables. Fluoroscopic screening can identify insulation failure, although the relation between mechanical failure and electrical failure is unclear.

Increasing lead burden correlates with externalized cables during systematic fluoroscopic screening of Riata leads.

Purpose: Riata and Riata ST defibrillator leads (St. Jude Medical, Sylmar, CA, USA) have been recalled due to increased risk of insulation failure leading to externalized cables. As this mechanical failure does not necessarily correlate with electrical failure, it can be difficult to diagnose.

Impact of the CHA2DS2-VASc score on anticoagulation recommendations for atrial fibrillation.

Background: The Congestive heart failure, Hypertension, Age ≥ 75 years, Diabetes mellitus, Stroke (CHADS(2)) score is used to predict the need for oral anticoagulation for stroke prophylaxis in patients with atrial fibrillation. The Congestive heart failure, Hypertension, Age ≥ 75 years, Diabetes mellitus, Stroke, Vascular disease, Age 65-74 years, Sex category (CHA(2)DS(2)-VASc) schema has been proposed as an improvement. Our objective is to determine how adoption of the CHA(2)DS(2)-VASc score alters anticoagulation recommendations.

Gender and Racial Characteristics of Patients Referred to a Tertiary Atrial Fibrillation Center.

Atrial Fibrillation Centers (AFCs) are becoming increasingly common and are often developed at institutions to provide comprehensive evaluation and management for patients with atrial fibrillation (AF) including catheter and surgical ablation. Studies have shown that women and racial minority patients are less likely to be offered aggressive or invasive therapies. The University of Virginia (UVA) AFC was opened in 2004.