Atrial Fibrillation (AFIB) in the ICU: Incidence, Risk Factors, and Outcomes: The International AFIB-ICU Cohort Study.

OBJECTIVES: To assess the incidence, risk factors, and outcomes of atrial fibrillation (AF) in the ICU and to describe current practice in the management of AF. DESIGN: Multicenter, prospective, inception cohort study. SETTING: Forty-four ICUs in 12 countries in four geographical regions. SUBJECTS: Adult, acutely admitted ICU patients without a history of persistent/permanent AF or recent cardiac surgery were enrolled; inception periods were from October 2020 to June 2021. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We included 1,423 ICU patients and analyzed 1,415 (99.4%), among whom 221 patients had 539 episodes of AF. Most (59%) episodes were diagnosed with continuous electrocardiogram monitoring. The incidence of AF was 15.6% (95% CI, 13.8-17.6), of which newly developed AF was 13.3% (11.5-15.1). A history of arterial hypertension, paroxysmal AF, sepsis, or high disease severity at ICU admission was associated with AF. Used interventions to manage AF were fluid bolus 19% (95% CI 16-23), magnesium 16% (13-20), potassium 15% (12-19), amiodarone 51% (47-55), beta-1 selective blockers 34% (30-38), calcium channel blockers 4% (2-6), digoxin 16% (12-19), and direct current cardioversion in 4% (2-6). Patients with AF had more ischemic, thromboembolic (13.6% vs 7.9%), and severe bleeding events (5.9% vs 2.1%), and higher mortality (41.2% vs 25.2%) than those without AF. The adjusted cause-specific hazard ratio for 90-day mortality by AF was 1.38 (95% CI, 0.95-1.99). CONCLUSIONS: In ICU patients, AF occurred in one of six and was associated with different conditions. AF was associated with worse outcomes while not statistically significantly associated with 90-day mortality in the adjusted analyses. We observed variations in the diagnostic and management strategies for AF.

Telemedicine to Expand Access to Critical Care Around the World.

This multiauthored communication gives a state-of-the-art global perspective on the increasing adoption of tele-critical care. Exponentially increasing sophistication in the deployment of Computers, Information, and Communication Technology has ensured extending the reach of limited intensivists virtually and reaching the unreached. Natural disasters, COVID-19 pandemic, and wars have made tele-intensive care a reality. Concerns and regulatory issues are being sorted out, cross-border cost-effective tele-critical care is steadily increasing Components to set up a tele-intensive care unit, and overcoming barriers is discussed. Importance of developing best practice guidelines and retraining is emphasized.

Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID).

In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individualized mortality risk scores based on the anonymized clinical and laboratory data at admission and determine the probability of Deaths at 7 and 28 days. Data of 1393 admitted patients (Expired-8.54%) was collected from six Apollo Hospital centers (from April to July 2020) using a standardized template and electronic medical records. 63 Clinical and Laboratory parameters were studied based on the patient's initial clinical state at admission and laboratory parameters within the first 24 h. The Machine Learning (ML) modelling was performed using eXtreme Gradient Boosting (XGB) Algorithm. 'Time to event' using Cox Proportional Hazard Model was used and combined with XGB Algorithm. The prospective validation cohort was selected of 977 patients (Expired-8.3%) from six centers from July to October 2020. The Clinical API for the Algorithm is  http://20.44.39.47/covid19v2/page1.php being used prospectively. Out of the 63 clinical and laboratory parameters, Age [adjusted hazard ratio (HR) 2.31; 95% CI 1.52-3.53], Male Gender (HR 1.72, 95% CI 1.06-2.85), Respiratory Distress (HR 1.79, 95% CI 1.32-2.53), Diabetes Mellitus (HR 1.21, 95% CI 0.83-1.77), Chronic Kidney Disease (HR 3.04, 95% CI 1.72-5.38), Coronary Artery Disease (HR 1.56, 95% CI - 0.91 to 2.69), respiratory rate > 24/min (HR 1.54, 95% CI 1.03-2.3), oxygen saturation below 90% (HR 2.84, 95% CI 1.87-4.3), Lymphocyte% in DLC (HR 1.99, 95% CI 1.23-2.32), INR (HR 1.71, 95% CI 1.31-2.13), LDH (HR 4.02, 95% CI 2.66-6.07) and Ferritin (HR 2.48, 95% CI 1.32-4.74) were found to be significant. The performance parameters of the current model is at AUC ROC Score of 0.8685 and Accuracy Score of 96.89. The validation cohort had the AUC of 0.782 and Accuracy of 0.93. The model for Mortality Risk Prediction provides insight into the COVID Clinical and Laboratory Parameters at admission. It is one of the early studies, reflecting on 'time to event' at the admission, accurately predicting patient outcomes.