Plasma Glycated Hemoglobin A1c Could Predict 30-Day All-cause Mortality of Intensive Care Unit Patients with Hyperglycemia

Background: An increased serum glucose level is a common finding among patients admitted to hospital with acute illness, including the intensive care unit (ICU), even without a history of previous diabetes mellitus (DM]. Glycated hemoglobin (HbAlc) is not only a diagnostic tool for DM but may also has prognostic value for diabetic and non-diabetic populations. Objective(s): To assess the relationship between HbA1c level on admission and clinical outcome among patients admitted to the ICU due to cardiopulmonary disorders with hyperglycemia. Method(s): Patients consecutively admitted to the ICU due to cardiopulmonary disorders who presented with hyperglycemia at admission were evaluated during a 6-month period. HbAlc and serum glucose levels were tested on admission and during the first 24-48 hours of hospitalization. Patients were divided according to HbA1c and compared in term of demographics. We evaluated the effect of HbA1c levels at admission on the clinical outcomes. Result(s): Of patients with cardiopulmonary disorders who presented with hyperglycemia at admission to the ICU, 73 had HbA1c levels 6%, 92 had HbA1c levels < 6%: 63/165 (38.2%) known as diabetic patients. The 30-day all-cause mortality was higher in the group with high HbA1c levels;38/73 vs. 32/98 (P= 0.02). Increased length of stay in the ICU and Acute Physiology and Chronic Health Evaluation II (APACHE II) score were associated with HbA1 c 6% (P < 0.022 and P < 0.026), respectively Conclusion(s): HbAlc 6% has an important clinical prognostic value among diabetic and non-diabetic patients with cardiopulmonary disorders and hyperglycemia. Copyright © 2022 Israel Medical Association. All rights reserved.

An overview of the infrastructure seismic resilience assessment using Artificial Intelligence and Machine-learning algorithms

The concept of resilience is increasingly attracting attention, specifically after the recent Covid-19 pandemic. In the context of seismic threat, the infrastructure seismic resilience is essential to keep the functionality of critical infrastructure and emergency departments during the occurrence of the disastrous earthquake event and aims to recover them quickly afterward. To achieve these targets, lots of preparation ahead are necessary, and prediction analysis of the great number of scenarios for damage and recovery needs to be simulated, compared, and analyzed to offer optimal resilient infrastructure designs and retrofitting. To perform these actions, several studies have used different technics such as Artificial Intelligence (AI) technology and machine learning (ML) techniques. These technics, moreover, have gained a rapid increase in the last years. This paper aims to review the available concepts of AI and ML techniques while used for seismic infrastructure resilience context, specifically in its four major analysis components i.e., hazards, damage, losses, and recovery. Limitations are discussed and recommendations are finally offered. This analysis can inspire future researchers by exploring the overall characteristics of the published literature. © 2022, National Technical University of Athens. All rights reserved.