Outcome Predictive Value of Serum Ferritin in ICU Patients with Long ICU Stay

Iron Metabolism in the Recovery Phase of Critical Illness with a Focus on Sepsis

Iron is an essential nutrient for humans and microbes, such as bacteria. Iron deficiency commonly occurs in critically ill patients, but supplementary iron therapy is not considered during the acute phase of critical illness since it increases iron availability for invading microbes and oxidative stress. However, persistent iron deficiency in the recovery phase is harmful and has potential adverse outcomes such as cognitive dysfunction, fatigue, and cardiopulmonary dysfunction. Therefore, it is important to treat iron deficiency quickly and efficiently. This article reviews current knowledge about iron-related biomarkers in critical illness with a focus on patients with sepsis, and provides possible criteria to guide decision-making for iron supplementation in the recovery phase of those patients.

A Comprehensive Review on Serum Ferritin as a Prognostic Marker in Intensive Care Units: Insights Into Ischemic Heart Disease

Serum ferritin has garnered considerable attention as a prognostic marker in intensive care units (ICUs), offering valuable insights into patient outcomes and clinical management strategies. This comprehensive review examines the role of serum ferritin in predicting outcomes among critically ill patients, with a particular focus on its implications for ischemic heart disease (IHD). Elevated serum ferritin levels have consistently been associated with adverse outcomes in ICU settings, including increased mortality, prolonged hospital stays, and higher morbidity rates. Furthermore, the relationship between serum ferritin levels and IHD underscores its potential as a biomarker for cardiovascular risk assessment in critically ill populations. The review synthesizes existing literature to highlight the predictive value of serum ferritin in assessing illness severity and guiding clinical decision-making in the ICUs. It also explores potential mechanisms linking serum ferritin to adverse outcomes and discusses implications for clinical practice. Integrating serum ferritin measurements into routine assessments could enhance prognostication and risk stratification in ICU patients, while further research is needed to elucidate optimal management strategies and therapeutic targets. Collaborative efforts between clinicians and researchers are essential to advance our understanding of serum ferritin's prognostic value in the ICUs and translate this knowledge into improved patient care and outcomes.

Potential Immune Indicators for Predicting the Prognosis of COVID-19 and Trauma: Similarities and Disparities

Although cellular and molecular mediators of the immune system have the potential to be prognostic indicators of disease outcomes, temporal interference between diseases might affect the immune mediators, and make them difficult to predict disease complications. Today one of the most important challenges is predicting the prognosis of COVID-19 in the context of other inflammatory diseases such as traumatic injuries. Many diseases with inflammatory properties are usually polyphasic and the kinetics of inflammatory mediators in various inflammatory diseases might be different. To find the most appropriate evaluation time of immune mediators to accurately predict COVID-19 prognosis in the trauma environment, researchers must investigate and compare cellular and molecular alterations based on their kinetics after the start of COVID-19 symptoms and traumatic injuries. The current review aimed to investigate the similarities and differences of common inflammatory mediators (C-reactive protein, procalcitonin, ferritin, and serum amyloid A), cytokine/chemokine levels (IFNs, IL-1, IL-6, TNF-α, IL-10, and IL-4), and immune cell subtypes (neutrophil, monocyte, Th1, Th2, Th17, Treg and CTL) based on the kinetics between patients with COVID-19 and trauma. The mediators may help us to accurately predict the severity of COVID-19 complications and follow up subsequent clinical interventions. These findings could potentially help in a better understanding of COVID-19 and trauma pathogenesis.

Predicting the severity of disease progression in COVID-19 at the individual and population level: A mathematical model

The impact of COVID-19 disease on health and economy has been global, and the magnitude of devastation is unparalleled in modern history. Any potential course of action to manage this complex disease requires the systematic and efficient analysis of data that can delineate the underlying pathogenesis. We have developed a mathematical model of disease progression to predict the clinical outcome, utilizing a set of causal factors known to contribute to COVID-19 pathology such as age, comorbidities, and certain viral and immunological parameters. Viral load and selected indicators of a dysfunctional immune response, such as cytokines IL-6 and IFNα, which contribute to the cytokine storm and fever, parameters of inflammation d-dimer and ferritin, aberrations in lymphocyte number, lymphopenia, and neutralizing antibodies were included for the analysis. The model provides a framework to unravel the multi-factorial complexities of the immune response manifested in SARS-CoV-2 infected individuals. Further, this model can be valuable to predict clinical outcome at an individual level, and to develop strategies for allocating appropriate resources to mitigate severe cases at a population level.

Predicting the Severity of Disease Progression in COVID-19 at the Individual and Population Level: A Mathematical Model

The impact of COVID-19 disease on health and economy has been global, and the magnitude of devastation is unparalleled in modern history. Any potential course of action to manage this complex disease requires the systematic and efficient analysis of data that can delineate the underlying pathogenesis. We have developed a mathematical model of disease progression to predict the clinical outcome, utilizing a set of causal factors known to contribute to COVID-19 pathology such as age, comorbidities, and certain viral and immunological parameters. Viral load and selected indicators of a dysfunctional immune response, such as cytokines IL-6 and IFNα which contribute to the cytokine storm and fever, parameters of inflammation D-Dimer and Ferritin, aberrations in lymphocyte number, lymphopenia, and neutralizing antibodies were included for the analysis. The model provides a framework to unravel the multi-factorial complexities of the immune response manifested in SARS-CoV-2 infected individuals. Further, this model can be valuable to predict clinical outcome at an individual level, and to develop strategies for allocating appropriate resources to manage severe cases at a population level.