Sex differences in D-dimer and critical illness in patients with COVID-19: A systematic review and meta-analysis

Dynamics of coagulation proteins upon ICU admission and after one year of recovery from COVID-19: a preliminary study

Objectives

This study aimed to investigate the association of baseline coagulation proteins with hospitalization variables in COVID-19 patients admitted to ICU, as well as coagulation system changes after one-year post-discharge, taking into account gender-specific bias in the coagulation profile.

Methods

We conducted a prospective longitudinal study on 49 ICU-admitted COVID-19 patients. Proteins were measured using a Luminex 200™. The association between coagulation protein levels and hospitalization variables was carried out by generalized linear models adjusted by the most relevant covariates.

Results

At ICU admission, lower factor XII, antithrombin, and protein C levels were linked to the need for invasive mechanical ventilation (IMV) or its duration (p=0.028; p=0.047 and p=0.015, respectively). Likewise, lower factor XII, antithrombin, and prothrombin levels were associated with longer ICU length of stay (ICU LOS) (p=0.045; p=0.022; p=0.036, respectively). From baseline to the end of the follow-up, factor XII, antithrombin, prothrombin, and protein C levels notably increased in patients with longer ICU LOS. One-year post-discharge, differences were found for factor IX, aPTT, and INR. Gender-stratified analysis showed sustained alterations in males.

Conclusions

Depleted specific coagulation factors on ICU admission are associated with increased severity in critically ill COVID-19 patients. Most coagulation alterations recover one-year post-discharge, except for factor IX, aPTT and INR, which remain reduced.

Exploring the Prognostic Role of D-dimer Levels in Pancreatic Cancer: A Comprehensive Review of Clinicopathological Associations

Pancreatic cancer is known for its dismal prognosis and high mortality rate, primarily due to late-stage diagnosis and aggressive disease progression. Finding reliable prognostic biomarkers is crucial in improving patient outcomes and guiding treatment strategies. D-dimer, a fibrin degradation product, has emerged as a potential biomarker of interest in various cancers due to its association with coagulation abnormalities. This comprehensive review investigates the prognostic role of D-dimer levels in pancreatic cancer by synthesizing current research and exploring its clinicopathological associations. Elevated D-dimer levels in pancreatic cancer patients have been linked to poorer clinical outcomes, including reduced overall survival and increased disease progression. The review examines how D-dimer levels correlate with tumor characteristics such as stage, grade, and metastatic spread, highlighting its potential utility as a prognostic marker. Additionally, the review addresses the methodological challenges in D-dimer measurement and the need for standardized protocols to enhance the reliability and applicability of results. Future research directions are identified, focusing on validating D-dimer's clinical utility and integrating it into routine practice for risk stratification and personalized treatment planning. By providing a comprehensive overview of D-dimer's prognostic value, this review aims to contribute to developing more effective management strategies for pancreatic cancer, ultimately improving patient care and outcomes.

Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers

The COVID-19 influenza emerged and proved to be fatal, causing millions of deaths worldwide. Vaccines were eventually discovered, effectively preventing the severe symptoms caused by the disease. However, some of the population (elderly and patients with comorbidities) are still vulnerable to severe symptoms such as breathlessness and chest pain. Identifying these patients in advance is imperative to prevent a bad prognosis. Hence, machine learning and deep learning algorithms have been used for early COVID-19 severity prediction using clinical and laboratory markers. The COVID-19 data was collected from two Manipal hospitals after obtaining ethical clearance. Multiple nature-inspired feature selection algorithms are used to choose the most crucial markers. A maximum testing accuracy of 95% was achieved by the classifiers. The predictions obtained by the classifiers have been demystified using five explainable artificial intelligence techniques (XAI). According to XAI, the most important markers are c-reactive protein, basophils, lymphocytes, albumin, D-Dimer and neutrophils. The models could be deployed in various healthcare facilities to predict COVID-19 severity in advance so that appropriate treatments could be provided to mitigate a severe prognosis. The computer aided diagnostic method can also aid the healthcare professionals and ease the burden on already suffering healthcare infrastructure.