Significant cross talk occurs between inflammation and coagulation. Thus, coagulopathy is common in sepsis, potentially aggravating the prognosis. Initially, septic patients tend to exhibit a prothrombotic state through extrinsic pathway activation, cytokine-induced coagulation amplification, anticoagulant pathways suppression, and fibrinolysis impairment. In late sepsis stages, with the establishment of disseminated intravascular coagulation (DIC), hypocoagulability ensues. Traditional laboratory findings of sepsis, including thrombocytopenia, increased prothrombin time (PT) and fibrin degradation products (FDPs), and decreased fibrinogen, only present late in the course of sepsis. A recently introduced definition of sepsis-induced coagulopathy (SIC) aims to identify patients at an earlier stage when changes to coagulation status are still reversible. Nonconventional assays, such as the measurement of anticoagulant proteins and nuclear material levels, and viscoelastic studies, have shown promising sensitivity and specificity in detecting patients at risk for DIC, allowing for timely therapeutic interventions. This review outlines current insights into the pathophysiological mechanisms and diagnostic options of SIC.

We described features of hospitalized Covid-19 patients and identified predictors of clinical deterioration. We included patients consecutively admitted at Humanitas Research Hospital (Rozzano, Milan, Italy); retrospectively extracted demographic; clinical; laboratory and imaging findings at admission; used survival methods to identify factors associated with clinical deterioration (defined as intensive care unit (ICU) transfer or death), and developed a prognostic index. Overall; we analyzed 239 patients (29.3% females) with a mean age of 63.9 (standard deviation [SD]; 14.0) years. Clinical deterioration occurred in 70 patients (29.3%), including 41 (17.2%) ICU transfers and 36 (15.1%) deaths. The most common symptoms and signs at admission were cough (77.8%) and elevated respiratory rate (34.1%), while 66.5% of patients had at least one coexisting medical condition. Imaging frequently revealed ground-glass opacity (68.9%) and consolidation (23.8%). Age; increased respiratory rate; abnormal blood gas parameters and imaging findings; coexisting coronary heart disease; leukocytosis; lymphocytopenia; and several laboratory parameters (elevated procalcitonin; interleukin-6; serum ferritin; C-reactive protein; aspartate aminotransferase; lactate dehydrogenase; creatinine; fibrinogen; troponin-I; and D-dimer) were significant predictors of clinical deterioration. We suggested a prognostic index to assist risk-stratification (C-statistic; 0.845; 95% CI; 0.802–0.887). These results could aid early identification and management of patients at risk, who should therefore receive additional monitoring and aggressive supportive care.

A large number of prediction models are published with the objective of allowing personalized decision making for diagnostic or prognostic purposes. Conventional statistical measures of discrimination, calibration, or other measures of model performance are not well-suited for directly and clearly assessing the clinical value of scores or biomarkers. Decision curve analysis is an increasingly popular technique used to assess the clinical utility of a prognostic or diagnostic score/rule, or even of a biomarker. Clinical utility is expressed as the net benefit, which represents the net balance of patients’ benefits and harms and considers, implicitly, the consequences of clinical actions taken in response to a certain prediction score, rule, or biomarker. The net benefit is plotted against a range of possible exchange rates, representing the spectrum of possible patients’ and clinicians’ preferences. Decision curve analysis is a powerful tool for judging whether newly published or existing scores may truly benefit patients, and represents a significant advancement in improving transparent clinical decision making. This paper is meant to be an introduction to decision curve analysis and its interpretation for clinical investigators. Given the extensive advantages, we advocate applying decision curve analysis to all models intended for use in clinical practice.

Historically, communicable diseases, such as HIV/AIDS, viral hepatitis, malaria, poliomyelitis, tuberculosis, influenza and, more recently, the coronavirus disease 2019, have been at the center of global health concerns and initiatives, as they are transmitted from one person to another with a variety of ways, easily spread across national borders, and threaten the lives of millions of people all over the globe