Background: Acute respiratory distress syndrome (ARDS) is a severe disease with high mortality, and its primary cause is sepsis. The aim of this study was to detect and evaluate the role of Human epididymis protein 4 (HE4) in sepsis-related ARDS. Methods: One hundred and twenty-three critically ill sepsis patients with/without ARDS and 102 healthy controls were enrolled in this study. Blood samples were collected upon admission for quantitative testing of HE4 by chemiluminescent microparticle immunoassay (CMIA). ROC curve analysis and Spearman’s correlation analysis were conducted to determine the diagnostic and prognostic value of HE4. Results: Compared with controls, the serum HE4 concentrations of sepsis patients were elevated, and levels in sepsis patients with ARDS were significantly higher (all P<0.0001). Moreover, HE4 concentrations were strongly correlated with the clinical severity characteristics of sepsis patients, and ROC curve suggested that the AUC of HE4 applied to discriminate sepsis-ARDS patients from sepsis patients was 0.903. HE4 was also found to be a prognostic biomarker of clinical severity and 28-day mortality among critically ill sepsis patients. Logistic regression analysis showed that HE4 was an independent factor for diagnosis of ARDS. Meanwhile, ROC curve analysis showed that the cutoff value of serum HE4 to discriminate 28-day mortality from sepsis patients (AUC: 0.782) was 646.5 pmol/L. Conclusions: The concentration of serum HE4 in patients with sepsis-related ARDS was markedly increased and was significantly correlated with mortality, which suggests that serum HE4 could be a promising diagnostic and prognostic biomarker for ARDS in sepsis patients.

Monocyte distribution width (MDW) is a blood monocyte morphological parameter that can be easily detected by an automated hemocyte analyzer and can provide clinicians with important information about cell volume variability in peripheral blood monocyte populations. The United States’ Food and Drug Administration and Conformite Europeenne have both been cleared for their clinical application in the detection of sepsis and developing sepsis in adult patients in the emergency department (ED). Recently, MDW has been found to have an early diagnosis and predictive value for sepsis in neonates and COVID-19 patients. Here, we summarize the findings of the studies investigating the clinical application of MDW in sepsis. Under different stimuli, especially in infectious diseases, the activation of innate immunity is the host’s first defense mechanism, and the change in monocyte volume is considered an early indicator reflecting the state of activation of innate immunity. Pivotal study data from a large multicenter patient cohort showed that abnormal MDW at presentation increases the odds of sepsis, considering the combination of MDW and White Blood Cell Count (WBC) as part of a standard sepsis assessment protocol for ED, which may increase the sensitivity and specificity of sepsis diagnosis. Meanwhile, MDW shares a diagnostic performance comparable to that of conventional biomarkers (C-reactive protein and procalcitonin) in sepsis. In addition, some evidence suggests that increased MDW, both in adults and neonates, may be associated with unfavorable short- and long-term outcomes, which indicates its prognostic value in sepsis. Taken together, MDW is a parameter of increased morphological variability of monocytes in response to infection, and numerous studies have shown that MDW could be used as a valuable diagnostic and prognostic index in patients with sepsis or suspected sepsis.

Background: The authors investigated a panel of novel biomarkers for diagnosis and prognosis assessment of sepsis using machine learning (ML) methods. Methods: Hematological parameters, liver function indices and inflammatory marker levels of 332 subjects were retrospectively analyzed. Results: The authors constructed sepsis diagnosis models and identified the random forest (RF) model to be the most optimal. Compared with PCT (procalcitonin) and CRP (C-reactive protein), the RF model identified sepsis patients at an earlier stage. The sepsis group had a mortality rate of 36.3%, and the RF model had greater predictive ability for the 30-day mortality risk of sepsis patients. Conclusion: The RF model facilitated the identification of sepsis patients and showed greater accuracy in predicting the 30-day mortality risk of sepsis patients.