Identification and analysis of genes associated with the severity and prognosis of sepsis

Significances of miRNAs for predicting sepsis mortality: a meta-analysis

Background

Sepsis is a life-threatening condition caused by a dysregulated immune response to infection and remains a major cause of mortality in intensive care units (ICUs). Recent studies have identified microRNAs (miRNAs), a class of small RNA molecules, as potential biomarkers for diagnosing and predicting outcomes in sepsis patients. However, the results of these studies have been inconsistent. This meta-analysis aims to comprehensively evaluate the diagnostic and prognostic value of miRNAs in predicting sepsis-related mortality.

Methods

A comprehensive literature search was performed across major databases, including PubMed, Cochrane Library, EMBASE, and CNKI, up to April 7, 2024. Data extraction and meta-analysis were conducted using Meta-disk 1.4 and STATA 15.1, employing both fixed- and random-effects models to ensure robust statistical analysis.

Results

A total of 55 studies met the inclusion criteria and were analyzed. The pooled sensitivity, specificity, and area under the summary receiver operating characteristic (SROC) curve for miRNA detection were calculated. The overall performance of total miRNA detection demonstrated a sensitivity of 0.76 (95% confidence interval [CI]: 0.74-0.77), a specificity of 0.72 (95% CI: 0.71-0.73), and an SROC value of 0.83. Subgroup analyses revealed that miR-133a-3p exhibited the highest diagnostic accuracy, with a pooled sensitivity of 0.83 (95% CI: 0.70-0.92), specificity of 0.79 (95% CI: 0.71-0.86), and an SROC value of 0.90. Additionally, other miRNAs, including miR-146a, miR-21, miR-210, miR-223-3p, miR-155, miR-25, miR-122, miR-125a, miR-125b, and miR-150, also demonstrated high SROC values (0.84 to 0.76).

Conclusion

This meta-analysis underscores the potential of several microRNAs (miRNAs) as reliable biomarkers for predicting sepsis mortality. Specifically, miR-133a-3p, miR-146a, miR-21, miR-210, miR-223-3p, miR-155, miR-25, miR-122, miR-125b, and miR-150 emerge as promising candidates for clinical applications in sepsis prognosis.

A predictive model for 28-day mortality after discharge in patients with sepsis associated with cerebrovascular disease

BACKGROUND

The present study investigated the association between cerebrovascular diseases and sepsis, including its occurrence, progression, and impact on mortality. However, there is currently a lack of predictive models for 28-day mortality in patients with cerebrovascular disease associated with sepsis.

OBJECTIVE

The objective of this study is to examine the mortality rate within 28 days after discharge in this population, while concurrently developing a corresponding predictive model.

METHODS

The data for this retrospective cohort study were obtained from the MIMIC-IV database. Patients with sepsis and cerebrovascular disease in the ICU were included. Laboratory indicators, vital signs, and demographic data were collected within 24 hours of ICU admission. Mortality rates within 28 days after discharge were calculated based on patient death times. Logistic regression analysis was used to identify potential variables for a predictive model. A nomogram visualized the prediction model. The performance of the model was evaluated using ROC curves, Calibration plots, and DCA.

RESULTS

The study enrolled a total of 2660 patients diagnosed with cerebrovascular disease complicated by sepsis, consisting of 1434 males (53.91%) with a median age of 70.97 (59.60, 80.73). Among this cohort of patients, a total of 751 fatalities occurred within 28 days following discharge. The multivariate regression analysis revealed that age, creatinine, arterial oxygen partial pressure (Pa O2), arterial carbon dioxide partial pressure (Pa CO2), respiratory rate, white blood cell (WBC) count, Body Mass Index (BMI), and race demonstrated potential predictive variables. The aforementioned model yielded an area under the ROC curve of 0.744, accompanied by a sensitivity of 66.2% and specificity of 71.2%. Furthermore, both calibration plots and DCA demonstrated robust performance in practical applications.

CONCLUSION

The proposed prediction model allows clinicians to promptly assess the mortality risk in patients with cerebrovascular disease complicated by sepsis within 28 days after discharge, facilitating early intervention strategies. Consequently, clinicians can implement additional advantageous medical interventions for individuals with cerebrovascular disease and sepsis.

Genetic polymorphisms associated with sepsis incidence, severity, and outcomes among neonates: A mini-review

Genetic variation remains a topic of great interest due to its potential as a risk factor for various diseases. Interactions between genes contribute to diverse phenotypes in response to factors such as infection. The impact of genetic background on susceptibility and clinical outcomes, particularly in neonatal sepsis, has gained recognition. The variability in sepsis susceptibility and outcomes can be attributed to the genetic diversity in coding regions and regulatory elements of genes related to innate immune response. Recent advances in genomics and technology have shed light on genetic polymorphisms among humans, often represented by single-nucleotide polymorphisms (SNPs). These SNPs encode proteins crucial for recognizing and responding to pathogenic bacteria, including Toll-like receptor 4, CD14, tumor necrosis factor-alpha, as well as interleukin-1-10. This literature review specifically discusses the involvement of genetic polymorphism during the pathogenesis stage of sepsis, with an emphasis on previous research findings in neonatal sepsis cases, aiming to discuss the implications of polymorphism in sepsis susceptibility and outcomes.