Sepsis: New Challenges and Future Perspectives for an Evolving Disease—Precision Medicine Is the Way!

Mirijello, Antonio; Tosoni, Alberto

doi:10.3390/medicina57101109

Cited by 6 publications

(13 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The key challenges in surviving sepsis revolve around early diagnosis and treatment, emphasizing the need for continued research efforts. Investigating methods to stratify patients using biomarkers and other tools holds promise in enhancing the speed of diagnosis and treatment, a critical factor in improving sepsis outcomes [ 55 ]. The landscape of sepsis treatment is expected to evolve towards more individualized approaches over the next five years.…”

Section: Reviewmentioning

confidence: 99%

“…The landscape of sepsis treatment is expected to evolve towards more individualized approaches over the next five years. This transformation comprehensively considers factors such as the individual's profile, type of infection, chronic conditions, and overall patient characteristics, contributing to a more tailored and effective treatment paradigm [ 55 ].…”

Section: Reviewmentioning

confidence: 99%

“…Biomarkers and predictive analytics represent another avenue of exploration for researchers in understanding the pathophysiological aspects and potential management strategies of sepsis. Ongoing studies aim to refine diagnostic and prognostic capabilities, improving the overall understanding and treatment of sepsis [ 55 ]. The future of sepsis treatment should also delve into the intricate relationship between microbiomes and immunomodulation in developing and progressing sepsis.…”

Section: Reviewmentioning

confidence: 99%

“…The future of sepsis treatment should also delve into the intricate relationship between microbiomes and immunomodulation in developing and progressing sepsis. Exploring these factors holds the potential to uncover novel therapeutic interventions that could revolutionize sepsis management [ 55 ]. In diagnostics, the development of new tools, including artificial intelligence, emerges as a critical component for achieving better, less invasive, and more accurate sepsis diagnoses.…”

Section: Reviewmentioning

confidence: 99%

See 3 more Smart Citations

Navigating the Complexity of Scoring Systems in Sepsis Management: A Comprehensive Review

Reddy,

Gemnani

et al. 2024

Cureus

View full text Add to dashboard Cite

This comprehensive review navigates the intricate landscape of sepsis scoring systems, aiming to provide healthcare professionals and researchers with a nuanced understanding of their role in contemporary sepsis management. Beginning with a succinct overview of sepsis, the review emphasizes the significance of scoring systems in standardizing assessments and guiding clinical decision-making. Through a detailed analysis of prominent systems such as SOFA, APACHE, and qSOFA, the review delineates their unique attributes, strengths, and limitations. The implications for sepsis management and patient outcomes are discussed, highlighting the potential for these tools to enhance early detection and intervention. The review concludes with a compelling call to action, urging healthcare professionals to integrate scoring systems into routine practice and researchers to explore novel approaches. By synthesizing current knowledge and addressing future directions, this review serves as a valuable resource for those seeking clarity and guidance in the dynamic landscape of sepsis management.

show abstract

Section: Reviewmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Navigating the Complexity of Scoring Systems in Sepsis Management: A Comprehensive Review

Reddy,

Gemnani

et al. 2024

Cureus

View full text Add to dashboard Cite

show abstract

“…According to the Sepsis-3 Consensus, the de nition of severe sepsis was abolished, and the presence of organ dysfunction was considered a necessary condition to de ne sepsis [1]. Sepsis is a leading cause of in-hospital mortality and morbidity globally [1,2,3]. Administrative databases are increasingly used in research studies to capture clinical outcomes such as sepsis.…”

mentioning

confidence: 99%

Sepsis in patients hospitalized in Sicily over the years 2016–2020: a retrospective study

Pipitò,

Puccio,

Marrali

et al. 2023

Preprint

View full text Add to dashboard Cite

Background: Sepsis is a leading cause of in-hospital mortality and morbidity globally. Epidemiological data regarding the incidence and mortality of sepsis are scarcely available. Most reports are based on administrative. Furthermore, sepsis in non-intensive care units has been considered only in a few studies. The present study aimed to describe the epidemiology of sepsis and related outcomes among patients hospitalized in Sicily and analyze the features associated with high mortality. Methods: Data on the discharge forms of all patients with sepsis in all Sicilian hospitals from January 2016 to December 2020 were retrospectively collected. Age, sex, comorbidities, infections, and blood alterations were evaluated using univariate analysis according to in-hospital mortality. The factors associated with mortality were included in the logistic regression model. Results: In Sicily, 15373 hospitalizations associated with sepsis occurred from 2016 to 2020 with a mortality of 36.3%. The percentage of hospitalizations associated with sepsis represented 0.65 % of all admissions with an increase over the years. The diagnosis of septic shock was reported in 42.0%, with a mortality of 67.7%. The average cost and length of stay were EUR 8849 ± 10395 and 18 ± 22 days, respectively. Septic shock was an independent predictor of mortality. Older age was an independent predictor of mortality in adults. Klebsiella pneumoniae, aspergillosis, peritonitis, chronic respiratory failure, and hypernatremia, were independently associated with death in non-Intensive Care Unit (ICU) adult patients. For paediatric patients, gastrointestinal infection was an independent predictor of mortality, and only for non-ICU hospitalizations, diabetes, and anaemia were. Conclusions: In Sicily, sepsis is a significant cause of in-hospital mortality, especially for ICU-admitted patients and patients with septic shock. From 2016 to 2020, there was an increase in hospitalizations with sepsis, mortality associated with sepsis and septic shock diagnosis. Hospitalizations associated with sepsis presented high charges, especially for ICU-admitted and septic shock diagnoses. Septic shock was an independent predictor of mortality for all patient groups. Older age, chronic respiratory failure, peritonitis, Klebsiella pneumoniae infection, aspergillosis, and hypernatremia were associated with higher mortality in non-ICU admitted adults, while diabetes, anaemia, and gastrointestinal disease for children non-ICU admitted.

show abstract

Identifying Predictors Associated with Risk of Death or Admission to Intensive Care Unit in Internal Medicine Patients with Sepsis: A Comparison of Statistical Models and Machine Learning Algorithms

et al. 2023

Self Cite

View full text Add to dashboard Cite

Background: Sepsis is a time-dependent disease: the early recognition of patients at risk for poor outcome is mandatory. Aim: To identify prognostic predictors of the risk of death or admission to intensive care units in a consecutive sample of septic patients, comparing different statistical models and machine learning algorithms. Methods: Retrospective study including 148 patients discharged from an Italian internal medicine unit with a diagnosis of sepsis/septic shock and microbiological identification. Results: Of the total, 37 (25.0%) patients reached the composite outcome. The sequential organ failure assessment (SOFA) score at admission (odds ratio (OR): 1.83; 95% confidence interval (CI): 1.41–2.39; p < 0.001), delta SOFA (OR: 1.64; 95% CI: 1.28–2.10; p < 0.001), and the alert, verbal, pain, unresponsive (AVPU) status (OR: 5.96; 95% CI: 2.13–16.67; p < 0.001) were identified through the multivariable logistic model as independent predictors of the composite outcome. The area under the receiver operating characteristic curve (AUC) was 0.894; 95% CI: 0.840–0.948. In addition, different statistical models and machine learning algorithms identified further predictive variables: delta quick-SOFA, delta-procalcitonin, mortality in emergency department sepsis, mean arterial pressure, and the Glasgow Coma Scale. The cross-validated multivariable logistic model with the least absolute shrinkage and selection operator (LASSO) penalty identified 5 predictors; and recursive partitioning and regression tree (RPART) identified 4 predictors with higher AUC (0.915 and 0.917, respectively); the random forest (RF) approach, including all evaluated variables, obtained the highest AUC (0.978). All models’ results were well calibrated. Conclusions: Although structurally different, each model identified similar predictive covariates. The classical multivariable logistic regression model was the most parsimonious and calibrated one, while RPART was the easiest to interpret clinically. Finally, LASSO and RF were the costliest in terms of number of variables identified.

show abstract

Sepsis: New Challenges and Future Perspectives for an Evolving Disease—Precision Medicine Is the Way!

Abstract: Sepsis still remains the leading cause of in-hospital death in the world [...]

Cited by 6 publications

References 18 publications

Navigating the Complexity of Scoring Systems in Sepsis Management: A Comprehensive Review

Navigating the Complexity of Scoring Systems in Sepsis Management: A Comprehensive Review

Sepsis in patients hospitalized in Sicily over the years 2016–2020: a retrospective study

Identifying Predictors Associated with Risk of Death or Admission to Intensive Care Unit in Internal Medicine Patients with Sepsis: A Comparison of Statistical Models and Machine Learning Algorithms

Contact Info

Product

Resources

About