A randomized trial to compare procalcitonin and C-reactive protein in assessing severity of sepsis and in guiding antibacterial therapy in Egyptian critically ill patients

Benefits and Harms of Procalcitonin- or C-Reactive Protein-Guided Antimicrobial Discontinuation in Critically Ill Adults With Sepsis: A Systematic Review and Network Meta-Analysis

OBJECTIVES

In sepsis treatment, antibiotics are crucial, but overuse risks development of antibiotic resistance. Recent guidelines recommended the use of procalcitonin to guide antibiotic cessation, but solid evidence is insufficient. Recently, concerns were raised that this strategy would increase recurrence. Additionally, optimal protocol or difference from the commonly used C-reactive protein (CRP) are uncertain. We aimed to compare the effectiveness and safety of procalcitonin- or CRP-guided antibiotic cessation strategies with standard of care in sepsis.

DATA SOURCES

A systematic search of PubMed, Embase, CENTRAL, Igaku Chuo Zasshi, ClinicalTrials.gov , and World Health Organization International Clinical Trials Platform.

STUDY SELECTION

Randomized controlled trials involving adults with sepsis in intensive care.

DATA EXTRACTION

A systematic review with network meta-analyses was performed. The Grading of Recommendations, Assessments, Developments, and Evaluation method was used to assess certainty.

DATA SYNTHESIS

Eighteen studies involving 5023 participants were included. Procalcitonin-guided and CRP-guided strategies shortened antibiotic treatment (-1.89 days [95% CI, -2.30 to -1.47], -2.56 days [95% CI, -4.21 to -0.91]) with low- to moderate-certainty evidence. In procalcitonin-guided strategies, this benefit was consistent even in subsets with shorter baseline antimicrobial duration (7-10 d) or in Sepsis-3, and more pronounced in procalcitonin cutoff of "0.5 μg/L and 80% reduction." No benefit was observed when monitoring frequency was less than half of the initial 10 days. Procalcitonin-guided strategies lowered mortality (-27 per 1000 participants [95% CI, -45 to -7]) and this was pronounced in Sepsis-3, but CRP-guided strategies led to no difference in mortality. Recurrence did not increase significantly with either strategy (very low to low certainty).

CONCLUSIONS

In sepsis, procalcitonin- or CRP-guided antibiotic discontinuation strategies may be beneficial and safe. In particular, the usefulness of procalcitonin guidance for current Sepsis-3, where antimicrobials are used for more than 7 days, was supported. Well-designed studies are needed focusing on monitoring protocol and recurrence.

Biomarkers in sepsis

Sepsis is a life-threatening condition characterized by dysregulated host response to infection leading to organ dysfunction. Despite advances in understanding its pathology, sepsis remains a global health concern and remains a major contributor to mortality. Timely identification is crucial for improving clinical outcomes, as delayed treatment significantly impacts survival. Accordingly, biomarkers play a pivotal role in diagnosis, risk stratification, and management. This review comprehensively discusses various biomarkers in sepsis and their potential application in antimicrobial stewardship and risk assessment. Biomarkers such as white blood cell count, neutrophil to lymphocyte ratio, erythrocyte sedimentation rate, C-reactive protein, interleukin-6, presepsin, and procalcitonin have been extensively studied for their diagnostic and prognostic value as well as in guiding antimicrobial therapy. Furthermore, this review explores the role of biomarkers in risk stratification, emphasizing the importance of identifying high-risk patients who may benefit from specific therapeutic interventions. Moreover, the review discusses the emerging field of transcriptional diagnostics and metagenomic sequencing. Advances in sequencing have enabled the identification of host response signatures and microbial genomes, offering insight into disease pathology and aiding species identification. In conclusion, this review provides a comprehensive overview of the current understanding and future directions of biomarker-based approaches in sepsis diagnosis, management, and personalized therapy.

Role of biomarkers in antimicrobial stewardship: physicians' perspectives

Biomarkers are playing an increasingly important role in antimicrobial stewardship. Their applications have included use in algorithms that evaluate suspected bacterial infections or provide guidance on when to start or stop antibiotic therapy, or when therapy should be repeated over a short period (6-12 h). Diseases in which biomarkers are used as complementary tools to determine the initiation of antibiotics include sepsis, lower respiratory tract infection (LRTI), COVID-19, acute heart failure, infectious endocarditis, acute coronary syndrome, and acute pancreatitis. In addition, cut-off values of biomarkers have been used to inform the decision to discontinue antibiotics for diseases such as sepsis, LRTI, and febrile neutropenia. The biomarkers used in antimicrobial stewardship include procalcitonin (PCT), C-reactive protein (CRP), presepsin, and interleukin (IL)-1β/IL-8. The cut-off values vary depending on the disease and study, with a range of 0.25-1.0 ng/mL for PCT and 8-50 mg/L for CRP. Biomarkers can complement clinical diagnosis, but further studies of microbiological biomarkers are needed to ensure appropriate antibiotic selection.

Procalcitonin-guided antibiotic therapy may shorten length of treatment and may improve survival-a systematic review and meta-analysis

BACKGROUND

Appropriate antibiotic (AB) therapy remains a challenge in the intensive care unit (ICU). Procalcitonin (PCT)-guided AB stewardship could help optimize AB treatment and decrease AB-related adverse effects, but firm evidence is still lacking. Our aim was to compare the effects of PCT-guided AB therapy with standard of care (SOC) in critically ill patients.

METHODS

We searched databases CENTRAL, Embase and Medline. We included randomized controlled trials (RCTs) comparing PCT-guided AB therapy (PCT group) with SOC reporting on length of AB therapy, mortality, recurrent and secondary infection, ICU length of stay (LOS), hospital LOS or healthcare costs. Due to recent changes in sepsis definitions, subgroup analyses were performed in studies applying the Sepsis-3 definition. In the statistical analysis, a random-effects model was used to pool effect sizes.

RESULTS

We included 26 RCTs (n = 9048 patients) in the quantitative analysis. In comparison with SOC, length of AB therapy was significantly shorter in the PCT group (MD - 1.79 days, 95% CI: -2.65, - 0.92) and was associated with a significantly lower 28-day mortality (OR 0.84, 95% CI: 0.74, 0.95). In Sepsis-3 patients, mortality benefit was more pronounced (OR 0.46 95% CI: 0.27, 0.79). Odds of recurrent infection were significantly higher in the PCT group (OR 1.36, 95% CI: 1.10, 1.68), but there was no significant difference in the odds of secondary infection (OR 0.81, 95% CI: 0.54, 1.21), ICU and hospital length of stay (MD - 0.67 days 95% CI: - 1.76, 0.41 and MD - 1.23 days, 95% CI: - 3.13, 0.67, respectively).

CONCLUSIONS

PCT-guided AB therapy may be associated with reduced AB use, lower 28-day mortality but higher infection recurrence, with similar ICU and hospital length of stay. Our results render the need for better designed studies investigating the role of PCT-guided AB stewardship in critically ill patients.

Biomarkers in the intensive care setting: A focus on using procalcitonin and C-reactive protein to optimize antimicrobial duration of therapy

Managing the critically ill patient with infection is complex, requiring clinicians to synthesize considerable information relating to antimicrobial efficacy and treatment duration. The use of biomarkers may play an important role in identifying variation in treatment response and providing information about treatment efficacy. Though a vast number of biomarkers for clinical application have been described, procalcitonin and C-reactive protein (CRP) are the most thoroughly investigated in the critically ill. However, the presence of heterogeneous populations, variable end points, and incongruent methodology in the literature complicates the use of such biomarkers to guide antimicrobial therapy. This review focuses on an appraisal of evidence for use of procalcitonin and CRP to optimize antimicrobial duration of therapy (DOT) in critically ill patients. Procalcitonin-guided antimicrobial therapy in mixed critically ill populations with varying degrees of sepsis appears to be safe and might assist in reducing antimicrobial DOT. Compared to procalcitonin, fewer studies exist examining the impact of CRP on antimicrobial DOT and clinical outcomes in the critically ill. Procalcitonin and CRP have been insufficiently studied in many key intensive care unit populations, including surgical patients with concomitant trauma, renally insufficient populations, the immunocompromised, and patients with septic shock. We believe the available evidence is not strong enough to warrant routine use of procalcitonin or CRP to guide antimicrobial DOT in critically ill patients with infection. So long as its limitations are recognized, procalcitonin could be considered to tailor antimicrobial DOT on a case-by-case basis in the critically ill patient.

Machine Learning Models for Prediction of Severe Pneumocystis carinii Pneumonia after Kidney Transplantation: A Single-Center Retrospective Study

BACKGROUND

The objective of this study was to formulate and validate a prognostic model for postoperative severe Pneumocystis carinii pneumonia (SPCP) in kidney transplant recipients utilizing machine learning algorithms, and to compare the performance of various models.

METHODS

Clinical manifestations and laboratory test results upon admission were gathered as variables for 88 patients who experienced PCP following kidney transplantation. The most discriminative variables were identified, and subsequently, Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), Light Gradient Boosting Machine (LGBM), and eXtreme Gradient Boosting (XGB) models were constructed. Finally, the models' predictive capabilities were assessed through ROC curves, sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1-scores. The Shapley additive explanations (SHAP) algorithm was employed to elucidate the contributions of the most effective model's variables.

RESULTS

Through lasso regression, five features-hemoglobin (Hb), Procalcitonin (PCT), C-reactive protein (CRP), progressive dyspnea, and Albumin (ALB)-were identified, and six machine learning models were developed using these variables after evaluating their correlation and multicollinearity. In the validation cohort, the RF model demonstrated the highest AUC (0.920 (0.810-1.000), F1-Score (0.8), accuracy (0.885), sensitivity (0.818), PPV (0.667), and NPV (0.913) among the six models, while the XGB and KNN models exhibited the highest specificity (0.909) among the six models. Notably, CRP exerted a significant influence on the models, as revealed by SHAP and feature importance rankings.

CONCLUSIONS

Machine learning algorithms offer a viable approach for constructing prognostic models to predict the development of severe disease following PCP in kidney transplant recipients, with potential practical applications.

One biomarker does not fit all: tailoring anti-infective therapy through utilization of procalcitonin and other specific biomarkers

INTRODUCTION

Considering the ongoing increase in antibiotic resistance, the importance of judicious use of antibiotics through reduction of exposure is crucial. Adding procalcitonin (PCT) and other biomarkers to pathogen-specific tests may help to further improve antibiotic therapy algorithms and advance antibiotic stewardship programs to achieve these goals.

AREAS COVERED

In recent years, several trials have investigated the inclusion of biomarkers such as PCT into clinical decision-making algorithms. For adult patients, findings demonstrated improvements in the individualization of antibiotic treatment, particularly for patients with respiratory tract infections and sepsis. While most trials were performed in hospitals with central laboratories, point-of-care testing might further advance the field by providing a cost-effective and rapid diagnostic tool in upcoming years. Furthermore, novel biomarkers including CD-64, presepsin, Pancreatic stone and sTREM-1, have all shown promising results for increased accuracy of sepsis diagnosis. Availability of these markers however is currently still limited and there is insufficient evidence for their routine use in clinical care.

EXPERT OPINION

In addition to new host-response markers, combining such biomarkers with pathogen-directed diagnostics present a promising strategy to increase algorithm accuracy in differentiating between bacterial and viral infections. Recent advances in microbiologic testing using PCR or nucleic amplification tests may further improve the diagnostic yield and promote more targeted pathogen-specific antibiotic therapy.

The diagnostic utility of IL-10, IL-17, and PCT in patients with sepsis infection

Objective

The purpose of this study is to determine the diagnostic value and net clinical benefit of interleukin-10 (IL-10), interleukin-17 (IL-17), procalcitonin (PCT), and combination tests in patients with sepsis, which will serve as a standard for sepsis early detection.

Patients and methods

An investigation of 84 sepsis patients and 81 patients with local inflammatory diseases admitted to the ICU of Tongji University Hospital in 2021. In addition to comparing inter-group variability, indicators relevant to sepsis diagnosis and therapy were screened.

Results

LASSO regression was used to examine PCT, WBC, CRP, IL-10, IFN-, IL-12, and IL-17. Multivariate logistic regression linked IL-10, IL-17, and PCT to sepsis risk. The AUC values of IL-10, IL-17, PCT, and the combination of the three tests were much higher than those of standard laboratory infection indicators. The combined AUC was greater than the sum of IL-10, IL-17, and PCT (P < 0.05). A clinical decision curve analysis of IL-10, IL-17, PCT, and the three combined tests found that the three combined tests outperformed the individual tests in terms of total clinical benefit rate. To predict the risk of sepsis using IL-10, IL-17, and PCT had an AUC of 0.951, and the model's predicted probability was well matched. An examination of the nomogram model's clinical value demonstrated a considerable net therapeutic benefit between 3 and 87%.

Conclusion

The IL-10, IL-17, and PCT tests all have a high diagnostic value for patients with sepsis, and the combination of the three tests outperforms the individual tests in terms of diagnostic performance, while the combined tests have a higher overall clinical benefit rate.