Daily Sepsis Research Analysis
Three studies advance sepsis care across diagnosis and prognosis: a Bayesian network meta-analysis finds CD64 offers the best diagnostic accuracy over clinical scores, a prospective cohort shows that combining urine sediment with TIMP-2*IGFBP7 and KIM-1 markedly improves prediction of SA-AKI progression and mortality, and a multicenter neonatal study identifies an actionable presepsin cut-off for late-onset sepsis.
Summary
Three studies advance sepsis care across diagnosis and prognosis: a Bayesian network meta-analysis finds CD64 offers the best diagnostic accuracy over clinical scores, a prospective cohort shows that combining urine sediment with TIMP-2*IGFBP7 and KIM-1 markedly improves prediction of SA-AKI progression and mortality, and a multicenter neonatal study identifies an actionable presepsin cut-off for late-onset sepsis.
Research Themes
- Diagnostic biomarkers and clinical scores in sepsis
- Risk stratification and prognosis in sepsis-associated acute kidney injury
- Neonatal late-onset sepsis diagnostics
Selected Articles
1. Comparison of the diagnostic accuracies of various biomarkers and scoring systems for sepsis: A systematic review and Bayesian diagnostic test accuracy network meta-analysis.
This Bayesian diagnostic test accuracy network meta-analysis of 78 studies (34,234 patients) shows that CD64 has the highest diagnostic performance for sepsis, outperforming sTREM-1, presepsin, and clinical scores such as qSOFA and SIRS. Heterogeneity was explained by study design, sepsis prevalence, sample size, and selection bias.
Impact: Provides comparative, post-Sepsis-3 evidence to guide biomarker selection for sepsis detection, likely shaping diagnostic pathways and future guideline updates.
Clinical Implications: Consider incorporating CD64 into sepsis screening/triage workflows, potentially alongside existing markers. Implementation should account for assay availability, turnaround time, and cost.
Key Findings
- Included 78 studies and 34,234 patients comparing biomarkers and clinical scores for sepsis diagnosis.
- CD64 achieved the highest diagnostic performance (DOR 20.17; moderate evidence), surpassing sTREM-1 and presepsin.
- Biomarkers outperformed qSOFA and SIRS in detecting sepsis under updated definitions.
- Meta-regression identified study design, sepsis proportion, sample size, and selection bias as major sources of heterogeneity.
Methodological Strengths
- Bayesian arm-based network meta-analysis of diagnostic accuracy with hierarchical modeling
- PROSPERO-registered protocol and multivariable meta-regression to explore heterogeneity
Limitations
- Variability in assay platforms and cut-offs across studies may affect comparability
- Overall evidence quality ranged from moderate to low; possible publication and selection biases
Future Directions: Prospective head-to-head diagnostic studies with standardized assays and cut-offs, evaluation of multi-marker panels, and implementation studies including cost-effectiveness.
PURPOSE: Sepsis affects approximately 50 million people worldwide, resulting in 11 million deaths annually. Conflicting results and insufficient evidence comparing performance biomarkers exist. The study aimed to comprehensively compare available biomarkers and clinical scores for detecting sepsis since its redefinition in 2016 with this systematic review and Bayesian diagnostic test accuracy network meta-analysis. MATERIALS AND METHODS: We conducted searches in the PubMed, EMBASE, and Scopus databases between January 2016 and December 2023. Eligible studies assessed the diagnostic accuracies of biomarkers, the quick Sequential Organ Failure Assessment (qSOFA) score, or Systemic Inflammatory Response Syndrome (SIRS) criteria in detecting sepsis. Bivariate hierarchical random effects arm-based beta-binomial models were used for quantitative synthesis (PROSPERO Registration Number: CRD42018086545). RESULTS: We included 78 studies representing 34,234 patients and compared qSOFA score, SIRS criteria alongside seven of the most studied biomarkers: procalcitonin, C-reactive protein (CRP), interleukin-6 (IL-6), presepsin (cluster of differentiation 14 subtypes), CD64, soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), and lipopolysaccharide-binding protein (LBP). CD64 demonstrated the highest superiority index, followed by sTREM-1 and presepsin (diagnostic odds ratio: 20.17 vs 18.73 and 10.04, 95 % credible interval [CrI]: 8.39-38.61 vs 1.31-83.98 and 6.71-14.24; quality of evidence: moderate vs low and low). Multivariable meta-regression analysis identified significant sources of heterogeneity, including study design, proportion of sepsis, sample size, and the risk of bias (patient selection). CONCLUSIONS: The best diagnostic accuracy for sepsis was shown by CD64, with a moderate quality of evidence. Compared to CD64, sTREM-1 and presepsin provided suboptimal and low evidence. These biomarkers were more effective at identifying updated sepsis than clinical scores. We recommend re-considering the addition of biomarkers in screening for sepsis or sepsis-related conditions, as this could lead to more accurate and timely decisions for future clinical interventions.
2. Does combining urine sediment examination to renal cell arrest and damage biomarkers improve prediction of progression and mortality of sepsis associated acute kidney injury?
In 96 patients with stage 1–2 SA-AKI, combining urine sediment scoring at day 3 with uTIMP-2*IGFBP7 and uKIM-1 markedly improved prediction of AKI progression (AUC up to 0.979) and mortality (AUC up to 0.807) versus biomarkers alone. Nearly half progressed to stage 3 and one-third died.
Impact: Demonstrates a pragmatic, low-cost enhancement to biomarker strategies by integrating urine microscopy, with substantial gains in prognostic accuracy for SA-AKI.
Clinical Implications: Incorporate day-3 urine sediment scoring with uTIMP-2*IGFBP7 (and KIM-1 where available) to identify SA-AKI patients at high risk for progression and death, enabling earlier nephrology consultation and kidney-protective strategies.
Key Findings
- Prospective cohort of 96 stage 1–2 SA-AKI patients; 48% progressed to stage 3 and 33.3% died.
- uTIMP-2*IGFBP7 predicted AKI progression with AUC 0.837; uKIM-1 AUC 0.657.
- Adding day-3 urine sediment (Perazella and Chawla scores) raised progression AUC to 0.977–0.979 and mortality AUC to 0.796–0.807.
- Trial registered (NCT06064487), with serial biomarker and sediment assessments at days 1, 3, and 7.
Methodological Strengths
- Prospective design with serial measurements and predefined outcomes
- Combination of microscopy-based sediment scoring with validated cell-cycle arrest and injury biomarkers
Limitations
- Single-cohort sample with modest size (n=96) limits generalizability
- External validation and assessment of clinical impact on management decisions are needed
Future Directions: Multicenter validation, integration into risk scores, and interventional studies testing biomarker- and sediment-guided management to reduce SA-AKI progression.
BACKGROUND: Sepsis associated acute kidney injury (SA-AKI) among hospitalized patients is common with higher morbidity and mortality. There is a need to discover new methods that allow better prediction of its outcomes and prognosis. We aimed to evaluate if combining serial examination of urine sediment to renal cell damage (KIM-1) and arrest (TIMP-2, IGFBP7) biomarkers could improve the prediction of progression and mortality of SA-AKI. METHODS: This prospective study enrolled 96 patients with stage 1 or 2 SA-AKI. Measuring of urinary TIMP-2, IGFBP7 and KIM-1 was done at time of AKI diagnosis and examination of urine sediment was performed by calculating Chawla score (CS) and Perazella score (PS) at days 1, 3 and 7. Main study outcomes included AKI progression to stage 3 and mortality. RESULTS: Ninety-six patients were included in the study. 48% of them progressed to AKI stage 3 and 33.3% died. uTIMP2IGFBP7 and uKIM-1 showed an area under the curve (AUC) of 0.837 and 0.657 respectively for predicting AKI progression and an AUC of 0.679 and 0.626 respectively for predicting mortality. Combining urine sediment examination at day 3 (P2 and C2) to uTIMP2IGFBP7, uKIM-1 and both biomarkers significantly improved their prediction ability to an AUC of to 0.977, 0.951 and 0.979 respectively to predict AKI progression, and to an AUC of 0.807, 0.796 and 0.803 respectively to predict mortality. CONCLUSIONS: Combining urine sediment examination with renal cell damage and arrest biomarkers significantly improved their performance of predicting AKI progression and mortality in patients with SA-AKI. CLINICAL TRIALS REGISTRATION: ClinicalTrials.gov Identifier: NCT06064487. First registration date: 21/09/2023.
3. The accuracy of presepsin in diagnosing neonatal late-onset sepsis in critically ill neonates: a prospective study.
In a multicenter prospective cohort of 351 critically ill neonates, presepsin increased at LOS onset and 24–48 hours, with AUC 0.71 (all cases) and 0.74 (confirmed cases). A cut-off of 713 ng/L classified roughly two-thirds correctly and achieved an NPV of 89%, and baseline values were unaffected by comorbidities.
Impact: Defines a practical presepsin threshold for neonatal LOS with prospective multicenter data, supporting integration into NICU diagnostic algorithms.
Clinical Implications: Use a 713 ng/L presepsin cut-off to aid LOS diagnosis and rule-out decisions in critically ill neonates; apply alongside cultures and clinical assessment to optimize antibiotic stewardship.
Key Findings
- Among 351 neonates, 69 developed LOS; presepsin rose at onset and at 24–48 hours.
- Diagnostic AUC at onset was 0.71 for all cases and 0.74 for culture-confirmed sepsis.
- A 713 ng/L cut-off correctly classified ~two-thirds of cases with an NPV of 89%.
- Baseline presepsin values were not affected by underlying pathologies in uninfected neonates.
Methodological Strengths
- Prospective multicenter design with serial biomarker sampling
- ROC analysis with predefined cut-off optimization and reporting of predictive values
Limitations
- Moderate discrimination (AUC ~0.71–0.74) means false positives/negatives remain
- Event count for confirmed sepsis is limited; external validation desirable
Future Directions: Evaluate presepsin within multi-marker panels and decision-support algorithms, assess impact on antibiotic stewardship and outcomes, and validate across diverse NICU settings.
OBJECTIVES: The diagnostic accuracy of presepsin (P-SEP) in the newborn is still under evaluation. METHODS: In a multicenter study, we studied the accuracy of P-SEP as a diagnostic marker of late-onset sepsis (LOS) in critical newborns with underlying disorders, to define the most accurate cut-off to distinguish infected from uninfected patients. RESULTS: Sixty-nine/351 newborns without infections at admission developed LOS. The median P-SEP value at T0 (admission) was 518.0 ng/L (IQR 313.0-789.0), without significant differences related to underlying diseases (p=0.52). In neonates who developed LOS, P-SEP increased at the onset of infection (T1) (median: 816.0 ng/L) and after 24-48 h (median: 901.0 ng/L) compared with their value at admission (median: 560.0 ng/L) (p<0.01 and p=0.03, respectively). The area under the ROC curve at T1 was 0.71 (95 % CI 0.65-0.78) when all cases of sepsis were included in the analysis and increased to 0.74 (95 % CI 0.66-0.81) considering only confirmed sepsis. Approximately two-thirds of patients were correctly classified, setting the cut-off at 713 ng/L, with a negative predictive value of 89.0 %. CONCLUSIONS: At a cut-off of 713 ng/L, P-SEP has good accuracy in diagnosing LOS in critically ill newborns. In uninfected newborns, the median value of P-SEP is not influenced by any underlying pathology.