Kidney Week

Abstract: TH-PO032

Early Biomarkers in the Detection and Risk Stratification of Sepsis Patients

Session Information

• AKI: Epidemiology, Risk Factors, Prevention
  October 25, 2018 | Location: Exhibit Hall, San Diego Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Acute Kidney Injury

• 101 AKI: Epidemiology, Risk Factors, and Prevention

Authors

• Patel, Vishal G., University of Florida College of Medicine, Lake Worth, Florida, United States

Vishal G. Patel,

• Madushani, R. w. m. anusha, University of Florida, Gainesville, Florida, United States

R. w. m. anusha Madushani,

• Ozrazgat-baslanti, Tezcan, University of Florida, Gainesville, Florida, United States

Tezcan Ozrazgat-baslanti,

• Adhikari, Lasith, University of Florida, Gainesville, Florida, United States

Lasith Adhikari,

• Wu, Quran, University of Florida, Gainesville, Florida, United States

Quran Wu,

• Lysak, Nicholas, University of Florida, Gainesville, Florida, United States

Nicholas Lysak,

• Bandyopadhyay, Sabyasachi, University of Florida, Gainesville, Florida, United States

Sabyasachi Bandyopadhyay,

• Bihorac, Azra, University of Florida, Gainesville, Florida, United States

Azra Bihorac,

Background

Treatment of sepsis requires early hemodynamic support and antibiotic initiation, making timely diagnosis and risk stratification critical to improving survival. The reliance on clinical judgment, rather than diagnostic guidelines, to diagnosis and risk stratify sepsis patients results in a wide range of clinical outcomes.

Methods

In a prospective cohort study of 157 sepsis patients in a surgical ICU, we used 42 biomarkers that includes laboratory and vital measurements as well as age and Charlson comorbidity index using the earliest measurement within 48 hours of sepsis onset. We used hierarchical clustering to group patients with similar clinical characteristics. We compared clinical characteristics and outcomes between two main clusters identified using Fisher’s exact test for categorical variables and student’s t-test or Wilcoxon rank sum test for continuous variables as appropriate. Composite biomarker mosaic images were created using clustering variables.

Results

We identified two main clusters with 18 and 139 subjects in Clusters I and II, respectively. Cluster I consisted of more septic shock patients (78% vs 17%) who had early multiorgan failure and acute physiologic derangements in the first 24 hours of sepsis onset (median APACHE II score of 30 vs 16 and total SOFA score of 13 vs 5, respectively, p<.0001). Chronic diseases that differentiated the two clusters were cardiovascular and renal diseases (p<.0001). Renal biomarkers were significantly elevated two to three folds with higher prevalence of AKI (93% vs 45%) in cluster I compared to cluster II (p<0.01). Cluster I had significantly higher hospital mortality of (33% vs 2.2%, p<.0001) and one year mortality (50% vs 18%, p=0.005).

Conclusion

Using early biomarkers, we were able to identify two major clusters that had clinically different profiles which were reflected in composite biomarker mosaic images. We would be able identify patients at risk for adverse outcomes using our clustering and imaging methodology in order to improve hospital and long-term outcomes.

Funding

• Other NIH Support