Heparin-binding protein-enhanced quick SOFA score improves mortality prediction in sepsis patients - Han, Xiaotong; Dou, Qingli; Zhu, Yimin; Ling, Peng; Shen, Yi-Hsuan; Liu, Jiangping; Zhang, Zhongwei; Zhou, Yucheng; Fan, Maiying; Huang, Sih-Shiang; Lee, Chien-Chang (Frontiers in Medicine, 2022)
Purpose The Quick Sequential Organ Failure Assessment (qSOFA) score proposed by Sepsis-3 as a sepsis screening tool has shown suboptimal accuracy. Heparin-binding protein (HBP) has been shown to identify early sepsis with high accuracy. Herein, we aim to investigate whether or not HBP improves the model performance of qSOFA. Methods We conducted a multicenter prospective observational study of 794 adult patients who presented to the emergency department (ED) with presumed sepsis between 2018 and 2019. For each participant, serum HBP levels were measured and the hospital course was followed. The qSOFA score was used as the comparator. The data was split into a training dataset (n=556) and a validation dataset (n=238). The primary endpoint was 30-day all-cause mortality. Results Compared with survivors, nonsurvivors had significantly higher serum HBP levels (median: 71.5 ng/mL vs 209.5 ng/mL, p<0.001). Serum level of HBP weakly correlated with qSOFA class (r2=0.240, p<0.001). Compared with the qSOFA model alone, the addition of admission HBP level to the qSOFA model significantly improved 30-day mortality discrimination (AUC, 0.70 vs. 0.80; P < 0.001), net reclassification improvement (26% [CI, 17% to 35%]; P < 0.001), and integrated discrimination improvement (12% [CI, 9% to 14%]; P < 0.001). Addition of C-reactive protein (CRP) level or neutrophil-to-lymphocyte ratio (NLR) to qSOFA did not improve its performance. A web-based mortality risk prediction calculator was created to facilitate clinical implementation. Conclusion This study confirms the value of combining qSOFA and HBP in predicting sepsis mortality. The web calculator provides a user-friendly tool for clinical implementation. Further validation in different patient populations is needed before widespread application of this prediction model.