Fa-Chao Chen1, Yin-Chuan Xu1, Zhao-Cai Zhang2. 1. Department of Cardiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China. 2. Intensive Care Unit, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
Abstract
OBJECTIVE: The present study was to evaluate the feasibility of using the multi-biomarker strategy for the prediction of sepsis-induced myocardial dysfunction (SIMD) and mortality in septic patients. METHODS: Brain natriuretic peptide (BNP), cardiac troponin I (cTnI), and heart-type fatty acid-binding protein (h-FABP) in 147 septic patients were assayed within 6 h after admission. We also determined the plasma levels of myeloperoxidase (MPO) and pregnancy-associated plasma protein-A (PAPP-A). The receiver operating characteristic (ROC) curve was used to assess the best cutoff values of various single-biomarkers for the diagnosis of SIMD and the prediction of mortality. Also, the ROC curve, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) indices were used to evaluate the feasibility of using multi-biomarkers to predict SIMD and mortality. RESULTS: Our statistics revealed that only h-FABP independently predicted SIMD (P<0.05). The addition of MPO and cTnI to h-FABP for SIMD prediction provided an NRI of 18.7% (P=0.025) and IDI of 3.3% (P=0.033). However, the addition of MPO or cTnI to h-FABP did not significantly improve the predictive ability of h-FABP to SIMD, as evidenced by the area under the curve (AUC), NRI, and IDI (all P>0.05). A history of shock and MPO were independent predictors of mortality in septic patients (both P<0.05). The addition of PAPP-A and h-FABP to MPO resulted in a mortality prediction with NRI of 25.5% (P=0.013) and IDI of 2.9% (P=0.045). However, this study revealed that the addition of h-FABP or PAPP-A to MPO did not significantly improve the ability to predict mortality, as evidenced by the AUC, NRI, and IDI (all P>0.05). CONCLUSIONS: The findings of this study indicate that a sensitive and specific strategy for early diagnosis of SIMD and mortality prediction in sepsis should incorporate three biomarkers.
OBJECTIVE: The present study was to evaluate the feasibility of using the multi-biomarker strategy for the prediction of sepsis-induced myocardial dysfunction (SIMD) and mortality in septic patients. METHODS:Brain natriuretic peptide (BNP), cardiac troponin I (cTnI), and heart-type fatty acid-binding protein (h-FABP) in 147 septic patients were assayed within 6 h after admission. We also determined the plasma levels of myeloperoxidase (MPO) and pregnancy-associated plasma protein-A (PAPP-A). The receiver operating characteristic (ROC) curve was used to assess the best cutoff values of various single-biomarkers for the diagnosis of SIMD and the prediction of mortality. Also, the ROC curve, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) indices were used to evaluate the feasibility of using multi-biomarkers to predict SIMD and mortality. RESULTS: Our statistics revealed that only h-FABP independently predicted SIMD (P<0.05). The addition of MPO and cTnI to h-FABP for SIMD prediction provided an NRI of 18.7% (P=0.025) and IDI of 3.3% (P=0.033). However, the addition of MPO or cTnI to h-FABP did not significantly improve the predictive ability of h-FABP to SIMD, as evidenced by the area under the curve (AUC), NRI, and IDI (all P>0.05). A history of shock and MPO were independent predictors of mortality in septic patients (both P<0.05). The addition of PAPP-A and h-FABP to MPO resulted in a mortality prediction with NRI of 25.5% (P=0.013) and IDI of 2.9% (P=0.045). However, this study revealed that the addition of h-FABP or PAPP-A to MPO did not significantly improve the ability to predict mortality, as evidenced by the AUC, NRI, and IDI (all P>0.05). CONCLUSIONS: The findings of this study indicate that a sensitive and specific strategy for early diagnosis of SIMD and mortality prediction in sepsis should incorporate three biomarkers.
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