BACKGROUND: The aim of this study was to test the diagnostic model of combining procalcitonin (PCT) and C-reactive protein (CRP) levels in the cord blood and routinely used biochemical parameters and clinical data in the prediction of early onset neonatal infection. METHODS: PCT and CRP concentrations were measured in cord blood of neonates with infection (group A, n=46) and compared with uninfected neonates (group B, n=240). Inclusion criteria for group A were based on obstetric history, clinical data and results of laboratory tests. Logistic regression was applied. The receiver operating characteristic (ROC) curves were constructed for PCT, CRP and the diagnostic model. RESULTS: There was a highly significant (p<0.000001) difference in PCT and CRP concentrations between both groups. The cut-off point for PCT in cord blood was 1.22 ng/mL [sensitivity % (SE%) 80.43, specificity % (SP%) 71.67, positive predictive value % (PPV%) 35.24, negative predictive value % (NPV%) 95.03], and 1.0 mg/L for CRP (SE% 73.91, SP% 77.92, PPV% 39.08, NPV% 93.97). In total, seven variables were included in the model (concentrations of PCT and CRP in cord blood, tocolysis, nutritional status of the newborn, Apgar score, neutrophil ratio and red blood cell count in neonatal venous blood), which proved to offer the highest sensitivity (91.3%; 95% CI: 83-99) and specificity (90%; 95% CI: 86-94) for the detection of early onset neonatal infection. The likelihood ratio for the model was high at 9.13, with PPV% 63.64 (95% CI: 52-75), NPV% 98.18 (95% CI: 96-100) and calculated area under the curve at 0.973. CONCLUSIONS: The diagnostic model based on seven clinical and laboratory parameters, using the concentration of PCT and CRP measurements in the cord blood, could be a useful tool for the prediction of early onset neonatal infection.
BACKGROUND: The aim of this study was to test the diagnostic model of combining procalcitonin (PCT) and C-reactive protein (CRP) levels in the cord blood and routinely used biochemical parameters and clinical data in the prediction of early onset neonatal infection. METHODS: PCT and CRP concentrations were measured in cord blood of neonates with infection (group A, n=46) and compared with uninfected neonates (group B, n=240). Inclusion criteria for group A were based on obstetric history, clinical data and results of laboratory tests. Logistic regression was applied. The receiver operating characteristic (ROC) curves were constructed for PCT, CRP and the diagnostic model. RESULTS: There was a highly significant (p<0.000001) difference in PCT and CRP concentrations between both groups. The cut-off point for PCT in cord blood was 1.22 ng/mL [sensitivity % (SE%) 80.43, specificity % (SP%) 71.67, positive predictive value % (PPV%) 35.24, negative predictive value % (NPV%) 95.03], and 1.0 mg/L for CRP (SE% 73.91, SP% 77.92, PPV% 39.08, NPV% 93.97). In total, seven variables were included in the model (concentrations of PCT and CRP in cord blood, tocolysis, nutritional status of the newborn, Apgar score, neutrophil ratio and red blood cell count in neonatal venous blood), which proved to offer the highest sensitivity (91.3%; 95% CI: 83-99) and specificity (90%; 95% CI: 86-94) for the detection of early onset neonatal infection. The likelihood ratio for the model was high at 9.13, with PPV% 63.64 (95% CI: 52-75), NPV% 98.18 (95% CI: 96-100) and calculated area under the curve at 0.973. CONCLUSIONS: The diagnostic model based on seven clinical and laboratory parameters, using the concentration of PCT and CRP measurements in the cord blood, could be a useful tool for the prediction of early onset neonatal infection.
Authors: Roberto Romero; Piya Chaemsaithong; Nikolina Docheva; Steven J Korzeniewski; Juan P Kusanovic; Bo Hyun Yoon; Jung-Sun Kim; Noppadol Chaiyasit; Ahmed I Ahmed; Faisal Qureshi; Suzanne M Jacques; Chong Jai Kim; Sonia S Hassan; Tinnakorn Chaiworapongsa; Lami Yeo; Yeon Mee Kim Journal: J Perinat Med Date: 2016-01 Impact factor: 1.901
Authors: S Lencot; B Cabaret; G Sauvage; C Laurans; E Launay; J-L Orsonneau; J Caillon; C Boscher; J-C Roze; C Gras-Le Guen Journal: Eur J Clin Microbiol Infect Dis Date: 2014-02-11 Impact factor: 3.267
Authors: Rebecca A Howman; Adrian K Charles; Angela Jacques; Dorota A Doherty; Karen Simmer; Tobias Strunk; Peter C Richmond; Catherine H Cole; David P Burgner Journal: PLoS One Date: 2012-12-13 Impact factor: 3.240
Authors: Edith H van den Hooven; Yvonne de Kluizenaar; Frank H Pierik; Albert Hofman; Sjoerd W van Ratingen; Peter Y J Zandveld; Jan Lindemans; Henk Russcher; Eric A P Steegers; Henk M E Miedema; Vincent W V Jaddoe Journal: Environ Health Perspect Date: 2012-02-03 Impact factor: 9.031