RESUMO
OBJECTIVES: To test the hypothesis that an exploratory proteomics analysis of urine proteins with subsequent development of validated urine biomarker panels would produce molecular classifiers for both the diagnosis and prognosis of infants with necrotizing enterocolitis (NEC). STUDY DESIGN: Urine samples were collected from 119 premature infants (85 NEC, 17 sepsis, 17 control) at the time of initial clinical concern for disease. The urine from 59 infants was used for candidate biomarker discovery by liquid chromatography/mass spectrometry. The remaining 60 samples were subject to enzyme-linked immunosorbent assay for quantitative biomarker validation. RESULTS: A panel of 7 biomarkers (alpha-2-macroglobulin-like protein 1, cluster of differentiation protein 14, cystatin 3, fibrinogen alpha chain, pigment epithelium-derived factor, retinol binding protein 4, and vasolin) was identified by liquid chromatography/mass spectrometry and subsequently validated by enzyme-linked immunosorbent assay. These proteins were consistently found to be either up- or down-regulated depending on the presence, absence, or severity of disease. Biomarker panel validation resulted in a receiver-operator characteristic area under the curve of 98.2% for NEC vs sepsis and an area under the curve of 98.4% for medical NEC vs surgical NEC. CONCLUSIONS: We identified 7 urine proteins capable of providing highly accurate diagnostic and prognostic information for infants with suspected NEC. This work represents a novel approach to improving the efficiency with which we diagnose early NEC and identify those at risk for developing severe, or surgical, disease.
Assuntos
Enterocolite Necrosante/diagnóstico , Biomarcadores/urina , Estudos de Casos e Controles , Cromatografia Líquida , Cistatina C/urina , Regulação para Baixo , Ensaio de Imunoadsorção Enzimática , Proteínas do Olho/urina , Feminino , Fibrinogênio/urina , Humanos , Recém-Nascido , Recém-Nascido Prematuro , Doenças do Prematuro/diagnóstico , Receptores de Lipopolissacarídeos/urina , Masculino , Espectrometria de Massas , Fatores de Crescimento Neural/urina , Fragmentos de Peptídeos/urina , Prognóstico , Estudos Prospectivos , Proteínas Plasmáticas de Ligação ao Retinol/urina , Sensibilidade e Especificidade , Sepse/diagnóstico , Serpinas/urina , Regulação para Cima , alfa-Macroglobulinas/urinaRESUMO
OBJECTIVE: To test whether statistical learning on clinical and laboratory test patterns would lead to an algorithm for Kawasaki disease (KD) diagnosis that could aid clinicians. STUDY DESIGN: Demographic, clinical, and laboratory data were prospectively collected for subjects with KD and febrile controls (FCs) using a standardized data collection form. RESULTS: Our multivariate models were trained with a cohort of 276 patients with KD and 243 FCs (who shared some features of KD) and validated with a cohort of 136 patients with KD and 121 FCs using either clinical data, laboratory test results, or their combination. Our KD scoring method stratified the subjects into subgroups with low (FC diagnosis, negative predictive value >95%), intermediate, and high (KD diagnosis, positive predictive value >95%) scores. Combining both clinical and laboratory test results, the algorithm diagnosed 81.2% of all training and 74.3% of all testing of patients with KD in the high score group and 67.5% of all training and 62.8% of all testing FCs in the low score group. CONCLUSIONS: Our KD scoring metric and the associated data system with online (http://translationalmedicine.stanford.edu/cgi-bin/KD/kd.pl) and smartphone applications are easily accessible, inexpensive tools to improve the differentiation of most children with KD from FCs with other pediatric illnesses.