The delivery of @entity1 before 37 weeks gestation is a leading cause of perinatal mortality and morbidity in the United States. Traditional methods of predicting @entity1 at risk relying on obstetric history or premonitory symptoms (detected clinically or by tocodynamometry) are neither sensitive nor specific. Recent approaches to predicting @entity3152 have included sonographic measurement of cervical length and various biochemical assays. Although more sensitive than traditional methods, none of these alone exhibits sufficient accuracy to warrant widespread use. We contend that the failure of current approaches to predicting @entity3152 reflects an inadequate understanding of the underlying pathogenesis. Clinical and experimental evidence support the concept that most cases of @entity3152 reflect four pathogenic processes, which share a common final biological pathway leading to uterine contractions and cervical changes with or without @entity3465 rupture of membranes. These pathogeneses are: (1) activation of the maternal or fetal @entity2339 ; (2) decidual-chorioamniotic or systemic @entity32 ; (3) decidual @entity548 (i.e. abruption); and (4) pathological distention of the uterus. Our research seeks to combine the most useful biophysical and biochemical markers of such processes with optimal clinical and epidemiological predictors into a composite, easily applied risk tool. This integrated approach has the potential to identify at-risk asymptomatic @entity1 with high sensitivity, specificity, and positive and negative predictive values, and also to ascertain underlying pathogenic processes that can lead to targeted therapy. To accomplish these goals, we employ logistic regression and artificial @entity1274 models to assess and apply the appropriate weight to markers associated with each of the above pathogenic pathways, in addition to markers of the final common pathway leading to fetal membrane rupture, cervical extracellular matrix degradation, and myometrial activation. By combining these markers, we expect ultimately to produce a predictive model that is more robust than any existing method, and that identifies the relative contribution of each pathogenic process. Further analysis of this model using a @entity1274 will enable us to identify asymptomatic @entity1 destined to deliver preterm with high sensitivity, specificity, positive and negative predictive values, and to assess the relative contribution of each of the four distinct pathogeneses to this @entity3152 risk.

Risk stratification and pathological mechanisms in XXXX .

@entity1 :: ('9606', 'Species') :: ['women', 'patients', 'infants'] @entity32 :: ('MESH:D007249', 'Disease') :: ['inflammation'] @entity3152 :: ('MESH:D007752', 'Disease') :: ['preterm delivery'] @entity548 :: ('MESH:D006470', 'Disease') :: ['haemorrhage'] @entity3465 :: ('MESH:C536271', 'Disease') :: ['premature'] @entity2339 :: ('MESH:D007029', 'Disease') :: ['hypothalamic-pituitary-adrenal axis'] @entity1274 :: ('MESH:D006319', 'Disease') :: ['neural network']
@entity3152 :: (MESH:D007752,Disease) :: ['preterm delivery']
