13 research outputs found
PE patients' presenting signs and symptoms.
<p>PE patients' presenting signs and symptoms.</p
Serum peptide biomarkers identified to separate PE and control subjects.
<p>FGA:</p>*<p>cluster 1;</p>**<p>cluster 2;</p>***<p>cluster 3;</p>****<p>cluster 4.</p><p>Score and minimal false discovery rate (<i>q</i> value) were computed using SAM algorithm.</p
Diagnosis of PE from control with serum biomarkers.
<p>Left panel: estimated PE scores were computed from the PE serum peptide panel PAM model as a function of the gestational weeks; right panel: the log sFlt-1/PIGF serum concentration ratio was plotted as a function of the gestational weeks. Red indicates known PE cases; green indicates known healthy pregnancy controls. For either PE or control sample category, a loess curve was fitted to represent the overall trend of biomarker scoring as a function of gestational age.</p
PAM predictive analysis of the 19-peptide biomarker panel differentiating PE from control samples.
<p>PAM prediction was performed with training data from PE (training, n = 21; testing, n = 10) and control (training, n = 21; testing, n = 10) samples evaluated with the biomarker panel. Samples are partitioned by the true class (upper) and predicted class (lower). The classification results from training and test sets are shown as 2 by 2 contingency tables, calculating the percentage of classifications that agreed with clinical diagnosis.</p
The serum concentrations of sFlt-1 (left) and PIGF (right) as a function of the gestation.
<p>For either PE (red) or control (green) data points, a loess curve was fitted to represent the overall trend of biomarker serum abundance as a function of gestation.</p
A Data-Driven Algorithm Integrating Clinical and Laboratory Features for the Diagnosis and Prognosis of Necrotizing Enterocolitis
<div><p>Background</p><p>Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting.</p><p>Study design</p><p>A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data.</p><p>Results</p><p>Machine learning using clinical and laboratory results at the time of clinical presentation led to two NEC models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner.</p><p>Algorithm availability</p><p><a href="http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl" target="_blank">http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl</a> and smartphone application upon request.</p></div
Clinical variable’s contribution (LD1) to the NEC outcome LDA model.
<p>LDA: Linear discriminant analysis. LD1: first discriminant variable.</p
A decision tree to guide the manual assignment of the modified Bell’s staging criteria to the study subjects.
<p>A decision tree to guide the manual assignment of the modified Bell’s staging criteria to the study subjects.</p
NEC outcome predictive LDA models with reduced number of variables (listed in descending order from right to left in Figure 3 by the absolute value of their weights).
<p>The model performance was gauged by ROC analysis. Vertical dotted line: the model performance deteriorates when the model’s panel size is less than 7 parameters.</p