Gaining the PROMIS perspective from children with nephrotic syndrome: a Midwest pediatric nephrology consortium study

Background and objectives Nephrotic syndrome (NS) represents a common disease in pediatric nephrology typified by a relapsing and remitting course and characterized by the presence of edema that can significantly affect the health-related quality of life in children and adolescents. The PROMIS pediatric measures were constructed to be publically available, efficient, precise, and valid across a variety of diseases to assess patient reports of symptoms and quality of life. This study was designed to evaluate the ability of children and adolescents with NS to complete the PROMIS assessment via computer and to initiate validity assessments of the short forms and full item banks in pediatric NS. Successful measurement of patient reported outcomes will contribute to our understanding of the impact of NS on children and adolescents. Design This cross-sectional study included 151 children and adolescents 8-17 years old with NS from 16 participating institutions in North America. The children completed the PROMIS pediatric depression, anxiety, social-peer relationships, pain interference, fatigue, mobility and upper extremity functioning measures using a web-based interface. Responses were compared between patients experiencing active NS (n = 53) defined by the presence of edema and patients with inactive NS (n = 96) defined by the absence of edema. Results All 151 children and adolescents were successfully able to complete the PROMIS assessment via computer. As hypothesized, the children and adolescents with active NS were significantly different on 4 self-reported measures (anxiety, pain interference, fatigue, and mobility). Depression, peer relationships, and upper extremity functioning were not different between children with active vs. inactive NS. Multivariate analysis showed that the PROMIS instruments remained sensitive to NS disease activity after adjusting for demographic characteristics. Conclusions Children and adolescents with NS were able to successfully complete the PROMIS instrument using a web-based interface. The computer based pediatric PROMIS measurement effectively discriminated between children and adolescents with active and inactive NS. The domain scores found in this study are consistent with previous reports investigating the health-related quality of life in children and adolescents with NS. This study establishes known-group validity and feasibility for PROMIS pediatric measures in children and adolescents with NS

Gaining the Patient Reported Outcomes Measurement Information System (PROMIS) perspective in chronic kidney disease: a Midwest Pediatric Nephrology Consortium study

Chronic kidney disease is a persistent chronic health condition commonly seen in pediatric nephrology programs. Our study aims to evaluate the sensitivity of the Patient Reported Outcomes Measurement Information System (PROMIS) pediatric instrument to indicators of disease severity and activity in pediatric chronic kidney disease

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

NEC outcome predictive results.

<p>A. ROC AUC analysis. To gauge the impact of different training/testing cohort partition on the statistical learning, we performed a bootstrapping analysis that randomly partitioned the cohorts into 100 different training/testing sets. The distribution of 100 ROC curves, training and testing respectively, are illustrated. B. Use of the NEC outcome prediction metric to risk-stratify NEC subjects into low, intermediate and high risk groups.</p

Demographics of NEC patients by Bell’s staging criteria.

†<p>Chi-square test is used. N is reported with percentages in parentheses.</p>‡<p>Fisher's exact test is used. N is reported with percentages in parentheses.</p>§<p>Kruskal-Wallis test is used. Median is reported with IQR in parentheses.</p

Clinical variable’s contribution (LD1) to the NEC outcome LDA model.

<p>LDA: Linear discriminant analysis. LD1: first discriminant variable.</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

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

Automated NEC staging assignment results.

<p>Left: modeling training. Right: blind testing. Bottom: manual versus automated NEC staging assignment comparative analysis. To gauge the impact of different training/testing cohort partition on the statistical learning, we performed a bootstrapping analysis that randomly partitioned the cohorts into 100 different training/testing sets. Results were summarized where median and interquartile range (IQR) values were calculated for each comparative category.</p