| Literature DB >> 23316102 |
Mark Duncan1, Brandie D Wagner, Keri Murray, Jenna Allen, Kelley Colvin, Frank J Accurso, D Dunbar Ivy.
Abstract
BACKGROUND: Management of pediatric pulmonary hypertension (PH) remains challenging. We have assessed a panel of circulating proteins in children with PH to investigate their value as predictive and/or prognostic biomarkers. From these determinations, we aim to develop a practical, noninvasive tool to aid in the management of pediatric PH.Entities:
Mesh:
Substances:
Year: 2012 PMID: 23316102 PMCID: PMC3536060 DOI: 10.1155/2012/143428
Source DB: PubMed Journal: Mediators Inflamm ISSN: 0962-9351 Impact factor: 4.711
Patient demographics and clinical measurements.
| Patient demographics (no of subjects = 70) | |
|---|---|
| Female—no. (%) | 37 (52.9%) |
| APAH—no. (%) | 45 (64.3%) |
| Congential heart disease | 31 (44.3%) |
| Chronic lung disease | 11 (15.7%) |
| Clotting disorders | 10 (14.3%) |
| Age (years)—median (IQR) | 8.0 (4.4–13.0) |
| Therapies | |
| Mono therapy | 23 (32.9%) |
| Dual therapy | 23 (32.9%) |
| Triple therapy | 10 (14.3%) |
| Calcium channel blockers | 22 (31.4%) |
| PDE-5 inhibitors | 29 (41.4%) |
| Endothelin receptor blockers | 25 (35.7%) |
| Prostacyclin | 23 (32.9%) |
| Epoprostenol | 12 (17.1%) |
| IV Treprostinil | 8 (11.4%) |
| Inh Iloprost | 3 (4.3%) |
| CATH variables—median (IQR) ( | |
| Pulmonary artery pressure, mm Hg | 34 (23–56) |
| Pulmonary capillary wedge pressure, mm Hg | 8 (6–10) |
| Right atrial pressure, mm Hg | 5 (3–7) |
| Cardiac index, L/min × m2 | 3.5 (3.0–4.3) |
| Pulmonary vascular resistance index, wood units × m2 | 5.6 (4.1–13.9) |
| PVR/SVR | 0.48 (0.27–0.76) |
| Vasoreactivity (% change in mPAP w/NO) | −21.1 (−29.5 to −12.8) |
| Follow-up time (months)—median (min–max) | 36 (12–89) |
| Adverse outcomes | 16 (22.9%) |
| Within 12 months | 9 (12.9%) |
| First observed outcome | |
| Expired | 10 |
| Transplantation | 0 |
| Initiation of IV prostanoids | 8 |
Figure 1Biomarker characteristics: the distribution of the 12 cytokine and growth factor measurements based on disease classification. The y-axis is displayed on the log (base 2) scale. Filled areas indicate the interquartile range (distance between 25th and 75th percentiles), the middle line corresponds to the median, and the whiskers contain data within 1.5 interquartile ranges. None of the differences was statistically significant.
Figure 2Dimension reduction using principal component analysis: the third PC was found to be the most associated with outcome. (a) The distribution of the 3rd PC is displayed separately by outcome. Filled areas indicate the interquartile range (distance between 25th and 75th percentiles), the middle line corresponds to the median, and the whiskers contain data within 1.5 interquartile ranges. (b) The PC loadings for the 3rd and 4th PCs are displayed. The 3rd PC is heavily loaded by positive IL-1α and IL-6 and negative VEGF values, whereas the 4th PC is heavily weighted by positive IL-6 and MCP-1 and negative IL-10 values.
Figure 3Added value of proteins toward outcome prediction. (a) Comparison of the ROC curves indicate an improvement in discriminative ability with the addition of the 4th PC calculated from the protein measurements. c-indices for each model are also displayed. (b) The reclassification of estimated probabilities for the logistic regression model which included protein markers as a predictor versus the model based on PAP alone. The dots represent those observations associated with an event and the open circles with a nonevent. Higher percentages of events and nonevents are desired above and below the reference line, respectively. The corresponding reclassification index was significant (P = 0.01) indicating a significant improvement in prediction with the addition of the proteins.