| Literature DB >> 32714886 |
Bruce Li1, Melissa McGrath2,3,4, Forough Farrokhyar4,5, Luis H Braga2,3,4,5.
Abstract
Background: Previous scoring systems have used renal scan parameters to assess severity of ureteropelvic junction obstruction-like hydronephrosis (UPJO-like HN), however this information is not always reliable due to protocol variation across centers and renogram limitations. Therefore, we sought to evaluate the Pyeloplasty Prediction Score (PPS), which utilizes only baseline ultrasound measurements to predict the likelihood of pyeloplasty in infants with UPJO-like.Entities:
Keywords: classification; prenatal hydronephrosis; pyeloplasty; ultrasonography; ureteropelvic junction obstruction
Year: 2020 PMID: 32714886 PMCID: PMC7343702 DOI: 10.3389/fped.2020.00353
Source DB: PubMed Journal: Front Pediatr ISSN: 2296-2360 Impact factor: 3.418
Figure 1Example measurements of ultrasound renal parameters for the Pyeloplasty Prediction Score in (A) longitudinal view of the left kidney with electronic calipers measuring renal length as the maximal distance between the upper and lower poles and (B) transverse view of the right kidney with green electronic calipers measuring true anteroposterior diameter (APD) as the distance between the parenchymal lips at the renal hilum in the mid-section. Blue electronic calipers represent the incorrect method of measuring APD as the calipers are not aligned to the parenchymal lips.
The Pyeloplasty Prediction Score is based on three parameters: society of fetal urology (SFU) grade of the ultrasound, transverse anteroposterior diameter (APD) measurement, and the absolute percentage difference between the lengths of the ipsilateral and contralateral kidneys.
| 0 | Normal |
| 1 | SFU Grade 1 |
| 2 | SFU Grade 2 |
| 3 | SFU Grade 3 |
| 4 | SFU Grade 4 |
| 0 | <5 mm |
| 1 | 5–10 mm |
| 2 | 11–15 mm |
| 3 | 16–19 mm |
| 4 | ≥20 mm |
| 0 | <5% |
| 1 | 5%–10% |
| 2 | 11%–15% |
| 3 | 16%–19% |
| 4 | ≥20% |
| PPS = A + B + C |
Each parameter is assigned a score from 0 to 4, 0 being least severe and 4 being most.
Figure 2Predictive ability of the Pyeloplasty Prediction Score modeled by (A) receiver operating characteristic curve for pyeloplasty and (B) sensitivity and specificity at various score cut-points.
Figure 3Pyeloplasty Prediction Score stratification according to likelihood ratios.