| Literature DB >> 30066729 |
Kellen Cristiane do Vale Lucio1, Maria Regina Bentlin1, Ana Carolina de Lima Augusto1, José Eduardo Corrente1, Taísa Bertoco Carregal Toscano1, Regina El Dib1, Eliane Chaves Jorge1.
Abstract
OBJECTIVES: To evaluate the accuracy of the ROPScore algorithm as a predictor of retinopathy of prematurity (ROP).Entities:
Mesh:
Year: 2018 PMID: 30066729 PMCID: PMC6055020 DOI: 10.6061/clinics/2018/e377
Source DB: PubMed Journal: Clinics (Sao Paulo) ISSN: 1807-5932 Impact factor: 2.365
Figure 1Excel spreadsheet (Microsoft) used to calculate the ROPScore. From Eckert et al. 2012.
Characteristics of the 181 premature infants included in the study.
| Characteristic | Total Cohort | Any stage ROP | Severe ROP |
|---|---|---|---|
| 181 | 32 | 22 | |
| 86/181 | 10/32 | 6/22 | |
| 1271.6 ± 354.6 | 884.0 ± 250.0 | 763.1±186.8 | |
| 29.2 ± 2.2 | 26.4±1.6 | 25.9±1.2 | |
| 596.9 ±248.0 | 407.4±190.8 | 390.7±162.8 | |
| 7.2 – 19.6 (13.5±3.0) | 12 – 19.6 (16.0±2.3) | 14.7 – 19.6 (17.9±1.0) |
Data are expressed as the mean ± SD; BW: Birth Weight; GA: Gestational Age; ROP: Retinopathy Of Prematurity; SD: Standard Deviation; WG: Weight Gain from birth to 6 weeks of life.
Figure 2Receiver operating characteristic (ROC) curves for the detection of any stage of retinopathy of prematurity (ROP) (A) and of severe ROP (B) according to the ROPScore algorithm.
Accuracy of the ROPScore for predicting the development of ROP.
| Any stage ROP | Severe ROP | |
|---|---|---|
| ROPScore: ≥16 | ROPScore: ≥16.6 | |
| Sensitivity | 87.5% (76%-98.9%) | 95.4% (86.7%-100%) |
| Specificity | 87.2% (81.9%-92.6%) | 83.6% (77.9%-89.4%) |
| PPV | 59.5% (45.5%-73.6%) | 44.7% (30.5%-58.9%) |
| NPV | 97.1% (94.1%-99.9%) | 99.2% (97.8%-100%) |
NPV: Negative Predictive Value; PPV: Positive Predictive Value; ROP: Retinopathy Of Prematurity.