Validation of postnatal growth and retinopathy of prematurity (G-ROP) screening guidelines in a tertiary care hospital of Pakistan: A report from low-middle income country.
Haroon TayyabRoha Ahmad ChoudharyHassan JabbarMohammad Abbas MotiwalaSehrish Nizar Ali MominShiraz HashmiAdnan MirzaKhadijah AbidPublished in: PloS one (2024)
Retinopathy of Prematurity (ROP) significantly contributes to childhood blindness globally, with a disproportionately high burden in low- and middle-income countries (LMICs) due to improved neonatal care alongside inadequate ROP screening and treatment facilities. This study aims to validate the performance of Postnatal Growth and Retinopathy of Prematurity (G-ROP) screening criteria in a cohort of premature infants presenting at a tertiary care setting in Pakistan. This cross-sectional study utilized retrospective chart review of neonates admitted to the neonatal intensive care unit (NICU) at The Aga Khan University Hospital, Pakistan from January 2018 to February 2022. The complete G-ROP criteria were applied as prediction tool for infants with type 1 ROP, type 2 ROP, and no ROP outcomes. Out of the 166 cases, 125 cases were included in the final analysis, and remaining cases were excluded due to incomplete data. ROP of any stage developed in 83 infants (66.4%), of whom 55 (44%) developed type 1 ROP, 28 (22.4%) developed type 2 ROP, and 19 (15.2%) were treated for ROP. The median BW was 1060 gm (IQR = 910 to 1240 gm) and the median gestational age was 29 wk (IQR = 27 to 30 wk). The G-ROP criteria demonstrated a sensitivity of 98.18% (95% CI: 90.28-99.95%) for triggering an alarm for type 1 ROP. The G-ROP criteria achieved 100% sensitivity (95% CI: 87.66 to 100%) for type 2 ROP. The overall sensitivity of G-ROP criteria to trigger an alarm for any type of ROP was 98.8% (95% CI: 93.47 to 99.97%). Thus, the G-ROP screening model is highly sensitive in detecting at-risk infants for ROP in a Pakistani tertiary care setting, supporting its use in LMICs where standard screening criteria may not suffice.
Keyphrases
- tertiary care
- preterm infants
- healthcare
- type diabetes
- physical activity
- mental health
- palliative care
- adipose tissue
- electronic health record
- gestational age
- insulin resistance
- body mass index
- cross sectional
- deep learning
- machine learning
- artificial intelligence
- preterm birth
- weight loss
- low birth weight
- birth weight
- quality improvement
- fluorescent probe
- living cells
- clinical evaluation