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Transcutaneous Microcirculatory Imaging in Preterm Neonates
Published on: December 31, 2015
Assessment of WINROP algorithm as screening tool for preterm infants in Manitoba to detect retinopathy of prematurity
Ebtihal Ali1,2, Nasser Al-Shafouri3, Abrar Hussain2
1Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba.
Insights
The WINROP algorithm showed 90% sensitivity but only 60% specificity for detecting vision-threatening retinopathy of prematurity. This computer-based tool requires reassessment for clinical use in preterm infants.
Area of Science:
- Neonatal care
- Ophthalmology
- Medical informatics
Background:
- Early detection of retinopathy of prematurity (ROP) is crucial for preventing blindness in premature infants.
- The WINROP algorithm, a computer-based tool, analyzes postnatal weight gain trends to predict ROP development.
- Less invasive ROP detection methods are needed.
Purpose of the Study:
- To evaluate the sensitivity and specificity of the WINROP algorithm for detecting vision-threatening ROP.
- To assess the clinical utility of the WINROP algorithm in a Canadian neonatal intensive care unit population.
Main Methods:
- Retrospective chart review of 215 preterm infants (<32 weeks gestation) from January 2008 to December 2013.
- Infants' weekly body weight data were entered into the WINROP algorithm.
- Paediatric ophthalmologist screening for ROP was used as the reference standard.
Main Results:
- The WINROP algorithm demonstrated a sensitivity of 90% (P=0.021) for detecting vision-threatening ROP.
- The specificity of the WINROP algorithm was found to be 60% (P=0.002).
- Mean gestational age was 28.6 ± 1.8 weeks and mean birth weight was 1244 ± 294 g.
Conclusions:
- The WINROP algorithm's sensitivity is insufficient for current clinical application in this population.
- Further reassessment of the WINROP algorithm in contemporary infant populations is recommended.
- The study highlights the need for improved predictive tools for ROP.
Objective:
Developing less invasive methods for early detection of retinopathy of prematurity (ROP) is vital to minimizing blindness in premature infants. Lofqvist and colleagues developed a computer-based ROP risk algorithm (WINROP) (https://winrop.com), which detects downtrends in postnatal weight gain that correlate with the development of sight-threatening ROP. The aim of this study is to investigate the sensitivity and specificity of the WINROP algorithm to detect vision-threatening ROP.
Methods:
This is a retrospective chart review study between January 2008 and December 2013. This study was conducted in the neonatal intensive care unit in Children's Hospital at Health Sciences Centre, Winnipeg, Manitoba, Canada. The study included preterm infants, less than 32 weeks' gestation, who were admitted to the hospital during the study period. The included 215 infants were eligible for ROP screening and had sufficient data to be entered into the WINROP algorithm. Infants were screened by a paediatric ophthalmologist for retinopathy of prematurity. The body weight of infants was measured weekly and entered into the WINROP algorithm; the sensitivity and the specificity of the WINROP algorithm were assessed.
Results:
The mean gestational age was 28.6 ± 1.8 weeks. The mean body weight was 1244 ± 294 g. The sensitivity of the WINROP algorithm to detect vision-threatening retinopathy of prematurity in our cohort was 90% (P=0.021) with a specificity of 60% (P=0.002).
Conclusion:
The WINROP algorithm lacks sufficient sensitivity to be used clinically in our population. The algorithm needs to be reassessed in contemporary populations.
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