Predictive algorithms for early detection of retinopathy of prematurity.
OBJECTIVE
To evaluate sensitivity, specificity and the safest cut-offs of three predictive algorithms (WINROP, ROPScore and CHOP ROP) for retinopathy of prematurity (ROP).


METHODS
A retrospective study was conducted in three centres from 2012 to 2014; 445 preterms with gestational age (GA) ≤ 30 weeks and/or birthweight (BW) ≤ 1500 g, and additional unstable cases, were included. No-ROP, mild and type 1 ROP were categorized. The algorithms were analysed for infants with all parameters (GA, BW, weight gain, oxygen therapy, blood transfusion) needed for calculation (399 babies).


RESULTS
Retinopathy of prematurity (ROP) was identified in both eyes in 116 patients (26.1%), and 44 (9.9%) had type 1 ROP. Gestational age and BW were significantly lower in ROP group compared with no-ROP subjects (GA: 26.7 ± 2.2 and 30.2 ± 1.9, respectively, p < 0.0001; BW: 839.8 ± 287.0 and 1288.1 ± 321.5 g, respectively, p = 0.0016). Customized alarms of ROPScore and CHOP ROP correctly identified all infants having any ROP or type 1 ROP. WINROP missed 19 cases of ROP, including three type 1 ROP. ROPScore and CHOP ROP provided the best performances with an area under the receiver operating characteristic curve for the detection of severe ROP of 0.93 (95% CI, 0.90-0.96, and 95% CI, 0.89-0.96, respectively), and WINROP obtained 0.83 (95% CI, 0.77-0.87). Median time from alarm to treatment was 11.1, 5.1 and 9.1 weeks, for WINROP, ROPScore and CHOP ROP, respectively.


CONCLUSIONS
ROPScore and CHOP ROP showed 100% sensitivity to identify sight-threatening ROP. Predictive algorithms are a reliable tool for early identification of infants requiring referral to an ophthalmologist, for reorganizing resources and reducing stressful procedures to preterm babies.
Department of Ophthalmology, University of Padova, Padova, Italy.
Stefano
S
Piermarocchi
Department of Ophthalmology, University of Padova, Padova, Italy.
Silvia
S
Bini
Department of Ophthalmology, University of Padova, Padova, Italy.
Ferdinando
F
Martini
Department of Ophthalmology, University of Padova, Padova, Italy.
Marianna
M
Berton
University of Verona, Verona, Italy.
Anna
A
Lavini
Ophthalmology Unit, Department of Neurological, Neuropsychological, Morphological and Movement Sciences, University of Verona, Verona, Italy.
Elena
E
Gusson
Ophthalmology Unit, Department of Neurological, Neuropsychological, Morphological and Movement Sciences, University of Verona, Verona, Italy.
Giorgio
G
Marchini
Department of Pediatrics, Section of Newborn Intensive Care Unit, University Hospital of Verona, Verona, Italy.
Ezio Maria
EM
Padovani
Department of Medical and Biological Sciences- Ophthalmology, University of Udine, Udine, Italy.
Sara
S
Macor
Department of Medical and Biological Sciences- Ophthalmology, University of Udine, Udine, Italy.
Silvia
S
Pignatto
Department of Medical and Biological Sciences- Ophthalmology, University of Udine, Udine, Italy.
Paolo
P
Lanzetta
Department of Pediatrics- Neonatology, University of Udine, Udine, Italy.
Luigi
L
Cattarossi
Department of Woman's and Child's Health, University of Padova, Padova, Italy.
Eugenio
E
Baraldi
Department of Woman's and Child's Health, University of Padova, Padova, Italy.
Paola
P
Lago
D000465:Algorithms; D042241:Early Diagnosis; D005260:Female; D005865:Gestational Age; D006801:Humans; D015994:Incidence; D007231:Infant, Newborn; D007558:Italy / Q000453:epidemiology; D008297:Male; D015997:Neonatal Screening / Q000379:methods*; D009887:Ophthalmoscopy / Q000379:methods*; D012372:ROC Curve; D012160:Retina / Q000000981:diagnostic imaging*; D012178:Retinopathy of Prematurity / Q000175:diagnosis* / Q000453:epidemiology; D012189:Retrospective Studies; D012307:Risk Factors

CHOP ROP
; 
WINROP
; RORScore; algorithms; retinopathy of prematurity