Stefano Piermarocchi1, Silvia Bini1, Ferdinando Martini1, Marianna Berton1, Anna Lavini2, Elena Gusson3, Giorgio Marchini3, Ezio Maria Padovani4, Sara Macor5, Silvia Pignatto5, Paolo Lanzetta5, Luigi Cattarossi6, Eugenio Baraldi7, Paola Lago7. 1. Department of Ophthalmology, University of Padova, Padova, Italy. 2. University of Verona, Verona, Italy. 3. Ophthalmology Unit, Department of Neurological, Neuropsychological, Morphological and Movement Sciences, University of Verona, Verona, Italy. 4. Department of Pediatrics, Section of Newborn Intensive Care Unit, University Hospital of Verona, Verona, Italy. 5. Department of Medical and Biological Sciences- Ophthalmology, University of Udine, Udine, Italy. 6. Department of Pediatrics- Neonatology, University of Udine, Udine, Italy. 7. Department of Woman's and Child's Health, University of Padova, Padova, Italy.
Abstract
PURPOSE: 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. CONCLUSION: 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.
PURPOSE: 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. CONCLUSION: 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.
Authors: Emily A McCourt; Gui-Shuang Ying; Anne M Lynch; Alan G Palestine; Brandie D Wagner; Erica Wymore; Lauren A Tomlinson; Gil Binenbaum Journal: JAMA Ophthalmol Date: 2018-04-01 Impact factor: 7.389
Authors: Gil Binenbaum; Edward F Bell; Pamela Donohue; Graham Quinn; James Shaffer; Lauren A Tomlinson; Gui-Shuang Ying Journal: JAMA Ophthalmol Date: 2018-09-01 Impact factor: 7.389
Authors: Kellen Cristiane do Vale Lucio; Maria Regina Bentlin; Ana Carolina de Lima Augusto; José Eduardo Corrente; Taísa Bertoco Carregal Toscano; Regina El Dib; Eliane Chaves Jorge Journal: Clinics (Sao Paulo) Date: 2018-07-26 Impact factor: 2.365