The Economic Model of Retinopathy of Prematurity (EcROP) Screening and Treatment: Mexico and the United States

Rothschild, Michael; Russ, Rebecca; Brennan, Kathryn; Williams, Christopher J.; Berrones, David; Patel, Brinda; Martinez-Castellanos, María A.; Fernandes, Alcides; Hubbard, G. Baker; Chan, Robison Vernon Paul; Zhou, Yang; Olsen, Timothy W.

doi:10.1016/j.ajo.2016.04.014

Cited by 31 publications

(52 citation statements)

References 19 publications

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“…In Mexico approximately 24% of childhood blindness occurs as a result of ROP, with large variations by regions. 27,28 It can be a challenge to acquire accurate healthcare statistics in Mexico. The last relevant reports by the Grupo ROP México are from 2011.…”

Section: Rop In Mexicomentioning

confidence: 99%

“…The economic impact of ROP on our society is high. 27 Our group participated in the development of the the Economic Model of Retinopathy of Prematurity (EcROP), which showed that the cost per neonate treated in Mexico is estimated to be $3,228 USD; parental indirect cost due to estimated lost wages and productivity was $305,584 USD; and a lifetime loss of productivity resulting from a blind individual was $142,172 USD.…”

Section: Rop In Mexicomentioning

confidence: 99%

“…Queen Sirikit National Institute of Child Health (QSNICH), a large tertiary referral hospital in Bangkok, reported that from 2006 to 2009, 40.7% (655/1609) of preterm infants born there had any stage of ROP. The mean GA of preterm infants with ROP was 29.1 weeks (range, [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39], and the mean BW was 1254.6 g (range, 545-2670 g). Prethreshold and threshold disease was found in 59% (n = 387), stage 4 and 5 ROP were found in 6% (n = 37), and aggressive posterior ROP (APROP) was found in 7% (n = 47).…”

Section: Rop In Thailandmentioning

confidence: 99%

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The current state of retinopathy of prematurity in India, Kenya, Mexico, Nigeria, Philippines, Romania, Thailand, and Venezuela

et al. 2019

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Retinopathy of prematurity (ROP) is one of the leading causes of preventable blindness in children worldwide. Middle-income nations are currently experiencing epidemic levels of ROP, because greater access to neonatal intensive care units has improved survival rates of premature infants, but without sophisticated oxygen regulation. The epidemiology, screening infrastructure, treatment options, and challenges that these countries face are often tied to unique local socioeconomic, cultural, geopolitical, and medical factors. We present an overview and narratives of the current state of ROP in eight countries that are or soon will be experiencing ROP epidemics

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Section: Rop In Mexicomentioning

confidence: 99%

Section: Rop In Mexicomentioning

confidence: 99%

Section: Rop In Thailandmentioning

confidence: 99%

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The current state of retinopathy of prematurity in India, Kenya, Mexico, Nigeria, Philippines, Romania, Thailand, and Venezuela

et al. 2019

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“…[42] ROP telemedicine screening has additionally been shown to be cost-effective in both developed and developing nations. [3, 43]…”

Section: Retinopathy Of Prematurity and Other Pediatric Retinal Diseasesmentioning

confidence: 99%

“…Nevertheless, telemedicine may prove to be cost-effective in the longer term. [3] Implementation of reliable, accurate and dependable new telemedicine systems additionally requires rigorous validation, standardization, and regulation.…”

Section: Introductionmentioning

confidence: 99%

Retinal Telemedicine

Chee

Darwish

Fernández‐Vega

et al. 2018

Curr Ophthalmol Rep

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Development and Validation of a Deep Learning Model to Predict the Occurrence and Severity of Retinopathy of Prematurity

et al. 2022

JAMA Netw Open

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IMPORTANCERetinopathy of prematurity (ROP) is the leading cause of childhood blindness worldwide. Prediction of ROP before onset holds great promise for reducing the risk of blindness. OBJECTIVE To develop and validate a deep learning (DL) system to predict the occurrence and severity of ROP before 45 weeks' postmenstrual age. DESIGN, SETTING, AND PARTICIPANTS This retrospective prognostic study included 7033 retinal photographs of 725 infants in the training set and 763 retinal photographs of 90 infants in the external validation set, along with 46 characteristics for each infant. All images of both eyes from the same infant taken at the first screening were labeled according to the final diagnosis made between the first screening and 45 weeks' postmenstrual age. The DL system was developed using retinal photographs from the first ROP screening and clinical characteristics before or at the first screening in infants born between June 3, 2017, and August 28, 2019. EXPOSURES Two models were specifically designed for predictions of the occurrence (occurrence network [OC-Net]) and severity (severity network [SE-Net]) of ROP. Five-fold cross-validation was applied for internal validation. MAIN OUTCOMES AND MEASURES Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity to evaluate the performance in ROP prediction. RESULTSThis study included 815 infants (450 [55.2%] boys) with mean birth weight of 1.91 kg (95% CI, 1.87-1.95 kg) and mean gestational age of 33.1 weeks (95% CI, 32.9-33.3 weeks).

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The Economic Model of Retinopathy of Prematurity (EcROP) Screening and Treatment: Mexico and the United States

Cited by 31 publications

References 19 publications

The current state of retinopathy of prematurity in India, Kenya, Mexico, Nigeria, Philippines, Romania, Thailand, and Venezuela

The current state of retinopathy of prematurity in India, Kenya, Mexico, Nigeria, Philippines, Romania, Thailand, and Venezuela

Retinal Telemedicine

Development and Validation of a Deep Learning Model to Predict the Occurrence and Severity of Retinopathy of Prematurity

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