Literature DB >> 28715553

Validation of the Children's Hospital of Philadelphia Retinopathy of Prematurity (CHOP ROP) Model.

Gil Binenbaum1,2, Gui-Shuang Ying2, Lauren A Tomlinson1.   

Abstract

Importance: The Children's Hospital of Philadelphia Retinopathy of Prematurity (CHOP ROP) model uses birth weight (BW), gestational age at birth (GA), and weight gain rate to predict the risk of severe retinopathy of prematurity (ROP). In a model development study, it predicted all infants requiring treatment, while greatly reducing the number of examinations compared with current screening guidelines. Objective: To validate the CHOP ROP model in a multicenter cohort that is large enough to obtain a precise estimate of the model's sensitivity for treatment-requiring ROP. Design, Setting, and Participants: This investigation was a secondary analysis of data from the Postnatal Growth and Retinopathy of Prematurity (G-ROP) Study. The setting was 30 hospitals in the United States and Canada between January 1, 2006, and June 30, 2012. The dates of analysis were September 28 to October 5, 2015. Participants were premature infants at risk for ROP with a known ROP outcome. Main Outcomes and Measures: Sensitivity for Early Treatment of Retinopathy of Prematurity type 1 ROP and potential reduction in the number of infants requiring examinations. In the primary analysis, the CHOP ROP model was applied weekly to predict the risk of ROP. If the risk was above a cut-point level (high risk), examinations were indicated, while low-risk infants received no examinations. In a secondary analysis, low-risk infants received fewer examinations rather than no examinations.
Results: Participants included 7483 premature infants at risk for ROP with a known ROP outcome. Their median BW was 1070 g (range, 310-3000 g), and their median GA was 28 weeks (range, 22-35 weeks). Among them, 3575 (47.8%) were female, and their race/ethnicity was 3615 white (48.3%), 2310 black (30.9%), 233 Asian (3.1%), 93 Pacific Islander (1.2%), and 40 American Indian/Alaskan native (0.5%). The original CHOP ROP model correctly predicted 452 of 459 infants who developed type 1 ROP (sensitivity, 98.5%; 95% CI, 96.9%-99.3%), reducing the number of infants requiring examinations by 34.3% if only high-risk infants received examinations. Lowering the cut point to capture all type 1 ROP cases (sensitivity, 100%; 95% CI, 99.2%-100%) resulted in only 6.8% of infants not requiring examinations. However, if low-risk infants were examined at 37 weeks' postmenstrual age and followed up only if ROP was present at that examination, all type 1 ROP cases would be captured, and the number of examinations performed among infants with GA exceeding 27 weeks would be reduced by 28.4%. Conclusion and Relevance: The CHOP ROP model demonstrated high but not 100% sensitivity and may be better used to reduce examination frequency. The model might be used reliably to guide a modified ROP screening schedule and decrease the number of examinations performed.

Entities:  

Mesh:

Year:  2017        PMID: 28715553      PMCID: PMC5710287          DOI: 10.1001/jamaophthalmol.2017.2295

Source DB:  PubMed          Journal:  JAMA Ophthalmol        ISSN: 2168-6165            Impact factor:   7.389


  36 in total

1.  UK retinopathy of prematurity guideline.

Authors:  A R Wilkinson; L Haines; K Head; A R Fielder
Journal:  Early Hum Dev       Date:  2008-02       Impact factor: 2.079

2.  Prediction of retinopathy of prematurity using the screening algorithm WINROP in a Mexican population of preterm infants.

Authors:  Luz Consuelo Zepeda-Romero; Anna-Lena Hård; Larissa Maria Gomez-Ruiz; Jose Alfonso Gutierrez-Padilla; Eusebio Angulo-Castellanos; Juan Carlos Barrera-de-Leon; Juan Manuel Ramirez-Valdivia; Cesareo Gonzalez-Bernal; Claudia Ivette Valtierra-Santiago; Esperanza Garnica-Garcia; Chatarina Löfqvist; Ann Hellström
Journal:  Arch Ophthalmol       Date:  2012-06

3.  Colorado retinopathy of prematurity model: a multi-institutional validation study.

Authors:  Jennifer H Cao; Brandie D Wagner; Ashlee Cerda; Emily A McCourt; Alan Palestine; Robert W Enzenauer; Rebecca S Braverman; Ryan K Wong; Irena Tsui; Charlotte Gore; Shira L Robbins; Michael A Puente; Levi Kauffman; Lingkun Kong; David G Morrison; Anne M Lynch
Journal:  J AAPOS       Date:  2016-05-07       Impact factor: 1.220

4.  Longitudinal growth of hospitalized very low birth weight infants.

Authors:  R A Ehrenkranz; N Younes; J A Lemons; A A Fanaroff; E F Donovan; L L Wright; V Katsikiotis; J E Tyson; W Oh; S Shankaran; C R Bauer; S B Korones; B J Stoll; D K Stevenson; L A Papile
Journal:  Pediatrics       Date:  1999-08       Impact factor: 7.124

5.  Predicting proliferative retinopathy in a Brazilian population of preterm infants with the screening algorithm WINROP.

Authors:  Anna-Lena Hård; Chatarina Löfqvist; Joao Borges Fortes Filho; Renato Soibelmann Procianoy; Lois Smith; Ann Hellström
Journal:  Arch Ophthalmol       Date:  2010-11

6.  The CHOP postnatal weight gain, birth weight, and gestational age retinopathy of prematurity risk model.

Authors:  Gil Binenbaum; Gui-Shuang Ying; Graham E Quinn; Jiayan Huang; Stephan Dreiseitl; Jules Antigua; Negar Foroughi; Soraya Abbasi
Journal:  Arch Ophthalmol       Date:  2012-12

7.  Weight gain measured at 6 weeks after birth as a predictor for severe retinopathy of prematurity: study with 317 very low birth weight preterm babies.

Authors:  Joao Borges Fortes Filho; Pedro P Bonomo; Mauricio Maia; Renato S Procianoy
Journal:  Graefes Arch Clin Exp Ophthalmol       Date:  2008-12-04       Impact factor: 3.117

Review 8.  Algorithms for the prediction of retinopathy of prematurity based on postnatal weight gain.

Authors:  Gil Binenbaum
Journal:  Clin Perinatol       Date:  2013-06       Impact factor: 3.430

9.  Early weight gain predicts retinopathy in preterm infants: new, simple, efficient approach to screening.

Authors:  Ann Hellström; Anna-Lena Hård; Eva Engström; Aimon Niklasson; Eva Andersson; Lois Smith; Chatarina Löfqvist
Journal:  Pediatrics       Date:  2009-03-16       Impact factor: 7.124

10.  Incidence of retinopathy of prematurity from 1996 to 2000: analysis of a comprehensive New York state patient database.

Authors:  Michael F Chiang; Raymond R Arons; John T Flynn; Justin B Starren
Journal:  Ophthalmology       Date:  2004-07       Impact factor: 12.079

View more
  13 in total

1.  Incidence and Early Course of Retinopathy of Prematurity: Secondary Analysis of the Postnatal Growth and Retinopathy of Prematurity (G-ROP) Study.

Authors:  Graham E Quinn; Gui-Shuang Ying; Edward F Bell; Pamela K Donohue; David Morrison; Lauren A Tomlinson; Gil Binenbaum
Journal:  JAMA Ophthalmol       Date:  2018-12-01       Impact factor: 7.389

Review 2.  Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review.

Authors:  Ryan M McAdams; Ravneet Kaur; Yao Sun; Harlieen Bindra; Su Jin Cho; Harpreet Singh
Journal:  J Perinatol       Date:  2022-05-13       Impact factor: 2.521

3.  Single-Examination Risk Prediction of Severe Retinopathy of Prematurity.

Authors:  Aaron S Coyner; Jimmy S Chen; Praveer Singh; Robert L Schelonka; Brian K Jordan; Cindy T McEvoy; Jamie E Anderson; R V Paul Chan; Kemal Sonmez; Deniz Erdogmus; Michael F Chiang; Jayashree Kalpathy-Cramer; J Peter Campbell
Journal:  Pediatrics       Date:  2021-12-01       Impact factor: 9.703

4.  Validation of the Colorado Retinopathy of Prematurity Screening Model.

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

5.  Development of Modified Screening Criteria for Retinopathy of Prematurity: Primary Results From the Postnatal Growth and Retinopathy of Prematurity Study.

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

6.  Monitoring Disease Progression With a Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning.

Authors:  Stanford Taylor; James M Brown; Kishan Gupta; J Peter Campbell; Susan Ostmo; R V Paul Chan; Jennifer Dy; Deniz Erdogmus; Stratis Ioannidis; Sang J Kim; Jayashree Kalpathy-Cramer; Michael F Chiang
Journal:  JAMA Ophthalmol       Date:  2019-07-03       Impact factor: 7.389

7.  Development and validation of a new clinical decision support tool to optimize screening for retinopathy of prematurity.

Authors:  Aldina Pivodic; Helena Johansson; Lois E H Smith; Anna-Lena Hård; Chatarina Löfqvist; Bradley A Yoder; M Elizabeth Hartnett; Carolyn Wu; Marie-Christine Bründer; Wolf A Lagrèze; Andreas Stahl; Abbas Al-Hawasi; Eva Larsson; Pia Lundgren; Lotta Gränse; Birgitta Sunnqvist; Kristina Tornqvist; Agneta Wallin; Gerd Holmström; Kerstin Albertsson-Wikland; Staffan Nilsson; Ann Hellström
Journal:  Br J Ophthalmol       Date:  2021-05-12       Impact factor: 5.908

8.  Factors associated with retinopathy of prematurity ophthalmology workload.

Authors:  Jack Jacob; Zinnia Matrix; Debra Skopec; Benjamin Ticho; Robert W Arnold
Journal:  J Perinatol       Date:  2018-08-31       Impact factor: 2.521

9.  Current evidence and outcomes for retinopathy of prematurity prevention: insight into novel maternal and placental contributions.

Authors:  Lara Carroll; Leah A Owen
Journal:  Explor Med       Date:  2020-02-29

10.  Validation of the DIGIROP-birth model in a Chinese cohort.

Authors:  Sizhe Chen; Rong Wu; He Chen; Wenbei Ma; Shaolin Du; Chao Li; Xiaohe Lu; Songfu Feng
Journal:  BMC Ophthalmol       Date:  2021-05-27       Impact factor: 2.209

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.