Mallika Prem Senthil1, Mohamad Aziz Salowi2, Mohamad Adam Bujang3, Adeline Kueh2, Chong Min Siew2, Kala Sumugam2, Chan Lee Gaik4, Tan Aik Kah1. 1. Department of Ophthalmology, Faculty of Medicine and Health Sciences, University Malaysia Sarawak (UNIMAS), Lot 77, Seksyen 22, Kuching Town Land District, Jalan Tun Ahmad Zaidi Adruce, 93150 Kuching, Sarawak, Malaysia. 2. Department of Ophthalmology, Sarawak General Hospital, 93150 Kuching, Sarawak, Malaysia. 3. Biostatistics Unit, Clinical Research Centre, Level 3 Dermatology Block, Hospital Kuala Lumpur, Jalan Pahang, 50586 Kuala Lumpur, Malaysia. 4. Departments of Pediatrics, Sarawak General Hospital, Kuching, 93150 Kuching, Sarawak, Malaysia.
Abstract
OBJECTIVES: To develop a simple prediction model for the pre-screening of Retinopathy of Prematurity (ROP) among preterm babies. METHODS: This was a prospective study. The test dataset (January 2007 until December 2010) was used to construct risk prediction models, and the validation dataset (January 2011 until March 2012) was used to validate the models developed from the test dataset. Two prediction models were produced using the test dataset based on logistic regression equations in which the development of ROP was used as the outcome. RESULTS: The sensitivity and specificity for model 1 [gestational age (GA), birth weight (BW), intraventricular haemorrhage (IVH) and respiratory distress syndrome (RDS)] was 82 % and 81.7%, respectively; for model 2, (GA and BW) the sensitivity and specificity were 80.5% and 80.3%, respectively. CONCLUSION: Model 2 was preferable, as it only required two predictors (GA and BW). Our prediction model can be used for early detection of ROP to avoid poor outcomes.
OBJECTIVES: To develop a simple prediction model for the pre-screening of Retinopathy of Prematurity (ROP) among preterm babies. METHODS: This was a prospective study. The test dataset (January 2007 until December 2010) was used to construct risk prediction models, and the validation dataset (January 2011 until March 2012) was used to validate the models developed from the test dataset. Two prediction models were produced using the test dataset based on logistic regression equations in which the development of ROP was used as the outcome. RESULTS: The sensitivity and specificity for model 1 [gestational age (GA), birth weight (BW), intraventricular haemorrhage (IVH) and respiratory distress syndrome (RDS)] was 82 % and 81.7%, respectively; for model 2, (GA and BW) the sensitivity and specificity were 80.5% and 80.3%, respectively. CONCLUSION: Model 2 was preferable, as it only required two predictors (GA and BW). Our prediction model can be used for early detection of ROP to avoid poor outcomes.
Authors: Sang Jin Kim; Alexander D Port; Ryan Swan; J Peter Campbell; R V Paul Chan; Michael F Chiang Journal: Surv Ophthalmol Date: 2018-04-19 Impact factor: 6.048