Risk score system accurate in predicting macular degeneration risk

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1. 8 predictors (age, sex, education level, race, smoking status, presence of pigment abnormality, soft drusen, and maximum drusen size) were utilized to create a macular risk scoring system (MRSS).

2. Area under the receiver operating characteristic (ROC) curve was excellent (internally validated c-index = 0.88; externally validated c-index = 0.91). Sensitivity and specificity at cutoff of 0 were 87.6% and 73.6% respectively.

Evidence Rating Level: 2 (Good)

Study Rundown: In this retrospective study, the authors created the macular risk scoring system (MRSS) to help clinicians in assessing how likely patients may develop age-related macular degeneration (AMD). Two large AMD datasets, the age-related eye disease study (AREDS) and blue mountain eye study (BMES), were utilized to identify crucial parameters for the study. Utilizing various statistical methods, the MRSS was created with a sensitivity and specificity of 87.6% and 73.6% respectively with a cutoff of 0. This is the first risk scoring system that also uses incidence data to help predict risk up to 10 years in the future. The authors also provide specificity & sensitivity for various cutoffs, providing flexibility users. Limitations are mostly based on the limitations of BMES & AREDS, which include limited number of AMD cases and a narrow study population respectively.

Click to read the study in Ophthalmology

Relevant Reading: Prediction Model for Prevalence and Incidence of Advanced Age-Related Macular Degeneration Based on Genetic, Demographic, and Environmental Variables

In-Depth [retrospective study]: This study attempted to identify a scoring system to assess risk of developing age-related macular degeneration. Two prior databases, AREDS and BMES, were utilized, and 5 demographic predictors (age, sex, education level, race, smoking status) and 3 ophthalmic predictors (presence of pigment abnormality, soft drusen, and maximum drusen size) were identified. Specific scores were assigned depending on the patient’s phenotype for each parameter. A logistic regression model was used to create the MRSS. The c-index, which represents of the area under the ROC curve, was 0.88-0.91 depending on the validation technique, which represents a very good model. Overall, the MRSS provides a valuable tool to assess whether patients are low or high-risk, allowing the physician to alter monitoring as needed.

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