Application of Machine Learning to Ranking Predictors of Anti-VEGF Response
Age-related macular degeneration (AMD) is a heterogeneous disease affecting the macula of individuals and is a cause of irreversible vision loss. Patients with neovascular AMD (nAMD) are candidates for the anti-vascular endothelial growth factor (anti-VEGF) treatment, designed to regress the growth...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/12/11/1926 |
| _version_ | 1797464839728136192 |
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| author | Janan Arslan Kurt K. Benke |
| author_facet | Janan Arslan Kurt K. Benke |
| author_sort | Janan Arslan |
| collection | DOAJ |
| description | Age-related macular degeneration (AMD) is a heterogeneous disease affecting the macula of individuals and is a cause of irreversible vision loss. Patients with neovascular AMD (nAMD) are candidates for the anti-vascular endothelial growth factor (anti-VEGF) treatment, designed to regress the growth of abnormal blood vessels in the eye. Some patients fail to maintain vision despite treatment. This study aimed to develop a prediction model based on features weighted in order of importance with respect to their impact on visual acuity (VA). Evaluations included an assessment of clinical, lifestyle, and demographic factors from patients that were treated over a period of two years. The methods included mixed-effects and relative importance modelling, and models were tested against model selection criteria, diagnostic and assumption checks, and forecasting errors. The most important predictors of an anti-VEGF response were the baseline VA of the treated eye, the time (in weeks), treatment quantity, and the treated eye. The model also ranked the impact of other variables, such as intra-retinal fluid, haemorrhage, pigment epithelium detachment, treatment drug, baseline VA of the untreated eye, and various lifestyle and demographic factors. The results identified variables that could be targeted for further investigation in support of personalised treatments based on patient data. |
| first_indexed | 2024-03-09T18:12:50Z |
| format | Article |
| id | doaj.art-788d3e09d8b14d2ebefcd95b0d6bed2f |
| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-03-09T18:12:50Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj.art-788d3e09d8b14d2ebefcd95b0d6bed2f2023-11-24T08:58:10ZengMDPI AGLife2075-17292022-11-011211192610.3390/life12111926Application of Machine Learning to Ranking Predictors of Anti-VEGF ResponseJanan Arslan0Kurt K. Benke1Sorbonne Université, Institut du Cerveau—Paris Brain Institute—ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013 Paris, FranceSchool of Engineering, University of Melbourne, Parkville, VIC 3010, AustraliaAge-related macular degeneration (AMD) is a heterogeneous disease affecting the macula of individuals and is a cause of irreversible vision loss. Patients with neovascular AMD (nAMD) are candidates for the anti-vascular endothelial growth factor (anti-VEGF) treatment, designed to regress the growth of abnormal blood vessels in the eye. Some patients fail to maintain vision despite treatment. This study aimed to develop a prediction model based on features weighted in order of importance with respect to their impact on visual acuity (VA). Evaluations included an assessment of clinical, lifestyle, and demographic factors from patients that were treated over a period of two years. The methods included mixed-effects and relative importance modelling, and models were tested against model selection criteria, diagnostic and assumption checks, and forecasting errors. The most important predictors of an anti-VEGF response were the baseline VA of the treated eye, the time (in weeks), treatment quantity, and the treated eye. The model also ranked the impact of other variables, such as intra-retinal fluid, haemorrhage, pigment epithelium detachment, treatment drug, baseline VA of the untreated eye, and various lifestyle and demographic factors. The results identified variables that could be targeted for further investigation in support of personalised treatments based on patient data.https://www.mdpi.com/2075-1729/12/11/1926age-related macular degenerationanti-VEGF treatmentexplainabilitystatistical modelling |
| spellingShingle | Janan Arslan Kurt K. Benke Application of Machine Learning to Ranking Predictors of Anti-VEGF Response Life age-related macular degeneration anti-VEGF treatment explainability statistical modelling |
| title | Application of Machine Learning to Ranking Predictors of Anti-VEGF Response |
| title_full | Application of Machine Learning to Ranking Predictors of Anti-VEGF Response |
| title_fullStr | Application of Machine Learning to Ranking Predictors of Anti-VEGF Response |
| title_full_unstemmed | Application of Machine Learning to Ranking Predictors of Anti-VEGF Response |
| title_short | Application of Machine Learning to Ranking Predictors of Anti-VEGF Response |
| title_sort | application of machine learning to ranking predictors of anti vegf response |
| topic | age-related macular degeneration anti-VEGF treatment explainability statistical modelling |
| url | https://www.mdpi.com/2075-1729/12/11/1926 |
| work_keys_str_mv | AT jananarslan applicationofmachinelearningtorankingpredictorsofantivegfresponse AT kurtkbenke applicationofmachinelearningtorankingpredictorsofantivegfresponse |