Identifying Characteristics Predictive of Lost-to-Follow-Up Status in Amblyopia
Copyright © 2021 Elsevier Inc. All rights reserved..
PURPOSE: To identify demographic and disease-related characteristics predictive of Lost-to-Follow-Up (LTFU) status in amblyopia treatment and create a risk model for predicting LTFU status.
DESIGN: Retrospective cohort study METHODS: Setting: Single-center, ophthalmology department at Boston Children's Hospital (BCH).
PATIENTS: 2037 patients treated for amblyopia at BCH between 2010 and 2014.
OBSERVATION PROCEDURE: LTFU was defined as patients who did not return after initial visit, excluding those who came for second opinion. Multiple variables were tested for association with LTFU status.
OUTCOME MEASURE: Odds ratio of LTFU risk associated with each variable. Multivariate logistic regression was used to create a risk score for predicting LTFU status.
RESULTS: A large proportion of patients (23%) were LTFU after first visit. Older age, nonwhite race, lack of insurance, previous glasses or atropine treatment, and longer requested follow-up intervals were independent predictors of LTFU status. A multivariable risk score was created to predict probability of LTFU (area under the curve 0.68).
CONCLUSIONS: Our comprehensive amblyopia database allows us to predict which patients are more likely to be LTFU after baseline visit and develop strategies to mitigate these effects. These findings may help with practice efficiency and improve patient outcomes in the future by transitioning these analyses to an electronic medical record that could be programmed to provide continually updated decision support for individual patients based on large data sets.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:230 |
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| Enthalten in: |
American journal of ophthalmology - 230(2021) vom: 15. Okt., Seite 200-206 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Shoshany, Talia N [VerfasserIn] |
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| Anmerkungen: |
Date Completed 24.01.2022 Date Revised 24.01.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.ajo.2021.05.002 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM325439176 |
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| 245 | 1 | 0 | |a Identifying Characteristics Predictive of Lost-to-Follow-Up Status in Amblyopia |
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| 500 | |a Date Completed 24.01.2022 | ||
| 500 | |a Date Revised 24.01.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021 Elsevier Inc. All rights reserved. | ||
| 520 | |a PURPOSE: To identify demographic and disease-related characteristics predictive of Lost-to-Follow-Up (LTFU) status in amblyopia treatment and create a risk model for predicting LTFU status | ||
| 520 | |a DESIGN: Retrospective cohort study METHODS: Setting: Single-center, ophthalmology department at Boston Children's Hospital (BCH) | ||
| 520 | |a PATIENTS: 2037 patients treated for amblyopia at BCH between 2010 and 2014 | ||
| 520 | |a OBSERVATION PROCEDURE: LTFU was defined as patients who did not return after initial visit, excluding those who came for second opinion. Multiple variables were tested for association with LTFU status | ||
| 520 | |a OUTCOME MEASURE: Odds ratio of LTFU risk associated with each variable. Multivariate logistic regression was used to create a risk score for predicting LTFU status | ||
| 520 | |a RESULTS: A large proportion of patients (23%) were LTFU after first visit. Older age, nonwhite race, lack of insurance, previous glasses or atropine treatment, and longer requested follow-up intervals were independent predictors of LTFU status. A multivariable risk score was created to predict probability of LTFU (area under the curve 0.68) | ||
| 520 | |a CONCLUSIONS: Our comprehensive amblyopia database allows us to predict which patients are more likely to be LTFU after baseline visit and develop strategies to mitigate these effects. These findings may help with practice efficiency and improve patient outcomes in the future by transitioning these analyses to an electronic medical record that could be programmed to provide continually updated decision support for individual patients based on large data sets | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Chinn, Ryan N |e verfasserin |4 aut | |
| 700 | 1 | |a Staffa, Steven J |e verfasserin |4 aut | |
| 700 | 1 | |a Bishop, Kaila |e verfasserin |4 aut | |
| 700 | 1 | |a Michalak, Suzanne |e verfasserin |4 aut | |
| 700 | 1 | |a Hunter, David G |e verfasserin |4 aut | |
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