Eye Injury and Demographic Parameters Associated with Poor Visual Outcome Lésion Oculaire Et Paramètres Démographiques Associés À Un Faible Résultat Visuel

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Eye Injury and Demographic Parameters Associated with Poor Visual Outcome Lésion Oculaire Et Paramètres Démographiques Associés À Un Faible Résultat Visuel HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author J Fr Ophtalmol Manuscript Author . Author Manuscript Author manuscript; available in PMC 2020 October 01. Published in final edited form as: J Fr Ophtalmol. 2019 October ; 42(8): 864–873. doi:10.1016/j.jfo.2019.03.018. Eye injury and demographic parameters associated with poor visual outcome Lésion oculaire et paramètres démographiques associés à un faible résultat visuel Nina Jovanovic1,*, Corinne Peek-Asa2, Tracy Young2, Faruk Nisic3, Sanjin Musa4 1.Ophthalmology Department, Canton Hospital, Zenica 2.Injury Prevention Research Center (IPRC), College of Public Health, The University of Iowa 3.Eye Clinic, Clinical Center University of Sarajevo 4.Institute for Public Health in Federation of Bosnia and Herzegovina, Sarajevo Abstract Background : Eye injuries can result in long-term disability, and healthcare providers need better tools to predict outcomes. Few prognostic models for poor visual acuity have been examined using variables usually present in very severe injuries, which creates a gap in prognosis. Therefore, a model associated with severe and less severe injuries is examined. Methods : Eye injuries hospitalized in Bosnia and Herzegovina from 2006 through 2014 were included. A total of 298 eye injuries met the inclusion criteria of being an acute mechanical, chemical or physical eye injury. Prognostic variables were grouped by patient characteristics, eye injury characteristics and eye injury diagnosis. Poor final visual acuity was the main outcome measure (vision less than 20/200). Multivariate regression analysis used stepwise selection to identify the strongest set of predictive variables. Results : Lens subluxation (95 % CI : 2.09–14.83), vitreous prolapse (95 % CI : 2.76–26.87), vitreous hemorrhage (95 % CI : 1.71–10.03), posterior segment intraocular foreign body (95 % CI : 1.19–39.09), and vitritis (95 % CI : 0.97–11.12) were significantly associated with poor final visual acuity. The predictive model identified the combination of age over 36, lens subluxation, vitreous prolapse and hemorrhage, vitritis, and macular hemorrhage as the combination most likely to have poor visual acuity. The final model resulted in a strong fit as measured by AIC, log likelihood, goodness-of-fit, and the c-statistic. *Corresponding author: Nina Jovanovic, [email protected], Tél. : +387 61 276 422, Ophthalmology Department, Canton Hospital Zenica, Crkvice 67, 72000 Zenica, Bosnia and Herzegovina. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. The partial result of the study was presented at the 51st Turkish Ophthalmological Society Congress, Annual Meeting, Antalya, Turkey 2017 in the Young Ophthalmology Session Conflict of Interest : The authors declare no conflict of interest or competing interests. Jovanovic et al. Page 2 Conclusions : This model can be used in clinical practice to evaluate severity and predict final Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author visual acuity in both severe and less severe eye injuries. The model accounts for characteristics of the injury as well as the patient. Additional studies with larger samples could further verify this model. Résumé Les lésions oculaires peuvent survenir après une invalidité à long terme et les fournisseurs de soins de santé ont besoin de meilleurs outils pour prédire les résultats. Peu de modèles pronostiques pour une faible acuité visuelle ont été examinés. en utilisant des variables généralement présentes dans les blessures très graves, ce qui crée un écart dans le pronostic. Par conséquent, un modèle associé à des blessures graves et moins graves est examiné. Les blessures aux yeux hospitalisées à l’hôpital de Bosnie-Herzégovine de 2006 à 2014 ont été incluses. Au total, 298 lésions oculaires répondaient aux critères d’inclusion suivants : lésion oculaire mécanique, chimique ou physique aiguë. Les variables pronostiques ont été regroupées comme caractéristiques du patient, caractéristiques des lésions oculaires et diagnostic des lésions oculaires. La principale mesure de résultat (vision inférieure à 20/200) était la faible acuité visuelle finale. L’analyse de régression multivariée a utilisé une sélection par étapes pour identifier le plus puissant ensemble de variables prédictives. subluxation du cristallin (IC 95 % : 2,09–14,83), prolapsus vitré (IC 95 % : 2,76–26,87), hémorragie vitrée (IC 95 % : 1,71–10,03), corps étranger intraoculaire dans le segment postérieur (IC 95 % : 1,19). −39,09) et la vitrite (IC 95 % : 0,97–11,12) étaient significativement associés à une faible acuité visuelle finale. Le modèle prédictif a identifié l’association comme étant l’âge le plus élevé de 36 ans, la subluxation de la lentille, le prolapsus et l’hémorragie vitreux, la vitrite et l’hémorragie maculaire comme la combinaison la plus susceptible d’avoir une faible acuité visuelle. Le modèle final a donné un fort ajustement, mesuré par AIC, log vraisemblance, qualité de l’ajustement et statistique c. Ce modèle peut être utilisé en pratique clinique pour évaluer la gravité et prédire l’acuité visuelle finale des lésions oculaires graves et moins graves. Le modèle s’appuie sur les caractéristiques de la blessure et du patient. Des études supplémentaires avec des échantillons plus grands pourraient confirmer davantage ce modèle. Mots clés : traumatisme oculaire, score pronostique, résultat visuel, modèle pronostique Keywords eye trauma; prognostic score; visual outcome; prognostic model Introduction Ocular trauma impacts up to 55 million people in the world each year, of which 1.6 million develop blindness [1]. Based on reports from developing countries, eye trauma was the cause for 5 % of blindness, with trauma disproportionately causing of blindness in young people. Ocular trauma is also disproportionately prevalent in low and middle-income countries [2]. Many risk factors for ocular trauma have been documented, including occupational risks, explosives, working with machinery, and eye injuries associated with motor vehicle crashes J Fr Ophtalmol. Author manuscript; available in PMC 2020 October 01. Jovanovic et al. Page 3 [3–5]. Eye injuries can produce substantial disability when vision loss occurs. Both patients Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author and their healthcare providers benefit from knowledge about the anticipated visual impact from different types and diagnoses of ocular trauma. Many studies validated the prognostic accuracy of the Ocular Trauma Score (OTS) in predicting visual outcome of injured eyes [6–13], and many studies have examined individual prognostic factors, such as poor visual acuity, vitreous prolapse or retinal detachment [14], age, and cause of injury [15] in predicting final visual acuity [14–19]. Accurate prognostic tools applied early in the process of care and treatment are helpful for decision making for the ophthalmologist and for informing the injured patient. The most widely used tools, the OTS [20] and The Classification and Regression Tree (CART) prognostic model [11,21] predict visual acuity using several individual variables. The OTS uses visual acuity at presentation, retinal detachment, endophthalmitis, relative afferent pupillary defect, globe rupture and perforating globe injury to calculate a numerical visual acuity scale that ranges from 1 (no perception of light) to 5 (more than 20/40). CART uses a recursive partitioning decision tree statistical method, which predicts functional outcomes of open globe injuries using dichotomized input variables : relative afferent pupillary defect, initial vision, lid laceration, and posterior wound location. Several studies have validated OTS as an accurate prognostic tool, primarily for eye injuries leading to severe impairments [11]. The OTS is helpful because it incorporates many variables, especially since studies of individual variables have less consistent prognostic ability. For example, age has been found in some studies to have prognostic value [16,22] and in other studies not [23]. However, OTS is not always possible to calculate, especially for less severe eye injuries, because these patients do not present with rupture globe, relative afferent pupillary defect, perforating injury, or other injury characteristics included in the OTS or CART. However, patients with less severe injuries may also be at risk for visual impairment. Therefore, there is a need to create a new prognostic tool which could be used to predict poor visual outcome in injuries with a wide range of severity using various clinical and patient characteristics. The aim of this study is to create a predictive model which can be used by ophthalmologist at patient admission to predict final visual acuity for both severely and less severely eye injured patients. Materials and methods Location and subjects The study was a clinical case series conducted at the Eye Department, Canton Hospital Zenica (CHZ), Bosnia and Herzegovina (B&H). Informed consent was obtained from all individual participants included in the study. The Ethical
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