Surgical Outcomes of a Novel Non-diffractive, Extended Depth-of-focus Intraocular Lens - Preliminary Results From a Phase 4 Study

Surgical Outcomes of a Novel Non-diffractive, Extended Depth-of-focus Intraocular Lens - Preliminary Results From a Phase 4 Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Surgical Outcomes of a Novel Non-diffractive, Extended Depth-of-focus Intraocular Lens - Preliminary Results From a Phase 4 Study Buğra Karasu, Enes Kesim, Ali Rıza Cenk Çelebi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8879910/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: Enova ADVANCED TM (VSY BIOTECHNOLOGY) is a new generation extended depth of focus (EDOF) intraocular lens (IOL) that provides continuous vision at various distances. The aim of this study was to present the first results of the EDOF IOL. Materials and Methods: This was a phase-4 retrospective cohort study. A total of 118 eyes of 59 patients data were reviewed. Mean aged of patients 66.62 ± 9.57 years (ranging, 32 - 84 years) with cataracts underwent bilateral implantation of Enova ADVANCED TM (VSY BIOTECHNOLOGY) (EDOF IOLs). The follow-up occurred on days 7 to 14, as well as at 2 months and 6 months post-treatment. The main goals included investigating monocular and binocular depth of focus at 6 months after surgery, uncorrected and best corrected distance, intermediate and near visual acuity (VA) up to 6 months following surgery, photopic and mesopic contrast sensitivity. Results: A total of 59 patients with 118 eyes were included in the final analysis. Depth of focus with a monocular vision was observed across various threshold values, including 0.1 log MAR (+0.43 to -0.47 D), 0.2 log MAR (+0.73 to -0.9 D), and 0.3 log MAR (+1.05 to -1.72 D). Enova ADVANCED TM (VSY BIOTECHNOLOGY) had values of 0.1 log MAR (+0.52 to -0.58 D), 0.2 log MAR (+1.07 to -1.54 D), and 0.3 log MAR (+1.50 to -2.07 D) in terms of binocular depth of focus. Throughout all follow-up periods, there was a statistically significant improvement in distance, intermediate, and near VA when compared to the preoperative period (p <0.0001). All patients showed an improvement in refraction compared to the baseline measurement. Enova ADVANCED TM (VSY BIOTECHNOLOGY) patients exhibited high contrast sensitivity and the EDOF lens demonstrated good results without use of glasses. Mesopic values were relatively lower than photopic values (p<0.05). At 6 months, all IOLs were properly aligned without any instances of tilting. No general safety concerns were expressed in any patient. Conclusion: The Enova ADVANCED TM (VSY BIOTECHNOLOGY) EDOF IOLs demonstrated favorable vision outcomes, showing similar VA across various distances. Enova ADVANCED TM (VSY BIOTECHNOLOGY) provides a wide monocular depth of focus at 0.1 and 0.2 log MAR. The levels of spectacle independence and patient satisfaction were noticeably higher. Cataract EDOF Defocus curve Contrast sensitivity Intermediate visual acuity Near visual acuity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Intraocular lenses (IOLs) are used to replace the natural human lens and/or correct refractive defects during refractive lens removal and cataract surgery. In recent years, a wide range of multifocal IOLs have been produced; these IOLs are more conspicuous in terms of the differences it brings to visual function than typical monofocal IOLs 1 . Patients' hopes of being able to see without glasses increase following cataract surgery. Although monofocal IOLs are ideal for distance vision. They frequently fail to offer good vision at all distances, including intermediate and short distances, when many patients require better vision for activities such as driving and using electronics like as computers and cell phones 2 . Randomized controlled clinical studies have demonstrated that monofocal IOLs are unable to deliver satisfactory vision at multiple distances and do not have the capability to enhance focus 3 , 4 , 5 . With rising life expectancy and lifestyle alterations, a high number of patients seek good distance vision as well as glasses-free near and intermediate vision for daily activities 1 . Presbyopia-correcting intraocular lenses (PC- IOLs) are also a choice for presbyopic patients who do not qualify for laser refractive surgery and do not wish to use near glasses 6 , 7 . Today, the utilization of computers and various devices that necessitate reading demands optimal visual acuity (VA) for intermediate distances; however, the majority of multifocal IOLs offer only satisfactory VA for both distance and near vision. For over a decade, PC-IOLs technology designed to address presbyopia has been accessible. Traditionally, designs of PC-IOL lenses have encompassed refractive types (such as Johnson & Johnson ReZoom), apodized diffractive types (for instance, Alcon Acrysof ReSTOR), non-apodized diffractive types (like Alcon Acrysof PanOptix), accommodating types (Bausch & Lomb Crystalens), and diffractive extended depth-of-focus technology (e.g. Johnson & Johnson Symfony). Although PC-IOLs can offer greater levels of spectacle independence compared to monofocal IOLs, they are not without their disadvantages. Any IOL that divides light to provide separate focal points for distance and near vision has the potential to cause photic phenomena, which patients might experience as glare, halos, starbursts, or blurred vision 8 , 9 . A trifocal IOLs superimposes far, intermediate, and near images on the retina, allowing brain processing to filter and provide crisp vision over a broad field of distances 2 . Extended depth of focus (EDOF) IOLs have been designed for those who want a continuous range of functional vision. These IOLs focus incident light waves along an extended longitudinal plane rather than at discrete locations, preventing near and far views from overlapping 10 – 12 . The aim of this study is to uncover the outcomes of depth of focus, contrast sensitivity, and VA when using Enova ADVANCED TM (VSY BIOTECHNOLOGY) EDOF IOLs. MATERIALS AND METHODS Study design This was a phase-4 retrospective cohort presenting the surgical outcomes of a single Enova ADVANCED TM (VSY BIOTECHNOLOGY) EDOF IOLs. The assessor and surgeon were chosen individually to avoid subjective results. All surgeries were performed by a single surgeon (B.K.). The study was conducted in compliance with the ethical principles outlined in the Declaration of Helsinki and ethical approval have been obtained by the local institutional review board (Istanbul Acıbadem University-2025-01/55). Patients were given detailed information about the study and provided written informed consent form. All patients underwent surgery on both of their eyes. Objectives The main goals included investigating monocular and binocular depth of focus at 6 months after surgery, uncorrected and best corrected distance, intermediate and near VA up to 6 months following surgery, photopic and mesopic contrast sensitivity, subjective refraction, visual disturbances, IOL stability, PCO, Nd:YAG ratio, and overall security. Patients and IOLs The study comprised patients aged 32 to 84 who had clinically significant bilateral age-related cataracts but no other serious ocular disease. There was one young patient (32 years old-taxi driver) who developed cataracts due to short-term oral steroid use (2 weeks). The Enova Advanced TM (VSY BIOTECHNOLOGY) EDOF IOL utilizes Non-diffractive "Light Tailoring Technology" to ensure dependable visual results. Technical Specifications: * Single piece, %100 glistening free, hydrophobic acrylic, UV filter *Asymetric, biconvex, neutral bi-aspheric EDOF * Refractive index 1.53 (546nm) *Optical diameter 6 mm, overall diameter 13mm *Water content %7 *Abbe number 42 *Haptic design C-loop, haptic angle 0°. *Additional range (Dioptre) 2.2 (approximately) Participants were provided with evaluations of the questionnaire during their most recent visit. One of the primary objectives of the study was to evaluate the visual impairment profiles associated with Enova ADVANCED™ (VSY BIOTECHNOLOGY) IOLs using the Visual Impairment Questionnaire. Participants initially report if they have encountered a specific visual disturbance, and if they have, they are requested to evaluate the frequency, severity, and discomfortness of these visual disturbances (for example, starbursts, halos, and glare). The resulting scores varied between 0 (indicating the worst functional impairment) to 100 (indicating no disability) 13 . Exclusion criteria Any eye condition other than cataracts that may lead to deterioration of VA throughout patients follow-up, any anterior segment disorders (e.g., chronic uveitis, iritis, corneal dystrophy) that could significantly influence the results, any corneal patologies, any ocular infection, any degenerative visual diseases, pseudoexfoliations syndrome, keratoconus, diabetic retinopathy, uncontrolled glaucoma and or IOP > 24 mmHg, choroidal haemorrhage, aniridia, microphthalmia, amblyopia, previous intraocular and corneal surgery, expected post-operative astigmatism greater than 1 Dioptre (D). Patients were monitored for a duration of 6 months post-operation. Assessments Before surgery, all patients received a thorough evaluation that included a complete medical history, a slit lamp and dilated fundus evaluation, subjective refraction, determination of the dominant eye, monocular and binocular distance, intermediate and near uncorrected visual acuity (UCVA) and best corrected visual acuity (BCVA). Optical biometry (Haag-Streit Lenstar) was used to measure IOLs for cataract surgery. And also photopic pupil size and intraocular pressure (IOP) were measured. VA, subjective refraction, IOP, biometry parameters, and IOL stability (by slit lamp assessment) were assessed at follow-up visits after 1–2 weeks, 2 months, and 6 months. Defocus curves were examined at 6 months after surgery. Contrast sensitivity, posterior capsule opacification (PCO), and the Nd:YAG ratio also were evaluated. The Barrett Universal II and a lens factor (LF) of 1.73 were utilized in all instances to ascertain the suitable IOL power. This LF was selected due to its correlation with an A-constant of 118.7, which serves as the manufacturer's recommended starting point for both lenses 14 . The Freiburg Visual Acuity and Contrast Test (FrACT) computerized charts were used to test monocular and binocular UCVA and BCVA at the following distances with 100% contrast: The far distance is 400 ± 12 cm, the intermediate is 67 ± 2 cm, and the near view 40 ± 1 cm. The charts brightness is regulated at 200 cd/m2. All VA measurements converted to logarithm of the minimum angle of resolution (log MAR). Both monocular and binocular defocus curves were measured using the similiar conditions and technique. The patient's vision was adjusted to the testing distance (4m), and VA was evaluated using defocus lenses in a randomized order ranging from + 1.50 D to -4.00 D. The defocus steps were 0.50 D, and applied three VA thresholds: 0.1, 0.2, and 0.3 log MAR. The depth of focus curves were made by adding negative lenses without changing the optotype distance. West and colleagues discovered that individuals with a VA of less than 0.3 log MAR (Snellen 6/12) experienced challenges in identifying faces and reading, drive at night 15 , 16 . Thus, we selected 0.3 log MAR as the threshold value. Depth of focus computed based on ANSI Z80.35-2018 recommendations. The dioptric interval between 0 defocus (or best distant vision) and the point corresponding to the maximum negative focusing surpassing the VA threshold was computed separately for each subject. Additionally, defocus curves were created using the average VAs acquired at each focusing step. The Functional Vision Analyzer Optec 6500 Vision Tester (Stereo Optical) was used to test monocular contrast sensitivity at 1.5, 3, 6, 12, and 18 cpd spatial frequencies under mesopic (3 cd/cm2), mesopic with glare, and photopic (85 cd/cm2) situations. After dark adaptation, the mesopic test was administered first, followed by the photopic test. Each test was repeated two or three times, and the mean of the results was used for analysis. For each spatial frequency, the number of individuals who could not view any contrast was enrolled. Monocular and binocular defocus curve and contrast sensitivity measurements were taken on the selected primary eye utilizing a randomization technique that included an equal number of dominant and non-dominant eyes. Surgical technique Topical or local anaesthesia was followed by continuous curvilinear capsulorhexis. In cases, the IOL was placed in the capsular bag using the recommended injector (Enova ADVANCED TM (VSY BIOTECHNOLOGY) EDOF IOLs). All procedures were carried out using typical self-sealing clear corneal incisions, capsulorhexis, and conventional phacoemulsification. Phacoemulsification was conducted utilizing a conventional stop-and-chop method with the ALCON INFINITI VISION system for all patients. The preference incision size was ≤ 2.4 mm for the IOL placement. The incision was tailored based on the requirement to adjust the level of astigmatism. A frown incision was utilized for minor astigmatism corrections, whereas a straight incision was favored for more significant adjustments. The choice of incision was affected by the nucleus's hardness across all types of incisions, allowing for effective management of all cataract varieties. Emmetropia was the aim in all situations. At the conclusion of the surgery, any remaining ophthalmic viscoelastic device was fully eliminated from the posterior chamber via irrigation, and the lateral ports and main incision were sealed with hydration. Postoperative care and medicines were administered in accordance with standard procedure for all patients. The topical steroid (10 mg/ml, Pred Forte; Allergan AbbVie, Chicago, USA) and the topical antibiotic (5 mg/ml, Vigamox (moxifloxacin ophthalmic solution), Novartis, Basel, Switzerland) were administered as eye drops four times a day for three weeks following the cataract surgery. Patients were advised to seek immediate medical attention and applied the hospital if they experienced symptoms after cataract surgery, such as worsening eye pain, eye redness, discharge and white or yellow pus from the eyes, swollen or puffy eyelids, and any deterioration, blurring, or decrease in VA. Statistical methods The current study utilized descriptive statistical techniques such as mean, standard deviation (SD), and minimum and maximum values. The safety population comprised all individuals who were administered an experimental device in one or both of their eyes. The complete analysis group was determined to include all individuals who completed a minimum of nine out of 12 measurements for the monocular and binocular defocus curve during the 6-month assessment. The ANCOVA model was utilized to analyze the primary endpoint, incorporating fixed terms for treatment, center, dominant eye, first operated eye, and the time between surgery and the 6-month measurement. During the examination of statistical data related to VA, MANOVA was selected as the parametric test while the Friedmann test was selected as the non-parametric test. Pearson correlation test was used to understand the statistical difference between mesopic and photopic values. The Kolmogorov-Smirnov test was utilized to assess the normal distribution of the groups. A p value less than 0.05 (p < 0.05) was deemed to be statistically significant. The analysis was conducted using SPSS version 25.0 software. RESULTS The study began with 128 eyes, but 10 were eliminated due to irregular follow-up visits, pseudophakic cystoid macular edema (CME) in 4 eyes, epiretinal membrane (ERM) development in 4 eyes, and a uveitic reaction in 2 eye. In this study, 118 eyes belonging to 59 patients were part of the research. All patients underwent surgery on both eyes. The study consisted of 33 male and 26 female participants, with an average age of 66.62 ± 9.57 years (ranging from, 32 to 84 years). Mean axial length was 23.91 ± 0.89 mm and anterior chamber depth was 3.12 ± 0.41 mm. And also mean IOL power was 21.35±1.92 mm, mean pupil diameter was 3.38±1.07 mm. Table 1 contains the demographic data of the patients. The surgery proceeded without any major complications. Depth of focus The monocular defocus at the 0.3 log MAR VA threshold, the range of the defocus curves was approximately +1.05 D to − 1.72 D for the Enova ADVANCED TM (VSY BIOTECHNOLOGY) EDOF IOLs. The binocular defocus at the 0.3 log MAR VA threshold, the extent of the defocus curves was roughly +1.52 D to − 2.07 D for the Enova ADVANCED EDOF IOLs. Figure 1 displays the table and graph illustrating the defocus curve. Visual acuity: monocular and binocular Tables 2 displays the VA results for distance, intermediate, and near vision using binocular measurements at 1 week, 2 weeks, 2 months, and 6 months. Tables 3 shows the VA outcomes for distance, intermediate, and near vision using monocular measurements at 1 week, 2 weeks, 2 months, and 6 months. Throughout all follow-up periods, a notable improvement in binocular and monocular VA was detected (p<0.001). The mean monocular intermediate BCVA exceeded 0.3 log MAR for 83.8% of eyes that were implanted with Enova ADVANCED TM (VSY BIOTECHNOLOGY) EDOF IOLs all follow ups after cataract surgery. For 56.2% of eyes that received Enova ADVANCED TM (VSY BIOTECHNOLOGY) EDOF IOLs implants, the monocular near BCVA was better than 0.3 log MAR. Distance, intermediate and near VAs were statistically significant for the Enova ADVANCED TM (VSY BIOTECHNOLOGY) EDOF IOL during all follow ups for corrected and uncorrected and monocular and binocular conditions (p < 0.001). A significant difference was found in distance, intermediate, and near VA (corrected or uncorrected, monocular or binocular) at 6 months (p < 0.001). Refraction There was a notable enhancement in refraction from the initial measurement to the 6th month, as indicated in figure 2. The Enova ADVANCED TM (VSY BIOTECHNOLOGY) EDOF IOLs achieved a ±0.5 D target spherical equivalent refraction for 78.1% of eyes. Values within ±1.0 of the target were 98.9% for Enova ADVANCED TM (VSY BIOTECHNOLOGY) EDOF IOLs. Contrast sensitivity Table 4 displays the corrected for distance contrast sensitivity curves measured at 6 months. Within the patient cohort, contrast sensitivity declined in mesopic settings and was further diminished in mesopic and glare conditions in comparison to photopic settings. Patient questionnaire Figure 4 contains a synopsis of the patient survey, while figure 3 displays the rates of spectacle independence for far, intermediate, and near distances. A total of 86% of the patients involved in the study reported being either completely satisfied or very satisfied with their overall experience at month 6. The main satisfaction rates of the patients in terms of their daily work and quality of life were as follows: Engaging in tasks or pastimes that necessitate clear vision at close range was %58.8. Reading ordinary articles in newspapers %59.3. Examining the small print in a phone book, a medicine bottle, or legal documents %33.7. Safe driving in daylight, in familiar areas %92.4. Driving comfortably at night %70.6. In the past week, the rates of double vision, glare, halo, and starburst were reported 1.3%, 28.7%, 11.3%, and 17.6%, respectively (Figure 4). Figure 5 presents the in vivo and in vitro imaging of EDOF IOL. Safety At 6 months, only one eye in the patient cohort had a displaced IOL measuring 0.5mm. During the final assessment, a minor tilt was observed in one case, but it had been resolved. The average IOP at 6 months varied from 13.76 ± 3.47 to 13.96 ± 2.73 mmHg in patient cohort. Throughout the Enova ADVANCED TM (VSY BIOTECHNOLOGY) EDOF IOLs study's follow-up periods, the following adverse events were recorded: 2 case of uveitic reaction, 4 cases of CME, 4 cases of ERM, and 2 cases of Nd:YAG capsulotomy due to clinically significant posterior capsular opacification (PCO). None of these incidents were classified as a severe adverse event. There were no instances of hypopyon, endophthalmitis, lens dislocation, pupillary block, or retinal detachment. DISCUSSION The goal of cataract surgery is to restore VA and increase the high patient's quality of life. However, with the traditional monofocal IOLs, patients are not able to drive, use computers, tablets, and mobile phones, or engage in hobbies such as reading without glasses or sewing. Choosing the ideal IOL for a patient should be based on more than just the lens's technical specifications. In addition to the patient's needs, lifestyle and interests (reading, driving, outdoor activities, etc.) should be considered. To obtain the clinical effects claimed for monofocal enhanced IOLs, lens manufacturers must diverge beams from monofocal optics to create a design that can produce a dot-spread function in focusing slightly differently than a standard monofocal design 17 . Multifocal IOLs, like as diffractive trifocal IOLs, have been shown to be useful for near, intermediate, and far vision in patients with healthy retinas, but for some patients they create discomfort by introducing dysphotopsia 18 . Various trials have shown that contrast sensitivity decrement and optical aberration increment in multifocal IOLs 19,20 . As previously established, EDOF IOLs result in reduced visual impairment compared to trifocal lenses 21 . Noninvasive psychophysical assessments have shown adequate sensitivity to identify variations in visual impairment resulting from monofocal IOLs and various multifocal designs 22 . In a research study carried out by Marius et al., a comparison was made between the Symfony (Johnson & Johnson Vision) and Acrysof IQ Vivity IOL (Alcon Laboratories, Inc.) EDOF IOLs in 138 eyes. The findings indicated a rise in the incidence of certain visual disturbances within the Symfony group, especially concerning the frequency, severity, and discomfort associated with starbursts and glare. However, the distance, intermediate, and near VAs were satisfactory and did not show significant differences between the Symfony (Johnson & Johnson Vision) and Acrysof IQ Vivity IOL (Alcon Laboratories, Inc.) ( EDOF IOLs.) 23 . In current study, it was found to be better than both IOLs in terms of double views and halo, and better than Symphony (Johnson & Johnson Vision) in terms of glare and starburst. In our research, superior outcomes were achieved according to both lenses (Symfony (Johnson & Johnson Vision) and Acrysof IQ Vivity IOL (Alcon Laboratories, Inc.)) regarding the BCVA (distance, intermediate and near). While the study conducted by Marius et al. reported results at the 3-month mark, this study provided findings at the 6-month interval. In our study, we can interpret that low visual disturbances such as double images, glare, halos and starbursts are probably caused by the 7% water content. In fact, IOL design of Enova ADVANCE TM (VSY BIOTECHNOLOGY) featuring optical edge curvature and fully functional optics may probably have exhibited the minimal levels of glare-type photic phenomena. However, since there is no previous study on Enova ADVANCED TM (VSY BIOTECHNOLOGY), controlled, randomized, comparative clinical studies are needed to support these data. A research conducted by Guarro et al examined the occurrence of visual disturbances among three EDOF IOLs and one monofocal IOL. The findings were documented for 22 patients in each group of the study. The non-diffractive extended-aperture IOL, AcrySof IQ Vivity, induces dysphotopsia that is comparable to that of the AcrySof IQ monofocal lens. These undesirable visual phenomena are considerably less severe than those generated by the AT LARA 829MO and TECNIS Symfony ZXR00 diffractive extended-aperture lens models. Both extended-aperture and monofocal lenses demonstrate fewer visual disturbances in binocular conditions as opposed to monocular conditions 24 . To address the dissatisfaction resulting from disphotopsia, EDOF IOLs are used in the clinical field. Non-diffractive EDOF IOLs create an extended focal point that provides for EDOF in comparison to monofocal IOLs, which elongate the light to achieve better intermediate vision. By generating a continuous range of vision and minimizing the overlap created by near and distant focal points, the EDOF lens is thought to deliver functional near, good intermediate, and sharp far vision while reducing dysphotopsia 25 . Patients defined positive dysphotopsia (PD) as light streaks, arcs, flashes, halos, and starbursts, all of which are triggered by an external light source. In contrast, negative dysphotopsia (ND) is characterized as a temporary arc-shaped dark shadow, usually stimulated by light sources that are oriented temporally 26 . While PD appears to be related to IOL material and edge design, previous research by Masket et al suggests that ND is more closely linked to IOL location rather than IOL material or design 27 . In alignment with the existing literature, high index of refraction hydrophobic IOLs with squared edges are the most probable to be linked with PD. It appears reasonable to regard a diminished optic size or effective optic size as factors contributing to PD; however, there is presently insufficient supporting evidence. Enova ADVANCED TM (VSY BIOTECHNOLOGY) IOL features a 360° all enhanced square edge and is made of hydrophobic acrylic; however, the PD may not reduce in size despite its high water content. When discussing the characteristics of the non-diffractive Enova ADVANCED TM (VSY BIOTECHNOLOGY) lens utilized in the current research: Light Tailoring Structure 1(LTS 1) offers a broader far and intermediate distribution of light energy. Light Tailoring Structure 2 (LTS 2) enhances light distribution to provide "tailor-made" visual acuity for far, midrange, and basic near vision. Enova ADVANCED TM (VSY BIOTECHNOLOGY) features a neutral spherical design. The innovative Light Tailoring Technology employs two structures (LTS 1 and 2) placed in the central 2 mm diameter to expand and fine-tune the light. LTS 1 broadens and spreads light from the distant focus into the intermediate and near vision ranges. LTS 2 fine-tunes light for improved intermediate and near VA. LTS 1 distributes light widely for EDOF. LTS 2 refines the light distribution to improve the relevant distances (far, intermediate, and basic near) 28-31 . The main focus of this research was to examine the depth of focus, along with various visual and safety factors of the EDOF IOL, in accordance with the guidelines set by ANSIZ80.352018 and the American Academy of Ophthalmology (AAO) Extended Working Group Consensus Statement for Depth of Focus Intraocular Lenses 32 . This study is the first phase 4 study to report results for Enova ADVANCED TM (VSY BIOTECHNOLOGY) EDOF IOL. In a study carried out by Reinhard et al., they compared 2 EDOF IOLs with 1 monofocal IOL. TECNIS Symfony IOL achieved -0.7 D monocular defocus at the 0.2 log MAR reference threshold, while ATLARA 829MP remained above this threshold up to -1.5 D defocus 33 . In our investigation, the monocular defocus was recorded as -0.9 D with a 0.2 log MAR. The AT LARA 829MP stands as an Enova ADVANCED TM (VSY BIOTECHNOLOGY) extended depth of focus (EDOF) intraocular lens (IOL) that delivers uninterrupted vision across a spectrum of distances. The research conducted on the AT LARA 829MP revealed that the monocular depth of focus varied between 0.1 log MAR at 0.87 D and 0.3 log MAR at 1.75 D 33 . The current investigation on Enova ADVANCED TM (VSY BIOTECHNOLOGY) EDOF IOLs demonstrated monocular depth of focus of 0.43 D at 0.1 log MAR and 1.72 D at 0.3 log MAR. The results from Enova ADVANCED TM (VSY BIOTECHNOLOGY) were very similar to those reported by AT LARA 829 MP and Schallhorn et al. In AT LARA 829 MP, mean binocular distance UCVA was 0.02 ± 0.17 log MAR and mean binocular near UCVA was 0.23 ± 0.23 log MAR. In the study conducted by Schallhorn et al., the mean binocular distance UCVA was −0.05±0.09 log MAR and the mean binocular near UCVA was 0.26 ± 0.14 log MAR 33, 34 . In Enova ADVANCED TM (VSY BIOTECHNOLOGY), the mean binocular distance UCVA was found to be 0.06 ± 0.04 log MAR, with the mean binocular near UCVA measuring at 0.15 ± 0.1 log MAR. Intermediate VA is becoming more and more crucial for essential for tasks, as it aids in working on computers and reading car instruments. In a study conducted by Reinhard et al., AT LARA 829MP demonstrated a VA level of 0.3 log MAR or better from a distance of 52 cm, while TECNIS Symfony showed similar results from a distance of 66 cm. However, the CT ASPHINA 409MP, offered satisfactory viewing quality starting from a distance of 100 cm 34 . In our study, visual gain equivalent to 0.3 log MAR VA was achieved from 43 cm. Reading a newspaper normally necessitates a VA of 0.4 log MAR at a distance of 40 cm; however, a greater level of VA is essential for smooth reading 35 . At 6 months, the intermediate VA of the Enova ADVANCED TM (VSY BIOTECHNOLOGY) EDOF IOLs exceeded 0.4 log MAR by a significant margin. The VA findings were in agreement with the patient survey findings. A large number of patients stated that they had no issues with activities that involved distance vision, such as driving and outdoor activities, while a slightly higher number of patients reported experiencing difficulty with activities that required near vision, such as reading and sewing. Patients simply reported that driving at night was more difficult than driving during the day. It is widely acknowledged that a small number of patients experience challenges with driving during daylight hours following cataract surgery. A different research conducted by Mönestam and colleagues revealed that 43% of individuals encountered challenges while driving at night because of glare, particularly in situations with low-contrast conditions below 20/50 where VA was significantly impaired (0.4 log MAR) 36 , in line with our results. It is widely acknowledged that a decrease in VA to 20/40 can greatly affect one's ability to drive at night 16 : in our research, all IOLs performed above this threshold on average. The patient questionnaire results also aligned with the findings on contrast sensitivity. In photopic conditions, the contrast sensitivity across all spatial frequencies was similar to the standard range for this population 37 . Nevertheless, as predicted, contrast sensitivity was decreased in mesopic and mesopic with glare situations. The refraction predictability was noted to be within ±1.0 D of the target value in 98.9% of cases. The presence of any remaining refractive error with multifocal IOLs diminishes visual quality at all distances in comparison to monofocal IOLs 38, 39 . The effectiveness of presbyopia correction greatly relies on the precision of post-operative refraction. Even minor inaccuracies in measuring eye dimensions can result in refractive changes that could affect the patient's vision quality after the surgery. Factors such as the management and care of the meibomian glands, the condition of the ocular surface, and the accuracy of biometers in measuring the curvature of the cornea are all crucial in determining the appropriate IOL power for implantation. EDOF IOLs are anticipated to demonstrate greater tolerance towards moderate refractive changes, making them more user-friendly compared to alternative IOLs 21 . Further research would be necessary to assess and verify the tolerance to refractive errors of EDOF IOLs. One limitation of this research stems from utilizing the FrACT to assess VA. The FrACT provides an automated method for individuals to self-administer VA measurements. Landolt rings, tumbling E, and Sloan letters are among the optotypes that are utilized. The FrACT has undergone validation and there is ample literature to substantiate its usage 34–36 . It provides certain benefits compared to traditional chart testing, especially in terms of objectivity and reliability 34 . Nevertheless, the ability to compare with existing literature may be restricted due to the potential for lower results from FrACT computerized charts compared to traditional chart testing methods, especially the ETDRS chart 16 . While the study's internal validity remains uncompromised, it is important to exercise caution when comparing it with previously published literature. Another significant limitation of the research is its retrospective design and the absence of a control group. The lack of aberrometric evaluations at different pupil sizes is an important limitation of the study. Although common, averaging a sphere and a cylinder is not the most accurate way to report refraction data. The inability to use vector or matrix formalisms is another limitation of the study. It is crucial to introduce the initial findings of the phase 4 study to the academic community, given that this lens is a recent addition to the research. As a result, in our research, the Enova ADVANCED TM (VSY BIOTECHNOLOGY) the AAO task force standards for EDOF IOLs by having a monocular depth of focus at least 0.5 D wider than the monofocal control at 0.2 logMAR 20 . The research showcased positive visual results after the Enova ADVANCED EDOF TM (VSY BIOTECHNOLOGY) lens was implanted. The Enova ADVANCED TM (VSY BIOTECHNOLOGY) EDOF lens demonstrated exceptional levels of photic phenomena, spectacle independence, and patient satisfaction. The findings validate earlier research indicating that the Enova ADVANCED EDOF TM (VSY BIOTECHNOLOGY) IOL delivers favorable optical and visual outcomes across all distances, allowing the majority of patients to perform most daily activities without the need for spectacles. Declarations Disclosure Statement The corresponding author is a member of the Editorial Board of BMC Ophthalmology and had no role in the editorial handling or peer review process of this manuscript. DECLARATION OF INTEREST STATEMENT Funding/Support: No funding or financial support was received for this study. Financial Disclosures: The authors declare no financial disclosures related to this study. DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from the corresponding author upon reasonable request. This study do not published anywhere. HUMAN ETHICS AND CONSENT TO PARTICIPATE The study was conducted in compliance with the ethical principles outlined in the Declaration of Helsinki and ethical approval have been obtained by the local institutional review board. The study was approved by the Istanbul Acıbadem University, Medicine of Acıbadem University Committee (approval date and number: January 9, 2025; No. 2025-01/55). CONSENT TO PUBLISH DECLARATION Not applicable. CONSENT TO PARTICIPATE DECLARATION Not applicable. AUTHOR CONTRIBUTIONS B.K. contributed to the conception and design of the study, data collection, analysis and interpretation, and drafting of the manuscript. A.R.C.C and E.K. contributed to data collection, analysis, and drafting of the manuscript. B.K., A.R.C.C and E.K. contributed to data analysis and interpretation, supervision, and critical revision of the manuscript for important intellectual content. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work. References Grzybowski A, Kanclerz P. Recent developments in cataract surgery. In: Grzybowski A, editor. Current Concepts in Ophthalmology. 2020: Vol 124. Cham: Springer International Publishing 55–97. Alfonso JF, Fernández-Vega L, Baamonde MB, et al. Prospective visual evaluation of apodized diffractive intraocular lenses. J Cataract Refract Surg. 2007: 33(7):1235–1243. Vega F, Millán MS, Gil MA, et al. Optical Performance of a Monofocal Intraocular Lens Designed to Extend Depth of Focus. J Refract Surg. 2020: Sep 1;36(9):625-632. Rocha KM, da Costa E, Dodhia M, et al. Tolerance to Induced Astigmatism With a Monofocal Intraocular Lens Designed to Extend the Depth of Focus. J Refract Surg. 2023: Apr;39(4):222-228. Song X, Liu X, Wang W, et al. Visual outcome and optical quality after implantation of zonal refractive multifocal and extended-range-of-vision IOLs: a prospective comparison. J Cataract Refract Surg. 2020: Apr;46(4):540-548. Alio JL , Plaza-Puche AB , Férnandez-Buenaga R , et al. Multifocal Intraocular Lenses: The Art and the Practice. Cham, Switzerland: Springer International Publishing 2019. Alio JL, Plaza-Puche AB, Fe´rnandez-Buenaga R, et al. Multifocal intraocular lenses: an overview. Surv Ophthalmol. 2017: 62:611 634. Calladine D, Evans JR, Shah S, et al. Multifocal versus monofocal intraocular lenses after cataract extraction. Cochrane Database Syst Rev. 2012: Sep 12;(9):CD003169. doi:10.1002/14651858.CD003169. pub3 Cochener B, Lafuma A, Khoshnood B, et al. Comparison of outcomes with multifocal intraocular lenses: a meta-analysis. Clin Ophthalmol. 2011: Jan 07; 5:45–56. doi:10.2147/OPTH.S14325 Akella SS, Juthani VV. Extended depth of focus intraocular lenses for presbyopia. Curr Opin Ophthalmol. 2018: 29(4):318–322. Attia MSA, Auffarth GU, Kretz FTA, et al. Clinical evaluation of an extended depth of focus intraocular lens with the Salzburg Reading Desk. J Refract Surg. 2017: 33(10):664–669. Weeber HA, Meijer ST, Piers PA. Extending the range of vision using diffractive intraocular lens technology. J Cataract Refract Surg. 2015: 41(12):2746–2754. Riusala A, Sarna S, Immonen I. Visual function index (VF-14) in exudative age-related macular degeneration of long duration. Am J Ophthalmol. 2003:135:206–212 IOL Con - Intraocular Lenses. Accessed July 3, 2022. http://www.iolcon.org Sheila K West, Gary S Rubin, Aimee T Broman, et al. How does visual impairment affect performance on tasks of everyday life? The SEE Project. Salisbury Eye Evaluation. Arch Ophthalmol. 2002: Jun;120(6):774-80. Wood J, Chaparro A, Carberry T, et al. Effect of simulated visual impairment on nightime driving performance. Optom Vis Sci. 2010: 87(6):379–386. Wang L, Dai E, Koch DD, et al. Optical aberrations of the human anterior cornea. J Cataract Refract Surg. 2003: 29:1514–1521. Berdahl J, Bala C, Dhariwal M, et al. Cost-benefit analysis of a trifocal intraocular lens versus a monofocal intraocular lens from the patient’s perspective in the United States. PLoS One. 2022: 17(11): e0277093. Wang SY, Stem MS, Oren G, et al. Patient-centered and visual quality outcomes of premium cataract surgery: a systematic review. Eur J Ophthalmol. 2017: 27(4):387–401 Cao K, Friedman DS, Jin S, et al. Multifocal versus monofocal intraocular lenses for age-related cataract patients: a system review and meta-analysis based on randomized controlled trials. Sur Ophthalmol. 2019: 64(5):647–658. Can İ, Bayhan HA. Clinical Outcomes of Enhanced Monofocal (Mono-EDOF) Intraocular Lenses with the Mini-Monovision Technique versus Trifocal Intraocular Lenses: A Comparative Study. Turk J Ophthalmol. 2024: 54(4):190-197 Piers PA, Fernandez EJ, Manzanera S, et al. Adaptive Optics Simulation of Intraocular Lenses with Modified Spherical Aberration. Investigative Ophthalmology & Visual Science 2004: December Vol.45, 4601-4610. Scheepers MA, Hall B. Randomized and double-blind comparison of clinical visual outcomes of 2 EDOF intraocular lenses. J Cataract Refract Surg. 2023: Apr 1;49(4):354-359. Guarro M, Sararols L, Londoño GJ, et al. Visual disturbances produced after the implantation of 3 EDOF intraocular lenses vs 1 monofocal intraocular lens. J Cataract Refract Surg. 2022: Dec 1;48(12):1354-1359. Kanclerz P, Toto F, Grzybowski A, et al. Extended depth-of-field intraocular lenses: an update. Asia Pac J Ophthalmol. 2020: 9(3):194–202. Davison JA. Positive and negative dysphotopsia in patients with acrylic intraocular lenses. J Cataract Refract Surg. 2000: 26:1346–1355. Masket S, Fram NR, Cho A, et al. Surgical management of negative dysphotopsia. J Cataract Refract Surg. 2018: 44:6–16. Data on file. Medical Report MR_EA_230605. VSY Biotechnology Laboratories. 06.2023. Data on file. L. Werner at. al. In vitro study evaluating the tendency of different intraocular lenses to form intraoptical glistenings. Study report. RDR_EGF3_05072020. Utah, USA 07.2020. Data on file. Technical Report TR_EA_200511. VSY Biotechnology Laboratories. 05.2020 Kohnen, T., & Suryakumar, R. Extended depth-of-focus technology in intraocular lenses. J Cataract Refract Surg. 2020: 46(2), 298–304. MacRae S, Holladay JT, Glasser A, et al. Special report: American Academy of Ophthalmology Task Force consensus state ment for extended depth of focus intraocular lenses. Ophthalmology 2017: 124(1):139–141. Reinhard T, Maier P, Böhringer D, et al. Comparison of two extended depth of focus intraocular lenses with a monofocal lens: a multi-centre randomised trial. Graefes Arch Clin Exp Ophthalmol. 2021: Feb;259(2):431-442. Schallhorn SC, Teenan D, Venter JA, et al. Initial clinical outcomes of a new extended depth of focus intraocular lens. J Refract Surg. 2019: 35(7):426–433. Whittaker SG, Lovie-Kitchin J. Visual requirements for reading. Optom Vis Sci. 1993: 70(1):54–65. Mönestam E, Lundqvist B. Long-time results and associations between subjective visual difficulties with car driving and objective visual function 5 years after cataract surgery. J Cataract Refract Surg. 2006: 32(1):50–55. Hohberger B, Laemmer R, Adler Wetal. Measuring contrast sensitivity in normal subjects with OPTEC 6500: influence of age and glare. Graefes Arch Clin Exp Ophthalmol. 2007: 245(12):1805–1814. Hayashi K, S-i M, Yoshida M, et al. Effect ofastigmatism on visual acuity in eyes with a diffractive multifocal intraocular lens. J Cataract Refract Surg. 2010: 36(8):1323–1329. Son H-S, Kim SH, Auffarth GU, et al. Prospective comparative study of tolerance to refractive errors after implantation of extended depth of focus and monofocal intraocular lenses with identical aspheric platform in Korean population. BMC Ophthalmol. 2019: 19(1):187. Tables Table 1. Clinical and demographic data of the patients. Enova ADVANCE EDOF IOLs group Baseline characteristics Eyes (n) 118 Age (mean±SD,years) 66.62 ± 9.57 (32-84) Sex (M/F) 33/26 Target SE (mean±SD,D) −0.07 ± 0.18 IOL power (mean±SD,D) 21.35±1.92 Pupil size(photopic) (mean±SD,mm) 3.38 ± 1.07 Primary eye (dominant/non-dominant) 49.4% / 50.6% Pre-op. optical biometry AL (mean±SD,mm) 23.91 ± 0.89 ACD (mean±SD,mm) 3.12 ± 0.41 Pre-op. Visual acuity (logMAR) and refraction Monocular UDVA (mean±SD) 0.73 ± 0.47 Binocular UDVA (mean±SD) 0.68 ± 0.44 Monocular CDVA (mean±SD) 0.71 ± 0.44 Binocular CDVA (mean±SD) 0.63 ± 0.40 Cylinder (mean±SD,D) −0.72 ± 0.58 Spherical (mean±SD,D) 0.78 ± 2.09 SE (mean±SD,D) 0.18 ± 2.04 Post-op. Refraction (primary eye, 6 months) Cylinder (mean±SD,D) −0.43 ± 0.37 Spherical (mean±SD,D) 0.21 ± 0.35 SE (mean±SD,D) −0.24 ± 0.31 IOL, intra ocular lens; AL, axial length; ACD, anterior chamber depth; UDVA, uncorrected distance visual acuity; CDVA, corrected distance visual acuity; SE, spherical equivalent; SD, standard deviation; D,dioptre Table 2. Mean binocular (primary eye) visual acuity, in logMAR Table 3. Mean monocular (primary eye) visual acuity, in logMAR Table 4. Contrast sensitivity curves with distance and intermediate correction under mesopic, mesopic with glare, photopic and photopic with glare conditions at 6 months post-operatively. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8879910","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598757891,"identity":"0b9d519a-b564-4682-8984-a8152dca8ef2","order_by":0,"name":"Buğra Karasu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACAwiVAKE+NoBIxsYDRGthnNnAIAGkGojXwswL1sLAgFeLuUT6tQ8/GNLkzduPP5O23WFTp9t+GGhLjU00Li2WM3KKZ/Yw5BjOOZNjJp17Jk3C7EwiUMuxtNwGXA67kZPMwMNQwTiDIYdNOrftsITZAaAWxobDeLUw/mGosJ/B//yZtCVIy/mHhLSkH2bmYchJnCGRYCbNCNJyg4Atlj1vmJllGNKSZ0i8MbbsbUuT3HYDaEsCHr+Ys6c/ZnzDkGw7gz/94Y2fbTb8ZufTHz74UGODUwsDA48BA+M/dMEEnMpBgP0BXulRMApGwSgYBQwAEY1eXM9KTEoAAAAASUVORK5CYII=","orcid":"","institution":"Derince Research and Educational Hospital","correspondingAuthor":true,"prefix":"","firstName":"Buğra","middleName":"","lastName":"Karasu","suffix":""},{"id":598757892,"identity":"6a431e13-6bc4-4b57-a6b1-d1047a335cac","order_by":1,"name":"Enes Kesim","email":"","orcid":"","institution":"Kocaeli University","correspondingAuthor":false,"prefix":"","firstName":"Enes","middleName":"","lastName":"Kesim","suffix":""},{"id":598757893,"identity":"292e33b6-4878-46c0-b811-378f7a8cd351","order_by":2,"name":"Ali Rıza Cenk Çelebi","email":"","orcid":"","institution":"Istinye University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Rıza Cenk","lastName":"Çelebi","suffix":""}],"badges":[],"createdAt":"2026-02-14 12:24:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8879910/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8879910/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103732295,"identity":"14bf9313-f6dc-4e04-bf93-5013566a7f08","added_by":"auto","created_at":"2026-03-02 09:22:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1202409,"visible":true,"origin":"","legend":"\u003cp\u003eMonocular and binocular defocus curve at 6 months.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8879910/v1/84c9e7a5a88ad10dd789b195.png"},{"id":103732296,"identity":"3b84eecb-0f2d-4d37-8aa4-38723dceca49","added_by":"auto","created_at":"2026-03-02 09:22:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":461832,"visible":true,"origin":"","legend":"\u003cp\u003ePost-operative spherical equivalent at 6 months.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8879910/v1/5220af18530b1f6e4290d187.png"},{"id":104400194,"identity":"f40dd792-a8c1-49d8-984b-e781359e9dde","added_by":"auto","created_at":"2026-03-11 12:09:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":977373,"visible":true,"origin":"","legend":"\u003cp\u003eRates of glasses independence for distant, intermediate, and close distances at the second and 6 months post-operation with Enova ADVANCED \u003csup\u003eTM\u003c/sup\u003e extended depth of focus intraocular lens (EDOF IOL).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8879910/v1/048b3b8f418e0447be43ca8a.png"},{"id":103732297,"identity":"9114cbd2-8190-4f46-aa81-d03be2824135","added_by":"auto","created_at":"2026-03-02 09:22:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":845668,"visible":true,"origin":"","legend":"\u003cp\u003eSurvey results at the 6-month follow-up for the patients cohort.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8879910/v1/28569293753d371005eb06f4.png"},{"id":103732298,"identity":"434029d8-9141-48c8-9966-ded3187a44b6","added_by":"auto","created_at":"2026-03-02 09:22:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1547047,"visible":true,"origin":"","legend":"\u003cp\u003eIn vivo and in vitro imaging of Enova ADVANCED \u003csup\u003eTM \u003c/sup\u003e(VSY BIOTECHNOLOGY) \u0026nbsp;EDOF IOL.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8879910/v1/eefd7d8c430588b2412ad5e2.png"},{"id":106392985,"identity":"399a13e8-7b99-42fe-bdb5-4be850d3bcfe","added_by":"auto","created_at":"2026-04-08 07:29:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5418611,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8879910/v1/a06c82f1-39a9-445c-b05d-dac8c4b1bc89.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSurgical Outcomes of a Novel Non-diffractive, Extended Depth-of-focus Intraocular Lens - Preliminary Results From a Phase 4 Study\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIntraocular lenses (IOLs) are used to replace the natural human lens and/or correct refractive defects during refractive lens removal and cataract surgery. In recent years, a wide range of multifocal IOLs have been produced; these IOLs are more conspicuous in terms of the differences it brings to visual function than typical monofocal IOLs \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePatients' hopes of being able to see without glasses increase following cataract surgery. Although monofocal IOLs are ideal for distance vision. They frequently fail to offer good vision at all distances, including intermediate and short distances, when many patients require better vision for activities such as driving and using electronics like as computers and cell phones \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRandomized controlled clinical studies have demonstrated that monofocal IOLs are unable to deliver satisfactory vision at multiple distances and do not have the capability to enhance focus \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWith rising life expectancy and lifestyle alterations, a high number of patients seek good distance vision as well as glasses-free near and intermediate vision for daily activities \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Presbyopia-correcting intraocular lenses (PC- IOLs) are also a choice for presbyopic patients who do not qualify for laser refractive surgery and do not wish to use near glasses \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eToday, the utilization of computers and various devices that necessitate reading demands optimal visual acuity (VA) for intermediate distances; however, the majority of multifocal IOLs offer only satisfactory VA for both distance and near vision. For over a decade, PC-IOLs technology designed to address presbyopia has been accessible. Traditionally, designs of PC-IOL lenses have encompassed refractive types (such as Johnson \u0026amp; Johnson ReZoom), apodized diffractive types (for instance, Alcon Acrysof ReSTOR), non-apodized diffractive types (like Alcon Acrysof PanOptix), accommodating types (Bausch \u0026amp; Lomb Crystalens), and diffractive extended depth-of-focus technology (e.g. Johnson \u0026amp; Johnson Symfony). Although PC-IOLs can offer greater levels of spectacle independence compared to monofocal IOLs, they are not without their disadvantages. Any IOL that divides light to provide separate focal points for distance and near vision has the potential to cause photic phenomena, which patients might experience as glare, halos, starbursts, or blurred vision \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA trifocal IOLs superimposes far, intermediate, and near images on the retina, allowing brain processing to filter and provide crisp vision over a broad field of distances \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eExtended depth of focus (EDOF) IOLs have been designed for those who want a continuous range of functional vision. These IOLs focus incident light waves along an extended longitudinal plane rather than at discrete locations, preventing near and far views from overlapping \u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe aim of this study is to uncover the outcomes of depth of focus, contrast sensitivity, and VA when using Enova ADVANCED \u003csup\u003eTM\u003c/sup\u003e (VSY BIOTECHNOLOGY) EDOF IOLs.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis was a phase-4 retrospective cohort presenting the surgical outcomes of a single Enova ADVANCED \u003csup\u003eTM\u003c/sup\u003e (VSY BIOTECHNOLOGY) EDOF IOLs. The assessor and surgeon were chosen individually to avoid subjective results. All surgeries were performed by a single surgeon (B.K.). The study was conducted in compliance with the ethical principles outlined in the Declaration of Helsinki and ethical approval have been obtained by the local institutional review board (Istanbul Acıbadem University-2025-01/55). Patients were given detailed information about the study and provided written informed consent form. All patients underwent surgery on both of their eyes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eObjectives\u003c/h3\u003e\n\u003cp\u003eThe main goals included investigating monocular and binocular depth of focus at 6 months after surgery, uncorrected and best corrected distance, intermediate and near VA up to 6 months following surgery, photopic and mesopic contrast sensitivity, subjective refraction, visual disturbances, IOL stability, PCO, Nd:YAG ratio, and overall security.\u003c/p\u003e\n\u003ch3\u003ePatients and IOLs\u003c/h3\u003e\n\u003cp\u003eThe study comprised patients aged 32 to 84 who had clinically significant bilateral age-related cataracts but no other serious ocular disease. There was one young patient (32 years old-taxi driver) who developed cataracts due to short-term oral steroid use (2 weeks).\u003c/p\u003e \u003cp\u003eThe Enova Advanced \u003csup\u003eTM\u003c/sup\u003e (VSY BIOTECHNOLOGY) EDOF IOL utilizes Non-diffractive \"Light Tailoring Technology\" to ensure dependable visual results.\u003c/p\u003e\n\u003ch3\u003eTechnical Specifications:\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003e*\u003c/em\u003eSingle piece, %100 glistening free, hydrophobic acrylic, UV filter\u003c/p\u003e \u003cp\u003e*Asymetric, biconvex, neutral bi-aspheric EDOF\u003c/p\u003e \u003cp\u003e* Refractive index 1.53 (546nm)\u003c/p\u003e \u003cp\u003e*Optical diameter 6 mm, overall diameter 13mm\u003c/p\u003e \u003cp\u003e*Water content %7\u003c/p\u003e \u003cp\u003e*Abbe number 42\u003c/p\u003e \u003cp\u003e*Haptic design C-loop, haptic angle 0\u0026deg;.\u003c/p\u003e \u003cp\u003e*Additional range (Dioptre) 2.2 (approximately)\u003c/p\u003e \u003cp\u003eParticipants were provided with evaluations of the questionnaire during their most recent visit. One of the primary objectives of the study was to evaluate the visual impairment profiles associated with Enova ADVANCED\u0026trade; (VSY BIOTECHNOLOGY) IOLs using the Visual Impairment Questionnaire. Participants initially report if they have encountered a specific visual disturbance, and if they have, they are requested to evaluate the frequency, severity, and discomfortness of these visual disturbances (for example, starbursts, halos, and glare). The resulting scores varied between 0 (indicating the worst functional impairment) to 100 (indicating no disability) \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eExclusion criteria\u003c/h3\u003e\n\u003cp\u003eAny eye condition other than cataracts that may lead to deterioration of VA throughout patients follow-up, any anterior segment disorders (e.g., chronic uveitis, iritis, corneal dystrophy) that could significantly influence the results, any corneal patologies, any ocular infection, any degenerative visual diseases, pseudoexfoliations syndrome, keratoconus, diabetic retinopathy, uncontrolled glaucoma and or IOP\u0026thinsp;\u0026gt;\u0026thinsp;24 mmHg, choroidal haemorrhage, aniridia, microphthalmia, amblyopia, previous intraocular and corneal surgery, expected post-operative astigmatism greater than 1 Dioptre (D). Patients were monitored for a duration of 6 months post-operation.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssessments\u003c/h2\u003e \u003cp\u003eBefore surgery, all patients received a thorough evaluation that included a complete medical history, a slit lamp and dilated fundus evaluation, subjective refraction, determination of the dominant eye, monocular and binocular distance, intermediate and near uncorrected visual acuity (UCVA) and best corrected visual acuity (BCVA). Optical biometry (Haag-Streit Lenstar) was used to measure IOLs for cataract surgery. And also photopic pupil size and intraocular pressure (IOP) were measured. VA, subjective refraction, IOP, biometry parameters, and IOL stability (by slit lamp assessment) were assessed at follow-up visits after 1\u0026ndash;2 weeks, 2 months, and 6 months. Defocus curves were examined at 6 months after surgery. Contrast sensitivity, posterior capsule opacification (PCO), and the Nd:YAG ratio also were evaluated.\u003c/p\u003e \u003cp\u003eThe Barrett Universal II and a lens factor (LF) of 1.73 were utilized in all instances to ascertain the suitable IOL power. This LF was selected due to its correlation with an A-constant of 118.7, which serves as the manufacturer's recommended starting point for both lenses \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Freiburg Visual Acuity and Contrast Test (FrACT) computerized charts were used to test monocular and binocular UCVA and BCVA at the following distances with 100% contrast: The far distance is 400\u0026thinsp;\u0026plusmn;\u0026thinsp;12 cm, the intermediate is 67\u0026thinsp;\u0026plusmn;\u0026thinsp;2 cm, and the near view 40\u0026thinsp;\u0026plusmn;\u0026thinsp;1 cm. The charts brightness is regulated at 200 cd/m2. All VA measurements converted to logarithm of the minimum angle of resolution (log MAR). Both monocular and binocular defocus curves were measured using the similiar conditions and technique. The patient's vision was adjusted to the testing distance (4m), and VA was evaluated using defocus lenses in a randomized order ranging from +\u0026thinsp;1.50 D to -4.00 D. The defocus steps were 0.50 D, and applied three VA thresholds: 0.1, 0.2, and 0.3 log MAR. The depth of focus curves were made by adding negative lenses without changing the optotype distance. West and colleagues discovered that individuals with a VA of less than 0.3 log MAR (Snellen 6/12) experienced challenges in identifying faces and reading, drive at night \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Thus, we selected 0.3 log MAR as the threshold value.\u003c/p\u003e \u003cp\u003eDepth of focus computed based on ANSI Z80.35-2018 recommendations. The dioptric interval between 0 defocus (or best distant vision) and the point corresponding to the maximum negative focusing surpassing the VA threshold was computed separately for each subject. Additionally, defocus curves were created using the average VAs acquired at each focusing step.\u003c/p\u003e \u003cp\u003eThe Functional Vision Analyzer Optec 6500 Vision Tester (Stereo Optical) was used to test monocular contrast sensitivity at 1.5, 3, 6, 12, and 18 cpd spatial frequencies under mesopic (3 cd/cm2), mesopic with glare, and photopic (85 cd/cm2) situations. After dark adaptation, the mesopic test was administered first, followed by the photopic test. Each test was repeated two or three times, and the mean of the results was used for analysis. For each spatial frequency, the number of individuals who could not view any contrast was enrolled.\u003c/p\u003e \u003cp\u003eMonocular and binocular defocus curve and contrast sensitivity measurements were taken on the selected primary eye utilizing a randomization technique that included an equal number of dominant and non-dominant eyes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSurgical technique\u003c/h3\u003e\n\u003cp\u003eTopical or local anaesthesia was followed by continuous curvilinear capsulorhexis. In cases, the IOL was placed in the capsular bag using the recommended injector (Enova ADVANCED \u003csup\u003eTM\u003c/sup\u003e (VSY BIOTECHNOLOGY) EDOF IOLs). All procedures were carried out using typical self-sealing clear corneal incisions, capsulorhexis, and conventional phacoemulsification. Phacoemulsification was conducted utilizing a conventional stop-and-chop method with the ALCON INFINITI VISION system for all patients.\u003c/p\u003e \u003cp\u003eThe preference incision size was \u0026le;\u0026thinsp;2.4 mm for the IOL placement. The incision was tailored based on the requirement to adjust the level of astigmatism. A frown incision was utilized for minor astigmatism corrections, whereas a straight incision was favored for more significant adjustments. The choice of incision was affected by the nucleus's hardness across all types of incisions, allowing for effective management of all cataract varieties. Emmetropia was the aim in all situations. At the conclusion of the surgery, any remaining ophthalmic viscoelastic device was fully eliminated from the posterior chamber via irrigation, and the lateral ports and main incision were sealed with hydration. Postoperative care and medicines were administered in accordance with standard procedure for all patients.\u003c/p\u003e \u003cp\u003eThe topical steroid (10 mg/ml, Pred Forte; Allergan AbbVie, Chicago, USA) and the topical antibiotic (5 mg/ml, Vigamox (moxifloxacin ophthalmic solution), Novartis, Basel, Switzerland) were administered as eye drops four times a day for three weeks following the cataract surgery. Patients were advised to seek immediate medical attention and applied the hospital if they experienced symptoms after cataract surgery, such as worsening eye pain, eye redness, discharge and white or yellow pus from the eyes, swollen or puffy eyelids, and any deterioration, blurring, or decrease in VA.\u003c/p\u003e\n\u003ch3\u003eStatistical methods\u003c/h3\u003e\n\u003cp\u003eThe current study utilized descriptive statistical techniques such as mean, standard deviation (SD), and minimum and maximum values. The safety population comprised all individuals who were administered an experimental device in one or both of their eyes. The complete analysis group was determined to include all individuals who completed a minimum of nine out of 12 measurements for the monocular and binocular defocus curve during the 6-month assessment. The ANCOVA model was utilized to analyze the primary endpoint, incorporating fixed terms for treatment, center, dominant eye, first operated eye, and the time between surgery and the 6-month measurement. During the examination of statistical data related to VA, MANOVA was selected as the parametric test while the Friedmann test was selected as the non-parametric test. Pearson correlation test was used to understand the statistical difference between mesopic and photopic values. The Kolmogorov-Smirnov test was utilized to assess the normal distribution of the groups. A p value less than 0.05 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was deemed to be statistically significant. The analysis was conducted using SPSS version 25.0 software.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe study began with 128 eyes, but 10 were eliminated due to irregular follow-up visits, pseudophakic cystoid macular edema (CME) in 4 eyes, epiretinal membrane (ERM) development in 4 eyes, and a uveitic reaction in 2 eye.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;In this study, 118 eyes belonging to 59 patients were part of the research. All patients underwent surgery on both eyes. The study consisted of 33 male and 26 female participants, with an average age of 66.62 ± 9.57 years (ranging from, 32 to 84 years). Mean axial length was 23.91 ± 0.89 mm and anterior chamber depth was 3.12 ± 0.41 mm. And also mean IOL power was 21.35±1.92 mm, mean pupil diameter was 3.38±1.07 mm.\u003c/p\u003e\n\u003cp\u003eTable 1 contains the demographic data of the patients. The surgery proceeded without any major complications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepth of focus\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe monocular defocus at the 0.3 log MAR VA threshold, the range of the defocus curves was approximately +1.05 D to − 1.72 D for the Enova ADVANCED\u003csup\u003e\u0026nbsp;TM\u003c/sup\u003e (VSY BIOTECHNOLOGY) EDOF IOLs. The binocular defocus at the 0.3 log MAR VA threshold, the extent of the defocus curves was roughly +1.52 D to − 2.07 D for the Enova ADVANCED EDOF IOLs. Figure 1 displays the table and graph illustrating the defocus curve.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVisual acuity: monocular and binocular\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTables 2 displays the VA results for distance, intermediate, and near vision using binocular measurements at 1 week, 2 weeks, 2 months, and 6 months. Tables 3 shows the VA outcomes for distance, intermediate, and near vision using monocular measurements at 1 week, 2 weeks, 2 months, and 6 months.\u003c/p\u003e\n\u003cp\u003eThroughout all follow-up periods, a notable improvement in binocular and monocular VA was detected (p\u0026lt;0.001). The mean monocular intermediate BCVA exceeded 0.3 log MAR for 83.8% of eyes that were implanted with Enova ADVANCED \u003csup\u003eTM\u003c/sup\u003e (VSY BIOTECHNOLOGY) EDOF IOLs all follow ups after cataract surgery. For 56.2% of eyes that received Enova ADVANCED\u003csup\u003e\u0026nbsp;TM\u003c/sup\u003e (VSY BIOTECHNOLOGY) EDOF IOLs implants, the monocular near BCVA was better than 0.3 log MAR.\u003c/p\u003e\n\u003cp\u003eDistance, intermediate and near VAs were statistically significant for the Enova ADVANCED\u0026nbsp;\u003csup\u003eTM\u003c/sup\u003e (VSY BIOTECHNOLOGY) EDOF IOL during all follow ups for corrected and uncorrected and monocular and binocular conditions (p \u0026lt; 0.001). A significant difference was found in distance, intermediate, and near VA (corrected or uncorrected, monocular or binocular) at 6 months (p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRefraction\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was a notable enhancement in refraction from the initial measurement to the 6th month, as indicated in figure 2. The Enova ADVANCED \u003csup\u003eTM\u003c/sup\u003e (VSY BIOTECHNOLOGY) EDOF IOLs achieved a ±0.5 D target spherical equivalent refraction for 78.1% of eyes. Values within ±1.0 of the target were 98.9% for Enova ADVANCED\u003csup\u003e\u0026nbsp;TM\u003c/sup\u003e (VSY BIOTECHNOLOGY) EDOF IOLs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContrast sensitivity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 displays the corrected for distance contrast sensitivity curves measured at 6 months. Within the patient cohort, contrast sensitivity declined in mesopic settings and was further diminished in mesopic and glare conditions in comparison to photopic settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient questionnaire\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Figure 4 contains a synopsis of the patient survey, while figure 3 displays the rates of spectacle independence for far, intermediate, and near distances. A total of 86% of the patients involved in the study reported being either completely satisfied or very satisfied with their overall experience at month 6.\u003c/p\u003e\n\u003cp\u003eThe main satisfaction rates of the patients in terms of their daily work and quality of life were as follows:\u003c/p\u003e\n\u003cp\u003eEngaging in tasks or pastimes that necessitate clear vision at close range was %58.8. Reading ordinary articles in newspapers %59.3. Examining the small print in a phone book, a medicine bottle, or legal documents %33.7. Safe driving in daylight, in familiar areas %92.4. Driving comfortably at night %70.6.\u003c/p\u003e\n\u003cp\u003eIn the past week, the rates of double vision, glare, halo, and starburst were reported 1.3%, 28.7%, 11.3%, and 17.6%, respectively (Figure 4). Figure 5 presents the in vivo and in vitro imaging of EDOF IOL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSafety\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;At 6 months, only one eye in the patient cohort had a displaced IOL measuring 0.5mm. During the final assessment, a minor tilt was observed in one case, but it had been resolved. The average IOP at 6 months varied from 13.76 ± 3.47 to 13.96 ± 2.73 mmHg in patient cohort.\u003c/p\u003e\n\u003cp\u003eThroughout the Enova ADVANCED\u003csup\u003e\u0026nbsp;TM\u003c/sup\u003e (VSY BIOTECHNOLOGY) EDOF IOLs study's follow-up periods, the following adverse events were recorded: 2 case of uveitic reaction, 4 cases of CME, 4 cases of ERM, and 2 cases of Nd:YAG capsulotomy due to clinically significant posterior capsular opacification (PCO). None of these incidents were classified as a severe adverse event. There were no instances of hypopyon, endophthalmitis, lens dislocation, pupillary block, or retinal detachment.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe goal of cataract surgery is to restore VA and increase the high patient's quality of life. However, with the traditional monofocal IOLs, patients are not able to drive, use computers, tablets, and mobile phones, or engage in hobbies such as reading without glasses or sewing. Choosing the ideal IOL for a patient should be based on more than just the lens's technical specifications. In addition to the patient's needs, lifestyle and interests (reading, driving, outdoor activities, etc.) should be considered. To obtain the clinical effects claimed for monofocal enhanced IOLs, lens manufacturers must diverge beams from monofocal optics to create a design that can produce a dot-spread function in focusing slightly differently than a standard monofocal design\u0026nbsp;\u003csup\u003e17\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMultifocal IOLs, like as diffractive trifocal IOLs, have been shown to be useful for near, intermediate, and far vision in patients with healthy retinas, but for some patients they create discomfort by introducing dysphotopsia \u003csup\u003e18\u003c/sup\u003e. Various trials have shown that contrast sensitivity decrement and optical aberration increment in multifocal IOLs \u003csup\u003e19,20\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAs previously established, EDOF IOLs result in reduced visual impairment compared to trifocal lenses \u003csup\u003e21\u003c/sup\u003e. Noninvasive psychophysical assessments have shown adequate sensitivity to identify variations in visual impairment resulting from monofocal IOLs and various multifocal designs \u003csup\u003e22\u003c/sup\u003e. In a research study carried out by Marius et al., a comparison was made between the Symfony (Johnson \u0026amp; Johnson Vision) \u0026nbsp;and Acrysof IQ Vivity IOL (Alcon Laboratories, Inc.) EDOF IOLs in 138 eyes. The findings indicated a rise in the incidence of certain visual disturbances within the Symfony group, especially concerning the frequency, severity, and discomfort associated with starbursts and glare. However, the distance, intermediate, and near VAs were satisfactory and did not show significant differences between the Symfony\u0026nbsp;(Johnson \u0026amp; Johnson Vision)\u0026nbsp;and\u0026nbsp;Acrysof IQ Vivity IOL (Alcon Laboratories, Inc.)\u0026nbsp;(\u0026nbsp;EDOF IOLs.) \u003csup\u003e23\u003c/sup\u003e. In current study, it was found to be better than both IOLs in terms of double views and halo, and better than Symphony\u0026nbsp;(Johnson \u0026amp; Johnson Vision)\u0026nbsp;in terms of glare and starburst. In our research, superior outcomes were achieved according to both lenses (Symfony\u0026nbsp;(Johnson \u0026amp; Johnson Vision)\u0026nbsp;and\u0026nbsp;Acrysof IQ Vivity IOL (Alcon Laboratories, Inc.)) regarding the BCVA (distance, intermediate and near). While the study conducted by Marius et al. reported results at the 3-month mark, this study provided findings at the 6-month interval. In our study, we can interpret that low visual disturbances such as double images, glare, halos and starbursts are probably caused by the 7% water content. In fact, IOL design of Enova ADVANCE\u003csup\u003e\u0026nbsp;TM\u003c/sup\u003e (VSY BIOTECHNOLOGY) featuring optical edge curvature and fully functional optics may probably have exhibited the minimal levels of glare-type photic phenomena. However, since there is no previous study on Enova ADVANCED\u003csup\u003e\u0026nbsp;TM\u003c/sup\u003e (VSY BIOTECHNOLOGY), controlled, randomized, comparative clinical studies are needed to support these data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA research conducted by Guarro et al examined the occurrence of visual disturbances among three EDOF IOLs and one monofocal IOL. The findings were documented for 22 patients in each group of the study. The non-diffractive extended-aperture IOL, AcrySof IQ Vivity, induces dysphotopsia that is comparable to that of the AcrySof IQ monofocal lens. These undesirable visual phenomena are considerably less severe than those generated by the AT LARA 829MO and TECNIS Symfony ZXR00 diffractive extended-aperture lens models. Both extended-aperture and monofocal lenses demonstrate fewer visual disturbances in binocular conditions as opposed to monocular conditions \u003csup\u003e24\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo address the dissatisfaction resulting from disphotopsia, EDOF IOLs are used in the clinical field. Non-diffractive EDOF IOLs create an extended focal point that provides for EDOF in comparison to monofocal IOLs, which elongate the light to achieve better intermediate vision. By generating a continuous range of vision and minimizing the overlap created by near and distant focal points, the EDOF lens is thought to deliver functional near, good intermediate, and sharp far vision while reducing dysphotopsia\u0026nbsp;\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePatients defined positive dysphotopsia (PD) as light streaks, arcs, flashes, halos, and starbursts, all of which are triggered by an external light source. In contrast, negative dysphotopsia (ND) is characterized as a temporary arc-shaped dark shadow, usually stimulated by light sources that are oriented temporally \u003csup\u003e26\u003c/sup\u003e. While PD appears to be related to IOL material and edge design, previous research by Masket et al suggests that ND is more closely linked to IOL location rather than IOL material or design \u003csup\u003e27\u003c/sup\u003e.\u0026nbsp; In alignment with the existing literature, high index of refraction hydrophobic IOLs with squared edges are the most probable to be linked with PD. It appears reasonable to regard a diminished optic size or effective optic size as factors contributing to PD; however, there is presently insufficient supporting evidence. Enova ADVANCED\u003csup\u003e\u0026nbsp;TM\u003c/sup\u003e (VSY BIOTECHNOLOGY) IOL features a 360° all enhanced square edge and is made of hydrophobic acrylic; however, the PD may not reduce in size despite its high water content.\u003c/p\u003e\n\u003cp\u003eWhen discussing the characteristics of the non-diffractive Enova ADVANCED\u003csup\u003e\u0026nbsp;TM\u003c/sup\u003e (VSY BIOTECHNOLOGY) lens utilized in the current research: Light Tailoring Structure 1(LTS 1) offers a broader far and intermediate distribution of light energy. Light Tailoring Structure 2 (LTS 2) enhances light distribution to provide \"tailor-made\" visual acuity for far, midrange, and basic near vision. Enova ADVANCED\u003csup\u003e\u0026nbsp;TM\u003c/sup\u003e (VSY BIOTECHNOLOGY)\u0026nbsp;features a neutral spherical design. \u0026nbsp;The innovative Light Tailoring Technology employs two structures (LTS 1 and 2) placed in the\u0026nbsp;central\u0026nbsp;2 mm diameter to expand and fine-tune the light. LTS 1 broadens and spreads light from the distant focus into the\u0026nbsp;intermediate\u0026nbsp;and near vision ranges. LTS 2 fine-tunes light for improved intermediate and near VA. LTS 1 distributes light widely for EDOF. LTS 2 refines the light distribution to improve the relevant distances (far, intermediate, and basic near) \u003csup\u003e28-31\u003c/sup\u003e.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe main focus of this research was to examine the depth of focus, along with various visual and safety factors of the EDOF IOL, in accordance with the guidelines set by ANSIZ80.352018 and the American Academy of Ophthalmology (AAO) Extended Working Group Consensus Statement for Depth of Focus Intraocular Lenses \u003csup\u003e32\u003c/sup\u003e. This study is the first phase 4 study to report results for Enova ADVANCED \u003csup\u003eTM\u003c/sup\u003e (VSY BIOTECHNOLOGY) EDOF IOL.\u003c/p\u003e\n\u003cp\u003eIn a study carried out by Reinhard et al., they compared 2 EDOF IOLs with 1 monofocal IOL. TECNIS Symfony IOL achieved -0.7 D monocular defocus at the 0.2 log MAR reference threshold, while ATLARA 829MP remained above this threshold up to -1.5 D defocus \u003csup\u003e33\u003c/sup\u003e. In our investigation, the monocular defocus was recorded as -0.9 D with a 0.2 log MAR. The AT LARA 829MP stands as an Enova ADVANCED \u003csup\u003eTM\u003c/sup\u003e (VSY BIOTECHNOLOGY) extended depth of focus (EDOF) intraocular lens (IOL) that delivers uninterrupted vision across a spectrum of distances. The research conducted on the AT LARA 829MP revealed that the monocular depth of focus varied between 0.1 log MAR at 0.87 D and 0.3 log MAR at 1.75 D \u003csup\u003e33\u003c/sup\u003e. The current investigation on Enova ADVANCED \u003csup\u003eTM\u003c/sup\u003e (VSY BIOTECHNOLOGY) EDOF IOLs demonstrated monocular depth of focus of 0.43 D at 0.1 log MAR and 1.72 D at 0.3 log MAR. The results from Enova ADVANCED\u003csup\u003e\u0026nbsp;TM\u003c/sup\u003e (VSY BIOTECHNOLOGY) were very similar to those reported by AT LARA 829 MP and Schallhorn et al. In AT LARA 829 MP, mean binocular distance UCVA was 0.02 ± 0.17 log MAR and mean binocular near UCVA was 0.23 ± 0.23 log MAR. In the study conducted by Schallhorn et al., the mean binocular distance UCVA was −0.05±0.09 log MAR and the mean binocular near UCVA was 0.26 ± 0.14 log MAR \u003csup\u003e33, 34\u003c/sup\u003e. \u0026nbsp;In Enova ADVANCED\u003csup\u003e\u0026nbsp;TM\u003c/sup\u003e (VSY BIOTECHNOLOGY), the mean binocular distance UCVA was found to be 0.06 ± 0.04 log MAR, with the mean binocular near UCVA measuring at 0.15 ± 0.1 log MAR.\u003c/p\u003e\n\u003cp\u003eIntermediate VA is becoming more and more crucial for essential for tasks, as it aids in working on computers and reading car instruments. In a study conducted by Reinhard et al., AT LARA 829MP demonstrated a VA level of 0.3 log MAR or better from a distance of 52 cm, while TECNIS Symfony showed similar results from a distance of 66 cm. However, the CT ASPHINA 409MP, offered satisfactory viewing quality starting from a distance of 100 cm \u003csup\u003e34\u003c/sup\u003e. In our study, visual gain equivalent to 0.3 log MAR VA was achieved from 43 cm.\u003c/p\u003e\n\u003cp\u003eReading a newspaper normally necessitates a VA of 0.4 log MAR at a distance of 40 cm; however, a greater level of VA is essential for smooth reading \u003csup\u003e35\u003c/sup\u003e. At 6 months, the intermediate VA of the Enova ADVANCED \u003csup\u003eTM\u003c/sup\u003e (VSY BIOTECHNOLOGY) EDOF IOLs exceeded 0.4 log MAR by a significant margin.\u003c/p\u003e\n\u003cp\u003eThe VA findings were in agreement with the patient survey findings. A large number of patients stated that they had no issues with activities that involved distance vision, such as driving and outdoor activities, while a slightly higher number of patients reported experiencing difficulty with activities that required near vision, such as reading and sewing. Patients simply reported that driving at night was more difficult than driving during the day. It is widely acknowledged that a small number of patients experience challenges with driving during daylight hours following cataract surgery. A different research conducted by Mönestam and colleagues revealed that 43% of individuals encountered challenges while driving at night because of glare, particularly in situations with low-contrast conditions below 20/50 where VA was significantly impaired (0.4 log MAR) \u003csup\u003e36\u003c/sup\u003e, in line with our results. It is widely acknowledged that a decrease in VA to 20/40 can greatly affect one's ability to drive at night \u003csup\u003e16\u003c/sup\u003e: in our research, all IOLs performed above this threshold on average.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The patient questionnaire results also aligned with the findings on contrast sensitivity. In photopic conditions, the contrast sensitivity across all spatial frequencies was similar to the standard range for this population \u003csup\u003e37\u003c/sup\u003e. Nevertheless, as predicted, contrast sensitivity was decreased in mesopic and mesopic with glare situations. The refraction predictability was noted to be within ±1.0 D of the target value in 98.9% of cases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe presence of any remaining refractive error with multifocal IOLs diminishes visual quality at all distances in comparison to monofocal IOLs \u003csup\u003e38, 39\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe effectiveness of presbyopia correction greatly relies on the precision of post-operative refraction. Even minor inaccuracies in measuring eye dimensions can result in refractive changes that could affect the patient's vision quality after the surgery. Factors such as the management and care of the meibomian glands, the condition of the ocular surface, and the accuracy of biometers in measuring the curvature of the cornea are all crucial in determining the appropriate IOL power for implantation. EDOF IOLs are anticipated to demonstrate greater tolerance towards moderate refractive changes, making them more user-friendly compared to alternative IOLs \u003csup\u003e21\u003c/sup\u003e. Further research would be necessary to assess and verify the tolerance to refractive errors of EDOF IOLs.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;One limitation of this research stems from utilizing the FrACT to assess VA. The FrACT provides an automated method for individuals to self-administer VA measurements. Landolt rings, tumbling E, and Sloan letters are among the optotypes that are utilized. The FrACT has undergone validation and there is ample literature to substantiate its usage \u003csup\u003e34–36\u003c/sup\u003e. It provides certain benefits compared to traditional chart testing, especially in terms of objectivity and reliability \u003csup\u003e34\u003c/sup\u003e. Nevertheless, the ability to compare with existing literature may be restricted due to the potential for lower results from FrACT computerized charts compared to traditional chart testing methods, especially the ETDRS chart \u003csup\u003e16\u003c/sup\u003e. While the study's internal validity remains uncompromised, it is important to exercise caution when comparing it with previously published literature. Another significant limitation of the research is its retrospective design and the absence of a control group. The lack of aberrometric evaluations at different pupil sizes is an important limitation of the study. Although common, averaging a sphere and a cylinder is not the most accurate way to report refraction data. The inability to use vector or matrix formalisms is another limitation of the study. It is crucial to introduce the initial findings of the phase 4 study to the academic community, given that this lens is a recent addition to the research.\u003c/p\u003e\n\u003cp\u003eAs a result, in our research, the Enova ADVANCED\u003csup\u003e\u0026nbsp;TM\u003c/sup\u003e (VSY BIOTECHNOLOGY) the AAO task force standards for EDOF IOLs by having a monocular depth of focus at least 0.5 D wider than the monofocal control at 0.2 logMAR \u003csup\u003e20\u003c/sup\u003e. The research showcased positive visual results after the Enova ADVANCED EDOF\u003csup\u003e\u0026nbsp;TM\u003c/sup\u003e (VSY BIOTECHNOLOGY) lens was implanted. The Enova ADVANCED\u003csup\u003e\u0026nbsp;TM\u003c/sup\u003e (VSY BIOTECHNOLOGY) EDOF lens demonstrated exceptional levels of photic phenomena, spectacle independence, and patient satisfaction. The findings validate earlier research indicating that the Enova ADVANCED EDOF\u003csup\u003e\u0026nbsp;TM\u003c/sup\u003e (VSY BIOTECHNOLOGY) IOL delivers favorable optical and visual outcomes across all distances, allowing the majority of patients to perform most daily activities without the need for spectacles.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe corresponding author is a member of the Editorial Board of BMC Ophthalmology and had no role in the editorial handling or peer review process of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDECLARATION OF INTEREST STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding/Support:\u003c/strong\u003e No funding or financial support was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Disclosures:\u003c/strong\u003e The authors declare no financial disclosures related to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request. This study do not published anywhere.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHUMAN ETHICS AND CONSENT TO PARTICIPATE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in compliance with the ethical principles outlined in the Declaration of Helsinki and\u0026nbsp;ethical approval have been obtained by the local institutional review board.\u0026nbsp;The study was approved by the Istanbul Acıbadem University, Medicine of Acıbadem University Committee (approval date and number: January 9, 2025; No. 2025-01/55).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT TO PUBLISH DECLARATION\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT TO PARTICIPATE DECLARATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB.K. contributed to the conception and design of the study, data collection, analysis and interpretation, and drafting of the manuscript. A.R.C.C and E.K. contributed to data collection, analysis, and drafting of the manuscript. B.K., A.R.C.C and E.K. contributed to data analysis and interpretation, supervision, and critical revision of the manuscript for important intellectual content. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGrzybowski A, Kanclerz P. Recent developments in cataract surgery. In: Grzybowski A, editor. Current Concepts in Ophthalmology. 2020: Vol 124. Cham: Springer International Publishing 55\u0026ndash;97.\u003c/li\u003e\n \u003cli\u003eAlfonso JF, Fern\u0026aacute;ndez-Vega L, Baamonde MB, et al. Prospective visual evaluation of apodized diffractive intraocular lenses. J Cataract Refract Surg. 2007: 33(7):1235\u0026ndash;1243.\u003c/li\u003e\n \u003cli\u003eVega F, Mill\u0026aacute;n MS, Gil MA, et al. Optical Performance of a Monofocal Intraocular Lens Designed to Extend Depth of Focus. J Refract Surg. 2020: Sep 1;36(9):625-632.\u003c/li\u003e\n \u003cli\u003eRocha KM, da Costa E, Dodhia M, et al. \u003csup\u003e\u0026nbsp;\u003c/sup\u003eTolerance to Induced Astigmatism With a Monofocal Intraocular Lens Designed to Extend the Depth of Focus. J Refract Surg. 2023: Apr;39(4):222-228.\u003c/li\u003e\n \u003cli\u003eSong X, Liu X, Wang W, et al. Visual outcome and optical quality after implantation of zonal refractive multifocal and extended-range-of-vision IOLs: a prospective comparison. J Cataract Refract Surg. 2020: Apr;46(4):540-548.\u003c/li\u003e\n \u003cli\u003eAlio JL\u003csup\u003e\u0026nbsp;\u003c/sup\u003e, Plaza-Puche AB\u003csup\u003e\u0026nbsp;\u003c/sup\u003e, F\u0026eacute;rnandez-Buenaga R\u003csup\u003e\u0026nbsp;\u003c/sup\u003e, et al. Multifocal Intraocular Lenses: The Art and the Practice. Cham, Switzerland: Springer International Publishing 2019.\u003c/li\u003e\n \u003cli\u003eAlio JL, Plaza-Puche AB, Fe\u0026acute;rnandez-Buenaga R, et al. Multifocal intraocular lenses: an overview. Surv Ophthalmol. 2017: 62:611 634.\u003c/li\u003e\n \u003cli\u003eCalladine D, Evans JR, Shah S, et al. Multifocal versus monofocal intraocular lenses after cataract extraction. Cochrane Database Syst Rev. 2012: Sep 12;(9):CD003169. doi:10.1002/14651858.CD003169. pub3\u003c/li\u003e\n \u003cli\u003eCochener B, Lafuma A, Khoshnood B, et al. Comparison of outcomes with multifocal intraocular lenses: a meta-analysis. Clin Ophthalmol. 2011: Jan 07; 5:45\u0026ndash;56. doi:10.2147/OPTH.S14325\u003c/li\u003e\n \u003cli\u003eAkella SS, Juthani VV. Extended depth of focus intraocular lenses for presbyopia. Curr Opin Ophthalmol. 2018: 29(4):318\u0026ndash;322.\u003c/li\u003e\n \u003cli\u003eAttia MSA, Auffarth GU, Kretz FTA, et al. Clinical evaluation of an extended depth of focus intraocular lens with the Salzburg Reading Desk. J Refract Surg. 2017: 33(10):664\u0026ndash;669.\u003c/li\u003e\n \u003cli\u003eWeeber HA, Meijer ST, Piers PA. Extending the range of vision using diffractive intraocular lens technology. J Cataract Refract Surg. 2015: 41(12):2746\u0026ndash;2754.\u003c/li\u003e\n \u003cli\u003eRiusala A, Sarna S, Immonen I. Visual function index (VF-14) in exudative age-related macular degeneration of long duration. Am J Ophthalmol. 2003:135:206\u0026ndash;212\u003c/li\u003e\n \u003cli\u003eIOL Con - Intraocular Lenses. Accessed July 3, 2022. http://www.iolcon.org\u003c/li\u003e\n \u003cli\u003eSheila K West, Gary S Rubin, Aimee T Broman, et al. How does visual impairment affect performance on tasks of everyday life? The SEE Project. Salisbury Eye Evaluation. Arch Ophthalmol. 2002: Jun;120(6):774-80.\u003c/li\u003e\n \u003cli\u003eWood J, Chaparro A, Carberry T, et al. Effect of simulated visual impairment on nightime driving performance. Optom Vis Sci. 2010: 87(6):379\u0026ndash;386.\u003c/li\u003e\n \u003cli\u003eWang L, Dai E, Koch DD, et al. Optical aberrations of the human anterior cornea. J Cataract Refract Surg. 2003: 29:1514\u0026ndash;1521.\u003c/li\u003e\n \u003cli\u003eBerdahl J, Bala C, Dhariwal M, et al. Cost-benefit analysis of a trifocal intraocular lens versus a monofocal intraocular lens from the patient\u0026rsquo;s perspective in the United States. PLoS One. 2022: 17(11): e0277093.\u003c/li\u003e\n \u003cli\u003eWang SY, Stem MS, Oren G, et al. Patient-centered and visual quality outcomes of premium cataract surgery: a systematic review. Eur J Ophthalmol. 2017: 27(4):387\u0026ndash;401\u003c/li\u003e\n \u003cli\u003eCao K, Friedman DS, Jin S, et al. Multifocal versus monofocal intraocular lenses for age-related cataract patients: a system review and meta-analysis based on randomized controlled trials. Sur Ophthalmol. 2019: 64(5):647\u0026ndash;658.\u003c/li\u003e\n \u003cli\u003eCan İ, Bayhan HA. Clinical Outcomes of Enhanced Monofocal (Mono-EDOF) Intraocular Lenses with the Mini-Monovision Technique versus Trifocal Intraocular Lenses: A Comparative Study. Turk J Ophthalmol. 2024: 54(4):190-197\u003c/li\u003e\n \u003cli\u003ePiers PA, Fernandez EJ, Manzanera S, et al. Adaptive Optics Simulation of Intraocular Lenses with Modified Spherical Aberration. Investigative Ophthalmology \u0026amp; Visual Science 2004: December Vol.45, 4601-4610.\u003c/li\u003e\n \u003cli\u003eScheepers MA, Hall B. Randomized and double-blind comparison of clinical visual outcomes of 2 EDOF intraocular lenses. J Cataract Refract Surg. 2023: Apr 1;49(4):354-359.\u003c/li\u003e\n \u003cli\u003eGuarro M, Sararols L, Londo\u0026ntilde;o GJ, et al. Visual disturbances produced after the implantation of 3 EDOF intraocular lenses vs 1 monofocal intraocular lens. J Cataract Refract Surg. 2022: Dec 1;48(12):1354-1359.\u003c/li\u003e\n \u003cli\u003eKanclerz P, Toto F, Grzybowski A, et al. Extended depth-of-field intraocular lenses: an update. Asia Pac J Ophthalmol. 2020: 9(3):194\u0026ndash;202.\u003c/li\u003e\n \u003cli\u003eDavison JA. Positive and negative dysphotopsia in patients with acrylic intraocular lenses. J Cataract Refract Surg. 2000: 26:1346\u0026ndash;1355.\u003c/li\u003e\n \u003cli\u003eMasket S, Fram NR, Cho A, et al. Surgical management of negative dysphotopsia. J Cataract Refract Surg. 2018: 44:6\u0026ndash;16.\u003c/li\u003e\n \u003cli\u003eData on file. Medical Report MR_EA_230605. VSY Biotechnology Laboratories. 06.2023.\u003c/li\u003e\n \u003cli\u003eData on file. L. Werner at. al.\u003cem\u003e\u0026nbsp;\u003c/em\u003eIn vitro study evaluating the tendency of different intraocular lenses to form intraoptical glistenings. Study report. RDR_EGF3_05072020. Utah, USA 07.2020.\u003c/li\u003e\n \u003cli\u003eData on file. Technical Report TR_EA_200511. VSY Biotechnology Laboratories. 05.2020\u003c/li\u003e\n \u003cli\u003eKohnen, T., \u0026amp; Suryakumar, R. Extended depth-of-focus technology in intraocular lenses. J Cataract Refract Surg. 2020: 46(2), 298\u0026ndash;304.\u003c/li\u003e\n \u003cli\u003eMacRae S, Holladay JT, Glasser A, et al. Special report: American Academy of Ophthalmology Task Force consensus state ment for extended depth of focus intraocular lenses. Ophthalmology 2017: 124(1):139\u0026ndash;141.\u003c/li\u003e\n \u003cli\u003eReinhard T, Maier P, B\u0026ouml;hringer D, et al. Comparison of two extended depth of focus intraocular lenses with a monofocal lens: a multi-centre randomised trial. Graefes Arch Clin Exp Ophthalmol. 2021: Feb;259(2):431-442.\u003c/li\u003e\n \u003cli\u003eSchallhorn SC, Teenan D, Venter JA, et al. Initial clinical outcomes of a new extended depth of focus intraocular lens. J Refract Surg. 2019: 35(7):426\u0026ndash;433.\u003c/li\u003e\n \u003cli\u003eWhittaker SG, Lovie-Kitchin J. Visual requirements for reading. Optom Vis Sci. 1993: 70(1):54\u0026ndash;65.\u003c/li\u003e\n \u003cli\u003eM\u0026ouml;nestam E, Lundqvist B. Long-time results and associations between subjective visual difficulties with car driving and objective visual function 5 years after cataract surgery. J Cataract Refract Surg. 2006: 32(1):50\u0026ndash;55.\u003c/li\u003e\n \u003cli\u003eHohberger B, Laemmer R, Adler Wetal. Measuring contrast sensitivity in normal subjects with OPTEC 6500: influence of age and glare. Graefes Arch Clin Exp Ophthalmol. 2007: 245(12):1805\u0026ndash;1814.\u003c/li\u003e\n \u003cli\u003eHayashi K, S-i M, Yoshida M, et al. Effect ofastigmatism on visual acuity in eyes with a diffractive multifocal intraocular lens. J Cataract Refract Surg. 2010: 36(8):1323\u0026ndash;1329.\u003c/li\u003e\n \u003cli\u003eSon H-S, Kim SH, Auffarth GU, et al. Prospective comparative study of tolerance to refractive errors after implantation of extended depth of focus and monofocal intraocular lenses with identical aspheric platform in Korean population. BMC Ophthalmol. 2019: 19(1):187.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eClinical and demographic data of the patients.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnova ADVANCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEDOF IOLs group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003eEyes (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003eAge (mean\u0026plusmn;SD,years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e66.62 \u0026plusmn; 9.57 (32-84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003eSex (M/F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e33/26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003eTarget SE (mean\u0026plusmn;SD,D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026minus;0.07 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003eIOL power (mean\u0026plusmn;SD,D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e21.35\u0026plusmn;1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003ePupil size(photopic) (mean\u0026plusmn;SD,mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e3.38 \u0026plusmn; 1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003ePrimary eye (dominant/non-dominant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e49.4% / 50.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-op. optical biometry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003eAL (mean\u0026plusmn;SD,mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e23.91 \u0026plusmn; 0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003eACD (mean\u0026plusmn;SD,mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e3.12 \u0026plusmn; 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-op. Visual acuity (logMAR) and refraction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003eMonocular UDVA (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e0.73 \u0026plusmn; 0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003eBinocular UDVA (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e0.68 \u0026plusmn; 0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003eMonocular CDVA (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e0.71 \u0026plusmn; 0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003eBinocular CDVA (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e0.63 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003eCylinder (mean\u0026plusmn;SD,D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026minus;0.72 \u0026plusmn; 0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003eSpherical (mean\u0026plusmn;SD,D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e0.78 \u0026plusmn; 2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003eSE (mean\u0026plusmn;SD,D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e0.18 \u0026plusmn; 2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-op. Refraction (primary eye, 6 months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003eCylinder (mean\u0026plusmn;SD,D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026minus;0.43 \u0026plusmn; 0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003eSpherical (mean\u0026plusmn;SD,D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e0.21 \u0026plusmn; 0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003eSE (mean\u0026plusmn;SD,D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026minus;0.24 \u0026plusmn; 0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 325px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;IOL, intra ocular lens; AL, axial length; ACD, anterior chamber depth; UDVA, uncorrected distance visual acuity; CDVA, corrected distance visual acuity; SE, spherical equivalent; SD, standard deviation; D,dioptre\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Mean binocular (primary eye) visual acuity, in logMAR\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Mean monocular (primary eye) visual acuity, in logMAR\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Contrast sensitivity curves with distance and intermediate correction under mesopic, mesopic with glare, photopic and photopic with glare conditions at 6 months post-operatively.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cataract, EDOF, Defocus curve, Contrast sensitivity, Intermediate visual acuity, Near visual acuity","lastPublishedDoi":"10.21203/rs.3.rs-8879910/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8879910/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e Enova ADVANCED\u003csup\u003eTM\u003c/sup\u003e (VSY BIOTECHNOLOGY) is a new generation extended depth of focus (EDOF) intraocular lens (IOL) that provides continuous vision at various distances. The aim of this study was to present the first results of the EDOF IOL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods:\u003c/strong\u003e This was a phase-4 retrospective cohort study. A total of 118 eyes of 59 patients data were reviewed. \u0026nbsp;Mean aged of patients 66.62 ± 9.57 years (ranging, 32 - 84 years) with cataracts underwent bilateral implantation of Enova ADVANCED \u003csup\u003eTM\u003c/sup\u003e (VSY BIOTECHNOLOGY) (EDOF IOLs). The follow-up occurred on days 7 to 14, as well as at 2 months and 6 months post-treatment. The main goals included investigating monocular and binocular depth of focus at 6 months after surgery, uncorrected and best corrected distance, intermediate and near visual acuity (VA) up to 6 months following surgery, photopic and mesopic contrast sensitivity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 59 patients with 118 eyes were included in the final analysis. Depth of focus with a monocular vision was observed across various threshold values, including 0.1 log MAR (+0.43 to -0.47 D), 0.2 log MAR (+0.73 to -0.9 D), and 0.3 log MAR (+1.05 to -1.72 D). Enova ADVANCED \u003csup\u003eTM\u003c/sup\u003e (VSY BIOTECHNOLOGY) had values of 0.1 log MAR (+0.52 to -0.58 D), 0.2 log MAR (+1.07 to -1.54 D), and 0.3 log MAR (+1.50 to -2.07 D) in terms of binocular depth of focus. Throughout all follow-up periods, there was a statistically significant improvement in distance, intermediate, and near VA when compared to the preoperative period (p \u0026lt;0.0001). All patients showed an improvement in refraction compared to the baseline measurement. Enova ADVANCED \u003csup\u003eTM\u003c/sup\u003e (VSY BIOTECHNOLOGY) patients exhibited high contrast sensitivity and the EDOF lens demonstrated good results without use of glasses. Mesopic values were relatively lower than photopic values\u003cbr\u003e\n(p\u0026lt;0.05). At 6 months, all IOLs were properly aligned without any instances of tilting.\u0026nbsp; No general safety concerns were expressed in any patient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Conclusion:\u003c/strong\u003e The Enova ADVANCED \u003csup\u003eTM\u003c/sup\u003e (VSY BIOTECHNOLOGY) EDOF IOLs demonstrated favorable vision outcomes, showing similar VA across various distances. Enova ADVANCED\u003csup\u003e TM\u003c/sup\u003e (VSY BIOTECHNOLOGY) provides a wide monocular depth of focus at 0.1 and 0.2 log MAR. The levels of spectacle independence and patient satisfaction were noticeably higher.\u003c/p\u003e","manuscriptTitle":"Surgical Outcomes of a Novel Non-diffractive, Extended Depth-of-focus Intraocular Lens - Preliminary Results From a Phase 4 Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 09:22:48","doi":"10.21203/rs.3.rs-8879910/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"82caf1c9-6c33-4ea4-8c42-47a2ff201436","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T07:25:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 09:22:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8879910","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8879910","identity":"rs-8879910","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}