Comparison of Machine and Human Expert Evaluation of Capsulorrhexis Creation Performance Through Analysis of Surgical Video Recordings.

Purpose: Achieving competency in cataract surgery is an essential component of ophthalmology residency training. Video-based analysis of surgery can change training through its objective, reliable, and timely assessment of resident performance. Methods: Using the Image Labeler application in MATLAB, the capsulorrhexis step of 208 surgical videos, recorded at the University of Michigan, was annotated for subjective and objective analysis. Two expert surgeons graded the creation of the capsulorrhexis based on the International Council of Ophthalmology's Ophthalmology Surgical Competency Assessment Rubric:Phacoemulsification (ICO-OSCAR:phaco) rating scale and a custom rubric (eccentricity, roundness, size, centration) that focuses on the objective aspects of this step. The annotated rhexis frames were run through an automated analysis to obtain objective scores for these components. The subjective scores were compared using both intra and inter-rater analyses to assess the consistency of a human-graded scale. The subjective and objective scores were compared using intraclass correlation methods to determine relative agreement. Results: All rhexes were graded as 4/5 or 5/5 by both raters for both items 4 and 5 of the ICO-OSCAR:phaco rating scale. Only roundness scores were statistically different between the subjective graders (mean difference = -0.149, p-value = 0.0023). Subjective scores were highly correlated for all components (>0.6). Correlations between objective and subjective scores were low (0.09 to 0.39). Conclusion: Video-based analysis of cataract surgery presents significant opportunities, including the ability to asynchronously evaluate performance and provide longitudinal assessment. Subjective scoring between two raters was moderately correlated for each component.

Evaluation of Methods for Detection and Semantic Segmentation of the Anterior Capsulotomy in Cataract Surgery Video.

Background: The capsulorhexis is one of the most important and challenging maneuvers in cataract surgery. Automated analysis of the anterior capsulotomy could aid surgical training through the provision of objective feedback and guidance to trainees. Purpose: To develop and evaluate a deep learning-based system for the automated identification and semantic segmentation of the anterior capsulotomy in cataract surgery video. Methods: In this study, we established a BigCat-Capsulotomy dataset comprising 1556 video frames extracted from 190 recorded cataract surgery videos for developing and validating the capsulotomy recognition system. The proposed system involves three primary stages: video preprocessing, capsulotomy video frame classification, and capsulotomy segmentation. To thoroughly evaluate its efficacy, we examined the performance of a total of eight deep learning-based classification models and eleven segmentation models, assessing both accuracy and time consumption. Furthermore, we delved into the factors influencing system performance by deploying it across various surgical phases. Results: The ResNet-152 model employed in the classification step of the proposed capsulotomy recognition system attained strong performance with an overall Dice coefficient of 92.21%. Similarly, the UNet model with the DenseNet-169 backbone emerged as the most effective segmentation model among those investigated, achieving an overall Dice coefficient of 92.12%. Moreover, the time consumption of the system was low at 103.37 milliseconds per frame, facilitating its application in real-time scenarios. Phase-wise analysis indicated that the Phacoemulsification phase (nuclear disassembly) was the most challenging to segment (Dice coefficient of 86.02%). Conclusion: The experimental results showed that the proposed system is highly effective in intraoperative capsulotomy recognition during cataract surgery and demonstrates both high accuracy and real-time capabilities. This system holds significant potential for applications in surgical performance analysis, education, and intraoperative guidance systems.

Neovascularization against autologous SLET donor grafts despite successful corneal epithelialization and graft adherence.

PURPOSE: To report a case of neovascularization against autologous grafts after simple limbal epithelial transplantation (SLET) despite successful corneal epithelialization, as well as its subsequent regression without intervention. METHODS: A case report and review of the literature. RESULTS: A 52-year-old woman underwent uncomplicated autologous SLET for asymmetric limbal stem cell deficiency (LSCD) in the left eye. One month after the surgery, the patient had successful adherence of the graft and corneal epithelialization; however, new neovascularization developed in the left eye towards the graft sites. With only a slow taper of topical prednisolone acetate and polymyxin b/trimethoprim, the neovascularization regressed to ghost vessels over the following three months with improvement of her LSCD symptoms and increased clarity of her cornea. CONCLUSION: The limbus does not enjoy relative immune privilege like other parts of the eye; therefore, autologous limbal stem cell transplantation (along with the minimal immune response generated) is valuable for restoration of the ocular surface. Here, we describe neovascularization against autologous donor grafts despite an otherwise uncomplicated surgery and expected epithelialization course. Inflammation-mediated angiogenesis likely initiated the neovascularization, suggesting that immune mediators of inflammation may be inadvertently part of the graft tissue in bilateral LSCD.

Adaptive Tensor-Based Feature Extraction for Pupil Segmentation in Cataract Surgery.

Cataract surgery remains the only definitive treatment for visually significant cataracts, which are a major cause of preventable blindness worldwide. Successful performance of cataract surgery relies on stable dilation of the pupil. Automated pupil segmentation from surgical videos can assist surgeons in detecting risk factors for pupillary instability prior to the development of surgical complications. However, surgical illumination variations, surgical instrument obstruction, and lens material hydration during cataract surgery can limit pupil segmentation accuracy. To address these problems, we propose a novel method named adaptive wavelet tensor feature extraction (AWTFE). AWTFE is designed to enhance the accuracy of deep learning-powered pupil recognition systems. First, we represent the correlations among spatial information, color channels, and wavelet subbands by constructing a third-order tensor. We then utilize higher-order singular value decomposition to eliminate redundant information adaptively and estimate pupil feature information. We evaluated the proposed method by conducting experiments with state-of-the-art deep learning segmentation models on our BigCat dataset consisting of 5,700 annotated intraoperative images from 190 cataract surgeries and a public CaDIS dataset. The experimental results reveal that the AWTFE method effectively identifies features relevant to the pupil region and improved the overall performance of segmentation models by up to 2.26% (BigCat) and 3.31% (CaDIS). Incorporation of the AWTFE method led to statistically significant improvements in segmentation performance (P < 1.29 × 10-10 for each model) and yielded the highest-performing model overall (Dice coefficients of 94.74% and 96.71% for the BigCat and CaDIS datasets, respectively). In performance comparisons, the AWTFE consistently outperformed other feature extraction methods in enhancing model performance. In addition, the proposed AWTFE method significantly improved pupil recognition performance by up to 2.87% in particularly challenging phases of cataract surgery.

CatStep: Automated Cataract Surgical Phase Classification and Boundary Segmentation Leveraging Inflated 3D-Convolutional Neural Network Architectures and BigCat.

Objective: Accurate identification of surgical phases during cataract surgery is essential for improving surgical feedback and performance analysis. Time spent in each surgical phase is an indicator of performance, and segmenting out specific phases for further analysis can simplify providing both qualitative and quantitative feedback on surgical maneuvers. Study Design: Retrospective surgical video analysis. Subjects: One hundred ninety cataract surgical videos from the BigCat dataset (comprising nearly 4 million frames, each labeled with 1 of 11 nonoverlapping surgical phases). Methods: Four machine learning architectures were developed for segmentation of surgical phases. Models were trained using cataract surgical videos from the BigCat dataset. Main Outcome Measures: Models were evaluated using metrics applied to frame-by-frame output and, uniquely in this work, metrics applied to phase output. Results: The final model, CatStep, a combination of a temporally sensitive model (Inflated 3D Densenet) and a spatially sensitive model (Densenet169), achieved an F1-score of 0.91 and area under the receiver operating characteristic curve of 0.95. Phase-level metrics showed considerable boundary segmentation performance with a median absolute error of phase start and end time of just 0.3 seconds and 0.1 seconds, respectively, a segmental F1-score @70 of 0.94, an oversegmentation score of 0.89, and a segmental edit score of 0.92. Conclusion: This study demonstrates the feasibility of high-performance automated surgical phase identification for cataract surgery and highlights the potential for improved surgical feedback and performance analysis. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Tensor-based Feature Extraction for Pupil Recognition in Cataract Surgery.

Cataract surgery remains the definitive treatment for cataracts, which are a major cause of preventable blindness worldwide. Adequate and stable dilation of the pupil are necessary for the successful performance of cataract surgery. Pupillary instability is a known risk factor for cataract surgery complications, and the accurate segmentation of the pupil from surgical video streams can enable the analysis of intraoperative pupil changes in cataract surgery. However, pupil segmentation performance can suffer due to variations in surgical illumination, obscuration of the pupil with surgical instruments, and hydration of the lens material intraoperatively. To overcome these challenges, we present a novel method called tensor-based pupil feature extraction (TPFE) to improve the accuracy of pupil recognition systems. We analyzed the efficacy of this approach with experiments performed on a dataset of 4,560 intraoperative annotated images from 190 cataract surgeries in human patients. Our results indicate that TPFE can identify features relevant to pupil segmentation and that pupil segmentation with state-of-the-art deep learning models can be significantly improved with the TPFE method.

Ray tracing intraocular lens calculation performance improved by AI-powered postoperative lens position prediction.

AIMS: To assess whether incorporating a machine learning (ML) method for accurate prediction of postoperative anterior chamber depth (ACD) improves cataract surgery refraction prediction performance of a commonly used ray tracing power calculation suite (OKULIX). METHODS AND ANALYSIS: A dataset of 4357 eyes of 4357 patients with cataract was gathered at the Kellogg Eye Center, University of Michigan. A previously developed machine learning (ML)-based method was used to predict the postoperative ACD based on preoperative biometry measured with the Lenstar LS900 optical biometer. Refraction predictions were computed with standard OKULIX postoperative ACD predictions and ML-based predictions of postoperative ACD. The performance of the ray tracing approach with and without ML-based ACD prediction was evaluated using mean absolute error (MAE) and median absolute error (MedAE) in refraction prediction as metrics. RESULTS: Replacing the standard OKULIX postoperative ACD with the ML-predicted ACD resulted in statistically significant reductions in both MAE (1.7% after zeroing mean error) and MedAE (2.1% after zeroing mean error). ML-predicted ACD substantially improved performance in eyes with short and long axial lengths (p<0.01). CONCLUSIONS: Using an ML-powered postoperative ACD prediction method improves the prediction accuracy of the OKULIX ray tracing suite by a clinically small but statistically significant amount, with the greatest effect seen in long eyes.

Evaluation of the Nallasamy formula: a stacking ensemble machine learning method for refraction prediction in cataract surgery.

AIMS: To develop a new intraocular lens power selection method with improved accuracy for general cataract patients receiving Alcon SN60WF lenses. METHODS AND ANALYSIS: A total of 5016 patients (6893 eyes) who underwent cataract surgery at University of Michigan's Kellogg Eye Center and received the Alcon SN60WF lens were included in the study. A machine learning-based method was developed using a training dataset of 4013 patients (5890 eyes), and evaluated on a testing dataset of 1003 patients (1003 eyes). The performance of our method was compared with that of Barrett Universal II, Emmetropia Verifying Optical (EVO), Haigis, Hoffer Q, Holladay 1, PearlDGS and SRK/T. RESULTS: Mean absolute error (MAE) of the Nallasamy formula in the testing dataset was 0.312 Dioptres and the median absolute error (MedAE) was 0.242 D. Performance of existing methods were as follows: Barrett Universal II MAE=0.328 D, MedAE=0.256 D; EVO MAE=0.322 D, MedAE=0.251 D; Haigis MAE=0.363 D, MedAE=0.289 D; Hoffer Q MAE=0.404 D, MedAE=0.331 D; Holladay 1 MAE=0.371 D, MedAE=0.298 D; PearlDGS MAE=0.329 D, MedAE=0.258 D; SRK/T MAE=0.376 D, MedAE=0.300 D. The Nallasamy formula performed significantly better than seven existing methods based on the paired Wilcoxon test with Bonferroni correction (p<0.05). CONCLUSIONS: The Nallasamy formula (available at https://lenscalc.com/) outperformed the seven other formulas studied on overall MAE, MedAE, and percentage of eyes within 0.5 D of prediction. Clinical significance may be primarily at the population level.

Analysis of Cataract Surgery Instrument Identification Performance of Convolutional and Recurrent Neural Network Ensembles Leveraging BigCat.

Purpose: To develop a method for accurate automated real-time identification of instruments in cataract surgery videos. Methods: Cataract surgery videos were collected at University of Michigan's Kellogg Eye Center between 2020 and 2021. Videos were annotated for the presence of instruments to aid in the development, validation, and testing of machine learning (ML) models for multiclass, multilabel instrument identification. Results: A new cataract surgery database, BigCat, was assembled, containing 190 videos with over 3.9 million annotated frames, the largest reported cataract surgery annotation database to date. Using a dense convolutional neural network (CNN) and a recursive averaging method, we were able to achieve a test F1 score of 0.9528 and test area under the receiver operator characteristic curve of 0.9985 for surgical instrument identification. These prove to be state-of-the-art results compared to previous works, while also only using a fraction of the model parameters of the previous architectures. Conclusions: Accurate automated surgical instrument identification is possible with lightweight CNNs and large datasets. Increasingly complex model architecture is not necessary to retain a well-performing model. Recurrent neural network architectures add additional complexity to a model and are unnecessary to attain state-of-the-art performance. Translational Relevance: Instrument identification in the operative field can be used for further applications such as evaluating surgical trainee skill level and developing early warning detection systems for use during surgery.

AI-powered effective lens position prediction improves the accuracy of existing lens formulas.

AIMS: To assess whether incorporating a machine learning (ML) method for accurate prediction of postoperative anterior chamber depth (ACD) improves the refraction prediction performance of existing intraocular lens (IOL) calculation formulas. METHODS: A dataset of 4806 patients with cataract was gathered at the Kellogg Eye Center, University of Michigan, and split into a training set (80% of patients, 5761 eyes) and a testing set (20% of patients, 961 eyes). A previously developed ML-based method was used to predict the postoperative ACD based on preoperative biometry. This ML-based postoperative ACD was integrated into new effective lens position (ELP) predictions using regression models to rescale the ML output for each of four existing formulas (Haigis, Hoffer Q, Holladay and SRK/T). The performance of the formulas with ML-modified ELP was compared using a testing dataset. Performance was measured by the mean absolute error (MAE) in refraction prediction. RESULTS: When the ELP was replaced with a linear combination of the original ELP and the ML-predicted ELP, the MAEs±SD (in Diopters) in the testing set were: 0.356±0.329 for Haigis, 0.352±0.319 for Hoffer Q, 0.371±0.336 for Holladay, and 0.361±0.331 for SRK/T which were significantly lower (p<0.05) than those of the original formulas: 0.373±0.328 for Haigis, 0.408±0.337 for Hoffer Q, 0.384±0.341 for Holladay and 0.394±0.351 for SRK/T. CONCLUSION: Using a more accurately predicted postoperative ACD significantly improves the prediction accuracy of four existing IOL power formulas.

Bilateral Anterior Subcapsular Cataract Development Following Initiation of Enfortumab Vedotin.

Enfortumab vedotin is an antibody-drug conjugate that was recently granted accelerated US Food and Drug Administration approval for the treatment of locally advanced or metastatic urothelial cancer. Early clinical trials identified blurry vision, increased lacrimation and other events associated with dry eye as potential side effects. We report a case of bilateral anterior subcapsular cataract development following initiation of enfortumab vedotin. Enfortumab vedotin is not previously known to cause cataract development or progression and, thus, our patient's presentation may reflect the first report of an undocumented adverse effect of this novel agent.

Descemet membrane endothelial keratoplasty in a patient with iris-fixated intraocular lens and prior radial keratotomy: a case report.

BACKGROUND: Anterior segment surgeries such as cataract surgery, intraocular lens (IOL) repositioning, and radial keratotomy (RK) may hasten endothelial dysfunction, particularly in the context of pre-existing Fuchs dystrophy, necessitating future corneal transplantation. CASE PRESENTATION: A 68-year-old woman with a history of RK with associated irregular astigmatism in both eyes and iris-fixated intraocular lens (IF-IOL) in the left eye presented with six months of decreased vision in the left eye. She was found to have Fuchs dystrophy and underwent DMEK surgery. She had an uncomplicated postoperative course, with uncorrected visual acuity improving to 20/20 three months after surgery. CONCLUSION: To our knowledge, this is the first reported case of a highly successful DMEK surgery in a patient with prior RK and IF-IOL.

Gender differences in refraction prediction error of five formulas for cataract surgery.

OBJECTIVES: To evaluate gender differences in optical biometry measurements and lens power calculations. METHODS: Eight thousand four hundred thirty-one eyes of five thousand five hundred nineteen patients who underwent cataract surgery at University of Michigan's Kellogg Eye Center were included in this retrospective study. Data including age, gender, optical biometry, postoperative refraction, implanted intraocular lens (IOL) power, and IOL formula refraction predictions were gathered and/or calculated utilizing the Sight Outcomes Research Collaborative (SOURCE) database and analyzed. RESULTS: There was a statistical difference between every optical biometry measure between genders. Despite lens constant optimization, mean signed prediction errors (SPEs) of modern IOL formulas differed significantly between genders, with predictions skewed more hyperopic for males and myopic for females for all 5 of the modern IOL formulas tested. Optimization of lens constants by gender significantly decreased prediction error for 2 of the 5 modern IOL formulas tested. CONCLUSIONS: Gender was found to be an independent predictor of refraction prediction error for all 5 formulas studied. Optimization of lens constants by gender can decrease refraction prediction error for certain modern IOL formulas.

Perioperative Prophylactic Antibiotics in 1,250 Orbital Surgeries.

PURPOSE: Intravenous antibiotic prophylaxis is used for many clean-contaminated surgeries or clean surgeries with an implant, but its value for clean orbital surgery has not been determined. This study investigated infection risks and adverse effects related to antibiotics in patients undergoing orbital surgery. METHODS: A prospective, nonrandomized comparative case series of all patients undergoing orbital surgery with participating surgeons between October 1, 2013, and March 1, 2015. Types of surgery, antibiotic regimens, corticosteroid use, antibiotic side effects, and surgical site infections (SSIs) were entered into an electronic database and subsequently analyzed. Cases in which patients received postoperative oral antibiotics were analyzed separately. RESULTS: Of 1,250 consecutive orbital surgeries, 1,225 met inclusion criteria. A total of 1208 patients were included in the primary analysis: 603 received no antibiotic prophylaxis (group A), and 605 received a single dose of intravenous antibiotic (group B). Five patients (0.42%) developed an SSI, 3 in group A and 2 in group B. The difference in SSI rates was not statistically significant between the 2 groups (p = 0.66). Antibiotic prophylaxis, alloplastic implants, paranasal sinus entry, and corticosteroid use were not associated with differences in SSI rates. All SSIs resolved on a single course of oral antibiotics; an implant was removed in 1 case. There were no complications associated with a single dose of intravenous prophylaxis. However, 12% of 17 patients (group C) who received 1 week of oral postoperative prophylactic antibiotics developed antibiotic-related complications (diarrhea, renal injury), yielding a number needed to harm of 8.5. CONCLUSIONS: In this large series, antibiotic prophylaxis does not appear to have reduced the already low incidence of SSI following orbital surgery. Given the detriments of systemic antibiotics, the rarity of infections related to orbital surgery, and the efficacy of treating such infections should they occur, patients undergoing orbital surgery should be educated to the early symptoms of postoperative infection and followed closely, but do not routinely require perioperative antibiotics.

Gradient Boosting Decision Tree Algorithm for the Prediction of Postoperative Intraocular Lens Position in Cataract Surgery.

Purpose: To develop a method for predicting postoperative anterior chamber depth (ACD) in cataract surgery patients based on preoperative biometry, demographics, and intraocular lens (IOL) power. Methods: Patients who underwent cataract surgery and had both preoperative and postoperative biometry measurements were included. Patient demographics and IOL power were collected from the Sight Outcomes Research Collaborative (SOURCE) database. A gradient-boosting decision tree model was developed to predict the postoperative ACD. The mean absolute error (MAE) and median absolute error (MedAE) were used as evaluation metrics. The performance of the proposed method was compared with five existing formulas. Results: In total, 847 patients were assigned randomly in a 4:1 ratio to a training/validation set (678 patients) and a testing set (169 patients). Using preoperative biometry and patient sex as predictors, the presented method achieved an MAE of 0.106 ± 0.098 (SD) on the testing set, and a MedAE of 0.082. MAE was significantly lower than that of the five existing methods (P < 0.01). When keratometry was excluded, our method attained an MAE of 0.123 ± 0.109, and a MedAE of 0.093. When IOL power was used as an additional predictor, our method achieved an MAE of 0.105 ± 0.091 and a MedAE of 0.080. Conclusions: The presented machine learning method achieved greater accuracy than previously reported methods for the prediction of postoperative ACD. Translational Relevance: Increasing accuracy of postoperative ACD prediction with the presented algorithm has the potential to improve refractive outcomes in cataract surgery.

Risk factors for strabismus following glaucoma drainage device implantation for refractory childhood glaucoma.

BACKGROUND: Strabismus is common in children after glaucoma drainage device (GDD) implantation, but the risk factors for postoperative strabismus remain speculative. The purpose of this study was to investigate possible risk factors for strabismus following GDD implantation for refractory childhood glaucoma. METHODS: The medical records of consecutive patients who underwent GDD implantation for refractory childhood glaucoma at Duke Eye Center from 2005 to 2016 were reviewed retrospectively. Pre- and postoperative motility and alignment, best-corrected visual acuity, and demographic and surgical data were extracted from the record for analysis. RESULTS: A total of 81 patients (mean age, 7.9 ± 4.8 years) met inclusion criteria. The most common glaucoma type was glaucoma following cataract surgery (GFCS), and the most common GDD was a Baerveldt 250 mm2 device. Before GDD surgery, 38 patients (47%) had documented strabismus. After GDD implantation, 25 (31%) had new or worsened strabismus, with vertical (16% of new/worsened), horizontal strabismus (exotropia, 48% of new/worsened; esotropia, 12% of new/worsened) and vertical and horizontal (24% of new/worsened) noted. New motility limitation occurred in 32 of 81 (40%) patients. Risk factors including age, type/location/number of GDD, revision, motility limitation, glaucoma type, asymmetric visual acuity, and visual impairment were not significantly associated with new or worsened post-GDD strabismus. CONCLUSIONS: Children with refractory childhood glaucoma are at high risk for strabismus, which increases after GDD implantation; this study identified no clear risk factors for new or worsened post-GDD strabismus.

Hydrogel ocular sealant for clear corneal incisions in cataract surgery.

PURPOSE: To assess the relationship between use of a hydrogel ocular sealant (Resure) to secure clear corneal incisions (CCIs) in cataract surgery and surgeon efficiency, patient symptomatology, and postoperative results. SETTING: Duke University Eye Center, Durham, North Carolina, USA. DESIGN: Retrospective case series. METHODS: A 1:1 matched cohort of hydrogel sealant exposure-discordant eyes of cataract surgery patients was retrospectively generated. Consecutive patients who had bilateral cataract surgery during the study period and in whom the hydrogel sealant was used to secure the CCI in only 1 of the 2 eyes were included in the study. The relationship between use of the hydrogel sealant and surgical time, 1-day postoperative foreign-body sensation, clinically noted corneal edema, and intraocular pressure (IOP) was evaluated. RESULTS: Ninety eyes of 45 patients were included in the study. One day postoperatively, no wound leak was found in any eye; the sealant was noted to be out of place in 2 (4.4%) of 45 cases. No statistically significant difference was found between sealant and non-sealant eyes in total surgical time (P = .16) or in IOP (P = .55), corneal edema (P = 1.00), or foreign-body sensation (P = .38) 1 day postoperatively. CONCLUSION: The hydrogel sealant was not observed to affect duration of surgery or 1-day postoperative IOP, corneal edema, or foreign-body sensation.

Everolimus to treat aggressive retinal astrocytic hamartoma in tuberous sclerosis complex.

Retinal astrocytic hamartomas (RAH) are the most frequent ocular manifestation of tuberous sclerosis complex and are usually indolent, requiring only observation. We report an aggressive RAH subtype in a child unresponsive to anti-VEGF and laser therapy. Treatment with systemic everolimus was well-tolerated and significantly reduced ocular (and nonocular) tumor size and fluid exudation.

Impact of botulinum toxin-A on corneal curvature assessed with Scheimpflug and Placido systems.

PURPOSE: To evaluate the effect of eyelid spasm treatment with botulinum toxin-A (BTX-A) on corneal curvature assessed with Scheimpflug (Pentacam) and Placido (Atlas) systems. SETTING: Federal University of São Paulo, São Paulo, Brazil. DESIGN: Prospective interventional case series. METHODS: Patients with hemifacial spasm were evaluated. Steep keratometry (K) and corneal astigmatism (magnitudes and treatment-induced astigmatism) obtained with the Placido and Scheimpflug systems were evaluated before BTX-A application and after 15 days and 2, 3, and 4 months. RESULTS: The study evaluated 48 eyes (24 affected and 24 normal contralateral eyes) of 24 patients. The mean steep K and astigmatism values obtained with the Placido system on the affected side before treatment were 46.91 diopters (D) ± 3.57 (SD) and 2.63 ± 2.46 D, respectively. A significant decrease in steep K (45.14 ± 1.20 D) and astigmatism (1.01 ± 0.58 D) was observed 2 months (P = .003 and P = .0004, respectively) and 3 months (45.64 ± 1.77 D and 1.36 ± 1.31 D, respectively) (P = .03 and P = .01, respectively) after treatment. The Scheimpflug system did not show significant changes in steep K measurements during the 4-month period. The mean astigmatism in the affected eye before treatment was 1.27 ± 0.88 D. A significant reduction was observed at 15 days (1.16 ± 1.16 D) and at 4 months (0.91 ± 0.59 D) (P = .02 and P = .03, respectively). CONCLUSION: The Placido system was more sensitive in detecting a temporary reduction in corneal curvature after eyelid-spasm treatment. FINANCIAL DISCLOSURE: None of the authors has a financial or proprietary interest in any material or method mentioned.

Multinational Comparison of Prophylactic Antibiotic Use for Eyelid Surgery.

IMPORTANCE: Antibiotic stewardship is important in controlling resistance, adverse reactions, and cost. The literature regarding antibiotic use for eyelid surgery is lacking. OBJECTIVES: To determine standard care and assess factors influencing antibiotic prescribing practices for eyelid surgery. DESIGN, SETTING, AND PARTICIPANTS: A survey study was conducted from February 2, 2014, to March 24, 2014. The survey was distributed to 2397 oculoplastic surgeons in private and academic oculoplastic surgery practices in 43 countries. All surgeons were members of ophthalmic plastic and reconstructive surgery societies. Data were analyzed by geographic location. Linear regression was performed to quantify contributions to rates of prescribing postoperative antibiotics for routine eyelid surgical procedures. MAIN OUTCOMES AND MEASURES: Rates of prescribing prophylactic intravenous, oral, and topical antibiotics as well as factors that influence surgeons' prescribing practices. RESULTS: A total of 782 responses were received from 2397 surgeons (average response rate, 36.7%; 2.5% margin of error) from 43 countries. Topical antibiotic use was common in all regions (85.2%). Perioperative intravenous antibiotic use was uncommon in all regions (13.5%). Geographic location was the greatest predictor of antibiotic prescribing practices (range, 2.9% in the United Kingdom to 86.7% in India; mean, 24%). Within Europe, Italy had the highest rate of antibiotic prescriptions for eyelid surgery (41.7%) and the United Kingdom had the lowest rate (2.9%.) In South America, Venezuela had the highest rate of antibiotic prescriptions for eyelid surgery (83.3%) and Chile had the lowest rate (0%). The practice locations that were associated with routinely prescribing postoperative oral antibiotics were India (odds ratio [OR], 15.83; 95% CI, 4.85-51.68; P < .001), Venezuela (OR, 13.47; 95% CI, 1.43-127.19; P = .02), and Southeast Asia (OR, 2.80; 95% CI, 1.15-6.84; P = .02). Conversely, practice location in the United Kingdom (OR, 0.048; 95% CI, 0.0063-0.37; P = .004), Australia and New Zealand (OR, 0.15; 95% CI, 0.033-0.67; P = .01), and the United States and Canada (OR, 0.41; 95% CI, 0.23-0.72; P = .002) were associated with decreased rates of postoperative oral antibiotic use. Surgeons' concern for allergic reactions was associated with decreased rates of prescribing antibiotics (OR, 0.34; 95% CI, 0.23-0.49; P < .001), while surgeons' concern for infection was associated with increased rates of prescribing antibiotics (OR 1.80; 95% CI, 1.45-2.23; P < .001). CONCLUSIONS AND RELEVANCE: These results from members of ophthalmic plastic and reconstructive surgery societies confirm that antibiotic prescribing practices for routine eyelid surgical procedures vary widely throughout the world. No standard of care has been established that would require the routine use of postoperative prophylactic antibiotics following eyelid surgery.