Comparison of automated corneal endothelial cell analysis in healthy and postoperative eyes with phakic intraocular lens: a cross-sectional study and literature review.

PURPOSE: Measurement of corneal endothelial cells is critical for postoperative evaluation of phakic intraocular lens (pIOL) surgery. However, inter-instrument differences in corneal endothelial cell density (ECD) after pIOL implantation have not yet been reported. This study aimed to compare automated corneal endothelial cell analysis between CellChek-20 (Konan Medical, Hyogo, Japan) and EM-4000 (Tomey, Nagoya, Japan) in healthy and postoperative eyes with pIOL. METHODS: We retrospectively analyzed 154 healthy and 236 postoperative eyes after pIOL surgery. Endothelial cell measurements were performed using CellChek-20 and EM-4000 with autofocusing and automated image analysis. ECD, percentage of hexagonal cells (HEX), coefficient of variation in cell size (CoV), and central corneal thickness (CCT) were compared between the two devices. RESULTS: The ECDs of the two devices were highly correlated in both healthy (Spearman's correlation coefficient [r] = 0.805; p < 0.001) and postoperative (r = 0.901; p < 0.001) groups. ECD from CellChek-20 was higher than EM-4000 in both healthy (mean difference = 228.9 cells/mm2; p < 0.001) and postoperative (mean difference = 115.6 cells/mm2; p < 0.001) groups. The CCT values also showed a strong correlation in healthy eyes (r = 0.974; p < 0.001) and in postoperative eyes (r = 0.936; p < 0.001); however, significant inter-instrument differences were observed. HEX and CV showed significant differences and relatively weak correlations (r < 0.7) between the two devices in both healthy and postoperative groups. CONCLUSION: The ECD values between the two instruments were correlated, but that of the CellChek-20 was significantly higher than that of the EM-4000 in both healthy and postoperative eyes after pIOL surgery. Most previous studies have also shown that the Konan software overestimated the ECD compared to other products in automatic measurement mode. The possibility of measurement bias should be considered when replacing equipment used for corneal endothelial cell measurements.

Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography.

BACKGROUND: The epiretinal membrane (ERM) is a common retinal disorder characterized by abnormal fibrocellular tissue at the vitreomacular interface. Most patients with ERM are asymptomatic at early stages. Therefore, screening for ERM will become increasingly important. Despite the high prevalence of ERM, few deep learning studies have investigated ERM detection in the color fundus photography (CFP) domain. In this study, we built a generative model to enhance ERM detection performance in the CFP. METHODS: This deep learning study retrospectively collected 302 ERM and 1,250 healthy CFP data points from a healthcare center. The generative model using StyleGAN2 was trained using single-center data. EfficientNetB0 with StyleGAN2-based augmentation was validated using independent internal single-center data and external datasets. We randomly assigned healthcare center data to the development (80%) and internal validation (20%) datasets. Data from two publicly accessible sources were used as external validation datasets. RESULTS: StyleGAN2 facilitated realistic CFP synthesis with the characteristic cellophane reflex features of the ERM. The proposed method with StyleGAN2-based augmentation outperformed the typical transfer learning without a generative adversarial network. The proposed model achieved an area under the receiver operating characteristic (AUC) curve of 0.926 for internal validation. AUCs of 0.951 and 0.914 were obtained for the two external validation datasets. Compared with the deep learning model without augmentation, StyleGAN2-based augmentation improved the detection performance and contributed to the focus on the location of the ERM. CONCLUSIONS: We proposed an ERM detection model by synthesizing realistic CFP images with the pathological features of ERM through generative deep learning. We believe that our deep learning framework will help achieve a more accurate detection of ERM in a limited data setting.

Association between metformin use and the risk of developing open-angle glaucoma among patients with diabetes: a retrospective cohort study and meta-analysis.

PURPOSE: Recent studies examining the neuroprotective effects of metformin on open-angle glaucoma (OAG) have failed to provide consistent results. In this study, we investigated the association between metformin use and OAG. METHODS: Data were obtained from a sample cohort of the Korean National Health Insurance database. Patients diagnosed with type-2 diabetes (T2DM) between 2004 and 2013 were included. We performed propensity score-matched analysis in a matched cohort (N = 20,646). The risk of the newly developed OAG was estimated using a Cox proportional hazards model. Including the present study, the meta-analysis included five studies to calculate the pooled risk for OAG based on metformin use. RESULTS: In the adjusted model, the analysis revealed no statistical association between metformin use and OAG incidence (hazard ratio [HR] 1.05; 95% confidence interval [CI] 0.79-1.40; P = 0.738). The highest tercile of metformin use demonstrated no statistical significance (HR 0.93 [95% CI 0.63-1.37]; P = 0.703). No significant dose-dependent association was observed between the cumulative dose and incidence of OAG (P-value for trend = 0.336). In a meta-analysis of four published articles and the present study, the common-effects and random-effects models indicated conflicting results in terms of significance. The random effects model demonstrated no significant association (pooled risk ratio 0.53; 95% CI 0.24-1.19; P = 0.123). CONCLUSION: We found no significant association between metformin use and OAG incidence in patients with T2DM in this population-based cohort study and meta-analysis. Further studies are needed to investigate the association between metformin use and the risk of OAG among patients with T2DM.

Development of an implantable collamer lens sizing model: a retrospective study using ANTERION swept-source optical coherence tomography and a literature review.

BACKGROUND: Optimal sizing for phakic intraocular lens (EVO-ICL with KS-AquaPort) implantation plays an important role in preventing postoperative complications. We aimed to formulate optimal lens sizing using ocular biometric parameters measured with a Heidelberg anterior segment optical coherence tomography (AS-OCT) device. METHODS: We retrospectively analyzed 892 eyes of 471 healthy subjects treated with an intraocular collamer lens (ICL) and assigned them to either the development (80%) or validation (20%) set. We built vault prediction models using the development set via classic linear regression methods as well as partial least squares and least absolute shrinkage and selection operator (LASSO) regression techniques. We evaluated prediction abilities based on the Bayesian information criterion (BIC) to select the best prediction model. The performance was measured using Pearson's correlation coefficient and the mean squared error (MAE) between the achieved and predicted results. RESULTS: Measurements of aqueous depth (AQD), anterior chamber volume, anterior chamber angle (ACA) distance, spur-to-spur distance, crystalline lens thickness (LT), and white-to-white distance from ANTERION were highly associated with the ICL vault. The LASSO model using the AQD, ACA distance, and LT showed the best BIC results for postoperative ICL vault prediction. In the validation dataset, the LASSO model showed the strongest correlation (r = 0.582, P < 0.001) and the lowest MAE (104.7 µm). CONCLUSION: This is the first study to develop a postoperative ICL vault prediction and lens-sizing model based on the ANTERION. As the measurements from ANTERION and other AS-OCT devices are not interchangeable, ANTERION may be used for optimal ICL sizing using our formula. Because our model was developed based on the East Asian population, further studies are needed to explore the role of this prediction model in different populations.

Focal Segmental Glomerulosclerosis Followed by Acute Hepatitis A Infection: Case Report.

Background and Objectives: Chronic viral hepatitis such as hepatitis B or hepatitis C is frequently related to nephropathies, yet acute hepatitis A virus (HAV) infection is an exception. Materials and Methods: A 43-year-old male presented with jaundice accompanied by nausea and vomiting. The patient was diagnosed with acute HAV infection. Although the liver function improved after conservative treatment, various symptoms such as proteinuria, hypoalbuminemia, generalized edema and pleural effusion persisted. Due to nephrotic syndrome, the patient was referred to the clinic of the nephrology department and a renal biopsy was performed. Results: The result of the renal biopsy was focal segmental glomerulosclerosis (FSGS) based on histology, electron microscopy and immunohistochemistry. Therefore, based on the clinical history and biopsy results, the patient was diagnosed as having FSGS aggravated by acute HAV infection. Proteinuria, hypoalbuminemia and generalized edema were improved after prednisolone treatment. Conclusions: Although less common, acute HAV infection can also present with an extrahepatic manifestation, for example, FSGS. Hence, clinical attention is required if proteinuria or hypoalbuminemia persists in patients with acute HAV infection.

Machine learning predicting myopic regression after corneal refractive surgery using preoperative data and fundus photography.

PURPOSE: Myopic regression after surgery is the most common long-term complication of refractive surgery, but it is difficult to identify myopic regression without long-term observation. This study aimed to develop machine learning models to identify high-risk patients for refractive regression based on preoperative data and fundus photography. METHODS: This retrospective study assigned subjects to the training (n = 1606 eyes) and validation (n = 403 eyes) datasets with chronological data splitting. Machine learning models with ResNet50 (for image analysis) and XGBoost (for integration of all variables and fundus photography) were developed based on subjects who underwent corneal refractive surgery. The primary outcome was the predictive performance for the presence of myopic regression at 4 years of follow-up examination postoperatively. RESULTS: By integrating all factors and fundus photography, the final combined machine learning model showed good performance to predict myopic regression of more than 0.5 D (area under the receiver operating characteristic curve [ROC-AUC], 0.753; 95% confidence interval [CI], 0.710-0.793). The performance of the final model was better than the single ResNet50 model only using fundus photography (ROC-AUC, 0.673; 95% CI, 0.627-0.716). The top-five most important input features were fundus photography, preoperative anterior chamber depth, planned ablation thickness, age, and preoperative central corneal thickness. CONCLUSION: Our machine learning algorithm provides an efficient strategy to identify high-risk patients with myopic regression without additional labor, cost, and time. Surgeons might benefit from preoperative risk assessment of myopic regression, patient counseling before surgery, and surgical option decisions.

Prediction of Heart Function and Volume Status in End-Stage Kidney Disease Patients through N-Terminal Pro-Brain Natriuretic Peptide.

Background and Objectives: N-terminal pro-brain natriuretic peptide (NT-proBNP) is a biomarker used to predict heart failure and evaluate volume status in hemodialysis (HD) patients. However, it is difficult to determine the cutoff value for NT-proBNP in HD patients. In this study, we analyzed whether NT-proBNP helps predict heart function and volume status in HD patients. Materials and Methods: This prospective observational study enrolled 96 end-stage kidney disease patients with HD. All patients underwent echocardiography and bioimpedance spectroscopy (BIS) after an HD session. Overhydration (OH) was measured by BIS. Laboratory data were obtained preHD, while serum NT-proBNP was measured after HD. Interventions for blood pressure control and dry weight control were performed, and NT-proBNP was re-assessed after a month. Results: There was an inverse correlation between NT-proBNP and ejection fraction (EF) (ß = -0.34, p = 0.001). OH (ß = 0.331, p = 0.001) and diastolic dysfunction (ß = 0.226, p = 0.027) were associated with elevated NT-proBNP. In a subgroup analysis of diastolic dysfunction grade, NT-proBNP increased according to dysfunction grade (normal, 4177 pg/mL [2637-10,391]; grade 1, 9736 pg/mL [5471-21,110]; and grades 2-3, 26,237 pg/mL [16,975-49,465]). NT-proBNP showed a tendency toward a decrease in the 'reduced dry weight' group and toward an increase in the 'increased dry weight' group compared to the control group (ΔNT-proBNP, -210 pg/mL [-12,899 to 3142], p = 0.104; 1575 pg/mL [-113 to 6439], p = 0.118). Conclusions: We confirmed that NT-proBNP is associated with volume status as well as heart function in HD patients.

Artificial intelligence-based nomogram for small-incision lenticule extraction.

BACKGROUND: Small-incision lenticule extraction (SMILE) is a surgical procedure for the refractive correction of myopia and astigmatism, which has been reported as safe and effective. However, over- and under-correction still occur after SMILE. The necessity of nomograms is emphasized to achieve optimal refractive results. Ophthalmologists diagnose nomograms by analyzing the preoperative refractive data with their individual knowledge which they accumulate over years of experience. Our aim was to predict the nomograms of sphere, cylinder, and astigmatism axis for SMILE accurately by applying machine learning algorithm. METHODS: We retrospectively analyzed the data of 3,034 eyes composed of four categorical features and 28 numerical features selected from 46 features. The multiple linear regression, decision tree, AdaBoost, XGBoost, and multi-layer perceptron were employed in developing the nomogram models for sphere, cylinder, and astigmatism axis. The scores of the root-mean-square error (RMSE) and accuracy were evaluated and compared. Subsequently, the feature importance of the best models was calculated. RESULTS: AdaBoost achieved the highest performance with RMSE of 0.1378, 0.1166, and 5.17 for the sphere, cylinder, and astigmatism axis, respectively. The accuracies of which error below 0.25 D for the sphere and cylinder nomograms and 25° for the astigmatism axis nomograms were 0.969, 0.976, and 0.994, respectively. The feature with the highest importance was preoperative manifest refraction for all the cases of nomograms. For the sphere and cylinder nomograms, the following highly important feature was the surgeon. CONCLUSIONS: Among the diverse machine learning algorithms, AdaBoost exhibited the highest performance in the prediction of the sphere, cylinder, and astigmatism axis nomograms for SMILE. The study proved the feasibility of applying artificial intelligence (AI) to nomograms for SMILE. Also, it may enhance the quality of the surgical result of SMILE by providing assistance in nomograms and preventing the misdiagnosis in nomograms.

Validation of an international prediction model including the Oxford classification in Korean patients with IgA nephropathy.

BACKGROUND: Recently, a new international risk prediction model including the Oxford classification was published which was validated in a large multi-ethnic cohort. Therefore, we aimed to validate this risk prediction model in Korean patients with IgA nephropathy. METHODS: This retrospective cohort study was conducted with 545 patients who diagnosed IgA nephropathy with renal biopsy in three medical centers. The primary outcome was defined as a reduction in estimated glomerular filtration rate (eGFR) of >50% or incident end-stage renal disease (ESRD). Continuous net reclassification improvement (cNRI) and integrated discrimination improvement (IDI) were used to validate models. RESULTS: During the median 3.6 years of follow-up period, 53 (9.7%) renal events occurred. In multivariable Cox regression model, M1 (hazard ratio [HR], 2.22; 95% confidence interval [CI], 1.02-4.82; p = .043), T1 (HR, 2.98; 95% CI, 1.39-6.39; p = .005) and T2 (HR, 4.80; 95% CI, 2.06-11.18; p < .001) lesions were associated with increased risk of renal outcome. When applied the international prediction model, the area under curve (AUC) for 5-year risk of renal outcome was 0.69, which was lower than previous validation and internally derived models. Moreover, cNRI and IDI analyses showed that discrimination and reclassification performance of the international model was inferior to the internally derived models. CONCLUSION: The international risk prediction model for IgA nephropathy showed not as good performance in Korean patients as previous validation in other ethnic group. Further validation of risk prediction model is needed for Korean patients with IgA nephropathy.

Differences in Perspectives of Medical Device Adverse Events: Observational Results in Training Program Using Virtual Cases.

BACKGROUND: Medical device adverse event reporting is an essential activity for mitigating device-related risks. Reporting of adverse events can be done by anyone like healthcare workers, patients, and others. However, for an individual to determine the reporting, he or she should recognize the current situation as an adverse event. The objective of this report is to share observed individual differences in the perception of a medical device adverse event, which may affect the judgment and the reporting of adverse events. METHODS: We trained twenty-three participants from twelve Asia-Pacific Economic Cooperation (APEC) member economies about international guidelines for medical device vigilance. We developed and used six virtual cases and six questions. We divided participants into six groups and compared their opinions. We also surveyed the country's opinion to investigate the beginning point of 'patient use'. The phases of 'patient use' are divided into: 1) inspecting, 2) preparing, and 3) applying medical device. RESULTS: As for the question on the beginning point of 'patient use,' 28.6%, 35.7%, and 35.7% of participants provided answers regarding the first, second, and third phases, respectively. In training for applying international guidelines to virtual cases, only one of the six questions reached a consensus between the two groups in all six virtual cases. For the other five questions, different judgments were given in at least two groups. CONCLUSION: From training courses using virtual cases, we found that there was no consensus on 'patient use' point of view of medical devices. There was a significant difference in applying definitions of adverse events written in guidelines regarding the medical device associated incidents. Our results point out that international harmonization effort is needed not only to harmonize differences in regulations between countries but also to overcome diversity in perspectives existing at the site of medical device use.

Overhydration Negatively Affects Quality of Life in Peritoneal Dialysis Patients: Evidence from a Prospective Observational Study.

UNLABELLED: Backgound: This study evaluated whether the hydration status affected health-related quality of life (HRQOL) during 12 months in peritoneal dialysis (PD) patients. METHODS: The hydration status and the HRQOL were examined at baseline and after 12 months using a bioimpedance spectroscopy and Kidney Disease Quality of Life-Short Form, respectively in PD patients. Four hundred eighty-one patients were included and divided according to the baseline overhydration (OH) value; normohydration group (NH group, -2L≤ OH ≤+2L, n=266) and overhydration group (OH group, OH >+2L, n=215). Baseline HRQOL scores were compared between the two groups. The subjects were re-stratified into quartiles according to the OH difference (OH value at baseline - OH value at 12 months; <-1, -1 - -0.1, -0.1 - +1, and ≥+1L). The relations of OH difference with HRQOL scores at 12 months and the association of OH difference with the HRQOL score difference (HRQOL score at baseline - HRQOL score at 12 months) were assessed. RESULTS: The OH group showed significantly lower baseline physical and mental health scores (PCS and MCS), and kidney disease component scores (KDCS) compared with the NH group (all, P<0.01). At 12 months, the adjusted PCS, MCS, and KDCS significantly increased as the OH difference quartiles increased (P<0.001, P=0.002, P<0.001, respectively). In multivariate analysis, the OH difference was independently associated with higher PCS (ß = 2.04, P< .001), MCS (ß=1.02, P=0.002), and KDCS (ß=1.06, P<0.001) at 12 months. The OH difference was independently associated with the PCS difference (ß = -1.81, P<0.001), MCS difference (ß=-0.92, P=0.01), and KDCS difference (ß=-0.90, P=0.001). CONCLUSION: The hydration status was associated with HRQOL and increased hydration status negatively affected HRQOL after 12 months in PD patients.

Factors Affecting the Separation Performance in Ionic Liquid/Cu Nanocomposite Membranes for Facilitated CO2 Transport.

Based on data reported for facilitated CO2 transport membranes, we analyzed the factors that would enhance their performance by utilizing an ionic liquid (IL)/Cu nanoparticle (NP) composite. The previously reported ILs were BMIM NO3, BMIM BF4, MOIM BF4, and HMIM NO3. Compared with neat ILs, the selectivity of the ILs/Cu increased with the enhancement of permeance. We investigated the effect of the percentage of free ions in ILs and the size of the Cu NPs on CO2 separation performance. In addition, we found that the viscosity and steric effect of ILs played an important role in enhancing separation performance. In this regard, CO2 easily dissolved in the low-viscosity ILs, and their low steric effect allowed the ILs to readily interact with the surface of the Cu NPs.

Could mean platelet volume be a promising biomarker of progression of chronic kidney disease?

The mean platelet volume (MPV), a readily available indicator of platelet activation and function, is a useful predictive and prognostic biomarker of cardiovascular and cerebrovascular disease (CVD). It is associated with a variety of prothrombotic and proinflammatory diseases. Larger platelets are more likely to aggregate and release greater quantities of adhesive molecules. MPV has seldom been investigated in patients with chronic kidney disease (CKD). This study aimed to investigate the relationship between MPV levels and the glomerular filtration rate (GFR) in patients with CKD. We reviewed the medical records of patients with CKD who visited the nephrology outpatient clinics of Soonchunhyang University Bucheon Hospital between January 2010 and May 2013. A total of 553 patients were included in the present retrospective study. According to the estimated GFR (eGFR) calculated by the abbreviated the Modification of Diet in Renal Disease (MDRD) equation, the patients were allocated to Group 1 (GFR, 60-89 ml/minute/1.73 m(2); n = 64), Group 2 (GFR, 30-59 ml/minute/1.73 m(2); n = 268), Group 3 (GFR, 15-29 ml/minute/1.73 m(2); n = 147), or Group 4 (GFR, <15 ml/minute/1.73 m(2) and non-dialysis; n = 74). Data were analyzed by Student's t-test, the chi-squared test, Pearson's correlation coefficient (r), Tukey's honestly significant difference (HSD) test, and one-way analysis of covariance. The MPV values had a negative correlation with eGFR in patients with CKD (Pearson's correlation coefficient = -0.553, p < 0.001). The mean MPV values in Groups 1-4 were 9.81 ± 0.13 fl, 10.34 ± 0.08 fl, 10.86 ± 0.09 fl, and 11.19 ± 0.11 fl, respectively (p < 0.001). Multiple comparisons of MPV values in the four groups by Tukey's HSD test showed statistically significant intergroup differences, with all p values <0.001. Platelet counts and PDW decreased along with eGFR, and there were no significant differences with respect to plateletcrit. Patients with prevalent coronary artery disease (CAD) or CVD had higher MPVs than did those without CAD or CVD. MPV was significantly increased with progression of CKD. MPV may be a useful indicator of increased risks of CAD or CVD in patients with CKD.

Efficacy of hemocontrol biofeedback system in intradialytic hypotension-prone hemodialysis patients.

We conducted a study to determine whether the hemocontrol biofeedback system (HBS) can improve intradialytic hypotension (IDH) in hypotension-prone hemodialysis (HD) patients compared with conventional HD. In this multicenter prospective crossover study, 60 hypotension-prone patients were serially treated by conventional HD for 8 weeks (period A), by HD with hemoscan blood volume monitoring for 2 weeks (period B0), and by HBS HD for 8 weeks (period B1). The number of sessions complicated by symptomatic IDH during 24 HD sessions (14.9 ± 5.8 sessions, 62.1% in period A vs 9.2 ± 7.2 sessions, 38.4% in period B1, P<0.001) and the number of IDH-related nursing interventions in a session (0.96 ± 0.66 in period A vs 0.56 ± 0.54 in period B1, P<0.001) significantly decreased in period B1 than in period A. Recovery time from fatigue after dialysis was significantly shorter in period B1 than in period A. The patients with higher post-dialysis blood pressure, lower difference between pre- and post-dialysis blood pressure, less frequent IDH, and higher pre- and post-dialysis body weight in period A responded better to HBS in period B1 in regard to the reduction of IDH. In conclusion, HBS may improve the patient tolerability to HD by reducing the IDH frequency and promoting faster recovery from fatigue after dialysis.

Novel polymer composites from waste ethylene-propylene-diene-monomer rubber by supercritical CO2 foaming technology.

In this study, a composite has been prepared by mixing waste rubber, such as ethylene-propylene-diene-monomer and low-density poly ethylene foaming, with supercritical carbon dioxide. In order to optimise the foaming process of the waste ethylene-propylene-diene-monomer-low-density poly ethylene composite, the variations of pressure and temperature on the foamed Microcell formation were studied. As indicated in scanning electron microscope photographs, the most uniform microcellular pattern was found at 200 bar and 100 °C using 30% by weight of waste ethylene-propylene-diene-monomer. Carbon dioxide could not be dissolved uniformly during foaming owing to extensive cross-linking of the waste ethylene-propylene-diene-monomer used for the composite. As a result the presence of un-uniform microcells after foaming were observed in the composite matrix to impart inferior mechanical properties of the composite. This problem was solved with uniform foaming by increasing the cross-link density of low-density poly ethylene using 1.5 parts per hundred dicumyl peroxide that enhances composite tensile and compressive strength up to 57% and 15%, respectively. The composite has the potential to be used as a foaming mat for artificial turf.

Limitations of NHIC claim code-based surveillance and the necessity of UDI implementation in Korea.

The E-Health Big Data Evidence Innovation Network (FeederNet) in Korea, based on the observational medical outcomes partnership (OMOP) common data model (CDM), had 72.3% participation from tertiary hospitals handling severe diseases as of October 2022. While this contributes to the activation of multi-institutional research, concerns about the comprehensiveness of device data persist due to the adoption of national health insurance corporation (NHIC) claim codes as device identifiers in the medical device field. This study critically evaluated the effectiveness and compatibility of NHIC claim codes and unique device identifier (UDI) within FeederNet to identify the optimal identifier for efficient Post-market surveillance (PMS). Specifically, this study addressed three main questions: (1) the number of UDIs classified as NHIC-covered items, (2) the number of UDIs included in each NHIC claim code, and (3) the number of NHIC claim codes each UDI covers. Among the 1,979,655 UDIs registered domestically, only 36.02% (712,983) were classified as covered by National Health Insurance. NHIC-covered medical devices were limited to categories (A) medical devices, (B) medical supplies, and (C) dental materials, excluding most software and in vitro diagnostics (IVD). Multiple UDIs could be registered under a single NHIC claim code, and a single UDI could be registered under multiple NHIC claim codes. Only 32.62% (13,756/42,171) of NHIC claim codes had registered UDIs, with an average of 53 UDIs per claim code. Of the UDIs listed as NHIC covered, 92.39% (659,046/713,341) had one claim code, while 7.25% (51,652) had multiple claim codes. Additionally, 2643 UDIs were listed as NHIC covered but had no registered claim codes. Due to this complex relationship, NHIC claim code-based PMS may pool safe and unsafe models or disperse problematic models across multiple claim codes, leading to a lower problem rate or insignificant differences between claim codes, thus reducing signal detection sensitivity compared to UDI-based PMS. In conclusion, NHIC claim code-based PMS has limitations in granularity and signal detection sensitivity, necessitating the adoption of UDI-based PMS to address these issues. The UDI system can enhance the accuracy of medical device identification and tracking, playing a crucial role in generating real-world evidence (RWE) by integrating data from various sources. Future research should explore specific strategies for integrating and utilizing UDI with NHIC claim codes, contributing to the implementation of a more reliable and comprehensive PMS in Korea's healthcare system.

Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era.

Recently, fundus photography (FP) is being increasingly used. Corneal curvature is an essential factor in refractive errors and is associated with several pathological corneal conditions. As FP-based examination systems have already been widely distributed, it would be helpful for telemedicine to extract information such as corneal curvature using FP. This study aims to develop a deep learning model based on FP for corneal curvature prediction by categorizing corneas into steep, regular, and flat groups. The EfficientNetB0 architecture with transfer learning was used to learn FP patterns to predict flat, regular, and steep corneas. In validation, the model achieved a multiclass accuracy of 0.727, a Matthews correlation coefficient of 0.519, and an unweighted Cohen's κ of 0.590. The areas under the receiver operating characteristic curves for binary prediction of flat and steep corneas were 0.863 and 0.848, respectively. The optic nerve and its peripheral areas were the main focus of the model. The developed algorithm shows that FP can potentially be used as an imaging modality to estimate corneal curvature in the post-COVID-19 era, whereby patients may benefit from the detection of abnormal corneal curvatures using FP in the telemedicine setting.

Comparison of early visual outcomes after SMILE using VISUMAX 800 and VISUMAX 500 for myopia: a retrospective matched case-control study.

VISUMAX 800 was introduced to improve the patient experience and clinical outcomes of small incision lenticule extraction (SMILE). This was a retrospective, matched, and case-control study (1:2) controlled for preoperative central corneal thickness and refractive error that compared early refractive and visual outcomes after SMILE using VISUMAX 800 and VISUMAX 500 to treat myopia. We included 50 eyes that underwent the VISUMAX 800 SMILE and 100 eyes that underwent the VISUMAX 500 SMILE. SMILE using VISUMAX 800 was performed using the CentraLign aid for vertex centration. Cyclotorsion was controlled by an OcuLign assistant in the VISUMAX 800 group after corneal marking. Corneal higher-order aberrations (HOAs) were evaluated using a Pentacam 1 month after surgery. No differences were observed in the pre- and post-operative refractive and visual outcomes at 1 day, 1 month, and 6 months after surgery. VISUMAX 800 induced less total HOAs than VISUMAX 500 (P = 0.036). No statistically significant differences were observed in the amounts of induced spherical aberrations or vertical and horizontal comas. No differences were observed in the 1 month and 6 months refractive and visual outcomes between two SMILE procedures, except for VISUMAX 800, which resulted in lower postoperative total HOAs than VISUMAX 500.

Association between cataract and fatty liver diseases from a nationwide cross-sectional study in South Korea.

This study examined the link between fatty liver disease (FLD) and cataracts, as previous research has suggested that FLD may contribute to metabolic syndrome, systemic inflammation, and potentially cataracts. We studied a nationwide cross-sectional cohort of the Fifth Korean National Health and Nutrition Examination Survey 2010-2011. FLD was defined as nonalcoholic FLD (NAFLD) and metabolic dysfunction-associated FLD (MAFLD). Multinomial logistic regression was utilized to investigate the relationship between cataracts and FLD after adjustment for potential confounders. Participants with cataracts had higher liver fibrosis scores, including the NAFLD fibrosis score (NFS; P < 0.001), fibrosis-4 index (FIB4; P < 0.001), and fatty liver index (FLI; P = 0.001). NAFLD was not associated with a higher odds ratio (OR) for cataracts in the fully adjusted model (OR = 1.23, P = 0.058). MAFLD was significantly associated with a higher OR (OR = 1.34, P = 0.006). After adjusting for all factors, the severity of FLD was linked to an increased risk of cataracts, with significant linear trends (P values for linear trends of NFS, FIB4, and FLI < 0.05). After adjusting for well-known cataract risk factors, MAFLD was significantly associated with cataracts. Our analysis suggests that FLD may serve as an independent risk factor for cataracts.

Efficacy and cost-effectiveness of darbepoetin alfa once every 4 weeks versus continuous erythropoietin receptor activator once every 4 weeks for anemia correction in patients with chronic kidney disease not on dialysis.

BACKGROUND: For anemia management in patients with chronic kidney disease not on dialysis, darbepoetin alfa (DA), which has a shorter half-life but is more inexpensive than continuous erythropoietin receptor activator (CERA), is preferred in Korea. This study evaluated the efficacy, safety, and cost-effectiveness of once-in-4-weeks DA compared with once-in-4-weeks CERA in patients with chronic kidney disease not on dialysis. METHODS: In this randomized, prospective, non-inferiority study, 40 erythropoiesis-stimulating agent-naïve patients with chronic kidney disease not on dialysis were randomized 1:1 to the DA group and CERA group. They received the study drug once in 4 weeks during 10- or 12-week correction period and 24-week efficacy evaluation period. The primary outcomes were the mean difference in the changes in hemoglobin levels between baseline and efficacy evaluation period and hemoglobin response rates during the correction period. The secondary outcomes included differences in adverse events and costs. RESULTS: DA was non-inferior to CERA for anemia correction; the mean difference in the change in hemoglobin levels between the groups was -0.070 g/dL (95% confidence interval, -0.730 to 0.590 g/dL). Hemoglobin response rates were 100% with DA and 94.1% with CERA. Adverse events were comparable. The mean cost of DA was approximately one-third that of CERA (34,100 ± 7,600 Korean won/4 weeks vs. 115,500 ± 23,600 Korean won/4 weeks; p < 0.001). CONCLUSION: Once-in-4-weeks DA safely corrects anemia in erythropoiesis-stimulating agent-naïve patients with chronic kidney disease not on dialysis and is more cost-effective than once-in-4-weeks CERA.