Real-Life Data in the Treatment of Neovascular Age-Related Macular Degeneration: Results from the Imaculaweb Registry Evaluated in a Single Italian Medical Retina Center.

INTRODUCTION: The aim of this study is to assess and compare the long-term clinical efficacy of anti-VEGF drugs using the Imaculaweb registry. METHODS: In this observational study based on the Imaculaweb registry, outcome measures were the number of injections, the change in mean visual acuity (VA) and central macular thickness (CMT), and the time between diagnosis and the first injection. RESULTS: In total, 126 eyes of 109 patients were included in the study. The mean VA was 49.4 ± 21.4, 54.1 ± 22.2, 51.6 ± 24.9, and 48.3 ± 25.7 letters at baseline and at the 1-, 2-, and 3-year follow-ups, respectively. Significant VA increases (p = 0.0002 for the first year and p = 0.045 for the second year) were documented at years 1 and 2 but not at year 3 (p = 0.8). The mean number of injections was 5.2, 2.6, and 2.3 at the 1-, 2-, and 3-year follow-ups, respectively. In the first year, 30% of the patients received at least 7 injections, while only 6.4% received <3 injections. CMT decreased significantly during the overall follow-up period, and intra- and subretinal fluid decreased (p < 0.0001). CONCLUSION: Imaculaweb turned out to be an effective tool to collect and share clinical data as well as to monitor patient outcome.

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach.

BACKGROUND: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) "black-box" approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). METHODS: Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients' attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. RESULTS: Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians' decision pathways to diagnose AMD. CONCLUSIONS: Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support.

Pilot Study for a Model for the Collection of Waiting Lists Data.

The analysis of data on waiting lists in Italy is regulated by the PNGLA (National Plan for the Governance of Waiting Lists). However, the Plan does not specify the characteristics of the data to be returned by the Regions for the purposes of monitoring, with the result that it is frequently either in aggregate form, unreadable, or incomplete, and therefore cannot be analysed in any meaningful way. Fondazione the Bridge and AGENAS, with the University of Genoa and the University of Pavia, conducted a pilot study on a methodological model for the collection of waiting lists data. The model proved to be effective and replicable, also providing a more valuable opportunity to analyse waiting lists data.

Tuberculosis Infection Control: Experiences and Considerations from a Web Based Tool Implementation.

Tuberculosis (TB) remains a significant global health challenge. Indeed, according to the World Health Organization (WHO), TB is classified as the second most common cause of death worldwide due to a single infectious agent in 2022, following COVID-19. To effectively manage tuberculosis patients, it is necessary to ensure accurate diagnosis, prompt treatment initiation, and vigilant monitoring of patients' progress. In 2017, the TB Ge network was implemented and launched in two primary hospitals within the Liguria Region in Italy, with the main purpose to manage tuberculosis infections. This system, organized as a web-based tool, simplifies the manual input of patient's data and therapies, while automating the integration of test results from hospitals' Laboratory Information Systems (LIS), without requiring human intervention. The goal of this paper is to highlight the outcomes achieved through the implementation of the TB Ge network in a period seriously affected by the COVID-19 pandemia and outline future directions. More specifically, the aim is to extend its adoption to all hospitals in the Liguria Region, thus improving the management of tuberculosis infections across healthcare facilities.

Autonomous artificial intelligence versus teleophthalmology for diabetic retinopathy.

Purpose: To assess the role of artificial intelligence (AI) based automated software for detection of Diabetic Retinopathy (DR) compared with the evaluation of digital retinography by two double masked retina specialists. Methods: Two-hundred one patients (mean age 65 ± 13 years) with type 1 diabetes mellitus or type 2 diabetes mellitus were included. All patients were undergoing a retinography and spectral domain optical coherence tomography (SD-OCT, DRI 3D OCT-2000, Topcon) of the macula. The retinal photographs were graded using two validated AI DR screening software (Eye Art TM and IDx-DR) designed to identify more than mild DR. Results: Retinal images of 201 patients were graded. DR (more than mild DR) was detected by the ophthalmologists in 38 (18.9%) patients and by the AI-algorithms in 36 patients (with 30 eyes diagnosed by both algorithms). Ungradable patients by the AI software were 13 (6.5%) and 16 (8%) for the Eye Art and IDx-DR, respectively. Both AI software strategies showed a high sensitivity and specificity for detecting any more than mild DR without showing any statistically significant difference between them. Conclusions: The comparison between the diagnosis provided by artificial intelligence based automated software and the reference clinical diagnosis showed that they can work at a level of sensitivity that is similar to that achieved by experts.

Standardization Proposal for the Transmission of Waiting List Data in Italy.

Each Italian region is required to manage and disclose data relating to waiting times for healthcare services which are provided by both public and private hospitals and local health units accredited to the Sistema Sanitario Nazionale (SSN - in English, National Healthcare System). The current law governing data relating to waiting times and their sharing is the Piano Nazionale di Governo delle Liste di Attesa (PNGLA - in English National Government Plan for Waiting Lists). However, this plan does not propose a standard to monitor such data, but only provides a few guidelines that the Italian regions are required to follow. The lack of a specific technical standard for managing sharing of waiting list data and the lack of precise and binding information in the PNGLA make the management and transmission of such data problematic, reducing the interoperability necessary to have an effective and efficient monitoring of the phenomenon. The proposal for a new standard for the transmission of waiting list data derives from these shortcomings. This proposed standard promotes greater interoperability, is easy to create with an implementation guide, and has sufficient degrees of freedom to assist the document author.

Healthcare Insights: Evaluating the Access to the Italian Healthcare System.

The Italian health system is organised on a regional basis and services are provided by both public and private operators, affecting the planning of services, access to services by citizens and their health rights. The creation of an observatory monitoring the methods and times of access to healthcare services has been pursued. The preliminary phase of the project is presented, which will lead to the comparison of the data obtained from 2019, with an eye on the Covid-19 pandemic impact.

Real-Life Management of Diabetic Macular Edema with Dexamethasone Intravitreal Implant: A Retrospective Analysis of Long-Term Clinical Outcomes.

PURPOSE: Inflammation plays a key role in the pathogenesis of diabetic macular edema (DME), and intravitreal corticosteroids are among the recommended therapies. The goal of this retrospective analysis was to describe outcomes with dexamethasone intravitreal implant (DEX implant) in real life. METHODS: Medical digital records of DME patients treated with DEX implant and followed up for 3 years were analyzed. Treatment with DEX implant was started either as first-line therapy in pseudophakic patients and in patients with cardiovascular comorbidities or as second-line therapy in patients refractory to the inhibitor of the vascular endothelial growth factor (anti-VEGF) therapy. Analyzed outcomes included central macular thickness (CMT) and best-corrected visual acuity (BCVA). Mean number of implant injections per patient and mean duration of the interval between injections were also estimated. RESULTS: Seventy-five patients (mean age 65.7 (±12.3) years; 53 phakic and 22 pseudophakic) with DME were included. Overall, 84 eyes were treated. Mean CMT improved from 380.1 (±100.3) µm at baseline to 306.8 (±77.0) µm at 36 months (p=0.0003). Mean BCVA improved for up to 6 months (p=0.08) and then started to decrease reaching values lower than baseline after 24 months. In pseudophakic patients, BCVA improvements were more pronounced and sustained up to 36 months (p=0.6). Over 36 months, each patient received on average 2.4 (±1.6) intravitreal injections of DEX implant. The time interval between consecutive injections was included between 180 and 240 days. No unexpected safety issues were reported. CONCLUSIONS: With fewer than 3 injections per patient over a 3-year period, DEX implant was able to improve anatomic outcomes in DME patients. Only pseudophakic eyes showed also a long lasting functional benefit at 36 months.

A SOA Based Solution for MDRO Surveillance and Improved Antibiotic Prescription in the Abruzzo Region.

Prevention and control of hospital and community acquired infections caused by multi drug resistant organisms (MDROs) are one major priority nowadays for health care systems worldwide. To improve actions and plans to tackle this problem, the creation of automated regional, national and international MDRO surveillance networks is a mandatory path for international health Institutions and Ministries. In this paper, the authors report on the surveillance system designed for the Abruzzo Region (Central Italy) to monitor the prevalence of MDROs in both infected and colonized patients, to verify appropriateness of antibiotic prescription in hospitalized patients and to interact with other national and sovra-national networks. Service Oriented Architecture (SOA) approach, different Healthcare Service Specification Project (HSSP) standards, local, national and international terminology and Clinical Document Architecture Release 2 (CDA R2) were adopted to design the overall architecture of this regional surveillance system. The Authors discuss the state of implementation of the project, itemizing specific design and implementation choices adopted so far and sketching next steps and reasons of some design and implementation choices, and indicate the next steps.

Real-Life Management of Patients with Retinal Vein Occlusion Using I-Macula Web Platform.

AIM: Real-life evaluation in the management of patients affected by macular edema secondary to retinal vein occlusion. MATERIAL AND METHODS: A retrospective, observational study using the I-Macula Web platform. RESULTS: Thirty-five patients (37 eyes; 15 females and 20 male) affected by RVO were analysed. At 12 months, there was a statistically significant improvement of best-corrected visual acuity (p = 0.0235) and central macular thickness (p < 0.0001). The mean change in visual acuity was 8.9 letters. Twenty-seven eyes underwent DEX implant (n = 62; mean: 2.29) only. Of these, 8, 4, 14, and 1 eyes underwent 1, 2, 3, and 4 DEX implants, respectively. The remaining 10 eyes were also injected with ranibizumab (n = 49; mean: 4.9). At 12 months, 12 eyes (32.5%) presented a dry macula, whereas the remaining 25 eyes (67.5%) still had macular edema. Mean interval between the first and second treatment (T1) and between the second and third treatment (T2) were 5.15 and (T2) 3.7 months, respectively. Where only DEX implants were received, T1 and T2 was 5.1 and 4.9 months, respectively. CONCLUSIONS: This study confirms that DEX implants and/or anti-VEGF drugs improve visual acuity and central macular thickness in patients affected by RVO.

I-Maculaweb: A Tool to Support Data Reuse in Ophthalmology.

This paper intends to present a Web-based application to collect and manage clinical data and clinical trials together in a unique tool. I-maculaweb is a user-friendly Web-application designed to manage, share, and analyze clinical data from patients affected by degenerative and vascular diseases of the macula. The unique and innovative scientific and technological elements of this project are the integration with individual and population data, relevant for degenerative and vascular diseases of the macula. Clinical records can also be extracted for statistical purposes and used for clinical decision support systems. I-maculaweb is based on an existing multilevel and multiscale data management model, which includes general principles that are suitable for several different clinical domains. The database structure has been specifically built to respect laterality, a key aspect in ophthalmology. Users can add and manage patient records, follow-up visits, treatment, diagnoses, and clinical history. There are two different modalities to extract records: one for the patient's own center, in which personal details are shown and the other for statistical purposes, where all center's anonymized data are visible. The Web-platform allows effective management, sharing, and reuse of information within primary care and clinical research. Clear and precise clinical data will improve understanding of real-life management of degenerative and vascular diseases of the macula as well as increasing precise epidemiologic and statistical data. Furthermore, this Web-based application can be easily employed as an electronic clinical research file in clinical studies.