BACKGROUND: The aim of this study was to investigate the predictive factors for persistent disease activity following anti-vascular endothelial growth factors (anti-VEGF) and their long-term effects in patients to be treated for neovascular age-related macular degeneration (nAMD) under real-world conditions.
METHODS: Retrospective data analysis of the PROOF study, a multi-center real-world retrospective chart review conducted across Korea in patients with nAMD included treatment-naive patients with nAMD who received first anti-VEGF (ranibizumab, bevacizumab, or aflibercept) between January 2017 and March 2019 was performed. All 600 patients (cohort 1) had a minimum follow-up of 12 months of which 453 patients (cohort 2) were followed-up for 24 months from baseline.
RESULTS: At month 12 after anti-VEGF therapy, 58.10% (95% confidence interval [CI]: 54.09, 62.12) of patients and at month 24, 66.02% of patients continued to have persistent retinal fluid. At both months 12 and 24, predictive factors for persistent disease activity were fibrovascular pigment epithelial detachments (PED) (P = 0.0494) and retinal fluid at month 3 after loading phase (P = 0.0082). The mean changes in visual acuity were + 6.2, + 10.1, and + 13.3 letters and in the central subfield thickness were - 79.1 µm, - 96.3 µm, and - 134.4 µm at 12 months from baseline, in the bevacizumab, aflibercept, and ranibizumab groups, respectively.
CONCLUSIONS: The presence of retinal fluid after loading phase and fibrovascular PED were predictors of persistent disease activity after at least 1 year of anti-VEGF treatment.

BACKGROUND: The aim of this study was to evaluate the efficacy and safety of escalating the dosage of intravitreal brolucizumab in patients with refractory neovascular age-related macular degeneration (AMD).
METHODS: This retrospective study included 17 eyes of 17 patients with refractory AMD treated with high-dose brolucizumab (12 mg/0.1 ml) for over 12 months. Patients initially received at least one anti-vascular endothelial growth factor (anti-VEGF) agent and were switched to standard-dose brolucizumab (6 mg/0.05 ml). Those who showed a suboptimal response to standard-dose treatment had their dosage of brolucizumab escalated.
RESULTS: Visual acuity was maintained from 68.3 ± 3.4 letters to 70.7 ± 3.2 letters after 12 months of high-dose treatment (P = 0.128). Central subfield thickness was 343.7 ± 17.0 μm before high-dose treatment and 316.7 ± 18.5 μm at 12 months (P = 0.083). The proportions of patients with subretinal fluid and serous pigment epithelial detachment significantly decreased from 82.4% to 41.2% and from 52.9% to 17.6%, respectively, after high-dose treatment (P = 0.039 and P = 0.031, respectively). The treatment interval extended from 7.2 ± 2.4 weeks to 10.2 ± 2.2 weeks after switching to standard-dose brolucizumab (P < 0.001) and was maintained at 13.5 ± 2.8 weeks after increasing the dose (P = 0.154). No severe ocular adverse events were observed.
CONCLUSIONS: High-dose brolucizumab was effective in patients who did not respond to standard-dose brolucizumab after switching from previous anti-VEGF agents. Increasing the dosage could offer sustained disease control and reduce the treatment burden for patients with refractory AMD.

In this study, we compared the similarities and differences in adverse events (AEs) among CAR T-cell products through signal mining via the FDA Adverse Event Reporting System (FAERS) and identified unknown AEs to provide a reference for safe clinical medication. Data from the FAERS database spanning from the fourth quarter of 2017 to the first quarter of 2024 were extracted. Signals were identified using the reporting odds ratio (ROR) method and the Medicines and Healthcare Products Regulatory Agency (MHRA) method. A total of 11,386 AE reports related to six CAR T-cell products were selected. The top three categories of AEs reported were nervous system disorders, immune system disorders, and general disorders and administration site conditions. However, there were variations in the AE spectra among the different CAR T-cell products. The BCMA-targeting drugs idecabtagene vicleucel (Ide-cel) and ciltacabtagene autoleucel (Cilta-cel) were found to be associated with parkinsonism, which were not observed in CD19-targeting drugs. Tisagenlecleucel (Tisa-cel) and axicabtagene ciloleucel (Axi-cel) exhibited cerebrovascular accident-related AEs, graft versus host disease, and abnormal coagulation indices. Cilta-cel was associated with cerebral hemorrhage, intracranial hemorrhage, cranial nerve disorder, and facial nerve disorder. Cardiopulmonary toxicity, including hypoxia, tachypnoea, cardiorenal syndrome, and hypotension, exhibited strong signal intensities and considerable overlap with CRS. The number of positive signals for cardiopulmonary toxicity associated with drugs targeting CD-19 is greater. Clinicians should assess patients prior to medication and closely monitor their vital signs, mental status, and laboratory parameters during treatment.

Randomized controlled trials have demonstrated responses to clinical parameters, but a significant proportion of allergy patients in real-life settings would have been excluded from such studies. Therefore, real-world research is needed, and there is a growing body of information on allergen immunotherapy\'s long-term effectiveness and safety. Real-world evidence can be a valuable instrument to better understand the patient\'s journey and the effectiveness and safety of therapies. For this purpose, a registry will be used for the first time in Italy to evaluate the impact of allergen immunotherapy on several outcomes, including quality of life and disease-related effects in the pediatric and adult allergic population with a socio-economic assessment and respect to real-world health.

OBJECTIVE: This study is aimed to compare the effectiveness of modern therapy including angiotensin receptor-neprilysin inhibitor (ARNI) and sodium-glucose cotransporter 2 inhibitors (SGLT2i) with conventional heart failure treatment in the real world.
BACKGROUND: Since ARNI and SGLT2i were introduced to treat heart failure (HF), its therapeutic regimen has modernized from previous treatment with beta-blocker (BB) and angiotensin-converting enzyme inhibitor (ACEi)/angiotensin II receptor blocker (ARB) with mineralocorticoid receptor antagonist (MRA) as added-on in HF with reduced ejection fraction (HFrEF). However, a comparison between conventional and modern treatment strategies with drugs in combination has not been performed.
METHODS: This observational study (2013-2020), using the Swedish HF Registry, involved 20,849 HF patients. Patients received either conventional (BB, ACEi/ARB, with/without MRA, n = 20,140) or modern (BB, ACEi/ARB, MRA, SGLT2i or BB, ARNI, MRA with/without SGLT2i, n = 709) treatment at the index visit. The endpoints were all-cause and cardiovascular (CV) mortality.
RESULTS: Modern HF therapy was associated with a significant 28% reduction in all-cause mortality (adjusted HR [aHR], 0.72 (0.54-0.96); p = 0.024) and a significant 62% reduction in CV mortality (aHR, 0.38 (0.21-0.68); p = 0.0013) compared to conventional HF treatment. Similar results emerged in a sensitivity analysis using propensity score matching. The interaction analyses did not reveal any trends for EF (< 40% and ≥ 40%), sex, age (< 70 and ≥ 70 years), eGFR (< 60 and ≥ 60 ml/min/1.73 m2), and etiology of HF subgroups.
CONCLUSIONS: In this nationwide study, modern HF therapy was associated with significantly reduced all-cause and CV mortality, regardless of EF, sex, age, eGFR, and etiology of HF.

As digital phenotyping, the capture of active and passive data from consumer devices such as smartphones, becomes more common, the need to properly process the data and derive replicable features from it has become paramount. Cortex is an open-source data processing pipeline for digital phenotyping data, optimized for use with the mindLAMP apps, which is used by nearly 100 research teams across the world. Cortex is designed to help teams (1) assess digital phenotyping data quality in real time, (2) derive replicable clinical features from the data, and (3) enable easy-to-share data visualizations. Cortex offers many options to work with digital phenotyping data, although some common approaches are likely of value to all teams using it. This paper highlights the reasoning, code, and example steps necessary to fully work with digital phenotyping data in a streamlined manner. Covering how to work with the data, assess its quality, derive features, and visualize findings, this paper is designed to offer the reader the knowledge and skills to apply toward analyzing any digital phenotyping data set. More specifically, the paper will teach the reader the ins and outs of the Cortex Python package. This includes background information on its interaction with the mindLAMP platform, some basic commands to learn what data can be pulled and how, and more advanced use of the package mixed with basic Python with the goal of creating a correlation matrix. After the tutorial, different use cases of Cortex are discussed, along with limitations. Toward highlighting clinical applications, this paper also provides 3 easy ways to implement examples of Cortex use in real-world settings. By understanding how to work with digital phenotyping data and providing ready-to-deploy code with Cortex, the paper aims to show how the new field of digital phenotyping can be both accessible to all and rigorous in methodology.

BACKGROUND: Pemigatinib demonstrated efficacy in fibroblast growth factor receptor (FGFR)-altered cholangiocarcinoma (CCA) in the FIGHT-202 trial. However, limited real-world evidence exists on treatment patterns and outcomes in this setting.
METHODS: Patient characteristics, treatment patterns, and outcomes of US adults who received pemigatinib for unresectable, locally advanced or metastatic CCA were collected via retrospective physician-abstracted chart review. Results were summarized using descriptive statistics.
RESULTS: Data from 120 patients (49.2% male; 55.0% White; 19.2% Hispanic; median age at initial pemigatinib prescription, 64.5 years) were collected from 18 physicians/practices. At the time of prescribing, 90.0% of patients had metastatic disease. FGFR2 testing was completed for 92.5% of patients; of those, all but one (result unknown) tested positive, and 95.5% were tested using next-generation sequencing. Pemigatinib was prescribed as second- and third-line therapy among 94.2% and 5.8% of patients, respectively. The most common starting dosage was 13.5 mg daily for 14 days of 21-day cycles (87.5% of patients). Among 60 patients (50.0% of the full cohort) who discontinued pemigatinib during the 6.5-month median study follow-up period, 68.3% discontinued due to disease progression. The median real-world progression-free survival (rwPFS) from the date of pemigatinib initiation was 7.4 months (95% CI: 6.4-8.6), and the real-world overall response rate (rwORR) was 59.2% (95% CI: 50.0%-68.4%).
CONCLUSIONS: This study complements the FIGHT-202 clinical trial by assessing the use of pemigatinib among a diverse population of patients with CCA under real-world conditions. Findings support the clinical benefit of pemigatinib demonstrated in FIGHT-202.

UNASSIGNED: Chronic atrophic gastritis (CAG) is a complex chronic disease caused by multiple factors that frequently occurs disease in the clinic. The worldwide prevalence of CAG is high. Interestingly, clinical CAG patients often present with a variety of symptom phenotypes, which makes it more difficult for clinicians to treat. Therefore, there is an urgent need to improve our understanding of the complexity of the clinical CAG population, obtain more accurate disease subtypes, and explore the relationship between clinical symptoms and medication. Therefore, based on the integrated platform of complex networks and clinical research, we classified the collected patients with CAG according to their different clinical characteristics and conducted correlation analysis on the classification results to identify more accurate disease subtypes to aid in personalized clinical treatment.
UNASSIGNED: Traditional Chinese medicine (TCM) offers an empirical understanding of the clinical subtypes of complicated disorders since TCM therapy is tailored to the patient\'s symptom profile. We gathered 6,253 TCM clinical electronic medical records (EMRs) from CAG patients and manually annotated, extracted, and preprocessed the data. A shared symptom-patient similarity network (PSN) was created. CAG patient subgroups were established, and their clinical features were determined through enrichment analysis employing community identification methods. Different clinical features of relevant subgroups were correlated based on effectiveness to identify symptom-botanical botanical drugs correspondence. Moreover, network pharmacology was employed to identify possible biological relationships between screened symptoms and medications and to identify various clinical and molecular aspects of the key subtypes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.
UNASSIGNED: 5,132 patients were included in the study: 2,699 males (52.60%) and 2,433 females (47.41%). The population was divided into 176 modules. We selected the first 3 modules (M29, M3, and M0) to illustrate the characteristic phenotypes and genotypes of CAG disease subtypes. The M29 subgroup was characterized by gastric fullness disease and internal syndrome of turbidity and poison. The M3 subgroup was characterized by epigastric pain and disharmony between the liver and stomach. The M0 subgroup was characterized by epigastric pain and dampness-heat syndrome. In symptom analysis, The top symptoms for symptom improvement in all three subgroups were stomach pain, bloating, insomnia, poor appetite, and heartburn. However, the three groups were different. The M29 subgroup was more likely to have stomach distention, anorexia, and palpitations. Citrus medica, Solanum nigrum, Jiangcan, Shan ci mushrooms, and Dillon were the most popular botanical drugs. The M3 subgroup has a higher incidence of yellow urine, a bitter tongue, and stomachaches. Smilax glabra, Cyperus rotundus, Angelica sinensis, Conioselinum anthriscoides, and Paeonia lactiflora were the botanical drugs used. Vomiting, nausea, stomach pain, and appetite loss are common in the M0 subgroup. The primary medications are Scutellaria baicalensis, Smilax glabra, Picrorhiza kurroa, Lilium lancifolium, and Artemisia scoparia. Through GO and KEGG pathway analysis, We found that in the M29 subgroup, Citrus medica, Solanum nigrum, Jiangcan, Shan ci mushrooms, and Dillon may exert their therapeutic effects on the symptoms of gastric distension, anorexia, and palpitations by modulating apoptosis and NF-κB signaling pathways. In the M3 subgroup, Smilax glabra, Cyperus rotundus, Angelica sinensis, Conioselinum anthriscoides, and Paeonia lactiflora may be treated by NF-κB and JAK-STAT signaling pathway for the treatment of stomach pain, bitter mouth, and yellow urine. In the M0 subgroup, Scutellaria baicalensis, Smilax glabra, Picrorhiza kurroa, Lilium lancifolium, and Artemisia scoparia may exert their therapeutic effects on poor appetite, stomach pain, vomiting, and nausea through the PI3K-Akt signaling pathway.
UNASSIGNED: Based on PSN identification and community detection analysis, CAG population division can provide useful recommendations for clinical CAG treatment. This method is useful for CAG illness classification and genotyping investigations and can be used for other complicated chronic diseases.

Aim: To evaluate real-world treatment patterns, survival and healthcare-resource utilization in US patients with advanced biliary tract cancers (BTC) receiving systemic therapy.Patients & methods: This study used claims data from the Healthcare Integrated Research Database (HIRD®) linked to clinical data from the Cancer Care Quality Program (January 1, 2015-September 30, 2020).Results: Of 413 patients, 84.5% received gemcitabine-based first-line (1L) treatment, 46% received second-line treatment, and 16.5% received third-line (3L) treatment. All-cause mortality was 53% and approximately 70% of patients had ≥1 inpatient visit. The total mean per-patient-per-month all-cause costs were $19,589 for 1L and $33,534 for 3L treatment.Conclusion: Results showed poor survival, significant resource use and high costs as treatment line progresses for patients with advanced BTC.
Our research explored which treatments US patients with advanced biliary tract cancers (BTC) received and how long they lived for. We analyzed information from a US database, called the Healthcare Integrated Research Database (HIRD®), which holds information related to healthcare insurance claims. Out of 413 patients in the database, 84.5% were initially given a combination treatment involving a chemotherapy called gemcitabine. We also found that 46% of patients received a type of second treatment (also known as second-line therapy), and 16.5% received a third treatment (third-line therapy). During treatment, around 70% of patients needed to stay in hospital at least once. The cost of healthcare was more expensive for patients receiving later lines of therapy, with the average monthly cost per patient for first-line treatment being $19,589 and third-line treatment being $33,534. Furthermore, just over half of the 413 patients died from any causes, showing the poor outlook these patients face. This information is important for understanding the real-world management of patients with BTC, so that their care can be improved in the future.

BACKGROUND: For medical artificial intelligence (AI) training and validation, human expert labels are considered the gold standard that represents the correct answers or desired outputs for a given data set. These labels serve as a reference or benchmark against which the model\'s predictions are compared.
OBJECTIVE: This study aimed to assess the accuracy of a custom deep learning (DL) algorithm on classifying diabetic retinopathy (DR) and further demonstrate how label errors may contribute to this assessment in a nationwide DR-screening program.
METHODS: Fundus photographs from the Lifeline Express, a nationwide DR-screening program, were analyzed to identify the presence of referable DR using both (1) manual grading by National Health Service England-certificated graders and (2) a DL-based DR-screening algorithm with validated good lab performance. To assess the accuracy of labels, a random sample of images with disagreement between the DL algorithm and the labels was adjudicated by ophthalmologists who were masked to the previous grading results. The error rates of labels in this sample were then used to correct the number of negative and positive cases in the entire data set, serving as postcorrection labels. The DL algorithm\'s performance was evaluated against both pre- and postcorrection labels.
RESULTS: The analysis included 736,083 images from 237,824 participants. The DL algorithm exhibited a gap between the real-world performance and the lab-reported performance in this nationwide data set, with a sensitivity increase of 12.5% (from 79.6% to 92.5%, P<.001) and a specificity increase of 6.9% (from 91.6% to 98.5%, P<.001). In the random sample, 63.6% (560/880) of negative images and 5.2% (140/2710) of positive images were misclassified in the precorrection human labels. High myopia was the primary reason for misclassifying non-DR images as referable DR images, while laser spots were predominantly responsible for misclassified referable cases. The estimated label error rate for the entire data set was 1.2%. The label correction was estimated to bring about a 12.5% enhancement in the estimated sensitivity of the DL algorithm (P<.001).
CONCLUSIONS: Label errors based on human image grading, although in a small percentage, can significantly affect the performance evaluation of DL algorithms in real-world DR screening.