Analyze of factors and prognosis of eyes lost to follow-up in retinal vein occlusive disease patients receiving anti-vascular endothelial growth factor therapy.

BACKGROUND: Patients with macular edema (ME) secondary to retinal vein occlusion (RVO) who received at least one intravitreal injection of anti-vascular endothelial growth factor therapy (VEGF) and lost to follow-up (LTFU) for more than six months were analyzed to investigate the factors contributing to the LTFU and the prognosis. METHOD: This was a retrospective, single-center study to analyze the causes and prognosis of LTFU over six months in RVO-ME patients treated with intravitreal anti-VEGF injections at our institution from January 2019 to August 2022 and to collect patients' baseline characteristics along with the number of injections before LTFU, primary disease, best corrected visual acuity (BCVA) before LTFU and after return visit, central macular thickness (CMT), months before LTFU and after LTFU, reasons for LTFU, and complications, to analyze the factors affecting visual outcome at a return visit. RESULTS: This study included 125 patients with LTFU; 103 remained LTFU after six months, and 22 returned after LTFU. The common reason for LTFU was "no improvement in vision" (34.4%), followed by "transport inconvenience" (22.4%), 16 patients (12.8%) were unwilling to visit the clinic, 15 patients (12.0%) had already elected to seek treatment elsewhere, 12 patients (9.6%) were not seen in time due to the 2019-nCov epidemic, and 11 patients (8.8%) cannot do it due to financial reasons. The number of injections before LTFU was a risk factor for LTFU (P < 0.05). LogMAR at the initial visit (P < 0.001), CMT at the initial visit (P < 0.05), CMT before the LTFU (P < 0.001), and CMT after the return visit (P < 0.05) were influential factors for logMAR at the return visit. CONCLUSION: Most RVO-ME patients were LTFU after anti-VEGF therapy. Long-term LTFU is greatly detrimental to the visual quality of patients; thus, the management of RVO-ME patients in follow-up should be considered.

Early triage of critically ill COVID-19 patients using deep learning.

The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources.

Development and Validation of a Clinical Risk Score to Predict the Occurrence of Critical Illness in Hospitalized Patients With COVID-19.

Importance: Early identification of patients with novel coronavirus disease 2019 (COVID-19) who may develop critical illness is of great importance and may aid in delivering proper treatment and optimizing use of resources. Objective: To develop and validate a clinical score at hospital admission for predicting which patients with COVID-19 will develop critical illness based on a nationwide cohort in China. Design, Setting, and Participants: Collaborating with the National Health Commission of China, we established a retrospective cohort of patients with COVID-19 from 575 hospitals in 31 provincial administrative regions as of January 31, 2020. Epidemiological, clinical, laboratory, and imaging variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-GRAM). The score provides an estimate of the risk that a hospitalized patient with COVID-19 will develop critical illness. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). Data from 4 additional cohorts in China hospitalized with COVID-19 were used to validate the score. Data were analyzed between February 20, 2020 and March 17, 2020. Main Outcomes and Measures: Among patients with COVID-19 admitted to the hospital, critical illness was defined as the composite measure of admission to the intensive care unit, invasive ventilation, or death. Results: The development cohort included 1590 patients. the mean (SD) age of patients in the cohort was 48.9 (15.7) years; 904 (57.3%) were men. The validation cohort included 710 patients with a mean (SD) age of 48.2 (15.2) years, and 382 (53.8%) were men and 172 (24.2%). From 72 potential predictors, 10 variables were independent predictive factors and were included in the risk score: chest radiographic abnormality (OR, 3.39; 95% CI, 2.14-5.38), age (OR, 1.03; 95% CI, 1.01-1.05), hemoptysis (OR, 4.53; 95% CI, 1.36-15.15), dyspnea (OR, 1.88; 95% CI, 1.18-3.01), unconsciousness (OR, 4.71; 95% CI, 1.39-15.98), number of comorbidities (OR, 1.60; 95% CI, 1.27-2.00), cancer history (OR, 4.07; 95% CI, 1.23-13.43), neutrophil-to-lymphocyte ratio (OR, 1.06; 95% CI, 1.02-1.10), lactate dehydrogenase (OR, 1.002; 95% CI, 1.001-1.004) and direct bilirubin (OR, 1.15; 95% CI, 1.06-1.24). The mean AUC in the development cohort was 0.88 (95% CI, 0.85-0.91) and the AUC in the validation cohort was 0.88 (95% CI, 0.84-0.93). The score has been translated into an online risk calculator that is freely available to the public (http://118.126.104.170/). Conclusions and Relevance: In this study, a risk score based on characteristics of COVID-19 patients at the time of admission to the hospital was developed that may help predict a patient's risk of developing critical illness.