Xiangrong Kong

To assess cross-sectional and longitudinal relationships between fundus autofluorescence (FAF) and visual acuity (VA) in ABCA4-associated Stargardt disease (STGD1). Secondary analysis used data from 301 STGD1 participants (566 study eyes) enrolled in the international ProgStar studies. The study reading center graded the FAF images qualitatively and quantitatively, including areas of atrophy reflected as definitely decreased autofluorescence (DDAF) and questionably decreased autofluorescence (QDAF). Linear models estimated cross-sectional and longitudinal associations between the growth of DDAF area and total DAF area (i.e., sum of DDAF and QDAF areas) and change in VA. At the first visit, the median age was 30 years (range, 7-69); 55% were women. Mean ± SD VA was 0.73 ± 0.36 logMAR; mean total DAF area was 3.4 ± 3.8 mm2; and 42% of eyes had no DDAF. Cross-sectionally, increasing age and duration of symptoms, younger age of symptom onset, and larger QDAF area were significantly associated with worse VA (P < 0.05). Mean follow-up was 3.3 years (range, 0.5-10.3). There was no statistically significant relationship between growth of total DAF or DDAF areas and change in VA (P > 0.05). Characteristics other than DAF area (e.g., flecks presence) may influence VA. Some patients remained DDAF free longitudinally and thus would be excluded in STGD1 trials that use DDAF as the primary endpoint. The cross-sectional relationship between DAF and VA confirmed the association of VA with retinal pigment epithelium (RPE) structure. The lack of a significant association between growth in DAF areas with change in VA over follow-up points to challenges resulting from slow progression and large VA variability. Exploring outcome measures from other modalities is warranted for designing STGD1 trials.

Introduction: The hierarchical healthcare delivery system is an important measure to improve the allocation of medical resources and promote equitable distribution of basic medical and health services. It is one of the key factors in the success or failure of China’s medical reform. This study aims to analyze the factors influencing patients’ healthcare-seeking behaviors, including socioeconomic and clinical outcomes, under China’s hierarchical healthcare delivery system, and to provide potential solutions.Methods: Patients receiving outpatient treatment in the past 14 days and inpatient care in the past 1 year were investigated. The multivariate logistic regression was used to analyze the influencing factors of patient’s medical treatment behavior selection, and to compare whether the clinical outcomes of primary medical institutions and grade A hospitals are the same.Results: Nine thousand and ninety-eight person-times were included in the study. Of these, 4,538 patients were outpatients, 68.27% of patients were treated in primary medical institutions; 4,560 patients were hospitalized, 58.53% chose to be hospitalized in grade A hospitals. Provinces and cities, urban and rural areas, occupation, education level, medical insurance type, income, whether there are comorbid diseases, and doctors’ medical behavior are the factors affecting the choice of medical treatment behavior. Patients who choose primary medical institutions and grade A hospitals have different control levels and control rate for the blood pressure, blood lipids, blood glucose.Conclusion: Under the hierarchical diagnosis and treatment system, the patients’ choice of hospital is mainly affected by their level of education, medical insurance types, and the inpatients are also affected by whether there are comorbid conditions. Clinical outcomes of choosing different levels of hospitals were different.

Background: Technology advancement has allowed more frequent monitoring of biomarkers. The resulting data structure entails more frequent follow-ups compared to traditional longitudinal studies where the number of follow-up is often small. Such data allow explorations of the role of intra-person variability in understanding disease etiology and characterizing disease processes. A specific example was to characterize pathogenesis of bacterial vaginosis (BV) using weekly vaginal microbiota Nugent assay scores collected over 2 years in post-menarcheeal women from Rakai, Uganda, and to identify risk factors for each vaginal microbiota pattern to inform epidemiological and etiological understanding of the pathogenesis of BV. Methods: We use a fully data-driven approach to characterize the longitudinal patters of vaginal microbiota by considering the densely sampled Nugent scores to be random functions over time and performing dimension reduction by functional principal components. Extending a current functional data clustering method, we use a hierarchical functional clustering framework considering multiple data features to help identify clinically meaningful patterns of vaginal microbiota fluctuations. Additionally, multinomial logistic regression was used to identify risk factors for each vaginal microbiota pattern to inform epidemiological and etiological understanding of the pathogenesis of BV. Results: Using weekly Nugent scores over 2 years of 211 sexually active and post-menarcheal women in Rakai, four patterns of vaginal microbiota variation were identified: persistent with a BV state (high Nugent scores), persistent with normal ranged Nugent scores, large fluctuation of Nugent scores which however are predominantly in the BV state; large fluctuation of Nugent scores but predominantly the scores are in the normal state. Higher Nugent score at the start of an interval, younger age group of less than 20 years, unprotected source for bathing water, a woman’s partner’s being not circumcised, use of injectable/Norplant hormonal contraceptives for family planning were associated with higher odds of persistent BV in women. Conclusion: The hierarchical functional data clustering method can be used for fully data driven unsupervised clustering of densely sampled longitudinal data to identify clinically informative clusters and risk-factors associated with each cluster.

Background: The financial hardships and social isolation experienced during the COVID-19 pandemic have been found to adversely affect children’s developmental outcomes. While many studies thus far have focused on school-aged children and the pandemic-related impacts on their academic skills and behavior problems, relatively less is known about pandemic hardships and associations with children’s development during their early years. Using a racially and economically diverse sample, we examined whether hardships experienced during the pandemic were associated with children’s development with a particular focus on communication and socioemotional development. Methods: Participants from eight cohorts of the Environmental influences on Child Health Outcomes program provided data on pandemic-related financial and social hardships as well as child developmental outcomes. Financial hardship was defined as at least one parent experiencing job loss or change, and social hardship was defined as families’ quarantining from household members or extended family and friends. The development of children under 4 was assessed longitudinally, before and during the pandemic (N = 684), using the Ages and Stages Questionnaire (ASQ). The Generalized Estimating Equations, which accounted for within-child correlation, were used for analysis. Results: Families from minority backgrounds and low socioeconomic status disproportionately experienced pandemic-related hardships. Male children had higher odds of experiencing negative changes in communication and personal social skills from pre- to during-pandemic visits (ORs ranged between 2.24 and 3.03 in analysis with binary ASQ outcomes and ranged from −0.34–0.36 in analyses with ASQ z-scores, ps = 0.000). Pandemic-related hardships in the social and financial areas did not explain within-individual changes in children’s developmental outcomes. Conclusion: Negative developmental changes from pre- to during-pandemic were found in boys, yet we did not find any associations between increased experience of pandemic-related hardships and children’s development. E how pandemic hardships affect development using a larger sample size and with longer follow-up is warranted.

Background: Prognostic risk factors for completely resected stage IA non-small-cell lung cancers (NSCLCs) have advanced minimally over recent decades. Although several biomarkers have been found to be associated with cancer recurrence, their added value to TNM staging and tumor grade are unclear. Methods: Features of preoperative low-dose CT image and histologic findings of hematoxylin- and eosin-stained tissue sections of resected lung tumor specimens were extracted from 182 stage IA NSCLC patients in the National Lung Screening Trial. These features were combined to predict the risk of tumor recurrence or progression through integrated deep learning evaluation (IDLE). Added values of IDLE to TNM staging and tumor grade in progression risk prediction and risk stratification were evaluated. Results: The 5-year AUC of IDLE was 0.817 ± 0.037 as compared to the AUC = 0.561 ± 0.042 and 0.573 ± 0.044 from the TNM stage and tumor grade, respectively. The IDLE score was significantly associated with cancer recurrence (p < 0.0001) even after adjusting for TNM staging and tumor grade. Synergy between chest CT image markers and histological markers was the driving force of the deep learning algorithm to produce a stronger prognostic predictor. Conclusions: Integrating markers from preoperative CT images and pathologist’s readings of resected lung specimens through deep learning can improve risk stratification of stage 1A NSCLC patients over TNM staging and tumor grade alone. Our study suggests that combining markers from nonoverlapping platforms can increase the cancer risk prediction accuracy.

Both linear regression with generalized estimating equations (GEE) and linear mixed-effects models (LMEM) can be used to estimate the marginal association of an exposure with clustered continuous o...

Clinical trials for retinitis pigmentosa (RP) likely require long follow-ups because of the slow progression of RP. Understanding patients’ attitudes toward participation in a long trial and their ...