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Post-Secondary Employment Outcomes (PSEO) are experimental tabulations developed by researchers at the U.S. Census Bureau. PSEO data provide earnings and employment outcomes for college and university graduates by degree level, degree major, and post-secondary institution. These statistics are generated by matching university transcript data with a national database of jobs, using state-of-the-art confidentiality protection mechanisms to protect the underlying data.
The PSEO are made possible through data sharing partnerships between universities, university systems, State Departments of Education, State Labor Market Information offices, and the U.S. Census Bureau. PSEO data are available for post-secondary institutions whose transcript data have been made available to the Census Bureau through a data-sharing agreement.
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The rapid pace of innovation for cell and gene therapy products holds promise for transforming medicine and creating treatment options for patients living with difficult, rare, and often incurable diseases. With an increasing number of cell and gene therapy products in development and the potential for more of these treatments to become available to patients in the future, it is important to understand the full spectrum of long-term effects and collect accurate, timely, and comprehensive data to ensure these products remain safe, effective, and of high quality. The FDA is seeking input on methods, approaches, logistics, privacy concerns, and other aspects related to efficacy and safety data collection in the post-approval setting for cell and gene therapies.
Comments received by mail/hand delivery/courier (for written/paper submissions) will be considered timely if they are postmarked or the delivery service acceptance receipt is on or before May 26. Due to the COVID-19 pandemic, Dockets Management Staff is receiving U.S. Postal Service mail intermittently, so electronic submissions are preferred.
Under the Prescription Drug User Fee Act (PDUFA) VII, by the end of fiscal year 2024, FDA is required to convene a meeting to solicit input on methods and approaches (e.g., use of RWE, Deve) for capturing post-approval safety and efficacy data for cell and gene therapy products. There will be a docket for the public comment period, which will be open for 30 days following the meeting. Within six months after the public meeting, FDA will issue a summary report or a transcript of the meeting.
Each row denotes a potential PASC diagnosis category defined by ICD-10 codes classified through CCSR, and each column denotes a particular PASC topic. Each PASC topic is characterized by a unique post-acute incidence probability distribution over all 137 individual potential PASC diagnosis categories.
We also plotted heat maps of the post-acute co-incidence rate matrix of the 137 potential PASC conditions for patients in each subphenotype and their matched controls in Supplementary Fig. 1. We observed that patients in these PASC subphenotypes are associated with higher co-incidence rates of PASC conditions compared to their matched controls. We further visualized in Fig. 4 the network patterns of 28 selected potential PASC conditions whose incidence rates were larger than 1% in any of the PASC subphenotypes, where the nodes in each network are particular potential PASC conditions with their sizes proportional to the incidence rate in the records of patients from the corresponding group. Each line linking a pair of nodes indicates a co-incidence of that pair of potential PASC conditions, with its thickness proportional to the co-incidence rate in the corresponding group. Figure 4 shows that the conditions used to characterize each PASC subphenotype were clearly associated with larger-sized nodes, representing higher incidence rates. There were no clear differences in node sizes for the groups with matched controls. We also observed denser connections in PASC subphenotypes, which suggested that the potential PASC conditions did not appear independently, but rather collectively, and those larger nodes included more interconnected network hubs.
Our analysis so far was based on a comprehensive list of 137 potential PASC conditions compiled from existing literature and clinician input. With respect to a specific patient cohort, it was challenging to guarantee that all of these conditions would be associated with excessive incidence risk in the follow-up period for patients who tested positive versus negative for SARS-CoV-2 infection due to population heterogeneity and incomplete information capture in the EHR. One key characteristic for TM was that it could effectively suppress the impact of conditions with low incidence rates in the EHR and drew more focus on the prevalent conditions (Fig. 2). However, it was still unclear if the subphenotypes would change if only the conditions that were associated with statistically significant excessive risks in the follow-up period for patients who tested positive for SARS-CoV-2 infection compared to patients who tested negative were considered.
Our results suggest that the identified subphenotypes are highly consistent across the two cohorts with distinct patient populations and geographical characteristics. These four subphenotypes also covered the major PASC conditions that have been reported from existing independent studies, such as cardiovascular3, respiratory25, neurological26 and gastrointestinal27 conditions. Our study verified the co-existence of these dominate subphenotypes and can inform focused disease areas of treatment development for PASC.
There is also an existing study on identifying Long-COVID symptom clusters with information reported from 233 patients enrolled in the All-Ireland Infectious Disease cohort13, whereas our study is based on the diagnosis information from the EHR of large general civilian patient populations. Some of these diagnoses were with a clear diagnostic criterion, whereas others were not. For example, the conditions in Subphenotype 1, such as heart failure, pneumonia and renal failure, were mostly with objective diagnostic criteria according to underlying disease etiologies. Many conditions in Subphenotype 2 (such as breathing abnormality and non-specific chest pain), Subphenotype 3 (such as musculoskeletal and nervous system pain) and Subphenotype 4 (such as abdominal and pelvic pain, nausea and vomiting) were more subjective to diagnose. In addition, the diagnosis of certain conditions, such as esophageal and gastrointestinal disorders in Subphenotype 4, was likely to encompass functional disorders rather than clearly defined disease etiologies. This meant that our identified subphenotypes, which separated severe COVID-19 complications (Subphenotype 1) and milder PASC conditions (Subphenotypes 2, 3 and 4) that could not be explained by alternative disease etiologies and were closer to those patient-reported symptoms. These subphenotypes would help tease out the heterogeneity of these conditions and provide guidance on patient management in practice.
Our study also has limitations. First, our analysis is based on longitudinal observational patient data, which cannot explain the biological mechanisms behind PASC. Second, the PASC diagnoses that we investigated were encoded as CCSR categories, which may not reflect the co-incidence patterns of fine-grained diagnosis conditions. Third, we focused on new incidences of conditions in the post-acute infection period for patients with COVID-19 and did not evaluate pre-existing conditions that may persist or worsen due to acute SARS-CoV-2 infection. Fourth, the goal of our study was to identify potential PASC subphenotypes, and we did not conduct rigorous analysis on the predictability of these subphenotypes, which was left as a future research topic. Finally, our study period did not include the COVID-19 wave dominated by the Omicron SARS-CoV-2 variant.
Two large-scale, de-identified, real-world EHR data warehouses were used in our analyses. Our first cohort data were based on the EHR from the INSIGHT CRN14, which contains the longitudinal clinical information of approximately 12 million patients in the NYC area. Our second cohort data were based on the EHR from the OneFlorida+ CRN15, which contains the information of nearly 15 million patients mainly from Florida and selected cities in Georgia and Alabama.
This study is part of the National Institutes of Health (NIH) Researching COVID to Enhance Recovery (RECOVER) Initiative31, which seeks to understand, treat and prevent the post-acute sequelae of SARS-CoV-2 infection (PASC).
Topic coherence is an important metric to evaluate the quality of topics based on the input data. It is measured based on a sliding window (in our case, the length of the window is the total number of PASCs), and then one can calculate the normalized pointwise mutual information (NPMI) between input data and the learned topics. We used the Python package GENSIM ( ) to calculate the topic coherence.
The prevalence of incident prescriptions of medications in the post-acute infection period for each subphenotype on the INSIGHT cohort, where medications are grouped into different categories shown by different colors. The most prevalent medication in each subphenotype is highlighted.
The heatmap of PASC topics learned from the OneFlorida+ cohort. Each row denotes a potential PASC category grouped by different CCSR domains, and each column denotes a particular PASC topic. Each PASC topic is characterized by a unique post-acute incidence probability distribution over all 137 individual potential PASC categories. 041b061a72