Real-World Effectiveness of Nirmatrelvir in Protecting Long COVID for Outpatient Adult Patients - A Large-Scale Observational Cohort Study from the RECOVER Initiative.

Paxlovid has been approved for use in patients who are at high risk for severe acute COVID-19 illness. Evidence regarding whether Paxlovid protects against Post-Acute Sequelae of SARS-CoV-2 infection (PASC), or Long COVID, is mixed in high-risk patients and lacking in low-risk patients. With a target trial emulation framework, we evaluated the association of Paxlovid treatment within 5 days of SARS-CoV-2 infection with incident Long COVID and hospitalization or death from any cause in the post-acute period (30-180 days after infection) using electronic health records from the Patient-Centered Clinical Research Networks (PCORnet) RECOVER repository. The study population included 497,499 SARS-CoV-2 positive patients between March 1, 2022, to February 1, 2023, and among which 165,256 were treated with Paxlovid within 5 days since infection and 307,922 were not treated with Paxlovid or other COVID-19 treatments. Compared with the non-treated group, Paxlovid treatment was associated with reduced risk of Long COVID with a Hazard Ratio (HR) of 0.88 (95% CI, 0.87 to 0.89) and absolute risk reduction of 2.99 events per 100 persons (95% CI, 2.65 to 3.32). Paxlovid treatment was associated with reduced risk of all-cause death (HR, 0.53, 95% CI 0.46 to 0.60; risk reduction 0.23 events per 100 persons, 95% CI 0.19 to 0.28) and hospitalization (HR, 0.70, 95% CI 0.68 to 0.73; risk reduction 2.37 events per 100 persons, 95% CI 2.19 to 2.56) in the post-acute phase. For those without documented risk factors, the associations (HR, 1.03, 95% CI 0.95 to 1.11; risk increase 0.80 events per 100 persons, 95% CI -0.84 to 2.45) were inconclusive. Overall, high-risk, nonhospitalized adult patients with COVID-19 who were treated with Paxlovid within 5 days of SARS-CoV-2 infection had a lower risk of Long COVID and all-cause hospitalization or death in the post-acute period. However, Long COVID risk reduction with Paxlovid was not observed in low-risk patients.

Identification of risk factors of Long COVID and predictive modeling in the RECOVER EHR cohorts.

BACKGROUND: SARS-CoV-2-infected patients may develop new conditions in the period after the acute infection. These conditions, the post-acute sequelae of SARS-CoV-2 infection (PASC, or Long COVID), involve a diverse set of organ systems. Limited studies have investigated the predictability of Long COVID development and its associated risk factors. METHODS: In this retrospective cohort study, we used electronic healthcare records from two large-scale PCORnet clinical research networks, INSIGHT (~1.4 million patients from New York) and OneFlorida+ (~0.7 million patients from Florida), to identify factors associated with having Long COVID, and to develop machine learning-based models for predicting Long COVID development. Both SARS-CoV-2-infected and non-infected adults were analysed during the period of March 2020 to November 2021. Factors associated with Long COVID risk were identified by removing background associations and correcting for multiple tests. RESULTS: We observed complex association patterns between baseline factors and a variety of Long COVID conditions, and we highlight that severe acute SARS-CoV-2 infection, being underweight, and having baseline comorbidities (e.g., cancer and cirrhosis) are likely associated with increased risk of developing Long COVID. Several Long COVID conditions, e.g., dementia, malnutrition, chronic obstructive pulmonary disease, heart failure, PASC diagnosis U099, and acute kidney failure are well predicted (C-index > 0.8). Moderately predictable conditions include atelectasis, pulmonary embolism, diabetes, pulmonary fibrosis, and thromboembolic disease (C-index 0.7-0.8). Less predictable conditions include fatigue, anxiety, sleep disorders, and depression (C-index around 0.6). CONCLUSIONS: This observational study suggests that association patterns between investigated factors and Long COVID are complex, and the predictability of different Long COVID conditions varies. However, machine learning-based predictive models can help in identifying patients who are at risk of developing a variety of Long COVID conditions.

Most people who develop COVID-19 make a full recovery, but some go on to develop post-acute sequelae of SARS-CoV-2 infection, commonly known as Long COVID. Up to now, we did not know why some people are affected by Long COVID whilst others are not. We conducted a study to identify risk factors for Long COVID and developed a mathematical modeling approach to predict those at risk. We find that Long COVID is associated with some factors such as experiencing severe acute COVID-19, being underweight, and having conditions including cancer or cirrhosis. Due to the wide variety of symptoms defined as Long COVID, it may be challenging to come up with a set of risk factors that can predict the whole spectrum of Long COVID. However, our approach could be used to predict a variety of Long COVID conditions.

Pediatric Gastrointestinal Outcomes During the Post-Acute Phase of COVID-19: Findings from RECOVER Initiative from 29 Hospitals in the US.

Importance: The profile of gastrointestinal (GI) outcomes that may affect children in post-acute and chronic phases of COVID-19 remains unclear. Objective: To investigate the risks of GI symptoms and disorders during the post-acute phase (28 days to 179 days after SARS-CoV-2 infection) and the chronic phase (180 days to 729 days after SARS-CoV-2 infection) in the pediatric population. Design: We used a retrospective cohort design from March 2020 to Sept 2023. Setting: twenty-nine healthcare institutions. Participants: A total of 413,455 patients aged not above 18 with SARS-CoV-2 infection and 1,163,478 patients without SARS-CoV-2 infection. Exposures: Documented SARS-CoV-2 infection, including positive polymerase chain reaction (PCR), serology, or antigen tests for SARS-CoV-2, or diagnoses of COVID-19 and COVID-related conditions. Main Outcomes and Measures: Prespecified GI symptoms and disorders during two intervals: post-acute phase and chronic phase following the documented SARS-CoV-2 infection. The adjusted risk ratio (aRR) was determined using a stratified Poisson regression model, with strata computed based on the propensity score. Results: Our cohort comprised 1,576,933 patients, with females representing 48.0% of the sample. The analysis revealed that children with SARS-CoV-2 infection had an increased risk of developing at least one GI symptom or disorder in both the post-acute (8.64% vs. 6.85%; aRR 1.25, 95% CI 1.24-1.27) and chronic phases (12.60% vs. 9.47%; aRR 1.28, 95% CI 1.26-1.30) compared to uninfected peers. Specifically, the risk of abdominal pain was higher in COVID-19 positive patients during the post-acute phase (2.54% vs. 2.06%; aRR 1.14, 95% CI 1.11-1.17) and chronic phase (4.57% vs. 3.40%; aRR 1.24, 95% CI 1.22-1.27). Conclusions and Relevance: In the post-acute phase or chronic phase of COVID-19, the risk of GI symptoms and disorders was increased for COVID-positive patients in the pediatric population. Key Points: Question: Does COVID-19 increase the risk of gastrointestinal (GI) symptoms and diseases during the post-acute phase in children and adolescents?Findings: Newly diagnosed GI symptoms and disorders such as diarrhea, constipation, and vomiting are seen more commonly in children and adolescents with SARS-CoV-2 infection.Meaning: Clinicians need to be mindful that after SARS-CoV-2 infection in children, lingering GI symptoms without a unifying diagnosis may be more common than among uninfected children.

Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER program.

IMPORTANCE: The frequency and characteristics of post-acute sequelae of SARS-CoV-2 infection (PASC) may vary by SARS-CoV-2 variant. OBJECTIVE: To characterize PASC-related conditions among individuals likely infected by the ancestral strain in 2020 and individuals likely infected by the Delta variant in 2021. DESIGN: Retrospective cohort study of electronic medical record data for approximately 27 million patients from March 1, 2020-November 30, 2021. SETTING: Healthcare facilities in New York and Florida. PARTICIPANTS: Patients who were at least 20 years old and had diagnosis codes that included at least one SARS-CoV-2 viral test during the study period. EXPOSURE: Laboratory-confirmed COVID-19 infection, classified by the most common variant prevalent in those regions at the time. MAIN OUTCOME(S) AND MEASURE(S): Relative risk (estimated by adjusted hazard ratio [aHR]) and absolute risk difference (estimated by adjusted excess burden) of new conditions, defined as new documentation of symptoms or diagnoses, in persons between 31-180 days after a positive COVID-19 test compared to persons without a COVID-19 test or diagnosis during the 31-180 days after the last negative test. RESULTS: We analyzed data from 560,752 patients. The median age was 57 years; 60.3% were female, 20.0% non-Hispanic Black, and 19.6% Hispanic. During the study period, 57,616 patients had a positive SARS-CoV-2 test; 503,136 did not. For infections during the ancestral strain period, pulmonary fibrosis, edema (excess fluid), and inflammation had the largest aHR, comparing those with a positive test to those without a COVID-19 test or diagnosis (aHR 2.32 [95% CI 2.09 2.57]), and dyspnea (shortness of breath) carried the largest excess burden (47.6 more cases per 1,000 persons). For infections during the Delta period, pulmonary embolism had the largest aHR comparing those with a positive test to a negative test (aHR 2.18 [95% CI 1.57, 3.01]), and abdominal pain carried the largest excess burden (85.3 more cases per 1,000 persons). CONCLUSIONS AND RELEVANCE: We documented a substantial relative risk of pulmonary embolism and a large absolute risk difference of abdomen-related symptoms after SARS-CoV-2 infection during the Delta variant period. As new SARS-CoV-2 variants emerge, researchers and clinicians should monitor patients for changing symptoms and conditions that develop after infection.

Association between acquiring SARS-CoV-2 during pregnancy and post-acute sequelae of SARS-CoV-2 infection: RECOVER electronic health record cohort analysis.

Background: Little is known about post-acute sequelae of SARS-CoV-2 infection (PASC) after acquiring SARS-CoV-2 infection during pregnancy. We aimed to evaluate the association between acquiring SARS-CoV-2 during pregnancy compared with acquiring SARS-CoV-2 outside of pregnancy and the development of PASC. Methods: This retrospective cohort study from the Researching COVID to Enhance Recovery (RECOVER) Initiative Patient-Centred Clinical Research Network (PCORnet) used electronic health record (EHR) data from 19 U.S. health systems. Females aged 18-49 years with lab-confirmed SARS-CoV-2 infection from March 2020 through June 2022 were included. Validated algorithms were used to identify pregnancies with a delivery at >20 weeks' gestation. The primary outcome was PASC, as previously defined by computable phenotype in the adult non-pregnant PCORnet EHR dataset, identified 30-180 days post-SARS-CoV-2 infection. Secondary outcomes were the 24 component diagnoses contributing to the PASC phenotype definition. Univariable comparisons were made for baseline characteristics between individuals with SARS-CoV-2 infection acquired during pregnancy compared with outside of pregnancy. Using inverse probability of treatment weighting to adjust for baseline differences, the association between SARS-CoV-2 infection acquired during pregnancy and the selected outcomes was modelled. The incident risk is reported as the adjusted hazard ratio (aHR) with 95% confidence intervals. Findings: In total, 83,915 females with SARS-CoV-2 infection acquired outside of pregnancy and 5397 females with SARS-CoV-2 infection acquired during pregnancy were included in analysis. Non-pregnant females with SARS-CoV-2 infection were more likely to be older and have comorbid health conditions. SARS-CoV-2 infection acquired in pregnancy as compared with acquired outside of pregnancy was associated with a lower incidence of PASC (25.5% vs 33.9%; aHR 0.85, 95% CI 0.80-0.91). SARS-CoV-2 infection acquired in pregnant females was associated with increased risk for some PASC component diagnoses including abnormal heartbeat (aHR 1.67, 95% CI 1.43-1.94), abdominal pain (aHR 1.34, 95% CI 1.16-1.55), and thromboembolism (aHR 1.88, 95% CI 1.17-3.04), but decreased risk for other diagnoses including malaise (aHR 0.35, 95% CI 0.27-0.47), pharyngitis (aHR 0.36, 95% CI 0.26-0.48) and cognitive problems (aHR 0.39, 95% CI 0.27-0.56). Interpretation: SARS-CoV-2 infection acquired during pregnancy was associated with lower risk of development of PASC at 30-180 days after incident SARS-CoV-2 infection in this nationally representative sample. These findings may be used to counsel pregnant and pregnant capable individuals, and direct future prospective study. Funding: National Institutes of Health (NIH) Other Transaction Agreement (OTA) OT2HL16184.

Navigating privacy and security in telemedicine for primary care.

OBJECTIVE: To examine patient and provider perspectives on privacy and security considerations in telemedicine during the COVID-19 pandemic. STUDY DESIGN: Qualitative study with patients and providers from primary care practices in 3 National Patient-Centered Clinical Research Network sites in New York, New York; North Carolina; and Florida. METHODS: Semistructured interviews were conducted, audio recorded, transcribed verbatim, and coded using an inductive process. Data related to privacy and information security were analyzed. RESULTS: Sixty-five patients and 21 providers participated. Patients and providers faced technology-related security concerns as well as difficulties ensuring privacy in the transformed shared space of telemedicine. Patients expressed increased comfort doing telemedicine from home but often did not like their providers to offer virtual visits from outside an office setting. Providers initially struggled to find secure and Health Insurance Portability and Accountability Act-compliant platforms and devices to host the software. Whereas some patients preferred familiar platforms such as FaceTime, others recognized potential security concerns. Audio-only encounters sometimes raised patient concerns that they would not be able to confirm the identity of the provider. CONCLUSIONS: Telemedicine led to novel concerns about privacy because patients and providers were often at home or in public spaces, and they shared concerns about software and hardware security. In addition to technological safeguards, our study emphasizes the critical role of physical infrastructure in ensuring privacy and security. As telemedicine continues to evolve, it is important to address and mitigate concerns around privacy and security to ensure high-quality and safe delivery of care to patients in remote settings.

Association between SARS-CoV-2 infection and select symptoms and conditions 31 to 150 days after testing among children and adults.

BACKGROUND: An increasing number of studies have described new and persistent symptoms and conditions as potential post-acute sequelae of SARS-CoV-2 infection (PASC). However, it remains unclear whether certain symptoms or conditions occur more frequently among persons with SARS-CoV-2 infection compared with those never infected with SARS-CoV-2. We compared the occurrence of specific COVID-associated symptoms and conditions as potential PASC 31- to 150-day following a SARS-CoV-2 test among adults and children with positive and negative test results. METHODS: We conducted a retrospective cohort study using electronic health record (EHR) data from 43 PCORnet sites participating in a national COVID-19 surveillance program. This study included 3,091,580 adults (316,249 SARS-CoV-2 positive; 2,775,331 negative) and 675,643 children (62,131 positive; 613,512 negative) who had a SARS-CoV-2 laboratory test during March 1, 2020-May 31, 2021 documented in their EHR. We used logistic regression to calculate the odds of having a symptom and Cox models to calculate the risk of having a newly diagnosed condition associated with a SARS-CoV-2 positive test. RESULTS: After adjustment for baseline covariates, hospitalized adults and children with a positive test had increased odds of being diagnosed with ≥ 1 symptom (adults: adjusted odds ratio[aOR], 1.17[95% CI, 1.11-1.23]; children: aOR, 1.18[95% CI, 1.08-1.28]) or shortness of breath (adults: aOR, 1.50[95% CI, 1.38-1.63]; children: aOR, 1.40[95% CI, 1.15-1.70]) 31-150 days following a SARS-CoV-2 test compared with hospitalized individuals with a negative test. Hospitalized adults with a positive test also had increased odds of being diagnosed with ≥ 3 symptoms or fatigue compared with those testing negative. The risks of being newly diagnosed with type 1 or type 2 diabetes (adjusted hazard ratio[aHR], 1.25[95% CI, 1.17-1.33]), hematologic disorders (aHR, 1.19[95% CI, 1.11-1.28]), or respiratory disease (aHR, 1.44[95% CI, 1.30-1.60]) were higher among hospitalized adults with a positive test compared with those with a negative test. Non-hospitalized adults with a positive test also had higher odds or increased risk of being diagnosed with certain symptoms or conditions. CONCLUSIONS: Patients with SARS-CoV-2 infection, especially those who were hospitalized, were at higher risk of being diagnosed with certain symptoms and conditions after acute infection.

Patient Experiences with and Preferences for Telemedicine Relative to In-Person Care During the COVID-19 Pandemic.

Introduction: Although telemedicine emerged during the COVID-19 pandemic as a critical mode of health care delivery, there may be differences in the perceived ease of patient-clinician communication and quality of care for telemedicine versus in-person visits, as well as variation in perceptions across patient subgroups. We examined patients' experiences with and preferences for telemedicine relative to in-person care, based on their most recent visit. Methods: We conducted a survey of 2,668 adults in a large academic health care system in November 2021. The survey captured patients' reasons for their most recent visit, perceptions on patient-clinician communication and quality of care, and attitudes toward telemedicine versus in-person care. Results: Among respondents, 552 (21%) had a telemedicine visit. Patients with telemedicine and in-person visits had similar agreement on ease of patient-clinician communication and perceived quality of the visit on average. However, for individuals 65 years of age or older, men, and those not needing urgent care, telemedicine was associated with worse perceptions of patient-clinician communication (65 years of age or older: adjusted odds ratio [aOR], 0.51; 95% confidence interval [CI], 0.31-0.85; men: aOR, 0.50; 95% CI, 0.31-0.81; urgent care: aOR 0.67; 95% CI, 0.49-0.91) and lower perceived quality (65 years of age or older, aOR 0.51; 95% CI, 0.30-0.86; men: 0.51; 95% CI, 0.32-0.83; urgent care: aOR 0.68; 95% CI, 0.49-0.93). Conclusion: Patient-perceived quality of care and patient-clinician communication were similar for telemedicine and in-person visits overall. However, among men, older adults, and those not seeking urgent care, patients using telemedicine had lower perceptions of patient-clinician communication and quality.

Avenues for Strengthening PCORnet's Capacity to Advance Patient-Centered Economic Outcomes in Patient-Centered Outcomes Research (PCOR).

PCORnet, the National Patient-Centered Clinical Research Network, provides the ability to conduct prospective and observational pragmatic research by leveraging standardized, curated electronic health records data together with patient and stakeholder engagement. PCORnet is funded by the Patient-Centered Outcomes Research Institute (PCORI) and is composed of 8 Clinical Research Networks that incorporate at total of 79 health system "sites." As the network developed, linkage to commercial health plans, federal insurance claims, disease registries, and other data resources demonstrated the value in extending the networks infrastructure to provide a more complete representation of patient's health and lived experiences. Initially, PCORnet studies avoided direct economic comparative effectiveness as a topic. However, PCORI's authorizing law was amended in 2019 to allow studies to incorporate patient-centered economic outcomes in primary research aims. With PCORI's expanded scope and PCORnet's phase 3 beginning in January 2022, there are opportunities to strengthen the network's ability to support economic patient-centered outcomes research. This commentary will discuss approaches that have been incorporated to date by the network and point to opportunities for the network to incorporate economic variables for analysis, informed by patient and stakeholder perspectives. Topics addressed include: (1) data linkage infrastructure; (2) commercial health plan partnerships; (3) Medicare and Medicaid linkage; (4) health system billing-based benchmarking; (5) area-level measures; (6) individual-level measures; (7) pharmacy benefits and retail pharmacy data; and (8) the importance of transparency and engagement while addressing the biases inherent in linking real-world data sources.

Development of a novel instrument to characterize telemedicine programs in primary care.

BACKGROUND: Given the rapid deployment of telemedicine at the onset of the COVID - 19 pandemic, updated assessment methods are needed to study and characterize telemedicine programs. We developed a novel semi - structured survey instrument to systematically describe the characteristics and implementation processes of telemedicine programs in primary care. METHODS: In the context of a larger study aiming to describe telemedicine programs in primary care, a survey was developed in 3 iterative steps: 1) literature review to obtain a list of telemedicine features, facilitators, and barriers; 2) application of three evaluation frameworks; and 3) stakeholder engagement through a 2-stage feedback process. During survey refinement, items were tested against the evaluation frameworks while ensuring it could be completed within 20-25 min. Data reduction techniques were applied to explore opportunity for condensed variables/items. RESULTS: Sixty initially identified telemedicine features were reduced to 32 items / questions after stakeholder feedback. Per the life cycle framework, respondents are asked to report a month in which their telemedicine program reached a steady state, i.e., "maturation". Subsequent questions on telemedicine features are then stratified by telemedicine services offered at the pandemic onset and the reported point of maturation. Several open - ended questions allow for additional telemedicine experiences to be captured. Data reduction techniques revealed no indication for data reduction. CONCLUSION: This 32-item semi-structured survey standardizes the description of primary care telemedicine programs in terms of features as well as maturation process. This tool will facilitate evaluation of and comparisons between telemedicine programs across the United States, particularly those that were deployed at the pandemic onset.

Social determinants of health and high-cost utilization among commercially insured population.

OBJECTIVES: To assess the impact of adding neighborhood social determinants of health (SDOH) data to demographic and clinical characteristics for predicting high-cost utilizers and to examine variations across age groups. STUDY DESIGN: Using US Census data and 2017-2018 commercial claims from a large national insurer, we estimated association between neighborhood-level SDOH and the probability of being a high-cost utilizer. METHODS: Observational study using administrative claims from a national insurer and US Census data. Data included a 50% random sample of commercially insured individuals who were younger than 89 years and had 1 year of continuous eligibility in 2017 and at least 30 days in 2018. Probit models assessed impact of SDOH and neighborhood conditions on predicting cost status. RESULTS: SDOH did not improve predictive power of evaluated models. However, disadvantaged neighborhood residence was still associated with being a high-cost utilizer. Adults 65 years and older in disadvantaged neighborhoods had increased likelihood of high-cost utilization. Children and younger adults in disadvantaged neighborhoods had lower risk of becoming high-cost utilizers. CONCLUSIONS: Policy makers and industry stakeholders should be aware of the mechanisms behind the relationship between neighborhood social conditions and health outcomes and how the relationship differs across age groups.

Many Intensive Care Units Were Overloaded While Nearby Hospitals Had Excess Capacity During The COVID-19 Pandemic.

Strained hospital capacity is associated with adverse patient outcomes. Anecdotal evidence suggests that during the COVID-19 pandemic in the US, some hospitals experienced capacity constraints while others in the same market had surplus capacity, a phenomenon known as "load imbalance." Our study evaluated the prevalence of intensive care unit load imbalance and the characteristics of hospitals most likely to be over capacity while other nearby hospitals were under capacity. Of the 290 hospital referral regions (HRRs) analyzed, 154 (53.1 percent) experienced load imbalance during the study period. HRRs experiencing the most imbalance had higher proportions of Black residents. Hospitals with the highest Medicaid patient shares and Black Medicare patient shares were significantly more likely to be over capacity, while other hospitals in their market were under capacity. Our findings highlight that hospital load imbalance was common during the COVID-19 pandemic. Policies to coordinate transfers may decrease strain during periods of high demand and ease the burden on hospitals that serve a higher proportion of patients from racial minority groups.

A Qualitative Study on Using Telemedicine for Precepting and Teaching in the Academic Setting.

PURPOSE: To examine the impact of telemedicine use on precepting and teaching among preceptors and patients during the COVID-19 pandemic. METHOD: The authors conducted a secondary analysis of a qualitative study focusing on providers' and patients' experiences with and attitudes toward telemedicine at 4 academic health centers. Teaching and precepting were emergent codes from the data and organized into themes. Themes were mapped to domains from the 2009 Consolidated Framework for Implementation Research (CFIR), a framework that assists with effective implementation and consists of 5 domains: intervention characteristics, outer settings, inner settings, characteristics of individuals, and process. RESULTS: In total, 86 interviews were conducted with 65 patients and 21 providers. Nine providers and 3 patients recounted descriptions related to teaching and precepting with telemedicine. Eight themes were identified, mapping across all 5 CFIR domains, with the majority of themes (n = 6) within the domains of characteristics of individuals, processes, and intervention characteristics. Providers and patients described how a lack of prepandemic telemedicine experience and inadequate processes in place to precept and teach with telemedicine affected the learning environment and perceived quality of care. They also discussed how telemedicine exacerbated existing difficulties in maintaining resident continuity. Providers described ways communication changed with telemedicine use during the pandemic, including having to wear masks while in the same room as the trainee and sitting closely to remain within range of the camera, as well as the benefit of observing trainees with the attending's camera off. Providers expressed a lack of protected structure and time for teaching and supervising with telemedicine, and a general view that telemedicine is here to stay. CONCLUSIONS: Efforts should focus on increasing knowledge of telemedicine skills and improving processes to implement telemedicine in the teaching setting in order to best integrate it into undergraduate and graduate medical education.

The Telemedicine Experience in Primary Care Practices in the United States: Insights From Practice Leaders.

PURPOSE: The need to rapidly implement telemedicine in primary care during the coronavirus disease 2019 (COVID-19) pandemic was addressed differently by various practices. Using qualitative data from semistructured interviews with primary care practice leaders, we aimed to report commonly shared experiences and unique perspectives regarding telemedicine implementation and evolution/maturation since March 2020. METHODS: We administered a semistructured, 25-minute, virtual interview with 25 primary care practice leaders from 2 health systems in 2 states (New York and Florida) included in PCORnet, the Patient-Centered Outcomes Research Institute clinical research network. Questions were guided by 3 frameworks (health information technology evaluation, access to care, and health information technology life cycle) and involved practice leaders' perspectives on the process of telemedicine implementation in their practice, with a specific focus on the process of maturation and facilitators/barriers. Two researchers conducted inductive coding of qualitative data open-ended questions to identify common themes. Transcripts were electronically generated by virtual platform software. RESULTS: Twenty-five interviews were administered for practice leaders representing 87 primary care practices in 2 states. We identified the following 4 major themes: (1) the ease of telemedicine adoption depended on both patients' and clinicians' prior experience using virtual health platforms, (2) regulation of telemedicine varied across states and differentially affected the rollout processes, (3) visit triage rules were unclear, and (4) there were positive and negative effects of telemedicine on clinicians and patients. CONCLUSIONS: Practice leaders identified several challenges to telemedicine implementation and highlighted 2 areas, including telemedicine visit triage guidelines and telemedicine-specific staffing and scheduling protocols, for improvement.

Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative.

Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with specific patient populations which makes their generalizability unclear. This study aims to characterize PASC using the EHR data warehouses from two large Patient-Centered Clinical Research Networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) area and 16.8 million patients in Florida respectively. With a high-throughput screening pipeline based on propensity score and inverse probability of treatment weighting, we identified a broad list of diagnoses and medications which exhibited significantly higher incidence risk for patients 30-180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients. We identified more PASC diagnoses in NYC than in Florida regarding our screening criteria, and conditions including dementia, hair loss, pressure ulcers, pulmonary fibrosis, dyspnea, pulmonary embolism, chest pain, abnormal heartbeat, malaise, and fatigue, were replicated across both cohorts. Our analyses highlight potentially heterogeneous risks of PASC in different populations.

Provider and Patient Experiences of Delays in Primary Care During the Early COVID-19 Pandemic.

BACKGROUND: The necessary suspension of nonacute services by healthcare systems early in the COVID-19 pandemic was predicted to cause delays in routine care in the United States, with potentially serious consequences for chronic disease management. However, limited work has examined provider or patient perspectives about care delays and their implications for care quality in future healthcare emergencies. OBJECTIVE: This study explores primary care provider (PCP) and patient experiences with healthcare delays during the COVID-19 pandemic. METHODS: PCPs and patients were recruited from four large healthcare systems in three states. Participants underwent semistructured interviews asking about their experiences with primary care and telemedicine. Data were analyzed using interpretive description. RESULTS: Twenty-one PCPs and 65 patients participated in interviews. Four main topics were identified: (1) types of care delayed, (2) causes for delays, (3) miscommunication contributing to delays, and (4) patient solutions to unmet care needs. CONCLUSIONS: Both patients and providers reported delays in preventive and routine care early in the pandemic, driven by healthcare system changes and patient concerns about infection risk. Primary care practices should develop plans for care continuity and consider new strategies for assessing care quality for effective chronic disease management in future healthcare system disruptions.

Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER Program.

Importance: The frequency and characteristics of post-acute sequelae of SARS-CoV-2 infection (PASC) may vary by SARS-CoV-2 variant. Objective: To characterize PASC-related conditions among individuals likely infected by the ancestral strain in 2020 and individuals likely infected by the Delta variant in 2021. Design: Retrospective cohort study of electronic medical record data for approximately 27 million patients from March 1, 2020-November 30, 2021. Setting: Healthcare facilities in New York and Florida. Participants: Patients who were at least 20 years old and had diagnosis codes that included at least one SARS-CoV-2 viral test during the study period. Exposure: Laboratory-confirmed COVID-19 infection, classified by the most common variant prevalent in those regions at the time. Main Outcomes and Measures: Relative risk (estimated by adjusted hazard ratio [aHR]) and absolute risk difference (estimated by adjusted excess burden) of new conditions, defined as new documentation of symptoms or diagnoses, in persons between 31-180 days after a positive COVID-19 test compared to persons with only negative tests during the 31-180 days after the last negative test. Results: We analyzed data from 560,752 patients. The median age was 57 years; 60.3% were female, 20.0% non-Hispanic Black, and 19.6% Hispanic. During the study period, 57,616 patients had a positive SARS-CoV-2 test; 503,136 did not. For infections during the ancestral strain period, pulmonary fibrosis, edema (excess fluid), and inflammation had the largest aHR, comparing those with a positive test to those with a negative test, (aHR 2.32 [95% CI 2.09 2.57]), and dyspnea (shortness of breath) carried the largest excess burden (47.6 more cases per 1,000 persons). For infections during the Delta period, pulmonary embolism had the largest aHR comparing those with a positive test to a negative test (aHR 2.18 [95% CI 1.57, 3.01]), and abdominal pain carried the largest excess burden (85.3 more cases per 1,000 persons). Conclusions and Relevance: We documented a substantial relative risk of pulmonary embolism and large absolute risk difference of abdomen-related symptoms after SARS-CoV-2 infection during the Delta variant period. As new SARS-CoV-2 variants emerge, researchers and clinicians should monitor patients for changing symptoms and conditions that develop after infection.

Risk Factors and Predictive Modeling for Post-Acute Sequelae of SARS-CoV-2 Infection: Findings from EHR Cohorts of the RECOVER Initiative.

Background: Patients who were SARS-CoV-2 infected could suffer from newly incidental conditions in their post-acute infection period. These conditions, denoted as the post-acute sequelae of SARS-CoV-2 infection (PASC), are highly heterogeneous and involve a diverse set of organ systems. Limited studies have investigated the predictability of these conditions and their associated risk factors. Method: In this retrospective cohort study, we investigated two large-scale PCORnet clinical research networks, INSIGHT and OneFlorida+, including 11 million patients in the New York City area and 16.8 million patients from Florida, to develop machine learning prediction models for those who are at risk for newly incident PASC and to identify factors associated with newly incident PASC conditions. Adult patients aged 20 with SARS-CoV-2 infection and without recorded infection between March 1st, 2020, and November 30th, 2021, were used for identifying associated factors with incident PASC after removing background associations. The predictive models were developed on infected adults. Results: We find several incident PASC, e.g., malnutrition, COPD, dementia, and acute kidney failure, were associated with severe acute SARS-CoV-2 infection, defined by hospitalization and ICU stay. Older age and extremes of weight were also associated with these incident conditions. These conditions were better predicted (C-index >0.8). Moderately predictable conditions included diabetes and thromboembolic disease (C-index 0.7-0.8). These were associated with a wider variety of baseline conditions. Less predictable conditions included fatigue, anxiety, sleep disorders, and depression (C-index around 0.6). Conclusions: This observational study suggests that a set of likely risk factors for different PASC conditions were identifiable from EHRs, predictability of different PASC conditions was heterogeneous, and using machine learning-based predictive models might help in identifying patients who were at risk of developing incident PASC.

Telemedicine Impact on the Patient-Provider Relationship in Primary Care During the COVID-19 Pandemic.

BACKGROUND: The COVID-19 pandemic has necessitated a rapid uptake of telemedicine in primary care requiring both patients and providers to learn how to navigate care remotely. This change can impact the patient-provider relationship that often defines care, especially in primary care. OBJECTIVE: This study aims to provide insight into the experiences of patients and providers with telemedicine during the pandemic, and the impact it had on their relationship. RESEARCH DESIGN: A qualitative study using thematic analysis of semistructured interviews. SUBJECTS: Primary care providers (n=21) and adult patients (n=65) with chronic disease across primary care practices in 3 National Patient-centered Clinical Research Network sites in New York City, North Carolina, and Florida. MEASURES: Experiences with telemedicine during the COVID-19 pandemic in primary care. Codes related to the patient-provider relationship were analyzed for this study. RESULTS: A recurrent theme was the challenge telemedicine posed on rapport building and alliance. Patients felt that telemedicine affected provider's attentiveness in varying ways, whereas providers appreciated that telemedicine provided unique insight into patients' lives and living situations. Finally, both patients and providers described communication challenges. CONCLUSIONS: Telemedicine has altered structure and process aspects of primary health care such as the physical spaces of encounters, creating a new setting to which both patients and providers must adjust. It is important to recognize the opportunities and limits that this new technology has to help providers maintain the type of one-on-one attention that patients expect and that contributes to relationship building.

Identifying environmental risk factors for post-acute sequelae of SARS-CoV-2 infection: An EHR-based cohort study from the recover program.

Post-acute sequelae of SARS-CoV-2 infection (PASC) affects a wide range of organ systems among a large proportion of patients with SARS-CoV-2 infection. Although studies have identified a broad set of patient-level risk factors for PASC, little is known about the association between "exposome"-the totality of environmental exposures and the risk of PASC. Using electronic health data of patients with COVID-19 from two large clinical research networks in New York City and Florida, we identified environmental risk factors for 23 PASC symptoms and conditions from nearly 200 exposome factors. The three domains of exposome include natural environment, built environment, and social environment. We conducted a two-phase environment-wide association study. In Phase 1, we ran a mixed effects logistic regression with 5-digit ZIP Code tabulation area (ZCTA5) random intercepts for each PASC outcome and each exposome factor, adjusting for a comprehensive set of patient-level confounders. In Phase 2, we ran a mixed effects logistic regression for each PASC outcome including all significant (false positive discovery adjusted p-value < 0.05) exposome characteristics identified from Phase I and adjusting for confounders. We identified air toxicants (e.g., methyl methacrylate), particulate matter (PM2.5) compositions (e.g., ammonium), neighborhood deprivation, and built environment (e.g., food access) that were associated with increased risk of PASC conditions related to nervous, blood, circulatory, endocrine, and other organ systems. Specific environmental risk factors for each PASC condition and symptom were different across the New York City area and Florida. Future research is warranted to extend the analyses to other regions and examine more granular exposome characteristics to inform public health efforts to help patients recover from SARS-CoV-2 infection.