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Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease. [ABSTRACT FROM AUTHOR]

Background: Virtual care increased dramatically during the COVID‐19 pandemic. The specific modality of virtual care (video, audio, eVisits, eConsults, and remote patient monitoring) has important implications for the accessibility and quality of care, but rates of use are relatively unknown. Methods for identifying virtual care modalities, especially in electronic health records (EHR) are inconsistent. This study (a) developed a method to identify virtual care modalities using EHR data and (b) described the distribution of these modalities over a 3‐year study period. Methods: EHR data from 316 primary care safety net clinics throughout the study period (4/1/2020‐3/31/2023) were included. Visit type (in‐person vs virtual) by adults >18 years old were classified. Expert consultation informed the development of two algorithms to classify virtual care visit modalities; these algorithms prioritized different EHR data elements. We conducted descriptive analyses comparing algorithms and the frequency of virtual care modalities. Results: Agreement between the algorithms was 96.5% for all visits and 89.3% for virtual care visits. The majority of disagreement between the algorithms was among encounters scheduled as audio‐only but billed as a video visit. Restricting to visits where the algorithms agreed on visit modality, there were 2‐fold more audio‐only than video visits. Conclusion: Visit modality classification varies depending upon which data in the EHR are prioritized. Regardless of which algorithm is utilized, safety net clinics rely on audio‐only and video visits to provide care in virtual visits. Elimination of reimbursement for audio visits may exacerbate existing inequities in care for low‐income patients. [ABSTRACT FROM AUTHOR]

Rationale: Shorter time-to-antibiotics is lifesaving in sepsis, but programs to hasten antibiotic delivery may increase unnecessary antibiotic use and adverse events. Objectives: We sought to estimate both the benefits and harms of shortening time-to-antibiotics for sepsis. Methods: We conducted a simulation study using a cohort of 1,559,523 hospitalized patients admitted through the emergency department with meeting two or more systemic inflammatory response syndrome criteria (2013-2018). Reasons for hospitalization were classified as septic shock, sepsis, infection, antibiotics stopped early, and never treated (no antibiotics within 48 h). We simulated the impact of a 50% reduction in time-toantibiotics for sepsis across 12 hospital scenarios defined by sepsis prevalence (low, medium, or high) and magnitude of "spillover" antibiotic prescribing to patients without infection (low, medium, high, or very high). Outcomes included mortality and adverse events potentially attributable to antibiotics (e.g., allergy, organ dysfunction, Clostridiodes difficile infection, and culture with multidrug-resistant organism). Results: A total of 933,458 (59.9%) hospitalized patients received antimicrobial therapy within 48 hours of presentation, including 38,572 (2.5%) with septic shock, 276,082 (17.7%) with sepsis, 370,705 (23.8%) with infection, and 248,099 (15.9%) with antibiotics stopped early. A total of 199,937 (12.8%) hospitalized patients experienced an adverse event; most commonly, acute liver injury (5.6%), new MDRO (3.5%), and Clostridiodes difficile infection (1.7%). Across the scenarios, a 50% reduction in timeto-antibiotics for sepsis was associated with a median of 1 to 180 additional antibiotic-treated patients and zero to seven additional adverse events per death averted from sepsis. Conclusions: The impacts of faster time-to-antibiotics for sepsis vary markedly across simulated hospital types. However, even in the worst-case scenario, new antibiotic-associated adverse events were rare. [ABSTRACT FROM AUTHOR]

Objective Data Sources and Study Setting Study Design Data Collection/Extraction Methods Principal Findings Conclusion To compare morbidity burden captured from multimorbidity indices and aggregated measures of clinically meaningful categories captured in primary care community‐based health center (CBHC) patients.Electronic health records of patients seen in 2019 in OCHIN's national network of CBHCs serving patients in rural and underserved communities.Age‐stratified analyses comparing the most common conditions captured by the Charlson, Elixhauser, and Multimorbidity Weighted (MWI) indices, and Classification Software Refined (CCSR) and Chronic Condition Indicator (CCI) algorithms.Active ICD‐10 conditions on patients' problem list in 2019.Approximately 35%–56% of patients with at least one condition are not captured by the Charlson, Elixhauser, and MWI indices. When stratified by age, this range broadens to 9%–90% with higher percentages in younger patients. The CCSR and CCI reflect a broader range of acute and chronic conditions prevalent among CBHC patients.Three commonly used indices to capture morbidity burden reflect conditions most prevalent among older adults, but do not capture those on problem lists for younger CBHC patients. An index with an expanded range of care conditions is needed to understand the complex care provided to primary care populations across the lifespan. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Understanding how the COVID-19 pandemic affected cardiovascular disease (CVD) risk monitoring in primary care may inform new approaches for addressing modifiable CVD risks. This study examined how pandemic-driven changes in primary care delivery affected CVD risk management processes. STUDY DESIGN: This retrospective study used electronic health record data from patients at 70 primary care community clinics with scheduled appointments from September 1, 2018, to September 30, 2021. METHODS: Analyses examined associations between appointment type and select care process measures: appointment completion rates, time to appointment, and up-to-date documentation for blood pressure (BP) and hemoglobin A1c (HbA1c). RESULTS: Of 1,179,542 eligible scheduled primary care appointments, completion rates were higher for virtual care (VC) vs in-person appointments (10.7 percentage points [PP]; 95% CI, 10.5-11.0; P < .001). Time to appointment was shorter for VC vs in-person appointments (--3.9 days; 95% CI, --4.1 to --3.7; P < .001). BP documentation was higher for appointments completed pre-- vs post pandemic onset (16.2 PP; 95% CI, 16.0-16.5; P < .001) and for appointments completed in person vs VC (54.9 PP; 95% CI, 54.6-55.2; P < .001). HbA1c documentation was higher for completed appointments after pandemic onset vs before (5.9 PP; 95% CI, 5.1-6.7; P < .001) and for completed VC appointments vs in-person appointments (3.9 PP; 95% CI, 3.0-4.7; P < .001). CONCLUSIONS: After pandemic onset, appointment completion rates were higher, time to appointment was shorter, HbA1c documentation increased, and BP documentation decreased. Future research should explore the advantages of using VC for CVD risk management while continuing to monitor for unintended consequences. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Limited research has assessed how virtual care (VC) affects cardiovascular disease (CVD) risk management, especially in community clinic settings. This study assessed change in community clinic patients' CVD risk management during the COVID-19 pandemic and CVD risk factor control among patients who had primarily in-person or primarily VC visits. STUDY DESIGN: Retrospective interrupted time-series analysis. METHODS: Data came from an electronic health record shared by 52 community clinics for index (March 1, 2019, to February 29, 2020) and follow-up (July 1, 2020, to February 28, 2022) periods. Analyses compared follow-up period changes in slope and level of population monthly means of 10-year reversible CVD risk score, blood pressure (BP), and hemoglobin A1c (HbA1c) among patients whose completed follow-up period visits were primarily in person vs primarily VC. Propensity score weighting minimized confounding. RESULTS: There were 10,028 in-person and 6593 VC patients in CVD risk analyses, 9874 in-person and 5390 VC patients in BP analyses, and 8221 in-person and 4937 VC patients in HbA1c analyses. The VC group was more commonly younger, female, White, and urban. Mean reversible CVD risk, mean systolic BP, and percentage of BP measurements that were 140/90 mm Hg or higher increased significantly from index to follow-up periods in both groups. Rate of change between these periods was the same for all outcomes in both groups, regardless of care modality. CONCLUSIONS: Among community clinic patients with CVD risk, receiving a majority of care in person vs a majority of care via VC was not significantly associated with longitudinal trends in reversible CVD risk score or key CVD risk factors. [ABSTRACT FROM AUTHOR]

Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease., Competing Interests: Competing Interests The authors declare no competing interests.

Introduction: Health centers provide primary and behavioral health care to the nation's safety net population. Many health centers served on the frontlines of the COVID-19 pandemic, which brought major changes to health center care delivery., Objective: To elucidate primary care and behavioral health service delivery patterns in health centers before and during the COVID-19 public health emergency (PHE)., Methods: We compared annual and monthly patients from 2019 to 2022 for new and established patients by visit type (primary care, behavioral health) and encounter visits by modality (in-person, telehealth) across 218 health centers in 13 states., Results: There were 1581,744 unique patients in the sample, most from health disparate populations. Review of primary care data over 4 years show that health centers served fewer pediatric patients over time, while retaining the capacity to provide to patients 65+. Monthly data on encounters highlights that the initial shift in March/April 2020 to telehealth was not sustained and that in-person visits rose steadily after November/December 2020 to return as the predominant care delivery mode. With regards to behavioral health, health centers continued to provide care to established patients throughout the PHE, while serving fewer new patients over time. In contrast to primary care, after initial uptake of telehealth in March/April 2020, telehealth encounters remained the predominant care delivery mode through 2022., Conclusion: Four years of data demonstrate how COVID-19 impacted delivery of primary care and behavioral health care for patients, highlighting gaps in pediatric care delivery and trends in telehealth over time., Competing Interests: Conflict of interest:: The authors have no conflicts of interest to declare., (© Copyright by the American Board of Family Medicine.)

Background: The purpose of this study was to evaluate the associations of (1) individual absolute and body size normalized weakness cut-points, and (2) the collective weakness classifications on time to diabetes in Americans., Methods: We analyzed data from 9577 adults aged at least 50-years from the Health and Retirement Study. Diabetes diagnosis was self-reported. A handgrip dynamometer measured handgrip strength (HGS). Males with HGS <35.5 kg (absolute), <0.45 kg/kg (normalized to body weight), or <1.05 kg/kg/m 2 (normalized to BMI) were categorized as weak. Females were classified as weak if their HGS was <20.0 kg, <0.337 kg/kg, or <0.79 kg/kg/m 2 . Compounding weakness included falling below 1, 2, or all 3 cut-points., Results: Persons below the body weight normalized weakness cut-points had a 1.29 (95% confidence interval (CI): 1.15-1.47) higher hazard for incident diabetes, while those below the BMI normalized cut-points had a 1.30 (CI: 1.13-1.51) higher hazard. The association between absolute weakness and incident diabetes was insignificant (hazard ratio: 1.06; CI: 0.91-1.24). Americans below 1, 2, or all 3 collective weakness categories had a 1.28 (CI: 1.10-1.50), 1.29 (CI: 1.08-1.52), and 1.33 (CI: 1.09-1.63) higher hazard for the incidence of diabetes, respectively., Conclusions: Our findings indicate that while absolute weakness, which is confounded by body size, was not associated with time to diabetes, adjusting for the influence of body size by normalizing HGS to body weight and BMI was significantly associated with time to diabetes. This suggests that muscle strength, not body size, may be driving such associations with time to diabetes., Competing Interests: CONFLICTS OF INTEREST The authors declare that they have no conflicts of interest.