Long COVID Clinical Phenotypes up to 6 Months After Infection Identified by Latent Class Analysis of Self-Reported Symptoms.

Background: The prevalence, incidence, and interrelationships of persistent symptoms after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection vary. There are limited data on specific phenotypes of persistent symptoms. Using latent class analysis (LCA) modeling, we sought to identify whether specific phenotypes of COVID-19 were present 3 months and 6 months post-infection. Methods: This was a multicenter study of symptomatic adults tested for SARS-CoV-2 with prospectively collected data on general symptoms and fatigue-related symptoms up to 6 months postdiagnosis. Using LCA, we identified symptomatically homogenous groups among COVID-positive and COVID-negative participants at each time period for both general and fatigue-related symptoms. Results: Among 5963 baseline participants (4504 COVID-positive and 1459 COVID-negative), 4056 had 3-month and 2856 had 6-month data at the time of analysis. We identified 4 distinct phenotypes of post-COVID conditions (PCCs) at 3 and 6 months for both general and fatigue-related symptoms; minimal-symptom groups represented 70% of participants at 3 and 6 months. When compared with the COVID-negative cohort, COVID-positive participants had higher occurrence of loss of taste/smell and cognition problems. There was substantial class-switching over time; those in 1 symptom class at 3 months were equally likely to remain or enter a new phenotype at 6 months. Conclusions: We identified distinct classes of PCC phenotypes for general and fatigue-related symptoms. Most participants had minimal or no symptoms at 3 and 6 months of follow-up. Significant proportions of participants changed symptom groups over time, suggesting that symptoms present during the acute illness may differ from prolonged symptoms and that PCCs may have a more dynamic nature than previously recognized. Clinical Trials Registration. NCT04610515.

Three-Month Symptom Profiles Among Symptomatic Adults With Positive and Negative Severe Acute Respiratory Syndrome Coronavirus 2 Tests: A Prospective Cohort Study From the INSPIRE Group.

BACKGROUND: Long-term symptoms following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are a major concern, yet their prevalence is poorly understood. METHODS: We conducted a prospective cohort study comparing adults with SARS-CoV-2 infection (coronavirus disease-positive [COVID+]) with adults who tested negative (COVID-), enrolled within 28 days of a Food and Drug Administration (FDA)-approved SARS-CoV-2 test result for active symptoms. Sociodemographic characteristics, symptoms of SARS-CoV-2 infection (assessed with the Centers for Disease Control and Prevention [CDC] Person Under Investigation Symptom List), and symptoms of post-infectious syndromes (ie, fatigue, sleep quality, muscle/joint pains, unrefreshing sleep, and dizziness/fainting, assessed with CDC Short Symptom Screener for myalgic encephalomyelitis/chronic fatigue syndrome) were assessed at baseline and 3 months via electronic surveys sent via text or email. RESULTS: Among the first 1000 participants, 722 were COVID+ and 278 were COVID-. Mean age was 41.5 (SD 15.2); 66.3% were female, 13.4% were Black, and 15.3% were Hispanic. At baseline, SARS-CoV-2 symptoms were more common in the COVID+ group than the COVID- group. At 3 months, SARS-CoV-2 symptoms declined in both groups, although were more prevalent in the COVID+ group: upper respiratory symptoms/head/eyes/ears/nose/throat (HEENT; 37.3% vs 20.9%), constitutional (28.8% vs 19.4%), musculoskeletal (19.5% vs 14.7%), pulmonary (17.6% vs 12.2%), cardiovascular (10.0% vs 7.2%), and gastrointestinal (8.7% vs 8.3%); only 50.2% and 73.3% reported no symptoms at all. Symptoms of post-infectious syndromes were similarly prevalent among the COVID+ and COVID- groups at 3 months. CONCLUSIONS: Approximately half of COVID+ participants, as compared with one-quarter of COVID- participants, had at least 1 SARS-CoV-2 symptom at 3 months, highlighting the need for future work to distinguish long COVID. CLINICAL TRIALS REGISTRATION: NCT04610515.

Associations Between Physical Activity Vital Sign in Patients and Health Care Utilization in a Health Care System, 2018-2020.

BACKGROUND: Physical inactivity is a risk factor for many chronic conditions. This retrospective cohort study examined associations between physical activity (PA) with health care utilization (HU). METHODS: A PA vital sign was recorded in clinics from January 2018 to December 2020. Patients were categorized as inactive, insufficiently active, or sufficiently active by US PA aerobic guidelines. Associations between PA vital sign and visits (inpatient admissions, emergency department, urgent care, and primary care) were estimated using population average regression by visit type. RESULTS: 23,721 patients had at least one PA vital sign recorded, with a mean age of 47.3 years and mean body mass index (BMI) of 28; 52% were female and 63% were White. Sufficiently active patients were younger, male, White, and had lower BMI than insufficiently active patients. Achieving 150 minutes per week of moderate to vigorous PA per 1000 patient years was associated with 34 fewer emergency department visits (P < .001), 19 fewer inpatient admissions (P < .001), and 38 fewer primary care visits (P < .001) compared with inactive patients. Stronger associations between lower PA and higher HU were present among those who were older or had a higher comorbidity. BMI, sex, ethnicity, and race did not modify the association between PA and HU. CONCLUSIONS: Meeting aerobic guidelines was associated with reduced HU for inpatient, primary care, and emergency department visits.

Ethnic and racial differences in self-reported symptoms, health status, activity level, and missed work at 3 and 6 months following SARS-CoV-2 infection.

Introduction: Data on ethnic and racial differences in symptoms and health-related impacts following SARS-CoV-2 infection are limited. We aimed to estimate the ethnic and racial differences in symptoms and health-related impacts 3 and 6 months after the first SARS-CoV-2 infection. Methods: Participants included adults with SARS-CoV-2 infection enrolled in a prospective multicenter US study between 12/11/2020 and 7/4/2022 as the primary cohort of interest, as well as a SARS-CoV-2-negative cohort to account for non-SARS-CoV-2-infection impacts, who completed enrollment and 3-month surveys (N = 3,161; 2,402 SARS-CoV-2-positive, 759 SARS-CoV-2-negative). Marginal odds ratios were estimated using GEE logistic regression for individual symptoms, health status, activity level, and missed work 3 and 6 months after COVID-19 illness, comparing each ethnicity or race to the referent group (non-Hispanic or white), adjusting for demographic factors, social determinants of health, substance use, pre-existing health conditions, SARS-CoV-2 infection status, COVID-19 vaccination status, and survey time point, with interactions between ethnicity or race and time point, ethnicity or race and SARS-CoV-2 infection status, and SARS-CoV-2 infection status and time point. Results: Following SARS-CoV-2 infection, the majority of symptoms were similar over time between ethnic and racial groups. At 3 months, Hispanic participants were more likely than non-Hispanic participants to report fair/poor health (OR: 1.94; 95%CI: 1.36-2.78) and reduced activity (somewhat less, OR: 1.47; 95%CI: 1.06-2.02; much less, OR: 2.23; 95%CI: 1.38-3.61). At 6 months, differences by ethnicity were not present. At 3 months, Other/Multiple race participants were more likely than white participants to report fair/poor health (OR: 1.90; 95% CI: 1.25-2.88), reduced activity (somewhat less, OR: 1.72; 95%CI: 1.21-2.46; much less, OR: 2.08; 95%CI: 1.18-3.65). At 6 months, Asian participants were more likely than white participants to report fair/poor health (OR: 1.88; 95%CI: 1.13-3.12); Black participants reported more missed work (OR, 2.83; 95%CI: 1.60-5.00); and Other/Multiple race participants reported more fair/poor health (OR: 1.83; 95%CI: 1.10-3.05), reduced activity (somewhat less, OR: 1.60; 95%CI: 1.02-2.51; much less, OR: 2.49; 95%CI: 1.40-4.44), and more missed work (OR: 2.25; 95%CI: 1.27-3.98). Discussion: Awareness of ethnic and racial differences in outcomes following SARS-CoV-2 infection may inform clinical and public health efforts to advance health equity in long-term outcomes.

Association of Initial SARS-CoV-2 Test Positivity With Patient-Reported Well-being 3 Months After a Symptomatic Illness.

Importance: Long-term sequelae after symptomatic SARS-CoV-2 infection may impact well-being, yet existing data primarily focus on discrete symptoms and/or health care use. Objective: To compare patient-reported outcomes of physical, mental, and social well-being among adults with symptomatic illness who received a positive vs negative test result for SARS-CoV-2 infection. Design, Setting, and Participants: This cohort study was a planned interim analysis of an ongoing multicenter prospective longitudinal registry study (the Innovative Support for Patients With SARS-CoV-2 Infections Registry [INSPIRE]). Participants were enrolled from December 11, 2020, to September 10, 2021, and comprised adults (aged ≥18 years) with acute symptoms suggestive of SARS-CoV-2 infection at the time of receipt of a SARS-CoV-2 test approved by the US Food and Drug Administration. The analysis included the first 1000 participants who completed baseline and 3-month follow-up surveys consisting of questions from the 29-item Patient-Reported Outcomes Measurement Information System (PROMIS-29; 7 subscales, including physical function, anxiety, depression, fatigue, social participation, sleep disturbance, and pain interference) and the PROMIS Short Form-Cognitive Function 8a scale, for which population-normed T scores were reported. Exposures: SARS-CoV-2 status (positive or negative test result) at enrollment. Main Outcomes and Measures: Mean PROMIS scores for participants with positive COVID-19 tests vs negative COVID-19 tests were compared descriptively and using multivariable regression analysis. Results: Among 1000 participants, 722 (72.2%) received a positive COVID-19 result and 278 (27.8%) received a negative result; 406 of 998 participants (40.7%) were aged 18 to 34 years, 644 of 972 (66.3%) were female, 833 of 984 (84.7%) were non-Hispanic, and 685 of 974 (70.3%) were White. A total of 282 of 712 participants (39.6%) in the COVID-19-positive group and 147 of 275 participants (53.5%) in the COVID-19-negative group reported persistently poor physical, mental, or social well-being at 3-month follow-up. After adjustment, improvements in well-being were statistically and clinically greater for participants in the COVID-19-positive group vs the COVID-19-negative group only for social participation (ß = 3.32; 95% CI, 1.84-4.80; P < .001); changes in other well-being domains were not clinically different between groups. Improvements in well-being in the COVID-19-positive group were concentrated among participants aged 18 to 34 years (eg, social participation: ß = 3.90; 95% CI, 1.75-6.05; P < .001) and those who presented for COVID-19 testing in an ambulatory setting (eg, social participation: ß = 4.16; 95% CI, 2.12-6.20; P < .001). Conclusions and Relevance: In this study, participants in both the COVID-19-positive and COVID-19-negative groups reported persistently poor physical, mental, or social well-being at 3-month follow-up. Although some individuals had clinically meaningful improvements over time, many reported moderate to severe impairments in well-being 3 months later. These results highlight the importance of including a control group of participants with negative COVID-19 results for comparison when examining the sequelae of COVID-19.

Implementation of a Physical Activity Vital Sign in Primary Care: Associations Between Physical Activity, Demographic Characteristics, and Chronic Disease Burden.

INTRODUCTION: Physical activity is important to prevent and manage multiple chronic medical conditions. The objective of this study was to describe the implementation of a physical activity vital sign (PAVS) in a primary care setting and examine the association between physical activity with demographic characteristics and chronic disease burden. METHODS: We extracted data from the electronic medical records of patients who had visits from July 2018 through January 2020 in a primary care clinic in which PAVS was implemented as part of the intake process. Data collected included self-reported physical activity, age, sex, body mass index, race, ethnicity, and a modified Charlson Comorbidity Index score indicating chronic disease burden. We classified PAVS into 3 categories of time spent in moderate to strenuous intensity physical activity: consistently inactive (0 min/wk), inconsistently active (<150 min/wk), and consistently active (≥150 min/wk). We used χ2 tests of independence to test for association between PAVS categories and all other variables. RESULTS: During the study period, 13,704 visits, corresponding to 8,741 unique adult patients, had PAVS recorded. Overall, 18.1% of patients reported being consistently inactive, 48.3% inconsistently active, and 33.7% consistently active. All assessed demographic and clinical covariates were associated with PAVS classification (all P < .001). Larger percentages of consistent inactivity were reported for female, older, and underweight or obese patients. Larger percentages of consistent activity were reported for male, younger, and normal weight or overweight patients. CONCLUSION: Using PAVS as a screening tool in primary care enables physicians to understand the physical activity status of their patients and can be useful in identifying inactive patients who may benefit from physical activity counseling.

The impact of a rapid home test on telehealth decision-making for influenza: a clinical vignette study.

BACKGROUND: Home testing for influenza has the potential to aid triage and management decisions for patients with influenza-like illness. As yet, little is known about the effect of the home influenza testing on clinical decision-making via telehealth. The goal of this study was to determine the clinicians' decision thresholds for influenza and whether the availability of a home influenza test affects clinical decisions. METHODS: We identified primary care physicians at 4 different sites in the US, largely via in-person continuing education meetings. Clinicians were asked for each vignette whether to treat empirically ("rule in"), ask the patient come to the clinic for further evaluation ("test"), or neither test nor treat ("rule out"). They were then given the results of a home influenza test, and were again asked to select from these three options. We measured the agreement of physician estimates of the likelihood of influenza with the probability based on a clinical prediction model. The test and treatment thresholds of influenza were determined based on mixed-effect logistic regressions. RESULTS: In total, 202 clinicians made 570 sets of clinical decisions. Agreement between estimated and actual probability of influenza was fair. The test and treatment thresholds were 24% (95% CI: 22% to 25%) and 63% (95% CI: 58% to 65%) before revealing the actual likelihood of influenza. After providing the results of a home flu test the thresholds were similar, 26% (95% CI: 24% to 29%) and 59% (95% CI: 56% to 62%). However, approximately half of clinicians changed their cliical management decision after being given the home influenza test result, largely by categorizing more patients in the "rule out" and "rule in" groups, and reducing the need for in-person evaluation from 41% of patients to only 20%. CONCLUSION: In the context of a telehealth visit for a patient with influenza-like illness, we identified a test threshold of approximately 25% and a treatment threshold of approximately 60%. Adding the home influenza test results reduced uncertainty and significantly decreased the need for in-person visits.

Point-of-care testing for disasters: needs assessment, strategic planning, and future design.

Objective evidence-based national surveys serve as a first step in identifying suitable point-of-care device designs, effective test clusters, and environmental operating conditions. Preliminary survey results show the need for point-of-care testing (POCT) devices using test clusters that specifically detect pathogens found in disaster scenarios. Hurricane Katrina, the tsunami in southeast Asia, and the current influenza pandemic (H1N1, "swine flu") vividly illustrate lack of national and global preparedness. Gap analysis of current POCT devices versus survey results reveals how POCT needs can be fulfilled. Future thinking will help avoid the worst consequences of disasters on the horizon, such as extensively drug-resistant tuberculosis and pandemic influenzas. A global effort must be made to improve POC technologies to rapidly diagnose and treat patients to improve triaging, on-site decision making, and, ultimately, economic and medical outcomes.

Assessing the performance of handheld glucose testing for critical care.

BACKGROUND: We assessed the performance of a point-of-care (POC) glucose meter system (GMS) with multitasking test strip by using the locally-smoothed (LS) median absolute difference (MAD) curve method in conjunction with a modified Bland-Altman difference plot and superimposed International Organization for Standardization (ISO) 15197 tolerance bands. We analyzed performance for tight glycemic control (TGC). METHODS: A modified glucose oxidase enzyme with a multilayer-gold, multielectrode, four-well test strip (StatStriptrade mark, NOVA Biomedical, Waltham, MA) was used. There was no test strip calibration code. Pragmatic comparison was done of GMS results versus paired plasma glucose measurements from chemistry analyzers in clinical laboratories. Venous samples (n = 1,703) were analyzed at 35 hospitals that used 20 types of chemistry analyzers. Erroneous results were identified using the Bland-Altman plot and ISO 15197 criteria. Discrepant values were analyzed for the TGC interval of 80-110 mg/dL. RESULTS: The GMS met ISO 15197 guidelines; 98.6% (410 of 416) of observations were within tolerance for glucose <75 mg/dL, and for > or =75 mg/dL, 100% were within tolerance. Paired differences (handheld minus reference) averaged -2.2 (SD 9.8) mg/dL; the median was -1 (range, -96 to 45) mg/dL. LS MAD curve analysis revealed satisfactory performance below 186 mg/dL; above 186 mg/dL, the recommended error tolerance limit (5 mg/dL) was not met. No discrepant values appeared. All points fell in Clarke Error Grid zone A. Linear regression showed y = 1.018x - 0.716 mg/dL, and r2 = 0.995. CONCLUSIONS: LS MAD curves draw on human ability to discriminate performance visually. LS MAD curve and ISO 15197 performance were acceptable for TGC. POC and reference glucose calibration should be harmonized and standardized.

Standardization, Harmonization, and Realization.

Point-of-care testing is useful when caring for patients in hospital settings and in emergency and disaster situations. However, point-of-care professional practice lacks components, such as standardization, harmonization, and consistency, which would substantially improve patient care if implemented. Therefore, we propose adoption of whole-blood standards, harmonization among testing methods, and tighter quality control constraints. Granting these 3 wishes will improve quality at the point of care and ultimately will improve diagnoses, treatment decisions, and patient outcomes.