Brain fog in long COVID limits function and health status, independently of hospital severity and preexisting conditions.

Importance: The U.S. government has named post-acute sequelae of COVID-19 (longCOVID) as influential on disability rates. We previously showed that COVID-19 carries a medical/functional burden at 1 year, and that age and other risk factors of severe COVID-19 were not associated with increased longCOVID risk. Long-term longCOVID brain fog (BF) prevalence, risk factors and associated medical/functional factors are poorly understood, especially after mild SARS-CoV-2 infection. Methods: A retrospective observational cohort study was conducted at an urban tertiary-care hospital. Of 1,032 acute COVID-19 survivors from March 3-May 15, 2020, 633 were called, 530 responded (59.2 ± 16.3 years, 44.5% female, 51.5% non-White) about BF prevalence, other longCOVID, post-acute ED/hospital utilization, perceived health/social network, effort tolerance, disability. Results: At approximately 1-year, 31.9% (n = 169) experienced BF. Acute COVID-19 severity, age, and premorbid cardiopulmonary comorbidities did not differ between those with/without BF at 1 year. Patients with respiratory longCOVID had 54% higher risk of BF than those without respiratory longCOVID. BF associated with sleep disturbance (63% with BF vs.29% without BF, p < 0.0001), shortness of breath (46% vs.18%, p < 0.0001), weakness (49% vs.22%, p < 0.0001), dysosmia/dysgeusia (12% vs.5%, p < 0.004), activity limitations (p < 0.001), disability/leave (11% vs.3%, p < 0.0001), worsened perceived health since acute COVID-19 (66% vs.30%, p < 0.001) and social isolation (40% vs.29%, p < 0.02), despite no differences in premorbid comorbidities and age. Conclusions and relevance: A year after COVID-19 infection, BF persists in a third of patients. COVID-19 severity is not a predictive risk factor. BF associates with other longCOVID and independently associates with persistent debility.

Long COVID in Cancer: A Matched Cohort Study of 1-year Mortality and Long COVID Prevalence Among Patients With Cancer Who Survived an Initial Severe SARS-CoV-2 Infection.

OBJECTIVES: The long-term effects of severe acute respiratory syndrome coronavirus 2 (coronavirus disease 2019 [COVID-19]) infection in patients with cancer are unknown. We examined 1-year mortality and prevalence of long COVID in patients with and without cancer after initial hospitalization for acute COVID-19 infection. METHODS: We previously studied 585 patients hospitalized from March to May 2020 with acute COVID-19 infection at Weill Cornell Medicine (117 patients with cancer and 468 age, sex, and comorbidity-matched non-cancer controls). Of the 456 patients who were discharged, we followed 359 patients (75 cancer and 284 non-cancer controls) for COVID-related symptoms and death, at 3, 6, and 12 months after initial symptom onset. Pearson χ 2 and Fisher exact tests were used to determine associations between cancer, postdischarge mortality, and long COVID symptoms. Multivariable Cox proportional hazards models adjusting for potential confounders were used to quantify the risk of death between patients with and without cancer. RESULTS: The cancer cohort had higher mortality after hospitalization (23% vs 5%, P < 0.001), a hazard ratio of 4.7 (95% CI: 2.34-9.46) for all-cause mortality, after adjusting for smoking and oxygen requirement. Long COVID symptoms were observed in 33% of patients regardless of cancer status. Constitutional, respiratory, and cardiac complaints were the most prevalent symptoms in the first 6 months, whereas respiratory and neurological complaints (eg, "brain fog" and memory deficits) were most prevalent at 12 months. CONCLUSIONS: Patients with cancer have higher mortality after hospitalization for acute severe acute respiratory syndrome coronavirus 2 infections. The risk of death was highest in the first 3 months after discharge. About one-third of all patients experienced long COVID.

Veterans' ambulatory care experience during COVID-19: veterans' access to and satisfaction with primary care early in the pandemic.

BACKGROUND: The COVID-19 pandemic caused widespread changes to healthcare, but few studies focus on ambulatory care during the early phase of the pandemic. We characterize veterans' ambulatory care experience, specifically access and satisfaction, early in the pandemic. METHODS: We employed a semi-structured telephone interview to capture quantitative and qualitative data from patients scheduled with a primary care provider between March 1 - June 30, 2020. Forty veterans were randomly identified at a single large urban Veterans Health Administration (VHA) medical center. The interview guide utilized 56 closed and open-ended questions to characterize veterans' perceptions of access to and satisfaction with their primary care experience at VHA and non-VHA primary care sources. We also explored the context of veterans' daily lives during the pandemic. We analyzed quantitative data using descriptive statistics and verbatim quotes using a matrix analysis. RESULTS: Veterans reported completing more appointments (mean 2.6 (SD 2.2)) than scheduled (mean 2.3 (SD 2.2)) mostly due to same-day or urgent visits, with a shift to telephone (mean 2.1 (SD 2.2)) and video (mean 1.5 (SD 0.6)). Among those who reported decreased access to care early in the pandemic (n = 27 (67%)), 15 (56%) cited administrative barriers ("The phone would hang up on me") and 9 (33%) reported a lack of provider availability ("They are not reaching out like they used to"). While most veterans (n = 31 (78%)) were highly satisfied with their VHA care (mean score 8.6 (SD 2.0 on a 0-10 scale), 9 (23%) reported a decrease in satisfaction since the pandemic. The six (15%) veterans who utilized non-VHA providers during the period of interest reported, on average, higher satisfaction ratings (mean 9.5 (SD 1.2)). Many veterans reported psychosocial effects such as the worsening of mental health (n = 6 (15%)), anxiety concerning the virus (n = 12 (30%)), and social isolation (n = 8 (20%), "I stay inside and away from people"). CONCLUSIONS: While the number of encounters reported suggest adequate access and satisfaction, the comments regarding barriers to care suggest that enhanced approaches may be warranted to improve and sustain veteran perceptions of adequate access to and satisfaction with primary care during times of crisis.

Association between city-wide lockdown and COVID-19 hospitalization rates in multigenerational households in New York City.

BACKGROUND: City-wide lockdowns and school closures have demonstrably impacted COVID-19 transmission. However, simulation studies have suggested an increased risk of COVID-19 related morbidity for older individuals inoculated by house-bound children. This study examines whether the March 2020 lockdown in New York City (NYC) was associated with higher COVID-19 hospitalization rates in neighborhoods with larger proportions of multigenerational households. METHODS: We obtained daily age-segmented COVID-19 hospitalization counts in each of 166 ZIP code tabulation areas (ZCTAs) in NYC. Using Bayesian Poisson regression models that account for spatiotemporal dependencies between ZCTAs, as well as socioeconomic risk factors, we conducted a difference-in-differences study amongst ZCTA-level hospitalization rates from February 23 to May 2, 2020. We compared ZCTAs in the lowest quartile of multigenerational housing to other quartiles before and after the lockdown. FINDINGS: Among individuals over 55 years, the lockdown was associated with higher COVID-19 hospitalization rates in ZCTAs with more multigenerational households. The greatest difference occurred three weeks after lockdown: Q2 vs. Q1: 54% increase (95% Bayesian credible intervals: 22-96%); Q3 vs. Q1: 48% (17-89%); Q4 vs. Q1: 66% (30-211%). After accounting for pandemic-related population shifts, a significant difference was observed only in Q4 ZCTAs: 37% (7-76%). INTERPRETATION: By increasing house-bound mixing across older and younger age groups, city-wide lockdown mandates imposed during the growth of COVID-19 cases may have inadvertently, but transiently, contributed to increased transmission in multigenerational households.

Assessing mortality differences across acute respiratory failure management strategies in Covid-19.

PURPOSE: Prolonged observation could avoid invasive mechanical ventilation (IMV) and related risks in patients with Covid-19 acute respiratory failure (ARF) compared to initiating early IMV. We aimed to determine the association between ARF management strategy and in-hospital mortality. MATERIALS AND METHODS: Patients in the Weill Cornell Covid-19 registry who developed ARF between March 5 - March 25, 2020 were exposed to an early IMV strategy; between March 26 - April 1, 2020 to an intermediate strategy; and after April 2 to prolonged observation. Cox proportional hazards regression was used to model in-hospital mortality and test an interaction between ARF management strategy and modified sequential organ failure assessment (mSOFA). RESULTS: Among 632 patients with ARF, 24% of patients in the early IMV strategy died versus 28% in prolonged observation. At lower mSOFA, prolonged observation was associated with lower mortality compared to early IMV (at mSOFA = 0, HR 0.16 [95% CI 0.04-0.57]). Mortality risk increased in the prolonged observation strategy group with each point increase in mSOFA score (HR 1.29 [95% CI 1.10-1.51], p = 0.002). CONCLUSION: In Covid-19 ARF, prolonged observation was associated with a mortality benefit at lower mSOFA scores, and increased mortality at higher mSOFA scores compared to early IMV.

Association between neighborhood overcrowdedness, multigenerational households, and COVID-19 in New York City

IMPACT: Patients living in overcrowded zip codes were at increased risk of contracting severe COVID-19 after controlling for confounding disease and socioeconomic factors OBJECTIVES/GOALS: This study sought to examine whether residences in over-crowded zip codes with higher reported over-crowding represented an independent risk factor for severe COVID-19 infection, defined by presentation to an emergency department. METHODS/STUDY POPULATION: In this zip code tabulated area (ZCTA)-level analysis, we used NYC Department of Health disease surveillance data in March 2020 merged with data from the CDC and ACS to model suspected COVID-19 case rates by zip code over-crowdedness (households with greater than 1 occupant per room, in quartiles). We defined suspected COVID-19 cases as emergency department reported cases of pneumonia and influenza-like illness. Our final model employed a multivariate Poisson regression models with controls for known COVID-19 clinical (prevalence of obesity, coronary artery disease, and smoking) and related socioeconomic risk factors (percentage below federal poverty line, median income by zip-code, percentage White, and proportion of multigenerational households) after accounting for multicollinearity. RESULTS/ANTICIPATED RESULTS: Our analysis examined 39,923 suspected COVID-19 cases across 173 ZCTAs in NYC between March 1 and March 30 2020. We found that, after adjusted analysis, for every quartile increase in defined over-crowdedness, case rates increased by 32.8% (95% CI: 22.7%% to 34.0%, P < 0.001). DISCUSSION/SIGNIFICANCE OF FINDINGS: Over-crowdedness by zip code may be an independent risk factor for severe COVID-19. Social distancing measures such as school closures that increase house-bound populations may inadvertently worsen the risk of COVID-19 contraction in this setting.

Predictors of life-threatening complications in relatively lower-risk patients hospitalized with COVID-19.

Older individuals with chronic health conditions are at highest risk of adverse clinical outcomes from COVID-19, but there is widespread belief that risk to younger, relatively lower-risk individuals is negligible. We assessed the rate and predictors of life-threatening complications among relatively lower-risk adults hospitalized with COVID-19. Of 3766 adults hospitalized with COVID-19 to three hospitals in New York City from March to May 2020, 963 were relatively lower-risk based on absence of preexisting health conditions. Multivariable logistic regression models examined in-hospital development of life-threatening complications (major medical events, intubation, or death). Covariates included age, sex, race/ethnicity, hypertension, weight, insurance type, and area-level sociodemographic factors (poverty, crowdedness, and limited English proficiency). In individuals ≥55 years old (n = 522), 33.3% experienced a life-threatening complication, 17.4% were intubated, and 22.6% died. Among those <55 years (n = 441), 15.0% experienced a life-threatening complication, 11.1% were intubated, and 5.9% died. In multivariable analyses among those ≥55 years, age (OR 1.03 [95%CI 1.01-1.06]), male sex (OR 1.72 [95%CI 1.14-2.64]), being publicly insured (versus commercial insurance: Medicare, OR 2.02 [95%CI 1.22-3.38], Medicaid, OR 1.87 [95%CI 1.10-3.20]) and living in areas with relatively high limited English proficiency (highest versus lowest quartile: OR 3.50 [95%CI 1.74-7.13]) predicted life-threatening complications. In those <55 years, no sociodemographic factors significantly predicted life-threatening complications. A substantial proportion of relatively lower-risk patients hospitalized with COVID-19 experienced life-threatening complications and more than 1 in 20 died. Public messaging needs to effectively convey that relatively lower-risk individuals are still at risk of serious complications.

Health Status, Persistent Symptoms, and Effort Intolerance One Year After Acute COVID-19 Infection.

BACKGROUND: The long-term prevalence and risk factors for post-acute COVID-19 sequelae (PASC) are not well described and may have important implications for unvaccinated populations and policy makers. OBJECTIVE: To assess health status, persistent symptoms, and effort tolerance approximately 1 year after COVID-19 infection DESIGN: Retrospective observational cohort study using surveys and clinical data PARTICIPANTS: Survey respondents who were survivors of acute COVID-19 infection requiring Emergency Department presentation or hospitalization between March 3 and May 15, 2020. MAIN MEASURE(S): Self-reported health status, persistent symptoms, and effort tolerance KEY RESULTS: The 530 respondents (median time between hospital presentation and survey 332 days [IQR 325-344]) had mean age 59.2±16.3 years, 44.5% were female and 70.8% were non-White. Of these, 41.5% reported worse health compared to a year prior, 44.2% reported persistent symptoms, 36.2% reported limitations in lifting/carrying groceries, 35.5% reported limitations climbing one flight of stairs, 38.1% reported limitations bending/kneeling/stooping, and 22.1% reported limitations walking one block. Even those without high-risk comorbid conditions and those seen only in the Emergency Department (but not hospitalized) experienced significant deterioration in health, persistent symptoms, and limitations in effort tolerance. Women (adjusted relative risk ratio [aRRR] 1.26, 95% CI 1.01-1.56), those requiring mechanical ventilation (aRRR 1.48, 1.02-2.14), and people with HIV (aRRR 1.75, 1.14-2.69) were significantly more likely to report persistent symptoms. Age and other risk factors for more severe COVID-19 illness were not associated with increased risk of PASC. CONCLUSIONS: PASC may be extraordinarily common 1 year after COVID-19, and these symptoms are sufficiently severe to impact the daily exercise tolerance of patients. PASC symptoms are broadly distributed, are not limited to one specific patient group, and appear to be unrelated to age. These data have implications for vaccine hesitant individuals, policy makers, and physicians managing the emerging longer-term yet unknown impact of the COVID-19 pandemic.

Impact of Severe Acute Respiratory Syndrome Coronavirus 2 Viral Load on Risk of Intubation and Mortality Among Hospitalized Patients With Coronavirus Disease 2019.

BACKGROUND: Patients hospitalized with coronavirus disease 2019 (COVID-19) frequently require mechanical ventilation and have high mortality rates. However, the impact of viral burden on these outcomes is unknown. METHODS: We conducted a retrospective cohort study of patients hospitalized with COVID-19 from 30 March 2020 to 30 April 2020 at 2 hospitals in New York City. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load was assessed using cycle threshold (Ct) values from a reverse transcription-polymerase chain reaction assay applied to nasopharyngeal swab samples. We compared characteristics and outcomes of patients with high, medium, and low admission viral loads and assessed whether viral load was independently associated with intubation and in-hospital mortality. RESULTS: We evaluated 678 patients with COVID-19. Higher viral load was associated with increased age, comorbidities, smoking status, and recent chemotherapy. In-hospital mortality was 35.0% (Ct <25; n = 220), 17.6% (Ct 25-30; n = 216), and 6.2% (Ct >30; n = 242) with high, medium, and low viral loads, respectively (P < .001). The risk of intubation was also higher in patients with a high viral load (29.1%) compared with those with a medium (20.8%) or low viral load (14.9%; P < .001). High viral load was independently associated with mortality (adjusted odds ratio [aOR], 6.05; 95% confidence interval [CI], 2.92-12.52) and intubation (aOR, 2.73; 95% CI, 1.68-4.44). CONCLUSIONS: Admission SARS-CoV-2 viral load among hospitalized patients with COVID-19 independently correlates with the risk of intubation and in-hospital mortality. Providing this information to clinicians could potentially be used to guide patient care.

Changes in SARS-CoV-2 viral load and mortality during the initial wave of the pandemic in New York City.

Public health interventions such as social distancing and mask wearing decrease the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but it is unclear whether they decrease the viral load of infected patients and whether changes in viral load impact mortality from coronavirus disease 2019 (COVID-19). We evaluated 6923 patients with COVID-19 at six New York City hospitals from March 15-May 14, 2020, corresponding with the implementation of public health interventions in March. We assessed changes in cycle threshold (CT) values from reverse transcription-polymerase chain reaction tests and in-hospital mortality and modeled the impact of viral load on mortality. Mean CT values increased between March and May, with the proportion of patients with high viral load decreasing from 47.7% to 7.8%. In-hospital mortality increased from 14.9% in March to 28.4% in early April, and then decreased to 8.7% by May. Patients with high viral loads had increased mortality compared to those with low viral loads (adjusted odds ratio 2.34). If viral load had not declined, an estimated 69 additional deaths would have occurred (5.8% higher mortality). SARS-CoV-2 viral load steadily declined among hospitalized patients in the setting of public health interventions, and this correlated with decreases in mortality.

Comparing automated vs. manual data collection for COVID-specific medications from electronic health records.

INTRODUCTION: Data extraction from electronic health record (EHR) systems occurs through manual abstraction, automated extraction, or a combination of both. While each method has its strengths and weaknesses, both are necessary for retrospective observational research as well as sudden clinical events, like the COVID-19 pandemic. Assessing the strengths, weaknesses, and potentials of these methods is important to continue to understand optimal approaches to extracting clinical data. We set out to assess automated and manual techniques for collecting medication use data in patients with COVID-19 to inform future observational studies that extract data from the electronic health record (EHR). MATERIALS AND METHODS: For 4,123 COVID-positive patients hospitalized and/or seen in the emergency department at an academic medical center between 03/03/2020 and 05/15/2020, we compared medication use data of 25 medications or drug classes collected through manual abstraction and automated extraction from the EHR. Quantitatively, we assessed concordance using Cohen's kappa to measure interrater reliability, and qualitatively, we audited observed discrepancies to determine causes of inconsistencies. RESULTS: For the 16 inpatient medications, 11 (69%) demonstrated moderate or better agreement; 7 of those demonstrated strong or almost perfect agreement. For 9 outpatient medications, 3 (33%) demonstrated moderate agreement, but none achieved strong or almost perfect agreement. We audited 12% of all discrepancies (716/5,790) and, in those audited, observed three principal categories of error: human error in manual abstraction (26%), errors in the extract-transform-load (ETL) or mapping of the automated extraction (41%), and abstraction-query mismatch (33%). CONCLUSION: Our findings suggest many inpatient medications can be collected reliably through automated extraction, especially when abstraction instructions are designed with data architecture in mind. We discuss quality issues, concerns, and improvements for institutions to consider when crafting an approach. During crises, institutions must decide how to allocate limited resources. We show that automated extraction of medications is feasible and make recommendations on how to improve future iterations.

Hyperglycemia in acute COVID-19 is characterized by insulin resistance and adipose tissue infectivity by SARS-CoV-2.

Individuals infected with SARS-CoV-2 who also display hyperglycemia suffer from longer hospital stays, higher risk of developing acute respiratory distress syndrome (ARDS), and increased mortality. Nevertheless, the pathophysiological mechanism of hyperglycemia in COVID-19 remains poorly characterized. Here, we show that hyperglycemia is similarly prevalent among patients with ARDS independent of COVID-19 status. Yet among patients with ARDS and COVID-19, insulin resistance is the prevalent cause of hyperglycemia, independent of glucocorticoid treatment, which is unlike patients with ARDS but without COVID-19, where pancreatic beta cell failure predominates. A screen of glucoregulatory hormones revealed lower levels of adiponectin in patients with COVID-19. Hamsters infected with SARS-CoV-2 demonstrated a strong antiviral gene expression program in the adipose tissue and diminished expression of adiponectin. Moreover, we show that SARS-CoV-2 can infect adipocytes. Together these data suggest that SARS-CoV-2 may trigger adipose tissue dysfunction to drive insulin resistance and adverse outcomes in acute COVID-19.

Thirty-Day Post-Discharge Outcomes Following COVID-19 Infection.

BACKGROUND: The clinical course of COVID-19 includes multiple disease phases. Data describing post-hospital discharge outcomes may provide insight into disease course. Studies describing post-hospitalization outcomes of adults following COVID-19 infection are limited to electronic medical record review, which may underestimate the incidence of outcomes. OBJECTIVE: To determine 30-day post-hospitalization outcomes following COVID-19 infection. DESIGN: Retrospective cohort study SETTING: Quaternary referral hospital and community hospital in New York City. PARTICIPANTS: COVID-19 infected patients discharged alive from the emergency department (ED) or hospital between March 3 and May 15, 2020. MEASUREMENT: Outcomes included return to an ED, re-hospitalization, and mortality within 30 days of hospital discharge. RESULTS: Thirty-day follow-up data were successfully collected on 94.6% of eligible patients. Among 1344 patients, 16.5% returned to an ED, 9.8% were re-hospitalized, and 2.4% died. Among patients who returned to the ED, 50.0% (108/216) went to a different hospital from the hospital of the index presentation, and 61.1% (132/216) of those who returned were re-hospitalized. In Cox models adjusted for variables selected using the lasso method, age (HR 1.01 per year [95% CI 1.00-1.02]), diabetes (1.54 [1.06-2.23]), and the need for inpatient dialysis (3.78 [2.23-6.43]) during the index presentation were independently associated with a higher re-hospitalization rate. Older age (HR 1.08 [1.05-1.11]) and Asian race (2.89 [1.27-6.61]) were significantly associated with mortality. CONCLUSIONS: Among patients discharged alive following their index presentation for COVID-19, risk for returning to a hospital within 30 days of discharge was substantial. These patients merit close post-discharge follow-up to optimize outcomes.

A predictive model of clinical deterioration among hospitalized COVID-19 patients by harnessing hospital course trajectories.

From early March through mid-May 2020, the COVID-19 pandemic overwhelmed hospitals in New York City. In anticipation of ventilator shortages and limited ICU bed capacity, hospital operations prioritized the development of prognostic tools to predict clinical deterioration. However, early experience from frontline physicians observed that some patients developed unanticipated deterioration after having relatively stable periods, attesting to the uncertainty of clinical trajectories among hospitalized patients with COVID-19. Prediction tools that incorporate clinical variables at one time-point, usually on hospital presentation, are suboptimal for patients with dynamic changes and evolving clinical trajectories. Therefore, our study team developed a machine-learning algorithm to predict clinical deterioration among hospitalized COVID-19 patients by extracting clinically meaningful features from complex longitudinal laboratory and vital sign values during the early period of hospitalization with an emphasis on informative missing-ness. To incorporate the evolution of the disease and clinical practice over the course of the pandemic, we utilized a time-dependent cross-validation strategy for model development. Finally, we validated our prediction model on an external validation cohort of COVID-19 patients served in a demographically distinct population from the training cohort. The main finding of our study is the identification of risk profiles of early, late and no clinical deterioration during the course of hospitalization. While risk prediction models that include simple predictors at ED presentation and clinical judgement are able to identify any deterioration vs. no deterioration, our methodology is able to isolate a particular risk group that remain stable initially but deteriorate at a later stage of the course of hospitalization. We demonstrate the superior predictive performance with the utilization of laboratory and vital sign data during the early period of hospitalization compared to the utilization of data at presentation alone. Our results will allow efficient hospital resource allocation and will motivate research in understanding the late deterioration risk group.

SARS-CoV-2 Viral Load Predicts Mortality in Patients with and without Cancer Who Are Hospitalized with COVID-19.

Patients with cancer may be at increased risk of severe coronavirus disease 2019 (COVID-19), but the role of viral load on this risk is unknown. We measured SARS-CoV-2 viral load using cycle threshold (CT) values from reverse-transcription polymerase chain reaction assays applied to nasopharyngeal swab specimens in 100 patients with cancer and 2,914 without cancer who were admitted to three New York City hospitals. Overall, the in-hospital mortality rate was 38.8% among patients with a high viral load, 24.1% among patients with a medium viral load, and 15.3% among patients with a low viral load (p < 0.001). Similar findings were observed in patients with cancer (high, 45.2% mortality; medium, 28.0%; low, 12.1%; p = 0.008). Patients with hematologic malignancies had higher median viral loads (CT = 25.0) than patients without cancer (CT = 29.2; p = 0.0039). SARS-CoV-2 viral load results may offer vital prognostic information for patients with and without cancer who are hospitalized with COVID-19.