Clinical Characteristics of Covid-19 in New York City Goyal, Parag; Choi, Justin J; Pinheiro, Laura C ...
New England journal of medicine/The New England journal of medicine,
06/2020, Volume:
382, Issue:
24
Journal Article
Peer reviewed
Open access
In this series of 393 consecutive patients admitted with Covid-19 to two New York City hospitals from March 3 to March 27, a third of patients received invasive mechanical ventilation, 10% of ...patients died, and 24% were still hospitalized as of April 10.
Sarcopenia and Cardiovascular Diseases Damluji, Abdulla A; Alfaraidhy, Maha; AlHajri, Noora ...
Circulation,
05/2023, Volume:
147, Issue:
20
Journal Article
Peer reviewed
Open access
Sarcopenia is the loss of muscle strength, mass, and function, which is often exacerbated by chronic comorbidities including cardiovascular diseases, chronic kidney disease, and cancer. Sarcopenia is ...associated with faster progression of cardiovascular diseases and higher risk of mortality, falls, and reduced quality of life, particularly among older adults. Although the pathophysiologic mechanisms are complex, the broad underlying cause of sarcopenia includes an imbalance between anabolic and catabolic muscle homeostasis with or without neuronal degeneration. The intrinsic molecular mechanisms of aging, chronic illness, malnutrition, and immobility are associated with the development of sarcopenia. Screening and testing for sarcopenia may be particularly important among those with chronic disease states. Early recognition of sarcopenia is important because it can provide an opportunity for interventions to reverse or delay the progression of muscle disorder, which may ultimately impact cardiovascular outcomes. Relying on body mass index is not useful for screening because many patients will have sarcopenic obesity, a particularly important phenotype among older cardiac patients. In this review, we aimed to: (1) provide a definition of sarcopenia within the context of muscle wasting disorders; (2) summarize the associations between sarcopenia and different cardiovascular diseases; (3) highlight an approach for a diagnostic evaluation; (4) discuss management strategies for sarcopenia; and (5) outline key gaps in knowledge with implications for the future of the field.
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.
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•Hyperglycemia is highly prevalent in acute respiratory distress syndrome ± COVID-19•Insulin resistance is the main cause for hyperglycemia in patients with severe COVID-19•Patients with COVID-19 and hamsters infected with SARS-CoV-2 have decreased adiponectin•SARS-CoV-2 can directly infect human and mouse adipocytes
Here, Reiterer et al. report that hyperglycemia in critically ill patients with COVID-19 is caused mainly by insulin resistance and is associated with decreased circulating adiponectin. SARS-CoV-2 is shown to directly infect human adipocytes, trigger an inflammatory antiviral response in the adipose tissue, and cause its dysfunction.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Despite potential harm that can result from polypharmacy, real-world data on polypharmacy in the setting of heart failure (HF) are limited. We sought to address this knowledge gap by studying older ...adults hospitalized for HF derived from the REGARDS study (Reasons for Geographic and Racial Differences in Stroke).
We examined 558 older adults aged ≥65 years with adjudicated HF hospitalizations from 380 hospitals across the United States. We collected and examined data from the REGARDS baseline assessment, medical charts from HF-adjudicated hospitalizations, the American Hospital Association annual survey database, and Medicare's Hospital Compare website. We counted the number of medications taken at hospital admission and discharge; and classified each medication as HF-related, non-HF cardiovascular-related, or noncardiovascular-related.
The vast majority of participants (84% at admission and 95% at discharge) took ≥5 medications; and 42% at admission and 55% at discharge took ≥10 medications. The prevalence of taking ≥10 medications (polypharmacy) increased over the study period. As the number of total medications increased, the number of noncardiovascular medications increased more rapidly than the number of HF-related or non-HF cardiovascular medications.
Defining polypharmacy as taking ≥10 medications might be more ideal in the HF population as most patients already take ≥5 medications. Polypharmacy is common both at admission and hospital discharge, and its prevalence is rising over time. The majority of medications taken by older adults with HF are noncardiovascular medications. There is a need to develop strategies that can mitigate the negative effects of polypharmacy among older adults with HF.
COVID-19-associated respiratory failure offers the unprecedented opportunity to evaluate the differential host response to a uniform pathogenic insult. Understanding whether there are distinct ...subphenotypes of severe COVID-19 may offer insight into its pathophysiology. Sequential Organ Failure Assessment (SOFA) score is an objective and comprehensive measurement that measures dysfunction severity of six organ systems, i.e., cardiovascular, central nervous system, coagulation, liver, renal, and respiration. Our aim was to identify and characterize distinct subphenotypes of COVID-19 critical illness defined by the post-intubation trajectory of SOFA score. Intubated COVID-19 patients at two hospitals in New York city were leveraged as development and validation cohorts. Patients were grouped into mild, intermediate, and severe strata by their baseline post-intubation SOFA. Hierarchical agglomerative clustering was performed within each stratum to detect subphenotypes based on similarities amongst SOFA score trajectories evaluated by Dynamic Time Warping. Distinct worsening and recovering subphenotypes were identified within each stratum, which had distinct 7-day post-intubation SOFA progression trends. Patients in the worsening suphenotypes had a higher mortality than those in the recovering subphenotypes within each stratum (mild stratum, 29.7% vs. 10.3%, p = 0.033; intermediate stratum, 29.3% vs. 8.0%, p = 0.002; severe stratum, 53.7% vs. 22.2%, p < 0.001). Pathophysiologic biomarkers associated with progression were distinct at each stratum, including findings suggestive of inflammation in low baseline severity of illness versus hemophagocytic lymphohistiocytosis in higher baseline severity of illness. The findings suggest that there are clear worsening and recovering subphenotypes of COVID-19 respiratory failure after intubation, which are more predictive of outcomes than baseline severity of illness. Distinct progression biomarkers at differential baseline severity of illness suggests a heterogeneous pathobiology in the progression of COVID-19 respiratory failure.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
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.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Background Outcomes following heart failure (HF) hospitalizations are poor, with 90-day mortality rates of 15% to 20%. Although prior studies found associations between individual social determinants ...of health (SDOH) and post-discharge mortality, less is known about how an individuals' total burden of SDOH affects 90-day mortality. Methods and Results We included participants of the REGARDS (Reasons for Geographic and Racial Differences in Stroke) Study who were Medicare beneficiaries aged ≥65 years discharged alive after an adjudicated HF hospitalization. Guided by the Healthy People 2020 Framework, we examined 9 SDOH. First, we examined age-adjusted associations between each SDOH and 90-day mortality; those associated with 90-day mortality were used to create an SDOH count. Next, we determined the hazard of 90-day mortality by the SDOH count, adjusting for confounders. Over 10 years, 690 participants were hospitalized for HF at 440 unique hospitals in the United States; there were a total of 79 deaths within 90 days. Overall, 28% of participants had 0 SDOH, 39% had 1, and 32% had ≥2. Compared with those with 0, the age-adjusted hazard ratio for 90-day mortality among those with 1 SDOH was 2.89 (95% CI, 1.46-5.72) and was 3.06 (1.51-6.19) among those with ≥2 SDOH. The adjusted hazard ratio was 2.78 (1.37-5.62) and 2.57 (1.19-5.54) for participants with 1 SDOH and ≥2, respectively. Conclusions While having any of the SDOH studied here markedly increased risk of 90-day mortality after an HF hospitalization, a greater burden of SDOH was not associated with significantly greater risk in our population.
Background The independent prognostic value of troponin and other biomarker elevation among patients with coronavirus disease 2019 (COVID‐19) are unclear. We sought to characterize biomarker levels ...in patients hospitalized with COVID‐19 and develop and validate a mortality risk score. Methods and Results An observational cohort study of 1053 patients with COVID‐19 was conducted. Patients with all of the following biomarkers measured—troponin‐I, B‐type natriuretic peptide, C‐reactive protein, ferritin, and d‐dimer (n=446) —were identified. Maximum levels for each biomarker were recorded. The primary end point was 30‐day in‐hospital mortality. Multivariable logistic regression was used to construct a mortality risk score. Validation of the risk score was performed using an independent patient cohort (n=440). Mean age of patients was 65.0±15.2 years and 65.3% were men. Overall, 444 (99.6%) had elevation of any biomarker. Among tested biomarkers, troponin‐I ≥0.34 ng/mL was the only independent predictor of 30‐day mortality (adjusted odds ratio, 4.38; P<0.001). Patients with a mortality score using hypoxia on presentation, age, and troponin‐I elevation, age (HA2T2) ≥3 had a 30‐day mortality of 43.7% while those with a score <3 had mortality of 5.9%. Area under the receiver operating characteristic curve of the HA2T2 score was 0.834 for the derivation cohort and 0.784 for the validation cohort. Conclusions Elevated troponin and other biomarker levels are commonly seen in patients hospitalized with COVID‐19. High troponin levels are a potent predictor of 30‐day in‐hospital mortality. A simple risk score can stratify patients at risk for COVID‐19–associated mortality.
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