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Cells respond to a myriad of stressors by senescing, acquiring stable growth arrest, morphologic and metabolic changes, and a senescence-associated-secretory-phenotype (SASP). The heterogeneity of senescent cells (SnCs) and their SASP is vast, yet poorly characterized. SnCs have diverse roles in health and disease and are therapeutically targetable, making characterization of SnCs and harmonization of their nomenclature a priority. The Cellular Senescence Network (SenNet), a NIH Common Fund initiative, will leverage emerging single cell and spatial-omics to identify and map SnCs in numerous organs across the lifespan of humans and mice. A common coordinate framework will integrate the data, using validated, standardized methods, creating public 4-dimensional SnC atlases. Key SenNet deliverables include development of innovative tools/technologies to detect SnCs, biomarker discovery, common annotations to describe SnCs and extensive public data sets. The goal is to comprehensively understand and map SnCs for diagnostic and therapeutic purposes to improve human health.

SARS-CoV-2 has caused over 100,000,000 cases and almost 2,500,000 deaths globally. Comprehensive assessment of the multifaceted antiviral Ab response is critical for diagnosis, differentiation of severity, and characterization of long-term immunity, especially as COVID-19 vaccines become available. Severe disease is associated with early, massive plasmablast responses. We developed a multiplex immunoassay from serum/plasma of acutely infected and convalescent COVID-19 patients and prepandemic and postpandemic healthy adults. We measured IgA, IgG, and/or IgM against SARS-CoV-2 nucleocapsid (N), spike domain 1 (S1), S1–receptor binding domain (RBD) and S1–N-terminal domain. For diagnosis, the combined [IgA + IgG + IgM] or IgG levels measured for N, S1, and S1-RBD yielded area under the curve values ≥0.90. Virus-specific Ig levels were higher in patients with severe/critical compared with mild/moderate infections. A strong prozone effect was observed in sera from severe/critical patients—a possible source of underestimated Ab concentrations in previous studies. Mild/moderate patients displayed a slower rise and lower peak in anti-N and anti-S1 IgG levels compared with severe/critical patients, but anti-RBD IgG and neutralization responses reached similar levels at 2–4 mo after symptom onset. Measurement of the Ab responses in sera from 18 COVID-19–vaccinated patients revealed specific responses for the S1-RBD Ag and none against the N protein. This highly sensitive, SARS-CoV-2–specific, multiplex immunoassay measures the magnitude, complexity, and kinetics of the Ab response and can distinguish serum Ab responses from natural SARS-CoV-2 infections (mild or severe) and mRNA COVID-19 vaccines.

Background Noninvasive ventilation (NIV), a form of positive airway pressure (PAP) therapy, is the standard of care for various forms of acute respiratory failure (ARF). Communication impairment is a side effect of NIV, impedes patient care, contributes to distress and intolerance, and potentially increases intubation rates. This study aimed to evaluate communication impairment during CPAP therapy and demonstrate communication device improvement with a standardized protocol. Research Question How does an oronasal mask affect communication intelligibility? How does use of an NIV communication device change this communication intelligibility? Study Design and Methods A single-center randomized controlled trial (36 outpatients with OSA on CPAP therapy) assessed exposure to CPAP 10 cm H2O and PAP communication devices (SPEAX, Ataia Medical). Communication impairment was evaluated by reading selected words and sentences for partners to record and were tabulated as %words correct. Each outpatient-partner pair performed three assessments: (1) baseline (conversing normally), (2) mask baseline (conversing with PAP), and (3) randomized to functioning device (conversing with PAP and device) or sham device. After each stage, both outpatients and partners completed Likert surveys regarding perceived intelligibility and comfort. Results While conversing with PAP, word and sentence intelligibility decreased relatively by 52% (87% vs 41%) and relatively by 57% (94% vs 40%), respectively, compared with normal conversation. Word and sentence intelligibility in the intervention arm increased relatively by 75% (35% vs 61%; P Interpretation Use of this PAP communication device significantly improves both intelligibility and comfort. This is one of the first studies quantifying communication impairment during PAP delivery. Trial Registry ClinicalTrials.gov ; No.: NCT03795753; URL: www.clinicaltrials.gov

To assess disparities in hypoxemia detection by pulse oximetry across self-identified racial groups and associations with clinical outcomes.Observational cohort study from May 5, 2018, to December 31, 2020.Three academic medical centers in the United States.Adults greater than or equal to 18 years who self-identified as White, Black, Asian, or American Indian admitted to the ICU or undergoing surgery during inpatient hospitalization with simultaneous measurements of pulse oximetry-estimated oxygen saturation and arterial blood gas-derived oxygen saturation.None.Multivariable models were employed to assess the relationships between race, occult hypoxemia (i.e., arterial blood gas-derived oxygen saturation88% despite pulse oximetry-estimated oxygen saturation ≥ 92%), and clinical outcomes of hospital mortality and hospital-free days. One-hundred twenty-eight-thousand two-hundred eighty-five paired pulse oximetry-estimated oxygen saturation-arterial blood gas-derived oxygen saturation measurements were included from 26,603 patients. Pulse oximetry-estimated oxygen saturation on average overestimated arterial blood gas-derived oxygen saturation by 1.57% (1.54-1.61%). Black, Asian, and American Indian patients were more likely to experience occult hypoxemia during hospitalization (estimated probability 6.2% [5.1-7.6%], 6.6% [4.9-8.8%], and 6.6% [4.4-10.0%], respectively) compared with White patients (3.6% [3.4-3.8%]). Black patients had increased odds of occult hypoxemia compared with White patients after adjustment (odds ratio, 1.65; 1.28-2.14; p0.001). Differences in occult hypoxemia between Asian and American Indian patients compared with White patients were not significant after adjustment (odds ratio, 1.53; 0.95-2.47; p = 0.077 and odds ratio, 1.31; 0.80-2.16; p = 0.288, respectively). Occult hypoxemia was associated with increased odds of mortality in surgical (odds ratio, 2.96; 1.20-7.28; p = 0.019) and ICU patients (1.36; 1.03-1.80; p = 0.033). Occult hypoxemia was associated with fewer hospital-free days in surgical (-2.5 d [-3.9 to -1.2 d]; p0.001) but not ICU patients (0.4 d [-0.7 to 1.4 d]; p = 0.500).Occult hypoxemia is more common in Black patients compared with White patients and is associated with increased mortality, suggesting potentially important outcome implications for undetected hypoxemia. It is imperative to validate pulse oximetry with expanded racial inclusion.

IMPORTANCE: Discrepancies in oxygen saturation measured by pulse oximetry (SpO(2)), when compared with arterial oxygen saturation (SaO(2)) measured by arterial blood gas (ABG), may differentially affect patients according to race and ethnicity. However, the association of these disparities with health outcomes is unknown. OBJECTIVE: To examine racial and ethnic discrepancies between SaO(2) and SpO(2) measures and their associations with clinical outcomes. DESIGN, SETTING, AND PARTICIPANTS: This multicenter, retrospective, cross-sectional study included 3 publicly available electronic health record (EHR) databases (ie, the Electronic Intensive Care Unit–Clinical Research Database and Medical Information Mart for Intensive Care III and IV) as well as Emory Healthcare (2014–2021) and Grady Memorial (2014–2020) databases, spanning 215 hospitals and 382 ICUs. From 141 600 hospital encounters with recorded ABG measurements, 87 971 participants with first ABG measurements and an SpO(2) of at least 88% within 5 minutes before the ABG test were included. EXPOSURES: Patients with hidden hypoxemia (ie, SpO(2) ≥88% but SaO(2)

BackgroundSARS-CoV-2 has caused over 36,000,000 cases and 1,000,000 deaths globally. Comprehensive assessment of the multifaceted anti-viral antibody response is critical for diagnosis, differentiation of severe disease, and characterization of long-term immunity. Initial observations suggest that severe disease is associated with higher antibody levels and greater B cell/plasmablast responses. A multi-antigen immunoassay to define the complex serological landscape and clinical associations is essential.MethodsWe developed a multiplex immunoassay and evaluated serum/plasma from adults with RT-PCR-confirmed SARS-CoV-2 infections during acute illness (N=52) and convalescence (N=69); and pre-pandemic (N=106) and post-pandemic (N=137) healthy adults. We measured IgA, IgG, and/or IgM against SARS-CoV-2 Nucleocapsid (N), Spike domain 1 (S1), receptor binding domain (S1-RBD) and S1-N-terminal domain (S1-NTD).ResultsTo diagnose infection, the combined [IgA+IgG+IgM] or IgG for N, S1, and S1-RBD yielded AUC values −0.90 by ROC curves. From days 6-30 post-symptom onset, the levels of antigen-specific IgG, IgA or [IgA+IgG+IgM] were higher in patients with severe/critical compared to mild/moderate infections. Consistent with excessive concentrations of antibodies, a strong prozone effect was observed in sera from severe/critical patients. Notably, mild/moderate patients displayed a slower rise and lower peak in anti-N and anti-S1 IgG levels compared to severe/critical patients, but anti-RBD IgG and neutralization responses reached similar levels at 2-4 months.ConclusionThis SARS-CoV-2 multiplex immunoassay measures the magnitude, complexity and kinetics of the antibody response against multiple viral antigens. The IgG and combined-isotype SARS-CoV-2 multiplex assay is highly diagnostic of acute and convalescent disease and may prognosticate severity early in illness.One Sentence SummaryIn contrast to patients with moderate infections, those with severe COVID-19 develop prominent, early antibody responses to S1 and N proteins.

Acute respiratory failure (ARF) is a common problem in medicine that utilizes significant healthcare resources and is associated with high morbidity and mortality. Classification of acute respiratory failure is complicated, and it is often determined by the level of mechanical support that is required, or the discrepancy between oxygen supply and uptake. These phenotypes make acute respiratory failure a continuum of syndromes, rather than one homogenous disease process. Early recognition of the risk factors for new or worsening acute respiratory failure may prevent that process from occurring. Predictive analytical methods using machine learning leverage clinical data to provide an early warning for impending acute respiratory failure or its sequelae. The aims of this review are to summarize the current literature on ARF prediction, to describe accepted procedures and common machine learning tools for predictive tasks through the lens of ARF prediction, and to demonstrate the challenges and potential solutions for ARF prediction that can improve patient outcomes.

Supplemental Digital Content is available in the text., Background: Acute respiratory failure occurs frequently in hospitalized patients and often begins outside the ICU, associated with increased length of stay, cost, and mortality. Delays in decompensation recognition are associated with worse outcomes. Objectives: The objective of this study is to predict acute respiratory failure requiring any advanced respiratory support (including noninvasive ventilation). With the advent of the coronavirus disease pandemic, concern regarding acute respiratory failure has increased. Derivation Cohort: All admission encounters from January 2014 to June 2017 from three hospitals in the Emory Healthcare network (82,699). Validation Cohort: External validation cohort: all admission encounters from January 2014 to June 2017 from a fourth hospital in the Emory Healthcare network (40,143). Temporal validation cohort: all admission encounters from February to April 2020 from four hospitals in the Emory Healthcare network coronavirus disease tested (2,564) and coronavirus disease positive (389). Prediction Model: All admission encounters had vital signs, laboratory, and demographic data extracted. Exclusion criteria included invasive mechanical ventilation started within the operating room or advanced respiratory support within the first 8 hours of admission. Encounters were discretized into hour intervals from 8 hours after admission to discharge or advanced respiratory support initiation and binary labeled for advanced respiratory support. Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment, our eXtreme Gradient Boosting-based algorithm, was compared against Modified Early Warning Score. Results: Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment had significantly better discrimination than Modified Early Warning Score (area under the receiver operating characteristic curve 0.85 vs 0.57 [test], 0.84 vs 0.61 [external validation]). Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment maintained a positive predictive value (0.31–0.21) similar to that of Modified Early Warning Score greater than 4 (0.29–0.25) while identifying 6.62 (validation) to 9.58 (test) times more true positives. Furthermore, Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment performed more effectively in temporal validation (area under the receiver operating characteristic curve 0.86 [coronavirus disease tested], 0.93 [coronavirus disease positive]), while achieving identifying 4.25–4.51× more true positives. Conclusions: Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment is more effective than Modified Early Warning Score in predicting respiratory failure requiring advanced respiratory support at external validation and in coronavirus disease 2019 patients. Silent prospective validation necessary before local deployment.

Objective: Vast 12-lead ECGs repositories provide opportunities to develop new machine learning approaches for creating accurate and automatic diagnostic systems for cardiac abnormalities. However, most 12-lead ECG classification studies are trained, tested, or developed in single, small, or relatively homogeneous datasets. In addition, most algorithms focus on identifying small numbers of cardiac arrhythmias that do not represent the complexity and difficulty of ECG interpretation. This work addresses these issues by providing a standard, multi-institutional database and a novel scoring metric through a public competition: the PhysioNet/Computing in Cardiology Challenge 2020. Approach: A total of 66 361 12-lead ECG recordings were sourced from six hospital systems from four countries across three continents; 43 101 recordings were posted publicly with a focus on 27 diagnoses. For the first time in a public competition, we required teams to publish open-source code for both training and testing their algorithms, ensuring full scientific reproducibility. Main results: A total of 217 teams submitted 1395 algorithms during the Challenge, representing a diversity of approaches for identifying cardiac abnormalities from both academia and industry. As with previous Challenges, high-performing algorithms exhibited significant drops ( ≲ 10%) in performance on the hidden test data. Significance: Data from diverse institutions allowed us to assess algorithmic generalizability. A novel evaluation metric considered different misclassification errors for different cardiac abnormalities, capturing the outcomes and risks of different diagnoses. Requiring both trained models and code for training models improved the generalizability of submissions, setting a new bar in reproducibility for public data science competitions.

Objectives With over 2 million cases of acute respiratory failure in the United States per year, noninvasive ventilation has become a leading treatment modality, often supplanting invasive mechanical ventilation as the initial treatment of choice. Most acute respiratory failure patients use a full face (oronasal) mask with noninvasive ventilation, which is known to impair communication, but its popularity and benefit has led many providers to accept the communication impairment. Medical staff periodically remove masks to communicate with patients, but patients are often limited to short utterances and risk lung derecruitment upon removal of positive pressure. These problems can lead to noninvasive ventilation failure, which is often linked to worse outcomes than first initiating invasive mechanical ventilation and can lead to increased hospitalization costs. Data sources We searched MEDLINE and Google Scholar for "speech," "communication," "impairment," "failure," "complications," "NIPPV," "NIV," and "noninvasive ventilation." Study selection We included articles with patients in acute respiratory failure. We excluded articles for patients using noninvasive ventilation therapy for obstructive sleep apnea. Data synthesis Communication impairment has been associated with increasing noninvasive ventilation anxiety (odds ratio, 1.25). Of patients using noninvasive ventilation, 48% require early discontinuation, 22% refuse noninvasive ventilation, and 9% are ultimately intubated. Improvements to communication have been shown to reduce fear and anxiety in invasive mechanical ventilation patients. Analogous communication problems exist with effective solutions in other fields, such as fighter pilot masks, that can be easily implemented to enhance noninvasive ventilation patient care, increase adherence to noninvasive ventilation treatment, and improve patient outcomes. Conclusions Communication impairment is an underappreciated cause of noninvasive ventilation complications and failure and requires further characterization. Analogous solutions-such as throat microphones and mask-based microphones-that can be easily implemented show potential as cost-effective methods to reduce noninvasive ventilation failure.

Objective Clinical event monitors are a type of active medication monitoring system that can use signals to alert clinicians to possible adverse drug reactions. The primary goal was to evaluate the positive predictive values of select signals used to automate the detection of ADRs in the medical intensive care unit. Method This is a prospective, case series of adult patients in the medical intensive care unit during a six-week period who had one of five signals presents: an elevated blood urea nitrogen, vancomycin, or quinidine concentration, or a low sodium or glucose concentration. Alerts were assessed using 3 objective published adverse drug reaction determination instruments. An event was considered an adverse drug reaction when 2 out of 3 instruments had agreement of possible, probable or definite. Positive predictive values were calculated as the proportion of alerts that occurred, divided by the number of times that alerts occurred and adverse drug reactions were confirmed. Results 145 patients were eligible for evaluation. For the 48 patients (50% male) having an alert, the mean ± SD age was 62 ± 19 years. A total of 253 alerts were generated. Positive predictive values were 1.0, 0.55, 0.38 and 0.33 for vancomycin, glucose, sodium, and blood urea nitrogen, respectively. A quinidine alert was not generated during the evaluation. Conclusions Computerized clinical event monitoring systems should be considered when developing methods to detect adverse drug reactions as part of intensive care unit patient safety surveillance systems, since they can automate the detection of these events using signals that have good performance characteristics by processing commonly available laboratory and medication information.

Introduction As many as 50% of all nursing home (NH) residents meet the World Health Organization criteria for anemia. The objectives of this study were to determine the prevalence and appropriateness of prescribing and monitoring of erythropoiesis-stimulating agents (ESAs) used to treat anemia in the NH setting. Methods Cross-sectional, 1-month study of all NH residents in 4 community-based, university-affiliated NHs between January and February 2008. Residents were included in the analysis if they received at least 1 dose of an ESA during the study duration. Data collected through chart review included basic demographic information, ESA indication, ESA dosage, concurrent administration of iron supplements, hemoglobin (Hgb) monitoring, and blood pressure measurements. Results A total of 4.5% (22/485) of NH residents received at least 1 dose of an ESA. Residents who received ESAs had a mean age of 80.4 (± 14.5) years. Most residents who received ESAs were female (64% [14/22]), white (68% [15/22]), and had a mean weight of 72.0 (± 20.84) kg. Only 27% (6/22) of residents were prescribed an ESA for an FDA-approved indication. Darbepoetin alfa was the most commonly prescribed ESA (64% [14/22]) with a mean weekly dose of 70.8 (± 68.1) μg, followed by epoetin alfa (37% [8/22]) with a mean weekly dose of 22,625 (± 21,232) units. More than one quarter (27% [6/22]) of those who received an ESA had an Hgb value of 12 g/dL or more, the maximum recommended threshold for use of these medications. Of the 18 residents who had blood pressure measurements, 11% (2/18) were hypertensive. Conclusion Suboptimal prescribing and monitoring of ESAs were common in the NHs we studied. Future studies are needed to determine if the development and use of computerized decision support systems can improve prescribing and monitoring of ESAs in the NH setting.