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Severe SARS-CoV-2 infection 1 has been associated with highly inflammatory immune activation since the earliest days of the COVID-19 pandemic 2 – 5 . More recently, these responses have been associated with the emergence of self-reactive antibodies with pathologic potential 6 – 10 , although their origins and resolution have remained unclear 11 . Previously, we and others have identified extrafollicular B cell activation, a pathway associated with the formation of new autoreactive antibodies in chronic autoimmunity 12 , 13 , as a dominant feature of severe and critical COVID-19 (refs. 14 – 18 ). Here, using single-cell B cell repertoire analysis of patients with mild and severe disease, we identify the expansion of a naive-derived, low-mutation IgG1 population of antibody-secreting cells (ASCs) reflecting features of low selective pressure. These features correlate with progressive, broad, clinically relevant autoreactivity, particularly directed against nuclear antigens and carbamylated proteins, emerging 10–15 days after the onset of symptoms. Detailed analysis of the low-selection compartment shows a high frequency of clonotypes specific for both SARS-CoV-2 and autoantigens, including pathogenic autoantibodies against the glomerular basement membrane. We further identify the contraction of this pathway on recovery, re-establishment of tolerance standards and concomitant loss of acute-derived ASCs irrespective of antigen specificity. However, serological autoreactivity persists in a subset of patients with postacute sequelae, raising important questions as to the contribution of emerging autoreactivity to continuing symptomology on recovery. In summary, this study demonstrates the origins, breadth and resolution of autoreactivity in severe COVID-19, with implications for early intervention and the treatment of patients with post-COVID sequelae. Single-cell B cell repertoire analysis identifies the expansion of a naive-derived population of antibody-secreting cells contributing to de novo autoreactivity in patients with severe COVID-19 and those with post-COVID symptoms.

Since the advent of critical care in the twentieth century, the core elements that are the foundation for critical care systems, namely to care for critically ill and injured patients and to save lives, have evolved enormously. The past half-century has seen dramatic advancements in diagnostic, organ support, and treatment modalities in critical care, with further improvements now needed to achieve personalized critical care of the highest quality. For critical care to be even higher quality in the future, advancements in the following areas are key: the physical ICU space; the people that care for critically ill patients; the equipment and technologies; the information systems and data; and the research systems that impact critically ill patients and families. With acutely and critically ill patients and their families as the absolute focal point, advancements across these areas will hopefully transform care and outcomes over the coming years.

An ideal test for COVID-19 would combine the sensitivity of laboratory-based PCR with the speed and ease of use of point-of-care (POC) or home-based rapid antigen testing. We evaluated clinical performance of the Diagnostic Analyzer for Selective Hybridization (DASH) SARS-CoV-2 POC rapid PCR test. We conducted a cross-sectional study of adults with and without symptoms of COVID-19 at four clinical sites where we collected two bilateral anterior nasal swabs and information on COVID-19 symptoms, vaccination, and exposure. One swab was tested with the DASH SARS-CoV-2 POC PCR and the second in a central laboratory using Cepheid Xpert Xpress SARS-CoV-2 PCR. We assessed test concordance and calculated sensitivity, specificity, negative and positive predictive values using Xpert as the \"gold standard\". We enrolled 315 and analyzed 313 participants with median age 42 years; 65% were female, 62% symptomatic, 75% had received ≥2 doses of mRNA COVID-19 vaccine, and 16% currently SARS-CoV-2 positive. There were concordant results for 307 tests indicating an overall agreement for DASH of 0.98 [95% CI 0.96, 0.99] compared to Xpert. DASH performed at 0.96 [95% CI 0.86, 1.00] sensitivity and 0.98 [95% CI 0.96, 1.00] specificity, with a positive predictive value of 0.85 [95% CI 0.73, 0.96] and negative predictive value of 0.996 [95% CI 0.99, 1.00]. The six discordant tests between DASH and Xpert all had high Ct values (>30) on the respective positive assay. DASH and Xpert Ct values were highly correlated (R = 0.89 [95% CI 0.81, 0.94]). DASH POC SARS-CoV-2 PCR was accurate, easy to use, and provided fast results (approximately 15 minutes) in real-life clinical settings with an overall performance similar to an EUA-approved laboratory-based PCR.

Abstract Rationale Accurate, early identification of patients at risk for developing acute lung injury (ALI) provides the opportunity to test and implement secondary prevention strategies. Objectives To determine the frequency and outcome of ALI development in patients at risk and validate a lung injury prediction score (LIPS). Methods In this prospective multicenter observational cohort study, predisposing conditions and risk modifiers predictive of ALI development were identified from routine clinical data available during initial evaluation. The discrimination of the model was assessed with area under receiver operating curve (AUC). The risk of death from ALI was determined after adjustment for severity of illness and predisposing conditions. Measurements and Main Results Twenty-two hospitals enrolled 5,584 patients at risk. ALI developed a median of 2 (interquartile range 1–4) days after initial evaluation in 377 (6.8%; 148 ALI-only, 229 adult respiratory distress syndrome) patients. The frequency of ALI varied according to predisposing conditions (from 3% in pancreatitis to 26% after smoke inhalation). LIPS discriminated patients who developed ALI from those who did not with an AUC of 0.80 (95% confidence interval, 0.78–0.82). When adjusted for severity of illness and predisposing conditions, development of ALI increased the risk of in-hospital death (odds ratio, 4.1; 95% confidence interval, 2.9–5.7). Conclusions ALI occurrence varies according to predisposing conditions and carries an independently poor prognosis. Using routinely available clinical data, LIPS identifies patients at high risk for ALI early in the course of their illness. This model will alert clinicians about the risk of ALI and facilitate testing and implementation of ALI prevention strategies. Clinical trial registered with www.clinicaltrials.gov (NCT00889772).

We present an interpretable machine learning algorithm called ‘eARDS’ for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the Berlin clinical criteria. The analysis was conducted on data collected from the Intensive care units (ICU) at Emory Healthcare, Atlanta, GA and University of Tennessee Health Science Center, Memphis, TN and the Cerner ® Health Facts Deidentified Database, a multi-site COVID-19 EMR database. The participants in the analysis consisted of adults over 18 years of age. Clinical data from 35,804 patients who developed ARDS and controls were used to generate predictive models that identify risk for ARDS onset up to 12-hours before satisfying the Berlin criteria. We identified salient features from the electronic medical record that predicted respiratory failure among this population. The machine learning algorithm which provided the best performance exhibited AUROC of 0.89 (95% CI = 0.88–0.90), sensitivity of 0.77 (95% CI = 0.75–0.78), specificity 0.85 (95% CI = 085–0.86). Validation performance across two separate health systems (comprising 899 COVID-19 patients) exhibited AUROC of 0.82 (0.81–0.83) and 0.89 (0.87, 0.90). Important features for prediction of ARDS included minimum oxygen saturation (SpO 2 ), standard deviation of the systolic blood pressure (SBP), O 2 flow, and maximum respiratory rate over an observational window of 16-hours. Analyzing the performance of the model across various cohorts indicates that the model performed best among a younger age group (18–40) (AUROC = 0.93 [0.92–0.94]), compared to an older age group (80+) (AUROC = 0.81 [0.81–0.82]). The model performance was comparable on both male and female groups, but performed significantly better on the severe ARDS group compared to the mild and moderate groups. The eARDS system demonstrated robust performance for predicting COVID19 patients who developed ARDS at least 12-hours before the Berlin clinical criteria, across two independent health systems.

While there has been significant progress in the development of rapid COVID-19 diagnostics, as the pandemic unfolds, new challenges have emerged, including whether these technologies can reliably detect the more infectious variants of concern and be viably deployed in non-clinical settings as “self-tests”. Multidisciplinary evaluation of the Abbott BinaxNOW COVID-19 Ag Card (BinaxNOW, a widely used rapid antigen test, included limit of detection, variant detection, test performance across different age-groups, and usability with self/caregiver-administration. While BinaxNOW detected the highly infectious variants, B.1.1.7 (Alpha) first identified in the UK, B.1.351 (Beta) first identified in South Africa, P.1 (Gamma) first identified in Brazil, B.1.617.2 (Delta) first identified in India and B.1.2, a non-VOC, test sensitivity decreased with decreasing viral loads. Moreover, BinaxNOW sensitivity trended lower when devices were performed by patients/caregivers themselves compared to trained clinical staff, despite universally high usability assessments following self/caregiver-administration among different age groups. Overall, these data indicate that while BinaxNOW accurately detects the new viral variants, as rapid COVID-19 tests enter the home, their already lower sensitivities compared to RT-PCR may decrease even more due to user error.

An ideal test for COVID-19 would combine the sensitivity of laboratory-based PCR with the speed and ease of use of point-of-care (POC) or home-based rapid antigen testing. We evaluated clinical performance of the Diagnostic Analyzer for Selective Hybridization (DASH) SARS-CoV-2 POC rapid PCR test. We conducted a cross-sectional study of adults with and without symptoms of COVID-19 at four clinical sites where we collected two bilateral anterior nasal swabs and information on COVID-19 symptoms, vaccination, and exposure. One swab was tested with the DASH SARS-CoV-2 POC PCR and the second in a central laboratory using Cepheid Xpert Xpress SARS-CoV-2 PCR. We assessed test concordance and calculated sensitivity, specificity, negative and positive predictive values using Xpert as the \"gold standard\". We enrolled 315 and analyzed 313 participants with median age 42 years; 65% were female, 62% symptomatic, 75% had received [greater than or equal to]2 doses of mRNA COVID-19 vaccine, and 16% currently SARS-CoV-2 positive. There were concordant results for 307 tests indicating an overall agreement for DASH of 0.98 [95% CI 0.96, 0.99] compared to Xpert. DASH performed at 0.96 [95% CI 0.86, 1.00] sensitivity and 0.98 [95% CI 0.96, 1.00] specificity, with a positive predictive value of 0.85 [95% CI 0.73, 0.96] and negative predictive value of 0.996 [95% CI 0.99, 1.00]. The six discordant tests between DASH and Xpert all had high Ct values (>30) on the respective positive assay. DASH and Xpert Ct values were highly correlated (R = 0.89 [95% CI 0.81, 0.94]). DASH POC SARS-CoV-2 PCR was accurate, easy to use, and provided fast results (approximately 15 minutes) in real-life clinical settings with an overall performance similar to an EUA-approved laboratory-based PCR.

Point-of-care screening tools are essential to expedite patient care and decrease reliance on slow diagnostic tools (e.g., microbial cultures) to identify pathogens and their associated antibiotic resistance. Analysis of volatile organic compounds (VOC) emitted from biological media has seen increased attention in recent years as a potential non-invasive diagnostic procedure. This work explores the use of solid phase micro-extraction (SPME) and ambient plasma ionization mass spectrometry (MS) to rapidly acquire VOC signatures of bacteria and fungi. The MS spectrum of each pathogen goes through a preprocessing and feature extraction pipeline. Various supervised and unsupervised machine learning (ML) classification algorithms are trained and evaluated on the extracted feature set. These are able to classify the type of pathogen as bacteria or fungi with high accuracy, while marked progress is also made in identifying specific strains of bacteria. This study presents a new approach for the identification of pathogens from VOC signatures collected using SPME and ambient ionization MS by training classifiers on just a few samples of data. This ambient plasma ionization and ML approach is robust, rapid, precise, and can potentially be used as a non-invasive clinical diagnostic tool for point-of-care applications.

Background Differentiating sepsis from the systemic inflammatory response syndrome (SIRS) in critical care patients is challenging, especially before serious organ damage is evident, and with variable clinical presentations of patients and variable training and experience of attending physicians. Our objective was to describe and quantify physician agreement in diagnosing SIRS or sepsis in critical care patients as a function of available clinical information, infection site, and hospital setting. Methods We conducted a post hoc analysis of previously collected data from a prospective, observational trial ( N  = 249 subjects) in intensive care units at seven US hospitals, in which physicians at different stages of patient care were asked to make diagnostic calls of either SIRS, sepsis, or indeterminate, based on varying amounts of available clinical information ( clinicaltrials.gov identifier: NCT02127502). The overall percent agreement and the free-marginal, inter-observer agreement statistic kappa ( κ free ) were used to quantify agreement between evaluators (attending physicians, site investigators, external expert panelists). Logistic regression and machine learning techniques were used to search for significant variables that could explain heterogeneity within the indeterminate and SIRS patient subgroups. Results Free-marginal kappa decreased between the initial impression of the attending physician and (1) the initial impression of the site investigator ( κ free 0.68), (2) the consensus discharge diagnosis of the site investigators ( κ free 0.62), and (3) the consensus diagnosis of the external expert panel ( κ free 0.58). In contrast, agreement was greatest between the consensus discharge impression of site investigators and the consensus diagnosis of the external expert panel ( κ free 0.79). When stratified by infection site, κ free for agreement between initial and later diagnoses had a mean value + 0.24 (range − 0.29 to + 0.39) for respiratory infections, compared to + 0.70 (range + 0.42 to + 0.88) for abdominal + urinary + other infections. Bioinformatics analysis failed to clearly resolve the indeterminate diagnoses and also failed to explain why 60% of SIRS patients were treated with antibiotics. Conclusions Considerable uncertainty surrounds the differential clinical diagnosis of sepsis vs. SIRS, especially before organ damage has become highly evident, and for patients presenting with respiratory clinical signs. Our findings underscore the need to provide physicians with accurate, timely diagnostic information in evaluating possible sepsis.

(1) Background: SeptiCyte RAPID is a molecular test for discriminating sepsis from non-infectious systemic inflammation, and for estimating sepsis probabilities. The objective of this study was the clinical validation of SeptiCyte RAPID, based on testing retrospectively banked and prospectively collected patient samples. (2) Methods: The cartridge-based SeptiCyte RAPID test accepts a PAXgene blood RNA sample and provides sample-to-answer processing in ~1 h. The test output (SeptiScore, range 0–15) falls into four interpretation bands, with higher scores indicating higher probabilities of sepsis. Retrospective (N = 356) and prospective (N = 63) samples were tested from adult patients in ICU who either had the systemic inflammatory response syndrome (SIRS), or were suspected of having/diagnosed with sepsis. Patients were clinically evaluated by a panel of three expert physicians blinded to the SeptiCyte test results. Results were interpreted under either the Sepsis-2 or Sepsis-3 framework. (3) Results: Under the Sepsis-2 framework, SeptiCyte RAPID performance for the combined retrospective and prospective cohorts had Areas Under the ROC Curve (AUCs) ranging from 0.82 to 0.85, a negative predictive value of 0.91 (sensitivity 0.94) for SeptiScore Band 1 (score range 0.1–5.0; lowest risk of sepsis), and a positive predictive value of 0.81 (specificity 0.90) for SeptiScore Band 4 (score range 7.4–15; highest risk of sepsis). Performance estimates for the prospective cohort ranged from AUC 0.86–0.95. For physician-adjudicated sepsis cases that were blood culture (+) or blood, urine culture (+)(+), 43/48 (90%) of SeptiCyte scores fell in Bands 3 or 4. In multivariable analysis with up to 14 additional clinical variables, SeptiScore was the most important variable for sepsis diagnosis. A comparable performance was obtained for the majority of patients reanalyzed under the Sepsis-3 definition, although a subgroup of 16 patients was identified that was called septic under Sepsis-2 but not under Sepsis-3. (4) Conclusions: This study validates SeptiCyte RAPID for estimating sepsis probability, under both the Sepsis-2 and Sepsis-3 frameworks, for hospitalized patients on their first day of ICU admission.