The immune checkpoints storm in COVID-19: Role as severity markers at emergency department admission.
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SUBMITTER: Avendano-Ortiz J
PROVIDER: S-EPMC8521292 | biostudies-literature | 2021 Oct
REPOSITORIES: biostudies-literature
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The immune checkpoints storm in COVID-19: Role as severity markers at emergency department admission.
Avendaño-Ortiz José J Lozano-Rodríguez Roberto R Martín-Quirós Alejandro A Terrón Verónica V Maroun-Eid Charbel C Montalbán-Hernández Karla K Valentín-Quiroga Jaime J García-Garrido Miguel Ángel MÁ Del Val Elena Muñoz EM Del Balzo-Castillo Álvaro Á Peinado María M Gómez Laura L Herrero-Benito Carmen C Rubio Carolina C Casalvilla-Dueñas José Carlos JC Gómez-Campelo Paloma P Pascual-Iglesias Alejandro A Del Fresno Carlos C Aguirre Luis A LA López-Collazo Eduardo E
Clinical and translational medicine 20211001 10
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Project description: Not available
| S-EPMC7475713 | biostudies-literature
Project description:Background and Objectives: Immune checkpoint inhibitors such as PD-1 and TIM-3 play an important role in regulating the host immune response and are proposed as potential prognostic markers and therapeutic targets in severe cases of COVID-19. We evaluated the expression of PD-1 and TIM-3 on T cells, as well as the concentration of sPD-1 in plasma, to clarify the role of these molecules in patients infected with SARS-CoV-2. Materials and Methods: In this retrospective observational study, we analysed the expression of PD-1 and TIM-3 on CD4+ and CD8+ T cells upon admission and after 7 days of hospitalisation in 770 adult patients. We also evaluated sPD-1 levels in the plasma of 145 patients at different stages of COVID-19 and of 11 control subjects. Molecules were determined using conventional flow cytometry and ELISA and the data were statistically processed. Results: We observed a significantly higher expression of PD-1 on CD4+ cells in deceased patients than in those with mild-to-moderate disease. All patients with COVID-19 exhibited a significantly higher expression of TIM-3 on both CD4+ and CD8+ T cells compared to controls. After 1 week of hospitalisation, there was no significant change in PD-1 or TIM-3 expression on CD4+ or CD8+ T cells across the studied groups. sPD-1 concentrations were not significantly different between survivors and non-survivors. Plasma sPD-1 levels did not correlate with PD-1 expression on T cells, but a significant correlation was observed between CD4+ PD-1 and CD8+ PD-1. Using machine-learning algorithms, we supported our observations and confirmed immunological variables capable of predicting survival, with AUC = 0.786. Conclusions: Analysis of the immune response may be useful for monitoring and predicting the course of COVID-19 upon admission. However, it is essential to evaluate complex immune parameters in conjunction with other key clinical and laboratory indicators.
| S-EPMC11857393 | biostudies-literature
Project description:Background/Objectives: Charlson Comorbidity Index (CCI) is one of the most reliable indicators to assess the impact of multimorbidity on COVID-19-related outcomes. Moreover, the patient's clinical conditions are associated with SARS-CoV-2 outcomes. This study aimed to analyze the association between multimorbidity and COVID-19-related outcomes, evaluating whether the National Early Warning Score 2 (NEWS2) mediated these associations. Methods: Data were obtained through the platform "EPICLIN". We analyzed all patients who tested positive for COVID-19 after accessing the emergency department (ED) of San Luigi Gonzaga (Orbassano) and Molinette (Turin) hospitals from 1 March to 30 June 2020. Different outcomes were assessed: non-discharge from the ED, 30-day mortality, ICU admission/death among hospitalized patients, and length of hospitalization among surviving patients. Two subgroups of patients (<65 and 65+ years old) were analyzed using logistic regressions, Cox models, and mediation analyses. Results: There was a greater risk of not being discharged or dying among those who were younger and with CCI ≥ 2. Moreover, the higher the CCI, the longer the length of hospitalization. Considering older subjects, a greater CCI was associated with a higher risk of death. Regarding the mediation analyses, multimorbidity significantly impacted the hospitalization length and not being discharged in the younger population. Instead, in the older population, the NEWS2 played a mediation role. Conclusions: This research showed that multimorbidity is a risk factor for a worse prognosis of COVID-19. Moreover, there was a strong direct effect of CCI on not being discharged, and the NEWS2 was found to act as mediator in the association between multimorbidity and COVID-19-related outcomes.
| S-EPMC11641911 | biostudies-literature
Project description:To identify predictors of hospital inpatient admission of older Medicare beneficiaries after discharge from the emergency department (ED).Retrospective cohort study.Nonfederal California hospitals (n = 284).Visits of Medicare beneficiaries aged 65 and older discharged from California EDs in 2007 (n = 505,315).Using the California Office of Statewide Health Planning and Development files, predictors of hospital inpatient admission within 7 days of ED discharge in older adults (?65) with Medicare were evaluated.Hospital inpatient admissions within 7 days of ED discharge occurred in 23,340 (4.6%) visits and were associated with older age (70-74: adjusted odds ratio (AOR) = 1.12, 95% confidence interval (CI) = 1.07-1.17; 75-79: AOR = 1.18, 95% CI = 1.13-1.23; ?80: AOR = 1.4, 95% CI = 1.35-1.46), skilled nursing facility use (AOR = 1.82, 95% CI = 1.72-1.94), leaving the ED against medical advice (AOR = 1.82, 95% CI = 1.67-1.98), and the following diagnoses with the highest odds of admission: end-stage renal disease (AOR = 3.83, 95% CI = 2.42-6.08), chronic renal disease (AOR = 3.19, 95% CI = 2.26-4.49), and congestive heart failure (AOR = 3.01, 95% CI = 2.59-3.50).Five percent of older Medicare beneficiaries have a hospital inpatient admission after discharge from the ED. Chronic conditions such as renal disease and heart failure were associated with the greatest odds of admission.
| S-EPMC5117639 | biostudies-literature
Project description:ObjectiveTo measure the hospital-level variation in admission rates for children receiving treatment of common pediatric illnesses across emergency departments (EDs) in US children's hospitals.MethodsWe performed a multi-center cross sectional study of children presenting to the EDs of 35 pediatric tertiary-care hospitals participating in the Pediatric Health Information System (PHIS). Admission rates were calculated for visits occurring between January 1, 2009, and December 31, 2012, associated with 1 of 7 common conditions, and corrected to adjust for hospital-level severity of illness. Conditions were selected systematically based on frequency of visits and admission rates.ResultsA total of 1288706 ED encounters (13.8% of all encounters) were associated with 1 of the 7 conditions of interest. After adjusting for hospital-level severity, the greatest variation in admission rates was observed for concussion (range 5%-72%), followed by pneumonia (19%-69%), and bronchiolitis (19%-65%). The least variation was found among patients presenting with seizures (7%-37%) and kidney and urinary tract infections (6%-37%). Although variability existed in disease-specific admission rates, certain hospitals had consistently higher, and others consistently lower, admission rates.ConclusionsWe observed greater than threefold variation in severity-adjusted admission rates for common pediatric conditions across US children's hospitals. Although local practices and hospital-level factors may partly explain this variation, our findings highlight the need for greater focus on the standardization of decisions regarding admission.
| S-EPMC4144003 | biostudies-literature
Project description:ObjectiveTo predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.MethodsThis retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.ResultsA total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).ConclusionMachine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.
| S-EPMC6054406 | biostudies-literature
Project description:BackgroundTo determine the utility of admission laboratory markers in the assessment and prognostication of coronavirus disease-2019 (COVID-19), a systematic review and meta-analysis were conducted on the association between admission laboratory values in hospitalized COVID-19 patients and subsequent disease severity and mortality.Material and methodsSearches were conducted in MEDLINE, Pubmed, Embase, and the WHO Global Research Database from December 1,2019 to May 1, 2020 for relevant articles. A random effects meta-analysis was used to calculate the weighted mean difference (WMD) and 95% confidence interval (95% CI) for each of 27 laboratory markers. The impact of age and sex on WMDs was estimated using meta-regression techniques for 11 markers.ResultsIn total, 64 studies met the inclusion criteria. The most marked WMDs were for neutrophils (ANC) at 3.82 × 109 /L (2.76, 4.87), lymphocytes (ALC) at -0.34 × 109 /L (-0.45, -0.23), interleukin-6 (IL-6) at 32.59 pg/mL (23.99, 41.19), ferritin at 814.14 ng/mL (551.48, 1076.81), C-reactive protein (CRP) at 66.11 mg/L (52.16, 80.06), D-dimer at 5.74 mg/L (3.91, 7.58), LDH at 232.41 U/L (178.31, 286.52), and high sensitivity troponin I at 90.47 pg/mL (47.79, 133.14) when comparing fatal to nonfatal cases. Similar trends were observed comparing severe to non-severe groups. There were no statistically significant associations between age or sex and WMD for any of the markers included in the meta-regression.ConclusionThe results highlight that hyper inflammation, blunted adaptive immune response, and intravascular coagulation play key roles in the pathogenesis of COVID-19. Markers of these processes are good candidates to identify patients for early intervention and, importantly, are likely reliable regardless of age or sex in adult patients.
| S-EPMC7436507 | biostudies-literature
Project description:AimsThis study aimed to re-evaluate whether the scoring systems, neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were effective in predicting prognosis and severity of COVID-19 patients in the emergency department (ED).MethodsCOVID-19 patients enrolled in this retrospective study divided into the death (DEA) and survival (SUR) groups, the severe/critical (SC) and non-severe/critical (non-SC) groups. The Acute Physiology and Chronic Health Evaluation (APACHE) II, Sequential Organ Failure Assessment (SOFA), National Early Warning Score (NEWS) and CCEDRRN COVID-19 Mortality Score were calculated. The neutrophil, lymphocyte and platelet counts were extracted from the first routine blood examination, and NLR and PLR were calculated accordingly. Receiver Operating Characteristic (ROC) curve and logistic regression were performed.ResultsAll the scoring systems, as well as NLR and PLR, significantly increased in both the DEA and SC groups. The ROC curve showed that the CCEDRRN COVID-19 Mortality Score had the highest predictive value for mortality and severity (AUC 0.779, 0.850, respectively), which outperformed the APACHE II, SOFA and NEWS. NLR presented better predictive ability for severity (AUC 0.741) than death (AUC 0.702). The APACHE II, NEWS and CCEDRRN COVID-19 Mortality Score were positively correlated with both prognosis and severity, whereas NLR only with severity.ConclusionThe NEWS and CCEDRRN COVID-19 Mortality Score were reconfirmed for early and rapid predicting the poor prognosis and severity of COVID-19 patients in ED, especially the CCEDRRN COVID-19 Mortality Score with the highest discrimination capacity, and NLR was more appropriate for predicting the severity.
| S-EPMC11497573 | biostudies-literature
Project description:IntroductionCoronavirus disease 2019 (Covid-19) has led to unprecedented healthcare demand. This study seeks to characterize Emergency Department (ED) discharges suspected of Covid-19 that are admitted within 72 h.MethodsWe abstracted all adult discharges with suspected Covid-19 from five New York City EDs between March 2nd and April 15th. Those admitted within 72 h were then compared against those who were not using descriptive and regression analysis of background and clinical characteristics.ResultsDischarged ED patients returning within 72 h were more often admitted if suspected of Covid-19 (32.9% vs 12.1%, p < .0001). Of 7433 suspected Covid-19 discharges, the 139 (1.9%) admitted within 72 h were older (55.4 vs. 45.6 years, OR 1.03) and more often male (1.32) or with a history of obstructive lung disease (2.77) or diabetes (1.58) than those who were not admitted (p < .05). Additional associations included non-English preference, cancer, heart failure, hypertension, renal disease, ambulance arrival, higher triage acuity, longer ED stay or time from symptom onset, fever, tachycardia, dyspnea, gastrointestinal symptoms, x-ray abnormalities, and decreased platelets and lymphocytes (p < .05 for all). On 72-h return, 91 (65.5%) subjects required oxygen, and 7 (5.0%) required mechanical ventilation in the ED. Twenty-two (15.8%) of the study group have since died.ConclusionSeveral factors emerge as associated with 72-h ED return admission in subjects suspected of Covid-19. These should be considered when assessing discharge risk in clinical practice.
| S-EPMC7434326 | biostudies-literature
Project description:BACKGROUND:Variation in hospitalization rates have been described for decades, yet little is known about variation in emergency department (ED) admission rates across clinical conditions. We sought to describe variation in ED risk-standardized admission rates (RSAR) and the consistency between condition-specific ED admission rates within hospitals. METHODS:Cross-sectional analysis of the 2009 National Emergency Department Sample, an all-payer administrative, claims dataset. We identify the 15 most frequently admitted conditions using Clinical Classification Software. To identify conditions with the highest ED RSAR variation, we compared both the ratio of the 75th percentile to the 25th percentile hospital and coefficient of variation between conditions. We calculate Spearman correlation coefficients to assess within-hospital correlation of condition-specific ED RSARs. RESULTS:Of 21,885,845 adult ED visits, 4,470,105 (20%) resulted in admission. Among the 15 most frequently admitted conditions, the 5 with the highest magnitude of variation were: mood disorders (ratio of 75th:25th percentile, 6.97; coefficient of variation, 0.81), nonspecific chest pain (2.68; 0.66), skin and soft tissue infections (1.82; 0.51), urinary tract infections (1.58; 0.43), and chronic obstructive pulmonary disease (1.57; 0.33). For these 5 conditions, the within-hospital RSAR correlations between each pair of conditions were >0.4, except for mood disorders, which was poorly correlated with all other conditions (r<0.3). CONCLUSIONS:There is significant condition-specific variation in ED admission rates across US hospitals. This variation appears to be consistent between conditions with high variation within hospitals.
| S-EPMC4858175 | biostudies-literature