The immune checkpoints storm in COVID-19: Role as severity markers at emergency department admission.
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ABSTRACT:
SUBMITTER: Avendano-Ortiz J
PROVIDER: S-EPMC8521292 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: Not available
| S-EPMC7475713 | 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:Triage systems play a vital role in emergency department (ED) operations and can determine how well a given ED serves its local population. We sought to describe ED utilization patterns for different triage levels using the National Hospital Ambulatory Medical Care Survey (NHAMCS) database.We conducted a multi-year secondary analysis of the NHAMCS database from 2009-2011. National visit estimates were made using standard methods in Analytics Software and Solutions (SAS, Cary, NC). We compared patients in the mid-urgency range in regard to ED lengths of stay, hospital admission rates, and numbers of tests and procedures in comparison to lower or higher acuity levels.We analyzed 100,962 emergency visits (representing 402,211,907 emergency visits nationwide). In 2011, patients classified as triage levels 1-3 had a higher number of diagnoses (5.5, 5.6 and 4.2, respectively) when compared to those classified as levels 4 and 5 (1.61 and 1.25). This group also underwent a higher number of procedures (1.0, 0.8 and 0.7, versus 0.4 and 0.4), had a higher ED length of stay (220, 280 and 237, vs. 157 and 135), and admission rates (32.2%, 32.3% and 15.5%, vs. 3.1% and 3.6%).Patients classified as mid-level (3) triage urgency require more resources and have higher indicators of acuity as those in triage levels 4 and 5. These patients' indicators are more similar to those classified as triage levels 1 and 2.
| S-EPMC6123086 | biostudies-other
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
Project description: Not available
| S-EPMC10873771 | biostudies-literature
Project description:Methods to accurately identify elderly patients with a high likelihood of hospital admission or subsequent return to the emergency department (ED) might facilitate the development of interventions to expedite the admission process, improve patient care, and reduce overcrowding. This study sought to identify variables found among elderly ED patients that could predict either hospital admission or return to the ED.All visits by patients 75 years of age or older during 2007 at an academic ED serving a large community of elderly were reviewed. Clinical and demographic data were used to construct regression models to predict admission or ED return. These models were then validated in a second group of patients 75 and older who presented during two 1-month periods in 2008.Of 4,873 visits, 3,188 resulted in admission (65.4%). Regression modeling identified five variables statistically related to the probability of admission: age, triage score, heart rate, diastolic blood pressure, and chief complaint. Upon validation, the c-statistic of the receiver operating characteristic (ROC) curve was 0.73, moderately predictive of admission. We were unable to produce models that predicted ED return for these elderly patients.A derived and validated triage-based model is presented that provides a moderately accurate probability of hospital admission of elderly patients. If validated experimentally, this model might expedite the admission process for elderly ED patients. Our models failed, as have others, to accurately predict ED return among elderly patients, underscoring the challenge of identifying those individuals at risk for early ED returns.
| S-EPMC5985811 | biostudies-literature