A Diagnostic Model to Predict SARS-CoV-2 Positivity in Emergency Department Using Routine Admission Hematological Parameters.
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ABSTRACT:
SUBMITTER: Soloperto R
PROVIDER: S-EPMC8471783 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Study objectiveOlder adults are frequently hospitalized from the emergency department (ED) after an episode of unexplained syncope. Current admission patterns are costly, with little evidence of benefit. We hypothesize that an ED observation syncope protocol will reduce resource use without adversely affecting patient-oriented outcomes.MethodsThis randomized trial at 5 EDs compared an ED observation syncope protocol to inpatient admission for intermediate-risk adults (≥50 years) presenting with syncope or near syncope. Primary outcomes included inpatient admission rate and length of stay. Secondary outcomes included 30-day and 6-month serious outcomes after hospital discharge, index and 30-day hospital costs, 30-day quality-of-life scores, and 30-day patient satisfaction.ResultsStudy staff randomized 124 patients. Observation resulted in a lower inpatient admission rate (15% versus 92%; 95% confidence interval [CI] difference -88% to -66%) and shorter hospital length of stay (29 versus 47 hours; 95% CI difference -28 to -8). Serious outcome rates after hospital discharge were similar for observation versus admission at 30 days (3% versus 0%; 95% CI difference -1% to 8%) and 6 months (8% versus 10%; 95% CI difference -13% to 9%). Index hospital costs in the observation group were $629 (95% CI difference -$1,376 to -$56) lower than in the admission group. There were no differences in 30-day quality-of-life scores or in patient satisfaction.ConclusionAn ED observation syncope protocol reduced the primary outcomes of admission rate and hospital length of stay. Analyses of secondary outcomes suggest reduction in index hospital costs, with no difference in safety events, quality of life, or patient satisfaction. Our findings suggest that an ED observation syncope protocol can be replicated and safely reduce resource use.
| S-EPMC4019722 | biostudies-literature
Project description:IntroductionRapid-response teams (RRTs) are interdisciplinary groups created to rapidly assess and treat patients with unexpected clinical deterioration marked by decline in vital signs. Traditionally emergency department (ED) disposition is partially based on the patients' vital signs (VS) at the time of hospital admission. We aimed to identify which patients will have RRT activation within 12 hours of admission based on their ED VS, and if their outcomes differed.MethodsWe conducted a case-control study of patients presenting from January 2009 to December 2012 to a tertiary ED who subsequently had RRT activations within 12 hours of admission (early RRT activations). The medical records of patients 18 years and older admitted to a non-intensive care unit (ICU) setting were reviewed to obtain VS at the time of ED arrival and departure, age, gender and diagnoses. Controls were matched 1:1 on age, gender, and diagnosis. We evaluated VS using cut points (lowest 10%, middle 80% and highest 10%) based on the distribution of VS for all patients. Our study adheres to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for reporting observational studies.ResultsA total of 948 patients were included (474 cases and 474 controls). Patients who had RRT activations were more likely to be tachycardic (odds ratio [OR] 2.02, 95% CI [1.25-3.27]), tachypneic (OR 2.92, 95% CI [1.73-4.92]), and had lower oxygen saturations (OR 2.25, 95% CI [1.42-3.56]) upon arrival to the ED. Patients who had RRT activations were more likely to be tachycardic at the time of disposition from the ED (OR 2.76, 95% CI [1.65-4.60]), more likely to have extremes of systolic blood pressure (BP) (OR 1.72, 95% CI [1.08-2.72] for low BP and OR 1.82, 95% CI [1.19-2.80] for high BP), higher respiratory rate (OR 4.15, 95% CI [2.44-7.07]) and lower oxygen saturation (OR 2.29, 95% CI [1.43-3.67]). Early RRT activation was associated with increased healthcare utilization and worse outcomes including increased rates of ICU admission within 72 hours (OR 38.49, 95%CI [19.03-77.87]), invasive interventions (OR 5.49, 95%CI [3.82-7.89]), mortality at 72 hours (OR 4.24, 95%CI [1.60-11.24]), and mortality at one month (OR 4.02, 95%CI [2.44-6.62]).ConclusionAfter matching for age, gender and ED diagnosis, we found that patients with an abnormal heart rate, respiratory rate or oxygen saturation at the time of ED arrival or departure are more likely to trigger RRT activation within 12 hours of admission. Early RRT activation was associated with higher mortality at 72 hours and one month, increased rates of invasive intervention and ICU admission. Determining risk factors of early RRT activation is of clinical, operational, and financial importance, as improved medical decision-making regarding disposition would maximize allocation of resources while potentially limiting morbidity and mortality.
| S-EPMC4899064 | biostudies-literature
Project description:Background:Complete blood count (CBC), red cell distribution width (RDW), mean platelet volume (MPV), mean corpuscular volume (MCV), mean cell hemoglobin (MCH), mean cell hemoglobin concentration (MCHC), or platelet (PLT) count are referred as predictors of adverse clinical outcomes in patients. The aim of the research was to identify potential factors of acute mortality in Polish emergency department (ED) patients by using selected hematological biomarkers and routine statistical tools. Methods:The study presents statistical results on patients who were recently discharged from inpatient facilities within one month prior to the index ED visit. In total, the analysis comprised 14,881 patients with the first RDW, MPV, MCV, MCH, MCHC, or PLT biomarkers' measurements recorded in the emergency department within the years 2016-2019 with a subsequent one month of all-cause mortality observation. The patients were classified with the codes of the International Statistical Classification of Diseases and Related Health Problems after 10th Revision (ICD10). Results:Based on the analysis of RDW, MPV, MCV, MCH, MCHC, and PLT on acute deaths in patients, we establish strong linear and quadratic relationships between the risk factors under study and the clinical response (P < 0.05), however, with different mortality courses and threats. In our statistical analysis, (1) gradient linear relationships were found for RDW and MPV along an entire range of the analyzed biomarkers' measurements, (2) following the quadratic modeling, an increasing risk of death above 95?fL was determined for MCV, and (3) no relation to excess death in ED patients was calculated for MCH, MCHC, and PLT. Conclusion:The study shows that there are likely relationships between blood counts and expected patient mortality at some time interval from measurements. Up to 1 month of observation since the first measurement of an hematological biomarker, RDW and MPV stand for a strong relationship with acute mortality of patients, whereas MCV, MCH, MCHC, and PLT give the U-shaped association, RDW and MPV can be established as the stronger predictors of early deaths of patients, MCV only in the highest levels (>95?fL), whereas MCH, MCHC, and PLT have no impact on the excess acute mortality in ED patients.
| S-EPMC7381964 | biostudies-literature
Project description:IntroductionOvercrowding in the emergency departments (ED) is linked to adverse clinical outcomes, a negative impact on patient safety, patient satisfaction, and physician efficiency. We aimed to design a medical admission prediction scoring system based on readily available clinical data during ED presentation.MethodsIn this retrospective cross-sectional study, data on ED presentations and medical admissions were extracted from the Emergency and Internal Medicine departments of a tertiary care facility in Qatar. Primary outcome was medical admission.ResultsOf 320299 ED presentations, 218772 were males (68.3%). A total of 11847 (3.7%) medical admissions occurred. Most patients were Asians (53.7%), followed by Arabs (38.7%). Patients who got admitted were older than those who did not (p <0.001). Admitted patients were predominantly males (56.8%), had a higher number of comorbid conditions and a higher frequency of recent discharge (within the last 30 days) (p <0.001). Age > 60 years, female gender, discharge within the last 30 days, and worse vital signs at presentations were independently associated with higher odds of admission (p<0.001). These factors generated the scoring system with a cut-off of >17, area under the curve (AUC) 0.831 (95% CI 0.827-0.836), and a predictive accuracy of 83.3% (95% CI 83.2-83.4). The model had a sensitivity of 69.1% (95% CI 68.2-69.9), specificity was 83.9% (95% CI 83.7-84.0), positive predictive value (PPV) 14.2% (95% CI 13.8-14.4), negative predictive value (NPV) 98.6% (95% CI 98.5-98.7) and positive likelihood ratio (LR+) 4.28% (95% CI 4.27-4.28).ConclusionMedical admission prediction scoring system can be reliably applied to the regional population to predict medical admissions and may have better generalizability to other parts of the world owing to the diverse patient population in Qatar.
| S-EPMC10637671 | biostudies-literature
Project description:To date, coronavirus disease 2019 (COVID-19) has affected over 100 million people globally. COVID-19 can present with a variety of different symptoms leading to manifestation of disease ranging from mild cases to a life-threatening condition requiring critical care-level support. At present, a rapid prediction of disease severity and critical care requirement in COVID-19 patients, in early stages of disease, remains an unmet challenge. Therefore, we assessed whether parameters from a routine clinical hematology workup, at the time of hospital admission, can be valuable predictors of COVID-19 severity and the requirement for critical care. Hematological data from the day of hospital admission (day of positive COVID-19 test) for patients with severe COVID-19 disease (requiring critical care during illness) and patients with non-severe disease (not requiring critical care) were acquired. The data were amalgamated and cleaned and modeling was performed. Using a decision tree model, we demonstrated that routine clinical hematology parameters are important predictors of COVID-19 severity. This proof-of-concept study shows that a combination of activated partial thromboplastin time, white cell count-to-neutrophil ratio, and platelet count can predict subsequent severity of COVID-19 with high sensitivity and specificity (area under ROC 0.9956) at the time of the patient's hospital admission. These data, pending further validation, indicate that a decision tree model with hematological parameters could potentially form the basis for a rapid risk stratification tool that predicts COVID-19 severity in hospitalized patients.
| S-EPMC8322583 | 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:ImportanceMore than 80% of patients who present to the emergency department (ED) with acute heart failure (AHF) are hospitalized. With more than 1 million annual hospitalizations for AHF in the US, safe and effective alternatives are needed. Care for AHF in short-stay units (SSUs) may be safe and more efficient than hospitalization, especially for lower-risk patients, but randomized clinical trial data are lacking.ObjectiveTo compare the effectiveness of SSU care vs hospitalization in lower-risk patients with AHF.Design, setting, and participantsThis multicenter randomized clinical trial randomly assigned low-risk patients with AHF 1:1 to SSU or hospital admission from the ED. Patients received follow-up at 30 and 90 days post discharge. The study began December 6, 2017, and was completed on July 22, 2021. The data were analyzed between March 27, 2020, and November 11, 2023.InterventionRandomized post-ED disposition to less than 24 hours of SSU care vs hospitalization.Main outcomes and measuresThe study was designed to detect at least 1-day superiority for a primary outcome of days alive and out of hospital (DAOOH) at 30-day follow-up for 534 participants, with an allowance of 10% participant attrition. Due to the COVID-19 pandemic, enrollment was truncated at 194 participants. Before unmasking, the primary outcome was changed from DAOOH to an outcome with adequate statistical power: quality of life as measured by the 12-item Kansas City Cardiomyopathy Questionnaire (KCCQ-12). The KCCQ-12 scores range from 0 to 100, with higher scores indicating better quality of life.ResultsOf the 193 patients enrolled (1 was found ineligible after randomization), the mean (SD) age was 64.8 (14.8) years, 79 (40.9%) were women, and 114 (59.1%) were men. Baseline characteristics were balanced between arms. The mean (SD) KCCQ-12 summary score between the SSU and hospitalization arms at 30 days was 51.3 (25.7) vs 45.8 (23.8) points, respectively (P = .19). Participants in the SSU arm had 1.6 more DAOOH at 30-day follow-up than those in the hospitalization arm (median [IQR], 26.9 [24.4-28.8] vs 25.4 [22.0-27.7] days; P = .02). Adverse events were uncommon and similar in both arms.Conclusions and relevanceThe findings show that the SSU strategy was no different than hospitalization with regard to KCCQ-12 score, superior for more DAOOH, and safe for lower-risk patients with AHF. These findings of lower health care utilization with the SSU strategy need to be definitively tested in an adequately powered study.Trial registrationClinicalTrials.gov Identifier: NCT03302910.
| S-EPMC10782263 | biostudies-literature
Project description:BackgroundTesting for COVID-19 remains limited in the United States and across the world. Poor allocation of limited testing resources leads to misutilization of health system resources, which complementary rapid testing tools could ameliorate.ObjectiveTo predict SARS-CoV-2 PCR positivity based on complete blood count components and patient sex.Study designA retrospective case-control design for collection of data and a logistic regression prediction model was used. Participants were emergency department patients > 18 years old who had concurrent complete blood counts and SARS-CoV-2 PCR testing. 33 confirmed SARS-CoV-2 PCR positive and 357 negative patients at Stanford Health Care were used for model training. Validation cohorts consisted of emergency department patients > 18 years old who had concurrent complete blood counts and SARS-CoV-2 PCR testing in Northern California (41 PCR positive, 495 PCR negative), Seattle, Washington (40 PCR positive, 306 PCR negative), Chicago, Illinois (245 PCR positive, 1015 PCR negative), and South Korea (9 PCR positive, 236 PCR negative).ResultsA decision support tool that utilizes components of complete blood count and patient sex for prediction of SARS-CoV-2 PCR positivity demonstrated a C-statistic of 78 %, an optimized sensitivity of 93 %, and generalizability to other emergency department populations. By restricting PCR testing to predicted positive patients in a hypothetical scenario of 1000 patients requiring testing but testing resources limited to 60 % of patients, this tool would allow a 33 % increase in properly allocated resources.ConclusionsA prediction tool based on complete blood count results can better allocate SARS-CoV-2 testing and other health care resources such as personal protective equipment during a pandemic surge.
| S-EPMC7286235 | biostudies-literature