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Emergency Department disposition decisions and associated mortality and costs in ICU patients with suspected infection.

ABSTRACT: Following emergency department (ED) assessment, patients with infection may be directly admitted to the intensive care unit (ICU) or alternatively admitted to hospital wards or sent home. Those admitted to the hospital wards or sent home may experience future deterioration necessitating ICU admission.We used a prospectively collected registry from two hospitals within a single tertiary care hospital network between 2011 and 2014. Patient information, outcomes, and costs were stored in the hospital data warehouse. Patients were categorized into three groups: (1) admitted directly from the ED to the ICU; (2) initially admitted to the hospital wards, with ICU admission within 72 hours of initial presentation; or (3) sent home from the ED, with ICU admission within 72 hours of initial presentation. Using multivariable logistic regression, we sought to compare outcomes and total costs between groups. Total costs were evaluated using a generalized linear model.A total of 657 patients were included; of these, 338 (51.4%) were admitted directly from the ED to the ICU, 246 (37.4%) were initially admitted to the wards and then to the ICU, and 73 (11.1%) were initially sent home and then admitted to the ICU. In-hospital mortality was lowest among patients admitted directly to the ICU (29.5%), as compared with patients admitted to the ICU from wards (42.7%) or home (61.6%) (P?

SUBMITTER: Fernando SM

PROVIDER: S-EPMC6034286 | biostudies-other | 2018 Jul

REPOSITORIES: biostudies-other

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Emergency Department disposition decisions and associated mortality and costs in ICU patients with suspected infection.

Fernando Shannon M SM Rochwerg Bram B Reardon Peter M PM Thavorn Kednapa K Seely Andrew J E AJE Perry Jeffrey J JJ Barnaby Douglas P DP Tanuseputro Peter P Kyeremanteng Kwadwo K

Critical care (London, England) 20180706 1

<h4>Background</h4>Following emergency department (ED) assessment, patients with infection may be directly admitted to the intensive care unit (ICU) or alternatively admitted to hospital wards or sent home. Those admitted to the hospital wards or sent home may experience future deterioration necessitating ICU admission.<h4>Methods</h4>We used a prospectively collected registry from two hospitals within a single tertiary care hospital network between 2011 and 2014. Patient information, outcomes, ...[more]

PMID: 29976238

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Project description:OBJECTIVES:ICU admission delays can negatively affect patient outcomes, but emergency department volume and boarding times may also affect these decisions and associated patient outcomes. We sought to investigate the effect of emergency department and ICU capacity strain on ICU admission decisions and to examine the effect of emergency department boarding time of critically ill patients on in-hospital mortality. DESIGN:A retrospective cohort study. SETTING:Single academic tertiary care hospital. PATIENTS:Adult critically ill emergency department patients for whom a consult for medical ICU admission was requested, over a 21-month period. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:Patient data, including severity of illness (Mortality Probability Model III on Admission), outcomes of mortality and persistent organ dysfunction, and hourly census reports for the emergency department, for all ICUs and all adult wards were compiled. A total of 854 emergency department requests for ICU admission were logged, with 455 (53.3%) as "accept" and 399 (46.7%) as "deny" cases, with median emergency department boarding times 4.2 hours (interquartile range, 2.8-6.3 hr) and 11.7 hours (3.2-20.3 hr) and similar rates of persistent organ dysfunction and/or death 41.5% and 44.6%, respectively. Those accepted were younger (mean ± SD, 61 ± 17 vs 65 ± 18 yr) and more severely ill (median Mortality Probability Model III on Admission score, 15.3% [7.0-29.5%] vs 13.4% [6.3-25.2%]) than those denied admission. In the multivariable model, a full medical ICU was the only hospital-level factor significantly associated with a lower probability of ICU acceptance (odds ratio, 0.55 [95% CI, 0.37-0.81]). Using propensity score analysis to account for imbalances in baseline characteristics between those accepted or denied for ICU admission, longer emergency department boarding time after consult was associated with higher odds of mortality and persistent organ dysfunction (odds ratio, 1.77 [1.07-2.95]/log10 hour increase). CONCLUSIONS:ICU admission decisions for critically ill emergency department patients are affected by medical ICU bed availability, though higher emergency department volume and other ICU occupancy did not play a role. Prolonged emergency department boarding times were associated with worse patient outcomes, suggesting a need for improved throughput and targeted care for patients awaiting ICU admission.

Project description:Urinary tract infections (UTIs) are a major cause of emergency hospital admissions, but it remains challenging to diagnose them reliably. Application of machine learning (ML) to routine patient data could support clinical decision-making. We developed a ML model predicting bacteriuria in the ED and evaluated its performance in key patient groups to determine scope for its future use to improve UTI diagnosis and thus guide antibiotic prescribing decisions in clinical practice. We used retrospective electronic health records from a large UK hospital (2011-2019). Non-pregnant adults who attended the ED and had a urine sample cultured were eligible for inclusion. The primary outcome was predominant bacterial growth ≥104 cfu/mL in urine. Predictors included demography, medical history, ED diagnoses, blood tests, and urine flow cytometry. Linear and tree-based models were trained via repeated cross-validation, re-calibrated, and validated on data from 2018/19. Changes in performance were investigated by age, sex, ethnicity, and suspected ED diagnosis, and compared to clinical judgement. Among 12,680 included samples, 4,677 (36.9%) showed bacterial growth. Relying primarily on flow cytometry parameters, our best model achieved an area under the ROC curve (AUC) of 0.813 (95% CI 0.792-0.834) in the test data, and achieved both higher sensitivity and specificity compared to proxies of clinician's judgement. Performance remained stable for white and non-white patients but was lower during a period of laboratory procedure change in 2015, in patients ≥65 years (AUC 0.783, 95% CI 0.752-0.815), and in men (AUC 0.758, 95% CI 0.717-0.798). Performance was also slightly reduced in patients with recorded suspicion of UTI (AUC 0.797, 95% CI 0.765-0.828). Our results suggest scope for use of ML to inform antibiotic prescribing decisions by improving diagnosis of suspected UTI in the ED, but performance varied with patient characteristics. Clinical utility of predictive models for UTI is therefore likely to differ for important patient subgroups including women <65 years, women ≥65 years, and men. Tailored models and decision thresholds may be required that account for differences in achievable performance, background incidence, and risks of infectious complications in these groups.

Project description:ObjectivesOne major goal of the emergency department (ED) is to decide, whether patients need to be hospitalised or can be sent home safely. We aim at providing criteria for these decisions without knowing the SARS-CoV-2 test result in suspected cases.SettingTertiary emergency medicine.ParticipantsAll patients were treated at the ED of the Charité during the pandemic peak and underwent SARS-CoV-2 testing. Patients with positive test results were characterised in detail and underwent a 14-day-follow-up.Primary and secondary outcome measuresLogistic regression and classification and regression tree (CART) analyses were performed to identify predictors (primary endpoint), which confirm safe discharge. The clinical endpoint was all-cause mortality or need for mechanical ventilation during index stay or after readmission.ResultsThe primary test population of suspected COVID-19 consisted of n=1255 cases, 45.2% were women (n=567). Of these, n=110 tested positive for SARS-CoV-2 (8.8%). The median age of SARS-CoV-2-positive cases was 45 years (IQR: 33-66 years), whereas the median age of the group tested negative for SARS-CoV-2 was 42 years (IQR: 30-60 years) (p=0.096). 43.6% were directly admitted to hospital care.CART analysis identified the variables oxygen saturation (<95%), dyspnoea and history of cardiovascular (CV) disease to distinguish between high and low-risk groups. If all three variables were negative, most patients were discharged from ED, and the incidence of the clinical endpoint was 0%. The validation cohort confirmed the safety of discharge using these variables and revealed an incidence of the clinical endpoint from 14.3% in patients with CV disease, 9.4% in patients with dyspnoea and 18.2% in patients with O2 satuaration below 95%.ConclusionsBased on easily available variables like dyspnoea, oxygen saturation, history of CV disease, approximately 25% of patients subsequently confirmed with COVID-19 can be identified for safe discharge.Trial registration numberDRKS00023117.

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Emergency Department disposition decisions and associated mortality and costs in ICU patients with suspected infection.

Publications

Emergency Department disposition decisions and associated mortality and costs in ICU patients with suspected infection.

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