From symptom onset to ED departure: understanding the acute care chain for patients with undifferentiated complaints: a prospective observational study.

BACKGROUND: For most acute conditions, the phase prior to emergency department (ED) arrival is largely unexplored. However, this prehospital phase has proven an important part of the acute care chain (ACC) for specific time-sensitive conditions, such as stroke and myocardial infarction. For patients with undifferentiated complaints, exploration of the prehospital phase of the ACC may also offer a window of opportunity for improvement of care. This study aims to explore the ACC of ED patients with undifferentiated complaints, with specific emphasis on time in ACC and patient experience. METHODS: This Dutch prospective observational study, included all adult (≥ 18 years) ED patients with undifferentiated complaints over a 4-week period. We investigated the patients' journey through the ACC, focusing on time in ACC and patient experience. Additionally, a multivariable linear regression analysis was employed to identify factors independently associated with time in ACC. RESULTS: Among the 286 ED patients with undifferentiated complaints, the median symptom duration prior to ED visit was 6 days (IQR 2-10), during which 58.6% of patients had contact with a healthcare provider before referral. General Practitioners (GPs) referred 80.4% of the patients, with the predominant patient journey (51.7%) involving GP referral followed by self-transportation to the ED. The median time in ACC was 5.5 (IQR 4.0-8.4) hours of which 40% was spent before the ED visit. GP referral and referral to pulmonology were associated with a longer time in ACC, while referral during evenings was associated with a shorter time in ACC. Patients scored both quality and duration of the provided care an 8/10. CONCLUSION: Dutch ED patients with undifferentiated complaints consulted a healthcare provider in over half of the cases before their ED visit. The median time in ACC is 5.5 h of which 40% is spent in the prehospital phase. Those referred by a GP and to pulmonology had a longer, and those in the evening a shorter time in ACC. The acute care journey starts hours before patients arrive at the ED and 6 days of complaints precede this journey. This timeframe could serve as a window of opportunity to optimise care.

Development and validation of a risk prediction model for hospital admission in COVID-19 patients presenting to primary care.

BACKGROUND: There is a paucity of prognostic models for COVID-19 that are usable for in-office patient assessment in general practice (GP). OBJECTIVES: To develop and validate a risk prediction model for hospital admission with readily available predictors. METHODS: A retrospective cohort study linking GP records from 8 COVID-19 centres and 55 general practices in the Netherlands to hospital admission records. The development cohort spanned March to June 2020, the validation cohort March to June 2021. The primary outcome was hospital admission within 14 days. We used geographic leave-region-out cross-validation in the development cohort and temporal validation in the validation cohort. RESULTS: In the development cohort, 4,806 adult patients with COVID-19 consulted their GP (median age 56, 56% female); in the validation cohort 830 patients did (median age 56, 52% female). In the development and validation cohort respectively, 292 (6.1%) and 126 (15.2%) were admitted to the hospital within 14 days, respectively. A logistic regression model based on sex, smoking, symptoms, vital signs and comorbidities predicted hospital admission with a c-index of 0.84 (95% CI 0.83 to 0.86) at geographic cross-validation and 0.79 (95% CI 0.74 to 0.83) at temporal validation, and was reasonably well calibrated (intercept -0.08, 95% CI -0.98 to 0.52, slope 0.89, 95% CI 0.71 to 1.07 at geographic cross-validation and intercept 0.02, 95% CI -0.21 to 0.24, slope 0.82, 95% CI 0.64 to 1.00 at temporal validation). CONCLUSION: We derived a risk model using readily available variables at GP assessment to predict hospital admission for COVID-19. It performed accurately across regions and waves. Further validation on cohorts with acquired immunity and newer SARS-CoV-2 variants is recommended.

A general practice prediction model based on signs and symptoms of COVID-19 patients reliably predicted hospitalisation.The model performed well in second-wave data with other dominant variants and changed testing and vaccination policies.In an emerging pandemic, GP data can be leveraged to develop prognostic models for decision support and to predict hospitalisation rates.

Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department.

BACKGROUND: Many prediction models have been developed to help identify emergency department (ED) patients at high risk of poor outcome. However, these models often underperform in clinical practice and their actual clinical impact has hardly ever been evaluated. We aim to perform a clinical trial to investigate the clinical impact of a prediction model based on machine learning (ML) technology. METHODS: The study is a prospective, randomized, open-label, non-inferiority pilot clinical trial. We will investigate the clinical impact of a prediction model based on ML technology, the RISKINDEX, which has been developed to predict the risk of 31-day mortality based on the results of laboratory tests and demographic characteristics. In previous studies, the RISKINDEX was shown to outperform internal medicine specialists and to have high discriminatory performance. Adults patients (18 years or older) will be recruited in the ED. All participants will be randomly assigned to the control group or the intervention group in a 1:1 ratio. Participants in the control group will receive care as usual in which the study team asks the attending physicians questions about their clinical intuition. Participants in the intervention group will also receive care as usual, but in addition to asking the clinical impression questions, the study team presents the RISKINDEX to the attending physician in order to assess the extent to which clinical treatment is influenced by the results. DISCUSSION: This pilot clinical trial investigates the clinical impact and implementation of an ML based prediction model in the ED. By assessing the clinical impact and prognostic accuracy of the RISKINDEX, this study aims to contribute valuable insights to optimize patient care and inform future research in the field of ML based clinical prediction models. TRIAL REGISTRATION: ClinicalTrials.gov NCT05497830. Machine Learning for Risk Stratification in the Emergency Department (MARS-ED). Registered on August 11, 2022. URL: https://clinicaltrials.gov/study/NCT05497830 .

Explainable Machine Learning Models for Rapid Risk Stratification in the Emergency Department: A Multicenter Study.

BACKGROUND: Risk stratification of patients presenting to the emergency department (ED) is important for appropriate triage. Diagnostic laboratory tests are an essential part of the workup and risk stratification of these patients. Using machine learning, the prognostic power and clinical value of these tests can be amplified greatly. In this study, we applied machine learning to develop an accurate and explainable clinical decision support tool model that predicts the likelihood of 31-day mortality in ED patients (the RISKINDEX). This tool was developed and evaluated in four Dutch hospitals. METHODS: Machine learning models included patient characteristics and available laboratory data collected within the first 2 h after ED presentation, and were trained using 5 years of data from consecutive ED patients from the Maastricht University Medical Center (Maastricht), Meander Medical Center (Amersfoort), and Zuyderland Medical Center (Sittard and Heerlen). A sixth year of data was used to evaluate the models using area under the receiver-operating-characteristic curve (AUROC) and calibration curves. The Shapley additive explanations (SHAP) algorithm was used to obtain explainable machine learning models. RESULTS: The present study included 266 327 patients with 7.1 million laboratory results available. Models show high diagnostic performance with AUROCs of 0.94, 0.98, 0.88, and 0.90 for Maastricht, Amersfoort, Sittard and Heerlen, respectively. The SHAP algorithm was utilized to visualize patient characteristics and laboratory data patterns that underlie individual RISKINDEX predictions. CONCLUSIONS: Our clinical decision support tool has excellent diagnostic performance in predicting 31-day mortality in ED patients. Follow-up studies will assess whether implementation of these algorithms can improve clinically relevant end points.

Head-to-head comparison of 19 prediction models for short-term outcome in medical patients in the emergency department: a retrospective study.

INTRODUCTION: Prediction models for identifying emergency department (ED) patients at high risk of poor outcome are often not externally validated. We aimed to perform a head-to-head comparison of the discriminatory performance of several prediction models in a large cohort of ED patients. METHODS: In this retrospective study, we selected prediction models that aim to predict poor outcome and we included adult medical ED patients. Primary outcome was 31-day mortality, secondary outcomes were 1-day mortality, 7-day mortality, and a composite endpoint of 31-day mortality and admission to intensive care unit (ICU).The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC). Finally, the prediction models with the highest performance to predict 31-day mortality were selected to further examine calibration and appropriate clinical cut-off points. RESULTS: We included 19 prediction models and applied these to 2185 ED patients. Thirty-one-day mortality was 10.6% (231 patients), 1-day mortality was 1.4%, 7-day mortality was 4.4%, and 331 patients (15.1%) met the composite endpoint. The RISE UP and COPE score showed similar and very good discriminatory performance for 31-day mortality (AUC 0.86), 1-day mortality (AUC 0.87), 7-day mortality (AUC 0.86) and for the composite endpoint (AUC 0.81). Both scores were well calibrated. Almost no patients with RISE UP and COPE scores below 5% had an adverse outcome, while those with scores above 20% were at high risk of adverse outcome. Some of the other prediction models (i.e. APACHE II, NEWS, WPSS, MEWS, EWS and SOFA) showed significantly higher discriminatory performance for 1-day and 7-day mortality than for 31-day mortality. CONCLUSIONS: Head-to-head validation of 19 prediction models in medical ED patients showed that the RISE UP and COPE score outperformed other models regarding 31-day mortality.

Sex differences in clinical presentation and mortality in emergency department patients with sepsis.

BACKGROUND: There is growing awareness that sex differences are associated with different patient outcomes in a variety of diseases. Studies investigating the effect of patient sex on sepsis-related mortality remain inconclusive and mainly focus on patients with severe sepsis and septic shock in the intensive care unit. We therefore investigated the association between patient sex and both clinical presentation and 30-day mortality in patients with the whole spectrum of sepsis severity presenting to the emergency department (ED) who were admitted to the hospital. MATERIALS AND METHODS: In our multi-centre cohort study, we retrospectively investigated adult medical patients with sepsis in the ED. Multivariable analysis was used to evaluate the association between patient sex and all-cause 30-day mortality. RESULTS: Of 2065 patients included, 47.6% were female. Female patients had significantly less comorbidities, lower Sequential Organ Failure Assessment score and abbreviated Mortality Emergency Department Sepsis score, and presented less frequently with thrombocytopenia and fever, compared to males. For both sexes, respiratory tract infections were predominant while female patients more often had urinary tract infections. Females showed lower 30-day mortality (10.1% vs. 13.6%; p = .016), and in-hospital mortality (8.0% vs. 11.1%; p = .02) compared to males. However, a multivariable logistic regression model showed that patient sex was not an independent predictor of 30-day mortality (OR 0.90; 95% CI 0.67-1.22; p = .51). CONCLUSIONS: Females with sepsis presenting to the ED had fewer comorbidities, lower disease severity, less often thrombocytopenia and fever and were more likely to have a urinary tract infection. Females had a lower in-hospital and 30-day mortality compared to males, but sex was not an independent predictor of 30-day mortality. The lower mortality in female patients may be explained by differences in comorbidity and clinical presentation compared to male patients.KEY MESSAGESOnly limited data exist on sex differences in sepsis patients presenting to the emergency department with the whole spectrum of sepsis severity.Female sepsis patients had a lower incidence of comorbidities, less disease severity and a different source of infection, which explains the lower 30-day mortality we found in female patients compared to male patients.We found that sex was not an independent predictor of 30-day mortality; however, the study was probably underpowered to evaluate this outcome definitively.

CT Scan-Derived Muscle, But Not Fat, Area Independently Predicts Mortality in COVID-19.

BACKGROUND: COVID-19 has demonstrated a highly variable disease course, from asymptomatic to severe illness and eventually death. Clinical parameters, as included in the 4C Mortality Score, can predict mortality accurately in COVID-19. Additionally, CT scan-derived low muscle and high adipose tissue cross-sectional areas (CSAs) have been associated with adverse outcomes in COVID-19. RESEARCH QUESTION: Are CT scan-derived muscle and adipose tissue CSAs associated with 30-day in-hospital mortality in COVID-19, independent of 4C Mortality Score? STUDY DESIGN AND METHODS: This was a retrospective cohort analysis of patients with COVID-19 seeking treatment at the ED of two participating hospitals during the first wave of the pandemic. Skeletal muscle and adipose tissue CSAs were collected from routine chest CT-scans at admission. Pectoralis muscle CSA was demarcated manually at the fourth thoracic vertebra, and skeletal muscle and adipose tissue CSA was demarcated at the first lumbar vertebra level. Outcome measures and 4C Mortality Score items were retrieved from medical records. RESULTS: Data from 578 patients were analyzed (64.6% men; mean age, 67.7 ± 13.5 years; 18.2% 30-day in-hospital mortality). Patients who died within 30 days demonstrated lower pectoralis CSA (median, 32.6 [interquartile range (IQR), 24.3-38.8] vs 35.4 [IQR, 27.2-44.2]; P = .002) than survivors, whereas visceral adipose tissue CSA was higher (median, 151.1 [IQR, 93.6-219.7] vs 112.9 [IQR, 63.7-174.1]; P = .013). In multivariate analyses, low pectoralis muscle CSA remained associated with 30-day in-hospital mortality when adjusted for 4C Mortality Score (hazard ratio, 0.98; 95% CI, 0.96-1.00; P = .038). INTERPRETATION: CT scan-derived low pectoralis muscle CSA is associated significantly with higher 30-day in-hospital mortality in patients with COVID-19 independently of the 4C Mortality Score.

The incidence and outcome of AKI in patients with sepsis in the emergency department applying different definitions of AKI and sepsis.

BACKGROUND: Sepsis is often accompanied with acute kidney injury (AKI). The incidence of AKI in patients visiting the emergency department (ED) with sepsis according to the new SOFA criteria is not exactly known, because the definition of sepsis has changed and many definitions of AKI exist. Given the important consequences of early recognition of AKI in sepsis, our aim was to assess the epidemiology of sepsis-associated AKI using different AKI definitions (RIFLE, AKIN, AKIB, delta check, and KDIGO) for the different sepsis classifications (SIRS, qSOFA, and SOFA). METHODS: We retrospectively enrolled patients with sepsis in the ED in three hospitals and applied different AKI definitions to determine the incidence of sepsis-associated AKI. In addition, the association between the different AKI definitions and persistent kidney injury, hospital length of stay, and 30-day mortality were evaluated. RESULTS: In total, 2065 patients were included. The incidence of AKI was 17.7-51.1%, depending on sepsis and AKI definition. The highest incidence of AKI was found in qSOFA patients when the AKIN and KDIGO definitions were applied (51.1%). Applying the AKIN and KDIGO definitions in patients with sepsis according to the SOFA criteria, AKI was present in 37.3% of patients, and using the SIRS criteria, AKI was present in 25.4% of patients. Crude 30-day mortality, prolonged length of stay, and persistent kidney injury were comparable for patients diagnosed with AKI, regardless of the definition used. CONCLUSION: The incidence of AKI in patients with sepsis is highly dependent on how patients with sepsis are categorised and how AKI is defined. When AKI (any definition) was already present at the ED, 30-day mortality was high (22.2%). The diagnosis of AKI in sepsis can be considered as a sign of severe disease and helps to identify patients at high risk of adverse outcome at an early stage.

Novice Doctors in the Emergency Department: A Scoping Review.

In many emergency departments (EDs), young, inexperienced doctors treat patients who are critically ill. At the start of their career, these novice doctors are not sufficiently qualified to take care of these potentially critically ill patients in the highly demanding environment of an ED. This not only poses a threat to the well-being of the doctor, who feels inadequately prepared and experiences a lot of stress, but also to that of the patients, who may not receive optimal care. Lastly, young doctors may influence the efficiency of the organization, with longer throughput times, more orders of ancillary investigations, and more admissions. Training novice doctors with regard to simple or complex skills using simulation techniques is part of the solution. However, the transfer of newly learned skills to clinical practice remains unexplored, and not everything can be trained before the actual skill is required. Therefore, it is important to train young doctors in their learning abilities, for instance, teach them how to be adaptive and how to use their skills in new situations. Lastly, the way care is organized is essential. Good supervision, leaving room for the learning processes of young doctors, developing a team with more experienced professionals (paramedics, nurses, and doctors), and well-organized processes, aiming to reduce the complexity of the work, are ways to improve the quality of care, independent of the experience level of the novice doctor.

Frequency of alterations in qSOFA, SIRS, MEWS and NEWS scores during the emergency department stay in infectious patients: a prospective study.

BACKGROUND: For emergency department (ED) patients with suspected infection, a vital sign-based clinical rule is often calculated shortly after the patient arrives. The clinical rule score (normal or abnormal) provides information about diagnosis and/or prognosis. Since vital signs vary over time, the clinical rule scores can change as well. In this prospective multicentre study, we investigate how often the scores of four frequently used clinical rules change during the ED stay of patients with suspected infection. METHODS: Adult (≥ 18 years) patients with suspected infection were prospectively included in three Dutch EDs between March 2016 and December 2019. Vital signs were measured in 30-min intervals and the quick Sequential Organ Failure Assessment (qSOFA) score, the Systemic Inflammatory Response Syndrome (SIRS) criteria, the Modified Early Warning Score and the National Early Warning Score (NEWS) score were calculated. Using the established cut-off points, we analysed how often alterations in clinical rule scores occurred (i.e. switched from normal to abnormal or vice versa). In addition, we investigated which vital signs caused most alterations. RESULTS: We included 1433 patients, of whom a clinical rule score changed once or more in 637 (44.5%) patients. In 6.7-17.5% (depending on the clinical rule) of patients with an initial negative clinical rule score, a positive score occurred later during ED stay. In over half (54.3-65.0%) of patients with an initial positive clinical rule score, the score became negative later on. The respiratory rate caused most (51.2%) alterations. CONCLUSION: After ED arrival, alterations in qSOFA, SIRS, MEWS and/or NEWS score are present in almost half of patients with suspected infection. The most contributing vital sign to these alterations was the respiratory rate. One in 6-15 patients displayed an abnormal clinical rule score after a normal initial score. Clinicians should be aware of the frequency of these alterations in clinical rule scores, as clinical rules are widely used for diagnosis and/or prognosis and the optimal moment of assessing them is unknown.

Vital signs, clinical rules, and gut feeling: an observational study among patients with fever.

BACKGROUND: GPs decide which patients with fever need referral to the emergency department (ED). Vital signs, clinical rules, and gut feeling can influence this critical management decision. AIM: To investigate which vital signs are measured by GPs, and whether referral is associated with vital signs, clinical rules, or gut feeling. DESIGN & SETTING: Prospective observational study at two out-of-hours (OOH) GP cooperatives in the Netherlands. METHOD: During two 9-day periods, GPs performed their regular work-up in patients aged ≥18 years with fever (≥38.0°C). Subsequently, researchers measured missing vital signs for completion of the systemic inflammatory response syndrome (SIRS) criteria and the quick Sequential Organ Failure Assessment (qSOFA) score. Associations between the number of referrals, positive SIRS and qSOFA scores, and GPs' gut feelings were investigated. RESULTS: GPs measured and recorded all vital signs required for SIRS criteria and qSOFA score calculations in 24 of 108 (22.2%) assessed patients, and referred 45 (41.7%) to the ED. Higher respiratory rates, temperatures, clinical rules, and gut feeling were associated with referral. During 7-day follow-up, nine (14.3%) of 63 patients who were initially not referred were admitted to hospital. CONCLUSION: GPs measured and recorded all vital signs for SIRS criteria and qSOFA score in one-in-five patients with fever, and referred half of 63 patients who were SIRS-positive and almost all of 22 patients who were qSOFA-positive. Some vital signs and gut feeling were associated with referral, but none were consistently present in all patients who were referred. The vast majority of patients who were not initially referred remained at home during follow-up.

[Clinical course of COVID-19 in the Netherlands: an overview of 2607 patients in hospital during the first wave]. / Klinisch beloop van covid-19 in Nederland.

OBJECTIVE: To systematically collect clinical data from patients with a proven COVID-19 infection in the Netherlands. DESIGN: Data from 2579 patients with COVID-19 admitted to 10 Dutch centers in the period February to July 2020 are described. The clinical data are based on the WHO COVID case record form (CRF) and supplemented with patient characteristics of which recently an association disease severity has been reported. METHODS: Survival analyses were performed as primary statistical analysis. These Kaplan-Meier curves for time to (early) death (3 weeks) have been determined for pre-morbid patient characteristics and clinical, radiological and laboratory data at hospital admission. RESULTS: Total in-hospital mortality after 3 weeks was 22.2% (95% CI: 20.7% - 23.9%), hospital mortality within 21 days was significantly higher for elderly patients (> 70 years; 35, 0% (95% CI: 32.4% - 37.8%) and patients who died during the 21 days and were admitted to the intensive care (36.5% (95% CI: 32.1% - 41.3%)). Apart from that, in this Dutch population we also see a risk of early death in patients with co-morbidities (such as chronic neurological, nephrological and cardiac disorders and hypertension), and in patients with more home medication and / or with increased urea and creatinine levels. CONCLUSION: Early death due to a COVID-19 infection in the Netherlands appears to be associated with demographic variables (e.g. age), comorbidity (e.g. cardiovascular disease) but also disease char-acteristics at admission.

Validating the RISE UP score for predicting prognosis in patients with COVID-19 in the emergency department: a retrospective study.

OBJECTIVE: To mitigate the burden of COVID-19 on the healthcare system, information on the prognosis of the disease is needed. The recently developed Risk Stratification in the Emergency Department in Acutely ill Older Patients (RISE UP) score has very good discriminatory value for short-term mortality in older patients in the emergency department (ED). It consists of six readily available items. We hypothesised that the RISE UP score could have discriminatory value for 30-day mortality in ED patients with COVID-19. DESIGN: Retrospective analysis. SETTING: Two EDs of the Zuyderland Medical Centre, secondary care hospital in the Netherlands. PARTICIPANTS: The study sample consisted of 642 adult ED patients diagnosed with COVID-19 between 3 March and until 25 May 2020. Inclusion criteria were (1) admission to the hospital with symptoms suggestive of COVID-19 and (2) positive result of the PCR or (very) high suspicion of COVID-19 according to the chest CT scan. OUTCOME: Primary outcome was 30-day mortality, secondary outcome was a composite of 30-day mortality and admission to intensive care unit (ICU). RESULTS: Within 30 days after presentation, 167 patients (26.0%) died and 102 patients (15.9%) were admitted to ICU. The RISE UP score showed good discriminatory value for 30-day mortality (area under the receiver operating characteristic curve (AUC) 0.77, 95% CI 0.73 to 0.81) and for the composite outcome (AUC 0.72, 95% CI 0.68 to 0.76). Patients with RISE UP scores below 10% (n=121) had favourable outcome (zero deaths and six ICU admissions), while those with scores above 30% (n=221) were at high risk of adverse outcome (46.6% mortality and 19.0% ICU admissions). CONCLUSION: The RISE UP score is an accurate prognostic model for adverse outcome in ED patients with COVID-19. It can be used to identify patients at risk of short-term adverse outcome and may help guide decision-making and allocating healthcare resources.

Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department: a retrospective study.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) has a high burden on the healthcare system. Prediction models may assist in triaging patients. We aimed to assess the value of several prediction models in COVID-19 patients in the emergency department (ED). METHODS: In this retrospective study, ED patients with COVID-19 were included. Prediction models were selected based on their feasibility. Primary outcome was 30-day mortality, secondary outcomes were 14-day mortality and a composite outcome of 30-day mortality and admission to medium care unit (MCU) or intensive care unit (ICU). The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC). RESULTS: We included 403 patients. Thirty-day mortality was 23.6%, 14-day mortality was 19.1%, 66 patients (16.4%) were admitted to ICU, 48 patients (11.9%) to MCU, and 152 patients (37.7%) met the composite endpoint. Eleven prediction models were included. The RISE UP score and 4 C mortality scores showed very good discriminatory performance for 30-day mortality (AUC 0.83 and 0.84, 95% CI 0.79-0.88 for both), significantly higher than that of the other models. CONCLUSION: The RISE UP score and 4 C mortality score can be used to recognise patients at high risk for poor outcome and may assist in guiding decision-making and allocating resources.

Value of biomarkers in predicting mortality in older medical emergency department patients: a Dutch prospective study.

OBJECTIVE: Older emergency department (ED) patients are at high risk of mortality, and it is important to predict which patients are at highest risk. Biomarkers such as lactate, high-sensitivity cardiac troponin T (hs-cTnT), N-terminal pro-B-type natriuretic peptide (NT-proBNP), D-dimer and procalcitonin may be able to identify those at risk. We aimed to assess the discriminatory value of these biomarkers for 30-day mortality and other adverse outcomes. DESIGN: Prospective cohort study. On arrival of patients, five biomarkers were measured. Area under the curves (AUCs) and interval likelihood ratios (LRs) were calculated to investigate the discriminatory value of the biomarkers. SETTING: ED in the Netherlands. PARTICIPANTS: Older (≥65 years) medical ED patients, referred for internal medicine or gastroenterology. PRIMARY AND SECONDARY OUTCOME MEASURES: 30-day mortality was the primary outcome measure, while other adverse outcomes (intensive care unit/medium care unit admission, prolonged length of hospital stay, loss of independent living and unplanned readmission) were the composite secondary outcome measure. RESULTS: The median age of the 450 included patients was 79 years (IQR 73-85). In total, 51 (11.3%) patients died within 30 days. The AUCs of all biomarkers for prediction of mortality were sufficient to good, with the highest AUC of 0.73 for hs-cTnT and NT-proBNP. Only for the highest lactate values, the LR was high enough (29.0) to be applicable for clinical decision making, but this applied to a minority of patients. The AUC for the composite secondary outcome (intensive and medium care admission, length of hospital stay >7 days, loss of independent living and unplanned readmission within 30 days) was lower, ranging between 0.58 and 0.67. CONCLUSIONS: Although all five biomarkers predict 30-day mortality in older medical ED patients, their individual discriminatory value was not high enough to contribute to clinical decision making. TRIAL REGISTRATION NUMBER: NCT02946398; Results.

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis.

INTRODUCTION: Patients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorized from low to high risk. Development of a risk stratification tool for these patients is important for appropriate triage and early treatment. The aim of this study was to develop machine learning models predicting 31-day mortality in patients presenting to the ED with sepsis and to compare these to internal medicine physicians and clinical risk scores. METHODS: A single-center, retrospective cohort study was conducted amongst 1,344 emergency department patients fulfilling sepsis criteria. Laboratory and clinical data that was available in the first two hours of presentation from these patients were randomly partitioned into a development (n = 1,244) and validation dataset (n = 100). Machine learning models were trained and evaluated on the development dataset and compared to internal medicine physicians and risk scores in the independent validation dataset. The primary outcome was 31-day mortality. RESULTS: A number of 1,344 patients were included of whom 174 (13.0%) died. Machine learning models trained with laboratory or a combination of laboratory + clinical data achieved an area-under-the ROC curve of 0.82 (95% CI: 0.80-0.84) and 0.84 (95% CI: 0.81-0.87) for predicting 31-day mortality, respectively. In the validation set, models outperformed internal medicine physicians and clinical risk scores in sensitivity (92% vs. 72% vs. 78%;p<0.001,all comparisons) while retaining comparable specificity (78% vs. 74% vs. 72%;p>0.02). The model had higher diagnostic accuracy with an area-under-the-ROC curve of 0.85 (95%CI: 0.78-0.92) compared to abbMEDS (0.63,0.54-0.73), mREMS (0.63,0.54-0.72) and internal medicine physicians (0.74,0.65-0.82). CONCLUSION: Machine learning models outperformed internal medicine physicians and clinical risk scores in predicting 31-day mortality. These models are a promising tool to aid in risk stratification of patients presenting to the ED with sepsis.

Lessons learned: Contribution to healthcare by medical students during COVID-19.

An overview of the experiences with deployment of undergraduate medical students in a Dutch university center during the COVID-19 pandemic is provided from organisational and educational perspectives. Medical students' and specialists' experiences during the first peak of COVID-19 underscore the preliminary suggestion that students can be given more enhanced (yet supervised) responsibility for patient care early in their practicums.

Chest CT in the Emergency Department for Diagnosis of COVID-19 Pneumonia: Dutch Experience.

Background Clinicians need to rapidly and reliably diagnose coronavirus disease 2019 (COVID-19) for proper risk stratification, isolation strategies, and treatment decisions. Purpose To assess the real-life performance of radiologist emergency department chest CT interpretation for diagnosing COVID-19 during the acute phase of the pandemic, using the COVID-19 Reporting and Data System (CO-RADS). Materials and Methods This retrospective multicenter study included consecutive patients who presented to emergency departments in six medical centers between March and April 2020 with moderate to severe upper respiratory symptoms suspicious for COVID-19. As part of clinical practice, chest CT scans were obtained for primary work-up and scored using the five-point CO-RADS scheme for suspicion of COVID-19. CT was compared with severe acute respiratory syndrome coronavirus 2 reverse-transcription polymerase chain reaction (RT-PCR) assay and a clinical reference standard established by a multidisciplinary group of clinicians based on RT-PCR, COVID-19 contact history, oxygen therapy, timing of RT-PCR testing, and likely alternative diagnosis. Performance of CT was estimated using area under the receiver operating characteristic curve (AUC) analysis and diagnostic odds ratios against both reference standards. Subgroup analysis was performed on the basis of symptom duration grouped presentations of less than 48 hours, 48 hours through 7 days, and more than 7 days. Results A total of 1070 patients (median age, 66 years; interquartile range, 54-75 years; 626 men) were included, of whom 536 (50%) had a positive RT-PCR result and 137 (13%) of whom were considered to have a possible or probable COVID-19 diagnosis based on the clinical reference standard. Chest CT yielded an AUC of 0.87 (95% CI: 0.84, 0.89) compared with RT-PCR and 0.87 (95% CI: 0.85, 0.89) compared with the clinical reference standard. A CO-RADS score of 4 or greater yielded an odds ratio of 25.9 (95% CI: 18.7, 35.9) for a COVID-19 diagnosis with RT-PCR and an odds ratio of 30.6 (95% CI: 21.1, 44.4) with the clinical reference standard. For symptom duration of less than 48 hours, the AUC fell to 0.71 (95% CI: 0.62, 0.80; P < .001). Conclusion Chest CT analysis using the coronavirus disease 2019 (COVID-19) Reporting and Data System enables rapid and reliable diagnosis of COVID-19, particularly when symptom duration is greater than 48 hours. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Elicker in this issue.

The prevalence of pulmonary embolism in patients with COVID-19 and respiratory decline: A three-setting comparison.

BACKGROUND: The risk of pulmonary embolism (PE) in patients with Coronavirus Disease 2019 (COVID-19) is recognized. The prevalence of PE in patients with respiratory deterioration at the Emergency Department (ED), the regular ward, and the Intensive Care Unit (ICU) are not well-established. OBJECTIVES: We aimed to investigate how often PE was present in individuals with COVID-19 and respiratory deterioration in different settings, and whether or not disease severity as measured by CT-severity score (CTSS) was related to the occurrence of PE. PATIENTS/METHODS: Between April 6th and May 3rd, we enrolled 60 consecutive adult patients with confirmed COVID-19 from the ED, regular ward and ICU who met the pre-specified criteria for respiratory deterioration. RESULTS: A total of 24 (24/60: 40% (95% CI: 28-54%)) patients were diagnosed with PE, of whom 6 were in the ED (6/23: 26% (95% CI: 10-46%)), 8 in the regular ward (8/24: 33% (95% CI: 16-55%)), and 10 in the ICU (10/13: 77% (95% CI: 46-95%)). CTSS (per unit) was not associated with the occurrence of PE (age and sex-adjusted OR 1.06 (95%CI 0.98-1.15)). CONCLUSION: The number of PE diagnosis among patients with COVID-19 and respiratory deterioration was high; 26% in the ED, 33% in the regular ward and 77% in the ICU respectively. In our cohort CTSS was not associated with the occurrence of PE. Based on the high number of patients diagnosed with PE among those scanned we recommend a low threshold for performing computed tomography angiography in patients with COVID-19 and respiratory deterioration.