Patients' Perspectives and Feasibility of Home Monitoring in Acute Care: The AcuteCare@Home Flash Mob Study.

Objective: To determine patients' perspectives on home monitoring at emergency department (ED) presentation and shortly after admission and compare these with their physicians' perspectives. Methods: Forty Dutch hospitals participated in this prospective flash mob study. Adult patients with acute medical conditions, treated by internal medicine specialties, presenting at the ED or admitted at the admission ward within the previous 24 h were included. The primary outcome was the proportion of patients who were able and willing to undergo home monitoring. Secondary outcomes included identifying barriers to home monitoring, patient's prerequisites, and assessing the agreement between the perspectives of patients and treating physicians. Results: On February 2, 2023, in total 665 patients [median age 69 (interquartile range: 55-78) years; 95.5% community dwelling; 29.3% Modified Early Warning Score ≥3; 29.5% clinical frailty score ≥5] were included. In total, 19.6% of ED patients were admitted and 26% of ward patients preferred home monitoring as continuation of care. Guaranteed readmission (87.8%), ability to contact the hospital 24/7 (77.3%), and a family caregiver at home (55.7%) were the most often reported prerequisites. Barriers for home monitoring were feeling too severely ill (78.8%) and inability to receive the required treatment at home (64.4%). The agreement between patients and physicians was fair (Cohens kappa coefficient 0.26). Conclusions: A substantial proportion of acutely ill patients stated that they were willing and able to be monitored at home. Guaranteed readmission, availability of a treatment team (24/7), and a home support system are needed for successful implementation of home monitoring in acute care.

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.

A Nationwide Flash-Mob Study for Suspected Acute Coronary Syndrome.

PURPOSE: Our primary objective was to evaluate the Marburg Heart Score (MHS), a clinical decision rule, or to develop an adapted clinical decision rule for family physicians (FPs) to safely rule out acute coronary syndrome (ACS) in patients referred to secondary care for suspected ACS. The secondary objective was to evaluate the feasibility of using the flash-mob method, an innovative study design, for large-scale research in family medicine. METHODS: In this 2-week, nationwide, prospective, observational, flash-mob study, FPs collected data on possible ACS predictors and assessed ACS probability (on a scale of 1-10) in patients referred to secondary care for suspected ACS. RESULTS: We collected data for 258 patients in 2 weeks by mobilizing approximately 1 in 5 FPs throughout the country via ambassadors. A final diagnosis was obtained for 243 patients (94.2%), of whom 45 (18.5%) received a diagnosis of ACS. Sex, sex-adjusted age, and ischemic changes on electrocardiography were significantly associated with ACS. The sensitivity of the MHS (cut-off ≤2) was 75.0%, specificity was 44.0%, positive predictive value was 24.3%, and negative predictive value was 88.0%. For the FP assessment (cut-off ≤5), these test characteristics were 86.7%, 41.4%, 25.2%, and 93.2%, respectively. CONCLUSIONS: For patients referred to emergency care, ACS could not be safely ruled out using the MHS or FP clinical assessment. The flash-mob study design may be a feasible alternative research method to investigate relatively simple, clinically relevant research questions in family medicine on a large scale and over a relatively short time frame.

Study protocol for a multicentre prospective cohort study to identify predictors of adverse outcome in older medical emergency department patients (the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) study).

BACKGROUND: Older patients (≥65 years old) experience high rates of adverse outcomes after an emergency department (ED) visit. Reliable tools to predict adverse outcomes in this population are lacking. This manuscript comprises a study protocol for the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) study that aims to identify predictors of adverse outcome (including triage- and risk stratification scores) and intends to design a feasible prediction model for older patients that can be used in the ED. METHODS: The RISE UP study is a prospective observational multicentre cohort study in older (≥65 years of age) ED patients treated by internists or gastroenterologists in Zuyderland Medical Centre and Maastricht University Medical Centre+ in the Netherlands. After obtaining informed consent, patients characteristics, vital signs, functional status and routine laboratory tests will be retrieved. In addition, disease perception questionnaires will be filled out by patients or their caregivers and clinical impression questionnaires by nurses and physicians. Moreover, both arterial and venous blood samples will be taken in order to determine additional biomarkers. The discriminatory value of triage- and risk stratification scores, clinical impression scores and laboratory tests will be evaluated. Univariable logistic regression will be used to identify predictors of adverse outcomes. With these data we intend to develop a clinical prediction model for 30-day mortality using multivariable logistic regression. This model will be validated in an external cohort. Our primary endpoint is 30-day all-cause mortality. The secondary (composite) endpoint consist of 30-day mortality, length of hospital stay, admission to intensive- or medium care units, readmission and loss of independent living. Patients will be followed up for at least 30 days and, if possible, for one year. DISCUSSION: In this study, we will retrieve a broad range of data concerning adverse outcomes in older patients visiting the ED with medical problems. We intend to develop a clinical tool for identification of older patients at risk of adverse outcomes that is feasible for use in the ED, in order to improve clinical decision making and medical care. TRIAL REGISTRATION: Retrospectively registered on clinicaltrials.gov ( NCT02946398 ; 9/20/2016).

Characteristics and outcome of acute medical admissions with hyponatremia: even mild hyponatremia is associated with higher mortality.

Hyponatremia is a common finding in hospitalized patients. In this retrospective cohort study we assessed the characteristics and outcome of acute medical admissions with hyponatremia. Compared to the normal sodium group, those with hyponatremia were significantly older and the Charlson Comorbidity Index (CCI) was higher. The number of admissions to MCU/ICU between both groups was similar, but hyponatremic patients had a longer length of stay and both 28-day and one-year mortality were higher, even in patients with mild hyponatremia. Hyponatremia was independently associated with mortality after adjustment for age, CCI and polypharmacy, as was found in the subgroup with mild hyponatremia.

Acute dyspnea caused by hypocalcemia-related laryngospasm.

BACKGROUND: Laryngospasm is a rare cause of stridor in adults, and laryngospasm due to hypocalcemia is an unusual finding. CASE REPORT: We present a case of an adult woman with acute dyspnea. A week prior to presentation, she experienced short episodes of a pinching feeling in her throat and difficulty breathing. On primary assessment, stridor and a positive Trousseau sign were noted. Laboratory examination showed hypocalcemia. We concluded that the dyspnea was caused by laryngospasm due to hypocalcemia. Hypocalcemia was treated promptly, and stridor and dyspnea resolved rapidly. WHY SHOULD AN EMERGENCY PHYSICIAN BE AWARE OF THIS?: Laryngospasm is a rare, but serious and potentially lethal, complication of hypocalcemia in adults. In every adult presenting with acute dyspnea and stridor, the possibility of hypocalcemia should be considered. Hypocalcemia should be treated promptly.

Risk stratification by abbMEDS and CURB-65 in relation to treatment and clinical disposition of the septic patient at the emergency department: a cohort study.

BACKGROUND: Sepsis leads to high mortality, therefore risk stratification is important. The abbMEDS (abbreviated Mortality Emergency Department Sepsis) score assesses sepsis severity and predicts mortality. In community-acquired pneumonia, the CURB-65 (Confusion, Urea, Respiration, Blood pressure, Age) also provides support in clinical decisions regarding antibiotic treatment and clinical disposition. We investigated the predictive value and feasibility of the abbMEDS and CURB-65 in sepsis patients at the ED and the relationship between the scores and antibiotic treatment and clinical disposition (i.e. admission and type of ward). METHODS: In this retrospective cohort study, we included 725 sepsis patients at the ED. We investigated the value in predicting 28-day mortality and feasibility of both scores. We calibrated the abbMEDS. We further assessed the relationship between the three risk categories per score and antibiotic treatment (i.e. oral and intravenous narrow or broad-spectrum) and clinical disposition. RESULTS: Both abbMEDS and CURB-65 were good predictors of 28-day mortality (13.0%) (AUC 0.77 [95% CI 0.72 - 0.83] and 0.73 [95% CI 0.67 - 0.78], respectively) and feasible (complete score 92.7 and 93.9%, respectively). In the high risk category of the abbMEDS, all patients were admitted and treated with intravenous broad-spectrum antibiotics. In the high risk category of the CURB-65, 2.5% were not admitted and 4.4% received no antibiotics. CONCLUSION: Both abbMEDS and CURB-65 are good predictors of 28-day mortality in septic ED patients. The abbMEDS is well calibrated and matches current clinical decisions concerning antibiotic treatment and clinical disposition, while this is less so for the CURB-65. In the future, use of the abbMEDS at the ED may improve sepsis care when its value as a decision support tool can be confirmed.

Direct health care costs of hospital admissions due to adverse events in The Netherlands.

BACKGROUND: Health care-related adverse events (AEs) are common, and the economic burden is substantial. Information on costs of health care-related AEs 'leading' to hospitalization is limited and has focused on adverse drug events. AIM: To provide insight into costs of admissions due to (preventable) health care-related AEs, not limited to adverse drug events. METHODS: This study was conducted during a 5-month period (May-September 2010) in The Netherlands, in a 600-bed university medical centre. All patients who were admitted via the emergency department to an internal medicine department because of a health care-related AE were included. We retrospectively retrieved all data on medical information as well as health care resource utilization from the patient's medical record. The cost of the admission was estimated (for each patient individually) by multiplying the number of resources by their specific unit cost and then summing all costs per patient. RESULTS: In total, 324 admissions due to a health care-related AE were included (28.7% of all admissions). Total direct health care costs of these hospitalizations amounted to €1,404,070 in a 5-month period. Medication-related AEs were most common (43.5%) and contributed most to the costs (€587,550; 41.8%). Inpatient days were most expensive (€1,076,385; 77.3%). Preventable health care-related AEs accounted for €277,665 (19.8%). CONCLUSION: We found that health care-related AEs are expensive, with preventable health care-related AEs accounting for one-fifth of the costs. Awareness of possible health care-related AEs following medical actions is necessary to reduce already high health care costs.

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 .

Acute internal medicine physicians' clinical intuition based on acute care telephone referral: A prospective study.

INTRODUCTION: In the Netherlands, most emergency department (ED) patients are referred by a general practitioner (GP) or a hospital specialist. Early risk stratification during telephone referral could allow the physician to assess the severity of the patients' illness in the prehospital setting. We aim to assess the discriminatory value of the acute internal medicine (AIM) physicians' clinical intuition based on telephone referral of ED patients to predict short-term adverse outcomes, and to investigate on which information their predictions are based. METHODS: In this prospective study, we included adult ED patients who were referred for internal medicine by a GP or a hospital specialist. Primary outcomes were hospital admission and triage category according to the Manchester Triage System (MTS). Secondary outcome was 31-day mortality. The discriminatory performance of the clinical intuition was assessed using an area under the receiver operating characteristics curve (AUC). To identify which information is important to predict adverse outcomes, we performed univariate regression analysis. Agreement between predicted and observed MTS triage category was assessed using intraclass and Spearman's correlation. RESULTS: We included 333 patients, of whom 172 (51.7%) were referred by a GP, 146 (43.8%) by a hospital specialist, and 12 (3.6%) by another health professional. The AIM physician's clinical intuition showed good discriminatory performance regarding hospital admission (AUC 0.72, 95% CI: 0.66-0.78) and 31-day mortality (AUC 0.73, 95% CI: 0.64-0.81). Univariate regression analysis showed that age ≥65 years and a sense of alarm were significant predictors. The predicted and observed triage category were similar in 45.2%, but in 92.5% the prediction did not deviate by more than one category. Intraclass and Spearman's correlation showed fair agreement between predicted and observed triage category (ICC 0.48, Spearman's 0.29). CONCLUSION: Clinical intuition based on relevant information during a telephone referral can be used to accurately predict short-term outcomes, allowing for early risk stratification in the prehospital setting and managing ED patient flow more effectively.

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.

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.

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.

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.

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.

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.

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.

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.