| 1. |
- Mokhtari, Arash, et al.
(författare)
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Effectiveness and Safety of the European Society of Cardiology 0-/1-h Troponin Rule-Out Protocol : The Design of the ESC-TROP Multicenter Implementation Study
- 2020
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Ingår i: Cardiology. - : S. Karger AG. - 1421-9751 .- 0008-6312. ; 145:11, s. 685-692
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Tidskriftsartikel (refereegranskat)abstract
- Chest pain is one of the most common complaints at the emergency department (ED), and it is commonly the perceived likelihood of acute coronary syndrome (ACS) that drives management. Guidelines from the European Society of Cardiology (ESC) recommend the use of a 0-/1-h high-sensitivity cardiac troponin T (hs-cTnT) protocol to rule out or in ACS, but this is mostly based on observational studies. The aim of the ESC-TROP trial is to determine the safety and effectiveness of the ESC 0-/1-h hs-cTnT protocol when implemented in routine care. Adult chest pain patients at 5 EDs in the Skåne Region, Sweden, are included in the trial. The 0-/1-h hs-cTnT ESC protocol supplemented with clinical assessment and electrocardiography (ECG) is implemented at 3 EDs, and the other 2 EDs act as concurrent controls. Outcomes will be evaluated during the 10 months after the implementation and the corresponding 10 months of the previous year. The 2 co-primary outcomes are (a) acute myocardial infarction (AMI) and all-cause death within 30 days in patients discharged from the ED, and (b) ED length of stay of the same patients. Secondary outcomes include the proportion of chest pain patients discharged from the ED and the number of ruled-out patients undergoing objective testing within 30 days. The ESC-TROP trial will determine the performance and applicability of the 0-/1-h hs-cTnT ESC protocol supplemented with clinical assessment and ECG when implemented in routine ED care. It will provide evidence whether 0-/1-h hs-cTnT testing is safe, effective, and feasible, and whether widespread implementation as recommended by ESC guidelines should be supported.
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| 2. |
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| 3. |
- André, Lars, et al.
(författare)
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The prevalence of clinically relevant delayed intracranial hemorrhage in head trauma patients treated with oral anticoagulants is very low: a retrospective cohort register study
- 2024
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Ingår i: Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine. - 1757-7241. ; 32, s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundCurrent guidelines from Scandinavian Neuro Committee mandate a 24-hour observation for head trauma patients on anticoagulants, even with normal initial head CT scans, as a means not to miss delayed intracranial hemorrhages. This study aimed to assess the prevalence, and time to diagnosis, of clinically relevant delayed intracranial hemorrhage in head trauma patients treated with oral anticoagulants.MethodUtilizing comprehensive two-year data from Region Skåne’s emergency departments, which serve a population of 1.3 million inhabitants, this study focused on adult head trauma patients prescribed oral anticoagulants. We identified those with intracranial hemorrhage within 30 days, defining delayed intracranial hemorrhage as a bleeding not apparent on their initial CT head scan. These cases were further defined as clinically relevant if associated with mortality, any intensive care unit admission, or neurosurgery.ResultsOut of the included 2,362 head injury cases (median age 84, 56% on a direct acting oral anticoagulant), five developed delayed intracranial hemorrhages. None of these five cases underwent neurosurgery nor were admitted to an intensive care unit. Only two cases (0.08%, 95% confidence interval [0.01–0.3%]) were classified as clinically relevant, involving subdural hematomas in patients aged 82 and 87 years, who both subsequently died. The diagnosis of these delayed intracranial hemorrhages was made at 4 and 7 days following initial presentation to the emergency department.ConclusionIn patients with head trauma, on oral anticoagulation, the incidence of clinically relevant delayed intracranial hemorrhage was found to be less than one in a thousand, with detection occurring four days or later after initial presentation. This challenges the effectiveness of the 24-hour observation period recommended by the Scandinavian Neurotrauma Committee guidelines, suggesting a need to reassess these guidelines to optimise care and resource allocation.
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| 4. |
- Berg, Johanna, et al.
(författare)
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Perceived usefulness of trauma audit filters in urban India: a mixed-methods multicentre Delphi study comparing filters from the WHO and low and middle-income countries
- 2022
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Ingår i: Bmj Open. - : BMJ. - 2044-6055. ; 12:6
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Tidskriftsartikel (refereegranskat)abstract
- Objective To compare experts' perceived usefulness of audit filters from Ghana, Cameroon, WHO and those locally developed; generate context-appropriate audit filters for trauma care in selected hospitals in urban India; and explore characteristics of audit filters that correlate to perceived usefulness. Design A mixed-methods approach using a multicentre online Delphi technique. Setting Two large tertiary hospitals in urban India. Methods Filters were rated on a scale from 1 to 10 in terms of perceived usefulness, with the option to add new filters and comments. The filters were categorised into three groups depending on their origin: low and middle-income countries (LMIC), WHO and New (locally developed), and their scores compared. Significance was determined using Kruskal-Wallis test followed by Wilcoxon rank-sum test. We performed a content analysis of the comments. Results 26 predefined and 15 new filter suggestions were evaluated. The filters had high usefulness scores (mean overall score 9.01 of 10), with the LMIC filters having significantly higher scores compared with those from WHO and those newly added. Three themes were identified in the content analysis relating to medical relevance, feasibility and specificity. Conclusions Audit filters from other LMICs were deemed highly useful in the urban India context. This may indicate that the transferability of defined trauma audit filters between similar contexts is high and that these can provide a starting point when implemented as part of trauma quality improvement programmes in low-resource settings.
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| 5. |
- Björkelund, Anders, et al.
(författare)
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Machine learning compared with rule‐in/rule‐out algorithms and logistic regression to predict acute myocardial infarction based on troponin T concentrations
- 2021
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Ingår i: Journal of the American College of Emergency Physicians Open. - Hoboken, NJ : John Wiley & Sons. - 2688-1152. ; 2:2
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Tidskriftsartikel (refereegranskat)abstract
- AbstractObjectiveComputerized decision-support tools may improve diagnosis of acute myocardial infarction (AMI) among patients presenting with chest pain at the emergency department (ED). The primary aim was to assess the predictive accuracy of machine learning algorithms based on paired high-sensitivity cardiac troponin T (hs-cTnT) concentrations with varying sampling times, age, and sex in order to rule in or out AMI.MethodsIn this register-based, cross-sectional diagnostic study conducted retrospectively based on 5695 chest pain patients at 2 hospitals in Sweden 2013–2014 we used 5-fold cross-validation 200 times in order to compare the performance of an artificial neural network (ANN) with European guideline-recommended 0/1- and 0/3-hour algorithms for hs-cTnT and with logistic regression without interaction terms. Primary outcome was the size of the intermediate risk group where AMI could not be ruled in or out, while holding the sensitivity (rule-out) and specificity (rule-in) constant across models.ResultsANN and logistic regression had similar (95%) areas under the receiver operating characteristics curve. In patients (n = 4171) where the timing requirements (0/1 or 0/3 hour) for the sampling were met, using ANN led to a relative decrease of 9.2% (95% confidence interval 4.4% to 13.8%; from 24.5% to 22.2% of all tested patients) in the size of the intermediate group compared to the recommended algorithms. By contrast, using logistic regression did not substantially decrease the size of the intermediate group.ConclusionMachine learning algorithms allow for flexibility in sampling and have the potential to improve risk assessment among chest pain patients at the ED.
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| 6. |
- Blodgett, Joanna M., et al.
(författare)
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Device-measured physical activity and cardiometabolic health : the Prospective Physical Activity, Sitting, and Sleep (ProPASS) consortium
- 2023
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Ingår i: European Heart Journal. - 0195-668X .- 1522-9645.
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Tidskriftsartikel (refereegranskat)abstract
- Background and Aims: Physical inactivity, sedentary behaviour (SB), and inadequate sleep are key behavioural risk factors of cardiometabolic diseases. Each behaviour is mainly considered in isolation, despite clear behavioural and biological interdependencies. The aim of this study was to investigate associations of five-part movement compositions with adiposity and cardiometabolic biomarkers.Methods: Cross-sectional data from six studies (n = 15 253 participants; five countries) from the Prospective Physical Activity, Sitting and Sleep consortium were analysed. Device-measured time spent in sleep, SB, standing, light-intensity physical activity (LIPA), and moderate-vigorous physical activity (MVPA) made up the composition. Outcomes included body mass index (BMI), waist circumference, HDL cholesterol, total:HDL cholesterol ratio, triglycerides, and glycated haemoglobin (HbA1c). Compositional linear regression examined associations between compositions and outcomes, including modelling time reallocation between behaviours.Results: The average daily composition of the sample (age: 53.7 ± 9.7 years; 54.7% female) was 7.7 h sleeping, 10.4 h sedentary, 3.1 h standing, 1.5 h LIPA, and 1.3 h MVPA. A greater MVPA proportion and smaller SB proportion were associated with better outcomes. Reallocating time from SB, standing, LIPA, or sleep into MVPA resulted in better scores across all outcomes. For example, replacing 30 min of SB, sleep, standing, or LIPA with MVPA was associated with −0.63 (95% confidence interval −0.48, −0.79), −0.43 (−0.25, −0.59), −0.40 (−0.25, −0.56), and −0.15 (0.05, −0.34) kg/m2 lower BMI, respectively. Greater relative standing time was beneficial, whereas sleep had a detrimental association when replacing LIPA/MVPA and positive association when replacing SB. The minimal displacement of any behaviour into MVPA for improved cardiometabolic health ranged from 3.8 (HbA1c) to 12.7 (triglycerides) min/day.Conclusions: Compositional data analyses revealed a distinct hierarchy of behaviours. Moderate-vigorous physical activity demonstrated the strongest, most time-efficient protective associations with cardiometabolic outcomes. Theoretical benefits from reallocating SB into sleep, standing, or LIPA required substantial changes in daily activity.
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| 7. |
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| 8. |
- Chiang, Cho-Han, et al.
(författare)
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Performance of the European Society of Cardiology 0/1-Hour, 0/2-Hour, and 0/3-Hour Algorithms for Rapid Triage of Acute Myocardial Infarction : An International Collaborative Meta-analysis
- 2022
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Ingår i: Annals of Internal Medicine. - 0003-4819. ; 175:1, s. 101-113
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Forskningsöversikt (refereegranskat)abstract
- BACKGROUND: The 2020 European Society of Cardiology (ESC) guidelines recommend using the 0/1-hour and 0/2-hour algorithms over the 0/3-hour algorithm as the first and second choices of high-sensitivity cardiac troponin (hs-cTn)-based strategies for triage of patients with suspected acute myocardial infarction (AMI).PURPOSE: To evaluate the diagnostic accuracies of the ESC 0/1-hour, 0/2-hour, and 0/3-hour algorithms.DATA SOURCES: PubMed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, and Scopus from 1 January 2011 to 31 December 2020. (PROSPERO: CRD42020216479).STUDY SELECTION: Prospective studies that evaluated the ESC 0/1-hour, 0/2-hour, or 0/3-hour algorithms in adult patients presenting with suspected AMI.DATA EXTRACTION: The primary outcome was index AMI. Twenty unique cohorts were identified. Primary data were obtained from investigators of 16 cohorts and aggregate data were extracted from 4 cohorts. Two independent authors assessed each study for methodological quality.DATA SYNTHESIS: A total of 32 studies (20 cohorts) with 30 066 patients were analyzed. The 0/1-hour algorithm had a pooled sensitivity of 99.1% (95% CI, 98.5% to 99.5%) and negative predictive value (NPV) of 99.8% (CI, 99.6% to 99.9%) for ruling out AMI. The 0/2-hour algorithm had a pooled sensitivity of 98.6% (CI, 97.2% to 99.3%) and NPV of 99.6% (CI, 99.4% to 99.8%). The 0/3-hour algorithm had a pooled sensitivity of 93.7% (CI, 87.4% to 97.0%) and NPV of 98.7% (CI, 97.7% to 99.3%). Sensitivity of the 0/3-hour algorithm was attenuated in studies that did not use clinical criteria (GRACE score <140 and pain-free) compared with studies that used clinical criteria (90.2% [CI, 82.9 to 94.6] vs. 98.4% [CI, 88.6 to 99.8]). All 3 algorithms had similar specificities and positive predictive values for ruling in AMI, but heterogeneity across studies was substantial. Diagnostic performance was similar across the hs-cTnT (Elecsys; Roche), hs-cTnI (Architect; Abbott), and hs-cTnI (Centaur/Atellica; Siemens) assays.LIMITATION: Diagnostic accuracy, inclusion and exclusion criteria, and cardiac troponin sampling time varied among studies.CONCLUSION: The ESC 0/1-hour and 0/2-hour algorithms have higher sensitivities and NPVs than the 0/3-hour algorithm for index AMI.PRIMARY FUNDING SOURCE: National Taiwan University Hospital.
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| 9. |
- Crilly, Julia, et al.
(författare)
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Research priority setting in emergency care : A scoping review
- 2022
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Ingår i: Journal of the American college of emergency physicians open. - : Wiley. - 2688-1152. ; 3:6
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Forskningsöversikt (refereegranskat)abstract
- Objective: Priority areas for emergency care research are emerging and becoming ever more important. The objectives of this scoping review were to (1) provide a comprehensive overview of published emergency care priority-setting studies by collating and comparing priority-setting methodology and (2) describe the resulting research priorities identified. Methods: The Joanna Briggs Institute methodological framework was used. Inclusion criteria were peer-review articles available in English, published between January 1, 2008 and March 31, 2019 and used 2 or more search terms. Five databases (Scopus, AustHealth, EMBASE, CINAHL, and Ovid MEDLINE) were searched. REporting guideline for PRIority SEtting of health research (REPRISE) criteria were used to assess the quality of evidence of included articles. Results: Forty-five studies were included. Fourteen themes for emergency care research were considered within 3 overarching research domains: emergency populations (pediatrics, geriatrics), emergency care workforce and processes (nursing, shared decision making, general workforce, and process), and emergency care clinical areas (imaging, falls, pain management, trauma care, substance misuse, infectious diseases, mental health, cardiology, general clinical care). Variation in the reporting of research priority areas was evident. Priority areas to drive the global agenda for emergency care research are limited given the country and professional group-specific context of existing studies. Conclusion: This comprehensive summary of generated research priorities across emergency care provides insight into current and future research agendas. With the nature of emergency care being inherently broad, future priorities may warrant population (eg, children, geriatrics) or subspecialty (eg, trauma, toxicology, mental health) focus and be derived using a rigorous framework and patient engagement.
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| 10. |
- de Capretz, Pontus Olsson, et al.
(författare)
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Machine learning for early prediction of acute myocardial infarction or death in acute chest pain patients using electrocardiogram and blood tests at presentation
- 2023
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Ingår i: BMC Medical Informatics and Decision Making. - London : BioMed Central (BMC). - 1472-6947. ; 23:1, s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- Aims: In the present study, we aimed to evaluate the performance of machine learning (ML) models for identification of acute myocardial infarction (AMI) or death within 30 days among emergency department (ED) chest pain patients. Methods and results: Using data from 9519 consecutive ED chest pain patients, we created ML models based on logistic regression or artificial neural networks. Model inputs included sex, age, ECG and the first blood tests at patient presentation: High sensitivity TnT (hs-cTnT), glucose, creatinine, and hemoglobin. For a safe rule-out, the models were adapted to achieve a sensitivity > 99% and a negative predictive value (NPV) > 99.5% for 30-day AMI/death. For rule-in, we set the models to achieve a specificity > 90% and a positive predictive value (PPV) of > 70%. The models were also compared with the 0 h arm of the European Society of Cardiology algorithm (ESC 0 h); An initial hs-cTnT < 5 ng/L for rule-out and ≥ 52 ng/L for rule-in. A convolutional neural network was the best model and identified 55% of the patients for rule-out and 5.3% for rule-in, while maintaining the required sensitivity, specificity, NPV and PPV levels. ESC 0 h failed to reach these performance levels. Discussion: An ML model based on age, sex, ECG and blood tests at ED arrival can identify six out of ten chest pain patients for safe early rule-out or rule-in with no need for serial blood tests. Future studies should attempt to improve these ML models further, e.g. by including additional input data. © 2023, The Author(s).
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