| 1. |
- Neumann, Johannes Tobias, et al.
(författare)
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Personalized diagnosis in suspected myocardial infarction
- 2023
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Ingår i: Clinical Research in Cardiology. - : Springer. - 1861-0684 .- 1861-0692. ; 112, s. 1288-1301
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Tidskriftsartikel (refereegranskat)abstract
- Background: In suspected myocardial infarction (MI), guidelines recommend using high-sensitivity cardiac troponin (hscTn)- based approaches. These require fixed assay-specific thresholds and timepoints, without directly integrating clinical information. Using machine-learning techniques including hs-cTn and clinical routine variables, we aimed to build a digital tool to directly estimate the individual probability of MI, allowing for numerous hs-cTn assays.Methods: In 2,575 patients presenting to the emergency department with suspected MI, two ensembles of machine-learning models using single or serial concentrations of six different hs-cTn assays were derived to estimate the individual MI probability ( ARTEMIS model). Discriminative performance of the models was assessed using area under the receiver operating characteristic curve (AUC) and logLoss. Model performance was validated in an external cohort with 1688 patients and tested for global generalizability in 13 international cohorts with 23,411 patients.Results: Eleven routinely available variables including age, sex, cardiovascular risk factors, electrocardiography, and hs-cTn were included in the ARTEMIS models. In the validation and generalization cohorts, excellent discriminative performance was confirmed, superior to hs-cTn only. For the serial hs-cTn measurement model, AUC ranged from 0.92 to 0.98. Good calibration was observed. Using a single hs-cTn measurement, the ARTEMIS model allowed direct rule-out of MI with very high and similar safety but up to tripled efficiency compared to the guideline- recommended strategy.Conclusion We developed and validated diagnostic models to accurately estimate the individual probability of MI, which allow for variable hs-cTn use and flexible timing of resampling. Their digital application may provide rapid, safe and efficient personalized patient care.
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| 2. |
- Akhtar, Zubair, et al.
(författare)
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Optimal timing of influenza vaccination among patients with acute myocardial infarction - Findings from the IAMI trial
- 2023
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Ingår i: Vaccine. - : Elsevier. - 0264-410X .- 1873-2518. ; 41:48, s. 7159-7165
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Tidskriftsartikel (refereegranskat)abstract
- Influenza vaccination reduces the risk of adverse cardiovascular events. The IAMI trial randomly assigned 2571 patients with acute myocardial infarction (AMI) to receive influenza vaccine or saline placebo during their index hospital admission. It was conducted at 30 centers in 8 countries from October 1, 2016 to March 1, 2020. In this post-hoc exploratory sub-study, we compare the trial outcomes in patients receiving early season vaccination (n = 1188) and late season vaccination (n = 1344). The primary endpoint was the composite of all-cause death, myocardial infarction (MI), or stent thrombosis at 12 months. The cumulative incidence of the primary and key secondary endpoints by randomized treatment and early or late vaccination was estimated using the Kaplan-Meier method. In the early vaccinated group, the primary composite endpoint occurred in 36 participants (6.0%) assigned to influenza vaccine and 49 (8.4%) assigned to placebo (HR 0.69; 95% CI 0.45 to 1.07), compared to 31 participants (4.7%) assigned to influenza vaccine and 42 (6.2%) assigned to placebo (HR 0.74; 95% CI 0.47 to 1.18) in the late vaccinated group (P = 0.848 for interaction on HR scale at 1 year). We observed similar estimates for the key secondary endpoints of all-cause death and CV death. There was no statistically significant difference in vaccine effectiveness against adverse cardiovascular events by timing of vaccination. The effect of vaccination on all-cause death at one year was more pronounced in the group receiving early vaccination (HR 0.50; 95% CI, 0.29 to 0.86) compared late vaccination group (HR 0.75; 35% CI, 0.40 to 1.40) but there was no statistically significant difference between these groups (Interaction P = 0.335). In conclusion, there is insufficient evidence from the trial to establish whether there is a difference in efficacy between early and late vaccination but regardless of vaccination timing we strongly recommend influenza vaccination in all patients with cardiovascular diseases.
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| 3. |
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| 4. |
- 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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| 5. |
- Borna, Catharina, et al.
(författare)
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The objective CORE score allows early rule out in acute chest pain patients
- 2018
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Ingår i: Scandinavian Cardiovascular Journal. - : Informa UK Limited. - 1401-7431 .- 1651-2006. ; 52:6, s. 308-314
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Tidskriftsartikel (refereegranskat)abstract
- Objectives. Chest pain is a common complaint in the emergency department (ED), and it is a challenge to identify low-risk chest pain patients eligible for early discharge. We aimed to develop a simple objective decision rule to exclude 30-day major adverse cardiac events (MACE) in ED chest pain patients. Design. We analyzed prospectively included patients presenting with chest pain. Low risk patients were identified with the clinical objective rule-out evaluation (CORE). CORE was based on high sensitivity cardiac troponin T (hs-cTnT) tests at ED presentation (0 h) and 2 h later together with a simplified risk score consisting of four objective variables: age ≥65 years and a history of arterial disease, hypertension or diabetes. For the patient to be classified as low risk in the CORE rule, hs-cTnT had to be ≤14 ng/L both at 0 and 2 h, and the sum of the risk score had to be 0. The primary outcome was MACE within 30 days. Results. Among the 751 patients in the final analysis, 90 (11.9%) had a MACE. CORE identified 248 (33%) of patients as low risk with a sensitivity of 98.9% (CI 93.1–99.9) and a negative predictive value of 99.6% (95% CI 97.4–100) for 30-day MACE. Adding the ED physician’s interpretation of the ECG to CORE did not improve diagnostic performance. Conclusion. A simple objective decision rule (CORE) identified one-third of all patients as having a very low 30-day risk of MACE. These patients may potentially be discharged without additional investigations for acute coronary syndrome.
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| 6. |
- 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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| 7. |
- Cwikiel, Wojciech, et al.
(författare)
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Endovascular Treatment of Two Pseudoaneurysms Originating From the Left Ventricle.
- 2013
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Ingår i: Cardiovascular and Interventional Radiology. - : Springer Science and Business Media LLC. - 1432-086X .- 0174-1551. ; 36:6, s. 1677-1680
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Tidskriftsartikel (refereegranskat)abstract
- A 67-year-old woman resented with an acute type A aortic dissection, which was treated surgically with aortic valve replacement as a composite graft with reimplantation of the coronary arteries. At the end of surgery, a left-ventricular venting catheter was placed through the apex and closed with a buffered suture. Consecutive computed tomography (CT) examinations verified a growing apex pseudoaneurysm. Communication between the ventricle and the pseudoaneurysm was successfully closed with an Amplatz septal plug by the transfemoral route. Follow-up CT showed an additional pseudoaneurysm, which also was successfully closed using the same method.
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| 8. |
- 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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| 9. |
- Dryver, Eric, et al.
(författare)
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Checklistor och »crowdsourcing« för ökad patientsäkerhet på akutmottagningen
- 2014
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Ingår i: Läkartidningen. - 0023-7205. ; 111:11, s. 493-494
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Checklists make it easier for the emergency physician. This is the idea behind a website launched this month. The site will contain suggestions for checklists on what information which should be obtained for the assessment of the patient at the emergency department. All emergency staff are invited to participate in the development of the project.
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| 10. |
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