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- Acosta, Stefan, et al.
(författare)
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Diagnostic pitfalls at admission in patients with acute superior mesenteric artery occlusion
- 2012
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Ingår i: Journal of Emergency Medicine. - : Elsevier. - 0736-4679 .- 1090-1280. ; 42:6, s. 635-641
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Acute superior mesenteric artery (SMA) occlusion leads to acute intestinal ischemia and is associated with high mortality. Early diagnosis is often missed, and confounding factors leading to diagnostic delays need to be highlighted.OBJECTIVES: To identify potential diagnostic laboratory pitfalls at admission in patients with acute SMA occlusion.METHODS: Fifty-five patients with acute SMA occlusion were identified from the in-hospital register during a 4-year period, 2005-2009.RESULTS: The median age was 76 years; 78% were women. The occlusion was embolic in 53% and thrombotic in 47% of patients. At admission, troponin I was above the clinical decision level (> 0.06 μg/L) for acute ischemic myocardial injury in 9/19 (47%) patients with embolic occlusion. Elevated pancreas amylase and normal plasma lactate were found in 12/45 and 13/27, respectively. A troponin I (TnI) above the clinical decision level was associated with a high frequency of referrals from the general surgeon to a specialist in internal medicine (p = 0.011) or a cardiologist (p = 0.024). The diagnosis was established after computed tomography angiography in 98% of the patients. The overall in-hospital mortality rate was 33%. Attempting intestinal revascularization (n = 43; p < 0.001), with a 95% frequency rate of completion control of the vascular procedure, was associated with a higher survival rate, whereas referral to the cardiologist was associated with a higher mortality rate (p = 0.018).CONCLUSION: Elevated TnI was common in acute SMA occlusion, and referral to the cardiologist was found to be associated with adverse outcome. Elevated pancreas amylase and normal plasma lactate values are also potential pitfalls at admission in patients with acute SMA occlusion.
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| 3. |
- Engström, Agnes, et al.
(författare)
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Direct Comparison of the European Society of Cardiology 0/1-Hour Vs. 0/2-Hour Algorithms in Patients with Acute Chest Pain
- 2024
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Ingår i: Journal of Emergency Medicine. - 0736-4679. ; 66:6, s. 651-659
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Tidskriftsartikel (refereegranskat)abstract
- Background: The recent guidelines from the European Society of Cardiology recommends using high-sensitivity cardiac troponin (hs-cTn) in either 0/1-h or 0/2-h algorithms to identify or rule out acute myocardial infarction (AMI). Several studies have reported good diagnostic accuracy with both algorithms, but few have compared the algorithms directly. Objective: We aimed to compare the diagnostic accuracy of the algorithms head-to-head, in the same patients. Methods: This was a secondary analysis of data from a prospective observational study; 1167 consecutive patients presenting with chest pain to the emergency department at Skåne University Hospital (Lund, Sweden) were enrolled. Only patients with a hs-cTnT sample at presentation AND after 1 AND 2 h were included in the analysis. We compared sensitivity, specificity, and negative (NPV) and positive predictive value (PPV). The primary outcome was index visit AMI. Results: A total of 710 patients were included, of whom 56 (7.9%) had AMI. Both algorithms had a sensitivity of 98.2% and an NPV of 99.8% for ruling out AMI, but the 0/2-h algorithm ruled out significantly more patients (69.3% vs. 66.2%, p < 0.001). For rule-in, the 0/2-h algorithm had higher PPV (73.4% vs. 65.2%) and slightly better specificity (97.4% vs. 96.3%, p = 0.016) than the 0/1-h algorithm. Conclusion: Both algorithms had good diagnostic accuracy, with a slight advantage for the 0/2-h algorithm. Which algorithm to implement may thus depend on practical issues such as the ability to exploit the theoretical time saved with the 0/1-h algorithm. Further studies comparing the algorithms in combination with electrocardiography, history, or risk scores are needed.
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| 4. |
- Eriksson, David, et al.
(författare)
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Diagnostic Accuracy of History and Physical Examination for Predicting Major Adverse Cardiac Events Within 30 Days in Patients With Acute Chest Pain
- 2020
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Ingår i: Journal of Emergency Medicine. - : Elsevier BV. - 0736-4679. ; 58:1, s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: The cornerstones in the assessment of emergency department (ED) patients with suspected acute coronary syndrome (ACS) are patient history and physical examination, electrocardiogram, and cardiac troponins. Although there are several prior studies on this subject, they have in some cases produced inconsistent results.OBJECTIVE: The aim of this study was to evaluate the diagnostic and prognostic accuracy of elements of patient history and the physical examination in ED chest pain patients for predicting major adverse cardiac events (MACE) within 30 days.METHODS: This was a prospective observational study that included 1167 ED patients with nontraumatic chest pain. We collected clinical data during the initial ED assessment of the patients. Our primaryoutcome was 30-day MACE.RESULTS: Pain radiating to both arms increased the probability of 30-day MACE (positive likelihood ratio [LR+] 2.7), whereas episodic chest pain lasting seconds (LR+ 0.0) and >24 h (LR+ 0.1) markedly decreased the risk. In the physical examination, pulmonary rales (LR+ 3.0) increased the risk of 30-day MACE, while pain reproduced by palpation (LR+ 0.3) decreased the risk. Among cardiac risk factors, a history of diabetes (LR+ 3.0) and peripheral arterial disease (LR+ 2.7) were the most predictive factors.CONCLUSIONS: No clinical findings reliably ruled in 30-day MACE, whereas episodic chest pain lasting seconds and pain lasting more than 24 h markedly decreased the risk of 30-day MACE. Consequently, these two findings can be adjuncts in ruling out 30-day MACE.
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| 6. |
- Heyman, Ellen Tolestam, et al.
(författare)
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Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths
- 2021
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Ingår i: Journal of Emergency Medicine. - Philadelphia, PA : Elsevier BV. - 0736-4679 .- 1090-1280. ; 61:6, s. 763-773
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion and palliative care, by using mortality as a proxy. But deaths, unforeseen by emergency physicians at time of the emergency department (ED) visit, might have a weaker association with the ED visit.OBJECTIVES: To develop an ML algorithm that predicts unsurprising deaths within 30 days after ED discharge.METHODS: In this retrospective registry study, we included all ED attendances within the Swedish region of Halland in 2015 and 2016. All registered deaths within 30 days after ED discharge were classified as either "surprising" or "unsurprising" by an adjudicating committee with three senior specialists in emergency medicine. ML algorithms were developed for the death subclasses by using Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM).RESULTS: Of all 30-day deaths (n = 148), 76% (n = 113) were not surprising to the adjudicating committee. The most common diseases were advanced stage cancer, multidisease/frailty, and dementia. By using LR, RF, and SVM, mean area under the receiver operating characteristic curve (ROC-AUC) of unsurprising deaths in the test set were 0.950 (SD 0.008), 0.944 (SD 0.007), and 0.949 (SD 0.007), respectively. For all mortality, the ROC-AUCs for LR, RF, and SVM were 0.924 (SD 0.012), 0.922 (SD 0.009), and 0.931 (SD 0.008). The difference in prediction performance between all and unsurprising death was statistically significant (P < .001) for all three models.CONCLUSION: In patients discharged to home from the ED, three-quarters of all 30-day deaths did not surprise an adjudicating committee with emergency medicine specialists. When only unsurprising deaths were included, ML mortality prediction improved significantly.
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| 8. |
- Lin, Zhen, et al.
(författare)
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Worse Outcomes After Readmission to a Different Hospital After Sepsis : A Nationwide Cohort Study
- 2022
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Ingår i: Journal of Emergency Medicine. - : Elsevier. - 0736-4679 .- 1090-1280. ; 63:4, s. 569-581
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: In the United States, sepsis accounts for 13% of the total hospital expenses and > 50% of hospital deaths. Moreover, people with sepsis are more likely to be readmitted.OBJECTIVE: The aim of this study was to assess the prevalence and outcomes of different hospital readmissions (DHRs) in patients with sepsis, and the factors associated with DHR.METHODS: We used data from the Nationwide Readmissions Database of the United States in 2017 to identify patients admitted for sepsis. Multivariable logistic regression analysis was used to evaluate the factors associated with DHR; five models were constructed to elucidate the relationship between DHR and in-hospital outcomes.RESULTS: In 2017, 85,120 (21.97%) of all patients with sepsis readmitted within 30 days in the United States were readmitted to a different hospital. The most common reason for readmission was infection irrespective of hospital status. Compared with the patients with sepsis who were readmitted to the same hospital, DHR was associated with higher hospitalization costs ($2264; 95% CI $1755-$2772; p < 0.001), longer length of stay (0.58 days; 95% CI 0.44-0.71 days; p < 0.001), and higher risk of in-hospital mortality (odds ratio 1.63; 95% CI 1.55-1.72; p < 0.001).CONCLUSIONS: DHR occurred in one-fifth of patients with sepsis in the United States. Our findings suggest that patients readmitted to a different hospital within 30 days may experience higher in-hospital mortality, longer length of stay, and higher hospitalization costs. Future studies need to examine whether continuity of care can improve the prognosis of patients with sepsis.
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