European journal of emergency medicine : official journal of the European Society for Emergency Medicine
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Diagnosing acute heart failure (AHF) is difficult in elderly patients presenting with acute dyspnea to the emergency department. ⋯ In this study, NT-proBNP alone exhibited the best diagnostic accuracy for diagnosing AHF in elderly patients presenting with acute dyspnea to the emergency departments. None of the other biomarkers alone or combined improved the accuracy compared to NT-proBNP, which is the only biomarker to use in this setting.
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Carbon monoxide (CO) poisoning is one of the most common causes of poisoning death and its diagnosis requires an elevated carboxyhemoglobin (COHb) level. Noninvasive CO saturation by pulse oximetry (SpCO) has been available since 2005 and has the advantage of being portable and easy to use, but its accuracy in determining blood COHb level is controversial. To evaluate the accuracy of SpCO (index test) to estimate COHb (reference test). ⋯ The mean bias was 0.75% and the LOA was -7.08% to 8.57%, 95% CI (-8.89 to 10.38) (2794 subjects and 4646 observations). Noninvasive measurement of COHb (SpCO) using current pulse CO oximeters do not seem to be highly accurate to estimate blood COHb (moderate sensitivity and specificity, large LOA). They should probably not be used to confirm (rule-in) or exclude (rule-out) CO poisoning with certainty.
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Background and importance Guidelines recommend that hospital emergency teams locally validate criteria for termination of cardiopulmonary resuscitation in patients with in-hospital cardiac arrest (IHCA). Objective To determine the value of a machine learning algorithm to predict failure to achieve return of spontaneous circulation (ROSC) and unfavourable functional outcome from IHCA using only data readily available at emergency team arrival. Design Retrospective cohort study. ⋯ Five hundred fifty-nine subjects experienced an unfavourable outcome (88.7%). The final classification model to predict unfavourable functional outcomes from IHCA at hospital discharge had an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92-0.93), a balanced accuracy of 0.59 (95% CI, 0.57-0.61), an F1-score of 0.94 (95% CI, 0.94-0.95), a positive predictive value of 0.91 (0.9-0.91), a negative predictive value of 0.57 (0.48-0.66), a sensitivity of 0.98 (0.97-0.99), and a specificity of 0.2 (0.16-0.24). Conclusion Using data readily available at emergency team arrival, machine learning algorithms had a high predictive power to forecast failure to achieve ROSC and unfavourable functional outcomes from IHCA while cardiopulmonary resuscitation was still ongoing; however, the positive predictive value of both models was not high enough to allow for early termination of resuscitation efforts.