A quick, objective, non-invasive means of identifying high-risk septic patients in the emergency department (ED) can improve hospital outcomes through early, appropriate management. Heart rate variability (HRV) analysis has been correlated with mortality in critically ill patients. We aimed to develop a Singapore ED sepsis (SEDS) predictive model to assess the risk of 30-day in-hospital mortality in septic patients presenting to the ED. We used demographics, vital signs, and HRV parameters in model building and compared it with the modified early warning score (MEWS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA) score.Adult patients clinically suspected to have sepsis in the ED and who met the systemic inflammatory response syndrome (SIRS) criteria were included. Routine triage electrocardiogram segments were used to obtain HRV variables. The primary endpoint was 30-day in-hospital mortality. Multivariate logistic regression was used to derive the SEDS model. MEWS, NEWS, and qSOFA (initial and worst measurements) scores were computed. Receiver operating characteristic (ROC) analysis was used to evaluate their predictive performances.Of the 214 patients included in this study, 40 (18.7%) met the primary endpoint. The SEDS model comprises of 5 components (age, respiratory rate, systolic blood pressure, mean RR interval, and detrended fluctuation analysis α2) and performed with an area under the ROC curve (AUC) of 0.78 (95% confidence interval [CI]: 0.72–0.86), compared with 0.65 (95% CI: 0.56–0.74), 0.70 (95% CI: 0.61–0.79), 0.70 (95% CI: 0.62–0.79), 0.56 (95% CI: 0.46–0.66) by qSOFA (initial), qSOFA (worst), NEWS, and MEWS, respectively.HRV analysis is a useful component in mortality risk prediction for septic patients presenting to the ED.
Before-after study designs are effective research tools and in some cases, have changed practice. These designs, however, are inherently susceptible to bias (ie, systematic errors) that are sometimes subtle but can invalidate their conclusions. This overview provides examples of before-after studies relevant to anesthesiologists to illustrate potential sources of bias, including selection/assignment, history, regression to the mean, test-retest, maturation, observer, retrospective, Hawthorne, instrumentation, attrition, and reporting/publication bias. Mitigating strategies include using a control group, blinding, matching before and after cohorts, minimizing the time lag between cohorts, using prospective data collection with consistent measuring/reporting criteria, time series data collection, and/or alternative study designs, when possible. Improved reporting with enforcement of the Enhancing Quality and Transparency of Health Research (EQUATOR) checklists will serve to increase transparency and aid in interpretation. By highlighting the potential types of bias and strategies to improve transparency and mitigate flaws, this overview aims to better equip anesthesiologists in designing and/or critically appraising before-after studies.
Summary:In 91 patients with bronchiectasis seen over 6 years, a positive mycobacterial culture was obtained in 12 cases (13%). The organisms isolated were Mycobacterium tuberculosis in nine cases, Mycobacterium avium in two cases and Mycobacterium tuberculosis and chelonei were obtained on separate occasions in one case. Computed tomography and/or bronchography showed that the bronchiectatic changes commonly involved the lower lobes and to a lesser extent, the middle and lingula lobes. In none of these 12 cases was tuberculosis strongly suspected on clinical or radiological grounds.We conclude that mycobacterial infections are common in patients with bronchiectasis and sputum should be cultured for mycobacteria periodically in these patients. In doubtful cases, bronchoscopy may be helpful to obtain a positive mycobacterial culture.
I n a randomized controlled trial (RCT) comparing treatments A and B, a null hypothesis (H 0 ) of no difference in a primary outcome of interest is defined. Whether any observed difference is statistically significant has traditionally been based on the P value and the confidence interval (CI). For a century, an arbitrary threshold of 0.05 (1/20) has been used to define statistical significance. 1 Because this probability is quite low, we conclude that P ≤ 0.05 suggests that the observed difference between A and B is incompatible with H 0 , and, with a ≥ 95% degree of certainty, that A and B are different. The 95% CI is a lower-upper limit in which the true effect estimate lies within a 95% certainty. A 95% CI that does not include the null effect size indicates the observed difference has reached statistical significance, and H 0 is rejected. The 95% CI provides information about the precision of the estimate and complements the P value. Reporting both is recommended.Use of a P value of 0.05 to dichotomize whether treatments A and B are "truly" different is appealing because of its simplicity. Indeed, for all its many limitations, 2 its use actually increased from 1990 to 2015 in MEDLINE and PubMed Central abstracts and articles. 3 Statistical significance carries considerable weight. Researchers, editors and reviewers, readers, and the press tend to become more excited about positive results. 4 However, a shocking number of scientific studies and meta-analyses are not reproducible or replicable. [5][6][7] A reasonable question to ask is: if a positive study was to be repeated, how "easy" might it be for the results to change from being statistically significant to non-significant (and thus, rightly or wrongly, lose some of their appeal)? What if there was, by sheer chance, one or several more (or less) outcome event(s) in one of the comparative groups? Would the coveted statistical significance be lost? In recent years, there has been much interest in a metric-the fragility index (FI), [8][9][10][11] first proposed 3 decades ago 12 -to test the robustness/fragility of statistically significant results. The various applications and implications of the FI are discussed herein.
Time-critical acute ischemic conditions such as ST-elevation myocardial infarction and acute ischemic stroke are staples in Emergency Medicine practice. While timely reperfusion therapy is a priority, the resultant acute ischemia/reperfusion injury contributes to significant mortality and morbidity. Among therapeutics targeting ischemia/reperfusion injury (IRI), remote ischemic conditioning (RIC) has emerged as the most promising. RIC, which consists of repetitive inflation and deflation of a pneumatic cuff on a limb, was first demonstrated to have protective effect on IRI through various neural and humoral mechanisms. Its attractiveness stems from its simplicity, low-cost, safety, and efficacy, while at the same time it does not impede reperfusion treatment. There is now good evidence for RIC as an effective adjunct to reperfusion in ST-elevation myocardial infarction patients for improving clinical outcomes. For other applications such as acute ischemic stroke, subarachnoid hemorrhage, traumatic brain injury, cardiac arrest, and spinal injury, there is varying level of evidence. This review aims to describe the RIC phenomenon, briefly recount its historical development, and appraise the experimental and clinical evidence for RIC in selected emergency conditions. Finally, it describes the practical issues with RIC clinical application and research in Emergency Medicine.
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