Dear Editor

“Trials are nothing like real life” is a complaint that defines a common problem in the practice of evidence-based medicine. Differences between patient populations or the circumstances of trials and real life often leave us bewildered when trying to determine the applicability of clinical research to the care of our own patients. Sometimes inclusion and exclusion criteria for subjects in trials mean that our own patients would never have received a treatment of interest. In other cases the way in which the treatment was delivered could not be replicated in our own practice. Alternatively, the outcomes reported in a trial only partly reflect outcomes of interest to our patients. Sometimes we can find evidence but the methodological validity of that evidence is poor, causing us to question the reliability of the results. In order to make a pragmatic decision in the real world, we must resolve these uncertainties if we are to make use of the evidence available and take decisions about the clinical care of patients.

Rather than do this on the basis of a hunch or a gut feeling, Mike Clancy has developed a remarkably useful and intuitive graphical representation of the “fit” of the evidence to a patient’s problems presented in the articles: A new way of demonstrating how close the evidence is to the question you asked and In patients with head injury undergoing rapid sequence intubation, does pre-treatment with intravenous lignocaine/lidocaine lead to an improved neurological outcome? A review of the literature.

In my opinion this graphical representation is as powerful as the forest plots used to depict the results of meta analyses. With one simple graphic, Clancy has devised a method for illustrating how well or how badly the evidence fits the question at hand. I would suggest that the area encompassed by the rhombus created in these graphs could be used to provide a weighting score for individual articles, with the smallest area representing the best fit. I consider this a major advance in the practice of evidence-based medicine and one that I will certainly adopt when teaching critical appraisal and the difficult step from the completed critical appraisal to the application of evidence. Thank you Mr Clancy!

Dear Sir,

We read with interest the article by Chen et al which demonstrated that a MEDS score greater than 12 may be discriminant for Emergency Department (ED) triage, ICU placement (1). These results of importance are in accordance with those of our group. We prospectively evaluated the accuracy of the MEDS score in 286 patients with documented sepsis admitted into our medical ICU (10 beds) after an ED consultation and we compared it with the performance of the SAPS II score calculated with the worst values of physiologic variables during the first 24 hr of ICU admission. The median age (interquartile) was 69 yr (52.1-78.8), for a median MEDS value of 10 (8-12) and a SAPS II score of 51 (35.7-70). Community-acquired pneumonia was the main source of sepsis (58.6%). In our series, the two scores discriminate well between survivors and non- survivors (in-hospital mortality rate: 32.9%) if we consider the area under the ROC curve which was 0.771 [95% confidence interval (CI), 0.714-0.828) for the MEDS score and 0.830 (95%CI, 0.781-0.880) for the SAPS II score. Furthermore, severity of illness, age, and mortality increased when stratifying patients into the five risk groups defined by Shapiro et al (p<. 000.1) (see table) (2). Whereas, Chen et al reported an increased mortality in case of MEDS score > 12 [calculated odds ratio (OR): 4.51], in our population a cut-off value of 10 was already an important predictor of death: OR, 5.4(95%CI: 3.01-9.72), sensitivity 0.475, specificity 0.857, positive predictive value 0.809, negative predictive value 0.562. As in Chen et al cohort, the MEDS score performs well and then may be an appropriate tool in identification of ED patients at high risks of death from sepsis. However, its usefulness should be tested in surgical ICU patients and prospectively evaluated in the ED to decide ICU admission.

Table 1

References

1 Chen CC, Chong CF, Liu YL, et al. Risk stratification of severe sepsis patients in the emergency department. Emerg Med J 2006; 23: 281-5

2 Shapiro NI, Wolfe RE, Moore RB, et al. Mortality in Emergency Department Sepsis (MEDS) score: a prospectively derivated and validated clinical prediction rule. Crit Care Med 2003; 31:670-5

Dear Editor:

ASSESSMENT SCORE OR NOT!!

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