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Carl Heneghan

Carl Heneghan

Director of the CEBM, GP and clinical lecturer at the University of Oxford.

Ami Banerjee

Ami Banerjee

Cardiology trainee and clinical research fellow at the University of Oxford

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    How good are we at diagnosing serious infection in children?

    Ami Banerjee
    Posted 8th February 2010 @ 01:58pm

    When I started in evidence-based medicine, it was a big shock that probably the most under-researched area of health is how health practitioners should diagnose illness; i.e. “diagnostic strategies”. Individual studies and systematic reviews have focused on drugs and interventions, but it is now recognised that such reviews are also necessary to evaluate diagnostic tests. In children, clinical signs (e.g. a raised temperature) or test results are even more important than in adults, because the child may not be able to describe his or her symptoms, particularly when they are very unwell.

    In the Lancet “online first” this week, a systematic review studied the value of rapid breathing, poor peripheral circulation and other factors in confirming or excluding the possibility of serious infection in children presenting to general practice or other outpatient care. After looking through nearly 2000 potentially relevant articles, the authors included 30 studies in their analysis. They collated information from the individual studies to calculate “likelihood ratios” for each factor or test; i.e. how likely a disease is after a test result. A likelihood ratio of >1 indicates the test result is associated with the disease. A likelihood ratio <1 indicates that the result is associated with absence of the disease. Clinical features with a positive likelihood ratio of more than 5 were termed “red flags” (i.e. warning signs for serious infection).

    Cyanosis, or a blue colouration of the skin, led to a positive likelihood ratio range of 3–52, whilst the same ranges for rapid breathing and petechial rash (the non-blanching rash parents are taught to look for if we are worried about meningitis) were 1–10 and 6–84 respectively. Put another way, a petechial rash increases the odds of serious infection by between 6-fold and 84-fold. The likelihood of the disease is influenced by the prevalence of the disease in the population. For example, in a setting where meningitis or infection is very rare, the likelihood of meningitis if you have a petechial rash is increased. No single clinical sign could rule-out serious infection but some combinations could be used to exclude the possibility of serious infection. For example, pneumonia is very unlikely if the child is not short of breath and there is no parental concern.

    It is amazing that only one study was set in primary care, where such information would change practice most since GPs often have to make decisions based on simple clinical signs. That study highlighted two red flags that are rarely evaluated: parental concern and doctor’s instinct. Parental concern gave a positive likelihood ratio of 14 and clinician instinct increased the likelihood of serious infection by 24 times. This study only looked at developed country research, and not research from developing countries, but the authors concluded, “Most of the red flags already recommended by WHO for use in developing countries can be used in the initial assessment of children presenting to ambulatory care settings in developed countries.” The next step is “to identify the level of risk at which clinical action should be taken”, based on these red flag tests.

    odds

    Dear Stefan
    Many thanks for your very astute comments and sorry for the tardy response. I am glad that at least one of our readers is wide awake and spotting my mistakes!

    You are right that "chance" tends to mean probability and as you point out, "odds" are more linked to likelihood ratios. I have changed the wording in the blog.

    I also made a mistake with the sentence you mentioned about likelihood ratios and prevalence. I meant to write:
    " The likelihood of disease is influenced by the prevalence of the disease in the population". For example, in the paper, the authors write,
    "The highest rule-in value was obtained
    in the setting with the lowest prevalence, where a
    temperature of 40°C or more increased the likelihood of
    disease from 0·8% to 5·0%". So this is to do with pre-test and post-test probabilities.

    Cheers
    Ami

    odds

    Dear Ami
    thanks for your interesting article.
    A comment, just to make things clearer:
    In the paragraph dealing with the results of the study, I believe you should change the notion of "chance" to "odds", as it is the odds that get multiplied by the likelihood ratio and not the probability i.e. a petechial rash increases the "odds" by a factor of 6 to 84.
    (http://en.wikipedia.org/wiki/Bayes'_theorem#In_terms_of_odds_and_likelihood_ratio) In other words, the effect of the likelihood ratio on the pretest probability is not linear e.g. a likelihood ratio of 100 does not increase the pretest probability 10 times more then a LR of 10.

    Further you write: "The likelihood ratio of the test or factor is influenced by the prevalence of the disease in the population"
    I'm unsure what you mean by this, could you elaborate on it? From a mathematical standpoint, the likelihood ratio of a test should be independent of the prevalence in the population, as far as I know.

    Thanks for your reply!

    Best wishes
    Stefan

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