But wait, one of your junior trainees has pointed out that their 2 year mortality is actually quite affected by this illness, and they’re not out of the woods yet…
The mood darkens slightly. The party hats are taken off. Killjoy! Can’t we celebrate the little victories?
And yet this is where we are in intensive care medicine much of the time. A spell on the unit is hardly a spa weekend that renews your vigour and health – the acute illness process is just the start. This topic is the focus of the paper from our recent journal club, available open-access through the link below. Specifically, it looks at the impact of sepsis on our patients in the long term.
Prescott H et al. Late mortality after sepsis: propensity matched cohort study. BMJ. 2016. 353. i2375
What's it about?
What did they do?
They have used an existing database from which to draw their samples; the US Health and Retirement Study (HRS). This is a longitudinal survey of US citizens over the age of 50 with regular questionnaires on topics such as health and employment. This is also linked to the patient’s Medicare records, thus allowing a link between the patient’s hospital care. With a database size of 37,000 adults that are broadly representative of the US population, the authors felt that this was a suitable source to take their sample.
The design of their study was overall a propensity matched cohort study. They were able to identify their primary cohort fairly easily i.e. the patients who developed sepsis within their inclusion criteria. The challenge was to find an appropriate control group with which to compare it to. The propensity matching was the approach used to try and create this control group from the patients within their large database. Now this all seems a little bit of statistics voodoo to me, but essentially they started by determining which criteria it was important to be similar between the patients to minimise confounding. They determined this based on age, gender and their ‘centile risk of sepsis’. This last parameter appeared to be a composite of a number of impacting factors; ethnicity, BMI, marital status, self-reported health, wealth, ADL scores, recent sepsis and Charlston comorbidities. All fairly clever stuff, but I suppose it does very much depend how confident you are that it is these parameters which are the true representative features of similarity between the groups. Either way, they used this approach to 1:1 match the septic patients with 3 other cohorts; those patients not in hospital, those admitted with a non-septic infection, and those admitted with a sterile SIRS process e.g. pancreatitis.
Having identified their cohorts the outcome they were interested in was all cause mortality between 31 days and 2 years. They also made note of the actual mode of death for these patients who did go on and die within 2 years.
What did they find?
The mortality results they observe from these comparisons are interesting:
- Sepsis vs not in hospital – adjusted odds ratio 3.5 (2.7 – 4.5)
- Sepsis vs infection – adjusted OR 1.6 (1.3 – 2.1)
- Sepsis vs sterile inflammatory process – adjusted OR 2.3 (1.7-2.1)
Interestingly the most common terminal illness in all groups was of further infection, either pneumonia or further sepsis. The similarity of this process across all the cohorts suggested that the increased mortality wasn’t particularly through a new modality, but rather an increased incidence of that modality overall.
Is it any good?
Now speaking of drawbacks, there is perhaps the one major issue with this study - propensity matching. Now I will get my confessions out that I am certainly no statistician, but I am willing to accept that the statistical basis of the matching is fairly sound. The main worry I have is about the step before this – how can we be confident about the parameters which we have chosen to match with? I know that authors love pointing out that groups are similar through their Chi squared’ and ‘t-tests’ etc. but all that shows is that they are similar for those particular parameters they are testing. On the surface it seems reasonable to accept that our cohorts are matched for age and comorbidities etc., but it remains a leap of faith that this makes them at a similar risk of dying from any particular illness, particularly here because I cannot see that these factors are well validated markers for this purpose. I know this will be the challenge whenever randomisation isn’t done, and it is clear that this is impossible in this case, so we may have to accept that this is the best we can do, but it leaves a sense of unease with the conclusions.
Other minor drawbacks relate to the methods of data collection. The patients are all rather elderly because of the nature of the HRS, limiting the generalisability of the results to younger patients with sepsis. The dependence on both appropriate coding and a claims based medical system is also an area with some potential for error. These limitations are not tiny, but in my mind are a rather more appreciable that that from the propensity matching.
Thanks for reading and also a big thanks to all the members of the journal club who were involved in the discussions. The brave amongst you may like to have a look at the link below looking at propensity matching.
References & Links
- Prescott H et al. Late mortality after sepsis: propensity matched cohort study. BMJ. 2016. 353. i2375
- Stuart E. Matching methods for causal interference: a review and a look forward. Stat Sci. 2010. 25(1); 1-21