Standardised Mortality Ratio
Reviewed and revised 26 August 2015
OVERVIEW
- Standardised Mortality Ratio SMR is the ratio of the observed or actual hospital mortality and the predicted hospital mortality for a specified time period.
- Requires an estimate of predicted mortality rate using a scoring system (e.g. APACHE, ANZROD, SAPS, MPM…)
- Interpretation:
- a value of 1 is considered normal or as expected
- > 1 worse than expected, and
- < 1 better than expected
USE
Used as a surrogate for good quality of care
- comparison of different ICUs
- monitor improvements or decline in ICU care over time
BENEFITS
- widely used
- quantitative
- better than a comparison of non-adjusted mortality data
- mortality is a hard endpoint that is clinically meaningful
LIMITATIONS
General
- acceptable deviations from SMR are not defined, therefore whether a unit significantly deviates from normal is unquantifiable
- needs to be considered in the context of case-mix and calibration
- inconsistencies and inaccuracies associated with data collection and scoring
- missing data limits inclusion of all patients
- original population used to calculate formulae possibly not generalisable
- model used for prediction may be inaccurate
- relies on mortality as a surrogate for quality care
- cost
- ideally samples should be very large
- does not account for post-ICU mortality or morbidity that may be related to ICU care
Comparison between different ICUs are limited by the following assumptions:
- that all pre-ICU care identical and that it has no impact on ICU or hospital outcome
- patients from different hospitals are all drawn from the same case-mix
- sample sizes are large enough to obey the laws of logistic regression
- data acquisition is flawless with respect to the rules of scoring systems and consistent between units
REASONS FOR A CHANGE IN SMR FOR AN ICU
Potential explanations:
- Need to ensure data entry is correct and accurate and consistent with prior practice (i.e. comparable)
- Issues like quantifying GCS accurately will have an impact on APACHE scores and consequently SMR. Quantification of GCS is a major source of inaccuracy in APACHE scores. Also source of admission and diagnosis;
- SMR reflects system wide performance rather than ICU performance alone, because based upon hospital mortality, not ICU mortality. Look at pre ICU and post ICU facilities in the hospital
- SMR affected by case-mix, so changes in case mix may account for increase in SMR and increased other hospital admissions
- Has there been increased practice variation and a deviation from clinical protocols in the ICU?
- Lead time bias (pre ICU care) has been shown to impact on SMR and this needs to be considered
- Any unusual events such as influenza epidemic or terrorist attack?
- Assess for changes in personnel, staffing levels, protocols and equipment
- New services introduced? (e.g. ECMO retrieval)
Critical Care
Compendium
Chris is an Intensivist and ECMO specialist at The Alfred ICU, where he is Deputy Director (Education). He is a Clinical Adjunct Associate Professor at Monash University, the Lead for the Clinician Educator Incubator programme, and a CICM First Part Examiner.
He is an internationally recognised Clinician Educator with a passion for helping clinicians learn and for improving the clinical performance of individuals and collectives. He was one of the founders of the FOAM movement (Free Open-Access Medical education) has been recognised for his contributions to education with awards from ANZICS, ANZAHPE, and ACEM.
His one great achievement is being the father of three amazing children.
On Bluesky, he is @precordialthump.bsky.social and on the site that Elon has screwed up, he is @precordialthump.
| INTENSIVE | RAGE | Resuscitology | SMACC
Hi there! I have been asked to compute SMRs using APACHE IV predicted mortality, and have already been asked to compute 95% CIs. What formula do you recommend for CIs in this case, since the SMR is not a mean nor proportion? Do you know of any examples of SMR CIs? Thanks in advance!