The significance of risk adjustment for the assessment of results in intensive care. : An analysis of risk adjustment models used in Swedish intensive care.

To study the development of mortality in intensive care over time or compare different departments, you need some kind of risk adjustment to make analysis meaningful since patient survival varies with severity of the disease. With the aid of a risk adjustment model, expected mortality can be calculated. The actual mortality rate observed can then be compared to the expected mortality rate, giving a risk-adjusted mortality. In-hospital mortality is commonly used when calculating riskadjusted mortality following intensive care, but in-hospital mortality is affected by the duration of care and transfer between units. Time-fixed measurements such as 30-day mortality are less affected by this and are a more objective measure, but the intensive care models that are available are not adapted for this measure. Furthermore, how length of follow-up affects risk adjusted mortality has not been studied. The degree and pattern of loss of physiological data that exists and how this affects performance of the model has not been properly studied. General intensive care models perform poorly for cardiothoracic intensive care where admission is often planned, where cardiovascular physiology is more affected by extra corporeal circulation and where the reasons for admission are usually not the same. The model used in Sweden for adult general intensive care patients is the Simplified Acute Physiology Score 3 (SAPS3). SAPS3 recalibrations were made for in-hospital mortality and 30-, 90- and 180-day mortality. Missing data were simulated, and the resulting performance compared to performance in datasets with originally missing data. We conclude that SAPS3 works equally well using 30-day mortality as in-hospital mortality. The performance with both 90- and 180-day mortality as outcome was also good. It was found that the model was stable when validated in other patients than it was recalibrated with. We conclude that the amount of data missing in the SIR has a limited effect on model performance, probably because of active data selection based on the patient's status and reason for admission. A model for cardiothoracic intensive care based on variables available on arrival at Swedish cardiothoracic intensive care units was developed and found to perform well.