New tools predict risk of complications for kidney patients following surgery
Researchers have created three new predictive models to help identify kidney patients at high risk of complications following surgery for a non-cardiac related matter.
People with kidney failure often require surgery and experience worse outcomes afterwards compared to the general population. Some prediction models have been created to help assess a person’s risk of complications after surgery, but these were often developed without inclusion of kidney patients and fail to capture outcomes in this population adequately.
To create prediction models specifically for kidney patients, Tyrone G. Harrison and colleagues analyzed data from 38,541 surgeries completed on kidney patients between 2005–2019 in Alberta, using administrative health, laboratory, and kidney failure datasets. During the study period, a total of 1,204 post-operative cardiac complications took place. The researchers then created three different models that predict an individual person’s risk of developing a similar post-operative complication (e.g., mortality, acute myocardial infarction, non-fatal ventricular arrhythmias, among others).
The first model simply accounts for patient’s age, sex, surgery type, surgery setting, and kidney failure type, while the second included additional comorbidities that a person may have (e.g., diabetes). The final model also includes physiological factors, such as hemoglobin and albumin, of a patient prior to surgery. Analysis shows that the third model offered the most accurate predictions of post-operative outcomes in kidney patients. The researchers are partnering with colleagues at the University of Manitoba to further validate the models, which can be used to inform shared decision-making and risk-guided strategies for the kidney population when considering to undergo surgery.