unnecessarily risky treatment
New method could help doctors avoid ineffective or unnecessarily risky treatments
In a usual management setting, after a person has had a heart attack or stroke, algorithmic risk models are used to calculate the risk of death for the patient. These algorithms or models utilize various factors such as age of the patient, gender, previous history, family history, ethnicity etc. Treatment of the patient is often guided by these models. A new study has shown that in many cases these models fail to predict the risks accurately. This may lead to treatment choices that are unnecessary or ineffective and even risky for the patients. The new study was published in the Digital Medicine.