Summary: Scientists have made significant strides in the field of multiple myeloma risk prediction by developing an individualized risk prediction model. The newly developed model, known as Individualized Risk in Newly Diagnosed Multiple Myeloma (IRMMa), incorporates tumor biology to provide a more accurate prognosis and treatment strategy. By incorporating tumor genomics, researchers were able to identify and classify 12 distinct subtypes of multiple myeloma. The IRMMa model aims to move towards precision medicine, allowing clinicians to tailor treatment options to individual patients rather than relying on population-level averages.
The original method for assessing multiple myeloma, which was developed in the 1970s, relied on staging solid tumors. However, with advancements in oncology and the availability of new treatments, this approach has become outdated. The IRMMa model takes into account the individualized risk of each patient and considers how treatment choices can impact prognosis.
To build the model, researchers used clinical, treatment, and genetic data from 2,000 newly diagnosed multiple myeloma patients. Patient DNA sequences were analyzed to identify “driver genes” that play a role in tumor growth. Machine learning was then employed to identify patterns in the data and make risk predictions.
While the IRMMa model is currently aimed at researchers, the research team hopes to enhance it by incorporating more patient data. The ultimate goal is to make it usable for clinical purposes, improving treatment decisions and patient outcomes.
In a similar vein, researchers have also made advancements in risk prediction for intensive care unit (ICU) admission and ICU survival among older adults. By developing a risk score using clinical data, experts were able to accurately identify patients who would require ICU admission or who were at risk of ICU mortality. With aging populations and increased demand for critical care services, such risk prediction models are crucial for improving patient care and resource allocation.
Overall, these breakthroughs in risk prediction using advanced technologies like artificial intelligence and machine learning have the potential to revolutionize the field of healthcare, allowing for more personalized and effective treatment strategies.
The source of the article is from the blog karacasanime.com.ve