A team of researchers used artificial intelligence to glean more insights into different disease categories and the prognosis for older adults who have them who entered long-term care. The authors said that being aware of the patterns of disease development and outcomes should help people in residential long-term care settings. It could also help clinicians and caregivers come up with tailored solutions for people with multiple diseases and differing combinations of diseases, the authors said.
The team used machine learning, a form of artificial intelligence, to group 4,648 people aged 65 or more years based on 22 diasess. All of the people were from Japan and had started using long-term care, according to the June report in Scientific Reports. The median age of participants was 83 years, and just over 60% were women. Data were collected between October 2014 and March 2019.
Those with cardiac disease, respiratory disease, cancer or diabetes reliant on insulin had a higher risk for death compared with those with musculoskeletal and sensory diseases. Cardiac disease, cancer and respiratory disease were associated with people needing more care within two years compared with those with musculoskeletal and sensory issues.
“In our opinion, some potential reasons contributing to each clinical subtype showing different outcomes could be the types and number of comorbidities involved,” the authors wrote, noting that the three leading causes of death in Japan in 2022 were malignancy, heart disease and senility. They said that reality may explain why the cardiac group and the respiratory and cancer group had worse mortality outcomes.
“In clinical settings, the findings suggest that medical and long-term care staff should be more conscious of the clinical subtypes among long-term care users,” the authors wrote.
The news comes as a recent report out earlier this month finds that about 4 in 10 cancer cases and about a half of all adult deaths from the disease are tied to potentially modifiable risk factors, a new study finds.
