Discussion
cancer, death
CANCER, SECONDARY, ADVANCED CANCER
In this secondary analysis of a large national randomized trial of more than 700 older adults with advanced cancer receiving active treatment, we identified 3 main symptom clusters (low, moderate, and high severity) using a clustering algorithm. The symptom severity score of patients in each cluster differed. Patients in moderate- and high-severity clusters at baseline were more likely to experience unplanned hospitalization and have worse overall survival but had similar risks of toxic effects. Our results demonstrate that unsupervised machine learning methods can be used to identify patients who are at higher risk for adverse outcomes based on their baseline patient-reported symptoms.Unsupervised machine learning proves to be an effective bottom-up method of stratifying patients into distinct symptom severity clusters. Both the total severity score and all individual items were higher in the moderate- and high-severity clusters compared with those in the low-severity cluster, confirming the validity of the k-means algorithm. Compared with traditional methods of determining patient subgroups, k-means is more flexible and does not need human input (eg, determining the cutoff values). Another strength of the unsupervised algorithm is that no outcome measure is needed to identify the clusters.A simple algorithm that does not require large computational resources, k-means has promising potential in clinical research and practice.A major finding of this study is that older adults with advanced cancer in the moderate- and high-severity clusters were more likely to experience unplanned hospitalization. This finding is consistent with prior literature that observed an association between higher baseline symptom burden and adverse outcomes in patients with cancer.Our results also suggest that moderate to severe baseline symptom severity are independently associated with all-cause mortality in older adults with advanced cancer receiving palliative systematic treatment. This suggests that assessment of symptom burden prior to treatment initiation contributes clinically useful prognostic information for treatment decision-making among older adults. Because of the expected limited survival in this population, baseline symptom burden could be more clinically meaningful for estimating outcomes than the response to treatment, the traditional oncology clinical trial end point.We hypothesize 3 possible mechanisms to explain the higher risks of hospitalization and death among patients in the moderate- and high-severity clusters. First, patients in the moderate- and high-severity clusters had more symptomatic burden that was not fully captured by GA and KPS, contributing to their vulnerabilities. These patients could potentially benefit from palliative care during their cancer treatments to reduce their symptom burden and improve their quality of life.Our study has clinical and public health implications. First, unsupervised machine learning may offer an option to obtain summary information from PRO-CATE, for which no total score was available. Second, the association between symptom severity at the initiation of a new palliative regimen and adverse outcomes among older adults with advanced cancer suggests that PRO-CTCAEs can provide information in addition to GA and KPS. The inclusion of patient-centered assessment tools, such as PRO-CTCAEs, in clinical practice can assist clinicians in treatment decision-making and supportive care recommendations.