Gait speed and survival of older surgical patient with cancer: Prediction after machine learning.

PURPOSE: Gait speed in older patients with cancer is associated with mortality risk. One approach to assess gait speed is with the 'Timed Up and Go' (TUG) test. We utilized machine learning algorithms to automatically predict the results of the TUG tests and its association with survival, using patient-generated responses. METHODS: A decision tree classifier was trained based on functional status data, obtained from preoperative geriatric assessment, and TUG test performance of older patients with cancer. The functional status data were used as input features to the decision tree, and the actual TUG data was used as ground truth labels. The decision tree was constructed to assign each patient to one of three categories: "TUG < 10 s", "TUG ≥ 10 s", and "uncertain." RESULTS: In total, 1901 patients (49% women) with a mean age of 80 years were assessed. The most commonly performed operations were urologic, colorectal, and head and neck. The machine learning algorithm identified three features (cane/walker use, ability to walk outside, and ability to perform housework), in predicting TUG results with the decision tree classifier. The overall accuracy, specificity, and sensitivity of the prediction were 78%, 90%, and 66%, respectively. Furthermore, survival rates in each predicted TUG category differed by approximately 1% from the survival rates obtained by categorizing the patients based on their actual TUG results. CONCLUSIONS: Machine learning algorithms can accurately predict the gait speed of older patients with cancer, based on their response to questions addressing other aspects of functional status.

Digital Health for Geriatric Oncology.

In this review, we describe state-of-the-art digital health solutions for geriatric oncology and explore the potential application of emerging remote health-monitoring technologies in the context of cancer care. We also discuss the benefits and motivations behind adopting technology for symptom monitoring of older adults with cancer. We provide an overview of common symptoms and of the digital solutions-designed remote symptom assessment. We describe state-of-the-art systems for this purpose and highlight the limitations and challenges for the full-scale adoption of such solutions in geriatric oncology. With rapid advances in Internet-of-things technologies, many remote assessment systems have been developed in recent years. Despite showing potential in several health care domains and reliable functionality, few of these solutions have been designed for or tested in older patients with cancer. As a result, the geriatric oncology community lacks a consensus understanding of a possible correlation between remote digital assessments and health-related outcomes. Although the recent development of digital health solutions has been shown to be reliable and effective in many health-related applications, there exists an unmet need for development of systems and clinical trials specifically designed for remote cancer management of older adults with cancer, including developing advanced remote technologies for cancer-related symptom assessment and psychological behavior monitoring at home and developing outcome-oriented study protocols for accurate evaluation of existing or emerging systems. We conclude that perhaps the clearest path to future large-scale use of remote digital health technologies in cancer research is designing and conducting collaborative studies involving computer scientists, oncologists, and patient advocates.