TY - JOUR
T1 - Gait speed and survival of older surgical patient with cancer
T2 - Prediction after machine learning
AU - Sasani, Keyvan
AU - Catanese, Helen N.
AU - Ghods, Alireza
AU - Rokni, Seyed Ali
AU - Ghasemzadeh, Hassan
AU - Downey, Robert J.
AU - Shahrokni, Armin
N1 - Funding Information:
The project was supported, in part, by the Beatriz and Samuel Seaver Foundation , the Memorial Sloan Kettering Cancer and Aging Program , the NIH/NCI Cancer Center Support Grant P30 CA008748 , and the National Science Foundation (NSF) under grants CNS-1566359 and CNS-1750679 . Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations.
Funding Information:
The project was supported, in part, by the Beatriz and Samuel Seaver Foundation, the Memorial Sloan Kettering Cancer and Aging Program, the NIH/NCI Cancer Center Support Grant P30 CA008748, and the National Science Foundation (NSF) under grants CNS-1566359 and CNS-1750679. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations.
Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2019/1
Y1 - 2019/1
N2 - 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.
AB - 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.
KW - Cancer
KW - Decision tree
KW - Gait speed
KW - Machine learning
KW - Predictive analytics
KW - Survival
KW - TUG
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UR - http://www.scopus.com/inward/citedby.url?scp=85049723929&partnerID=8YFLogxK
U2 - 10.1016/j.jgo.2018.06.012
DO - 10.1016/j.jgo.2018.06.012
M3 - Article
C2 - 30017733
AN - SCOPUS:85049723929
SN - 1879-4068
VL - 10
SP - 120
EP - 125
JO - Journal of Geriatric Oncology
JF - Journal of Geriatric Oncology
IS - 1
ER -