PREDICTIVE POWER OF COMPREHENSIVE GERIATRIC ASSESSMENT QUESTIONNAIRES TO ESTIMATE THE RISK OF EARLY DEATH IN ELDERLY CANCER PATIENTS USING MACHINE LEARNING

2019 ◽  
Vol 10 (6) ◽  
pp. S14
Author(s):  
G.R. Sena ◽  
T.P. Lima ◽  
M.G. Mello ◽  
J.T. Lima ◽  
L.S. Thuler
Keyword(s):  
Machine Learning ◽  
Cancer Patients ◽  
Geriatric Assessment ◽  
Comprehensive Geriatric Assessment ◽  
Predictive Power ◽  
Early Death ◽  
Elderly Cancer Patients

The comprehensive geriatric assessment in malignancies and prognostic factors: Description of a prospective cohort of 889 Brazilian elderly cancer patients.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e23035-e23035
Author(s):  
Jurema Telles O Lima ◽  
Raissa Viana ◽  
Mirella Rebello ◽  
Maria Julia Gonçalves Mello ◽  
Letícia telles Sales ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Cancer Patients ◽  
Geriatric Assessment ◽  
Comprehensive Geriatric Assessment ◽  
Prospective Cohort ◽  
Early Death ◽  
Northeastern Brazil ◽  
World Health ◽  
Elderly Cancer Patients ◽  
Risk Of Death

e23035 Background: According to the World Health Organization (WHO), the definition of "elderly" varies according to the degree of development of the country. In Low and Medium Development Countries (LMDC), a person aged 60 or older is considered elder, opposed to developed countries (65 or older). It is in LMDC that is occurring the largest relative increase in the incidence of cancer, specially those related to aging. The Comprehensive Geriatric Assessment (CGA) is still underutilized in oncological clinical practice, especially in LMDC. Objectives: To determine predictive factors for the occurrence of early death (in the first six months of surveillance) and to perform the development and temporal validation of a practical prognostic score based on the CGA to predict early death (up to 180 days) in elderly cancer patients. Methods: A prospective cohort enrolled elderly patients ≥ 60 years with a recent cancer diagnosis admitted between 2015-2017. The CGA performed at the time of admission included the following instruments: CCI; KPS; MMSE; TUG test; IPAQ; ADL; MNA; MNA-SF; GDS15; PPS and Polypharmacy. The studied outcome was early death, defined as the one that occurred within the first six months after the diagnosis. Survival analysis (Kaplan-Meier) and Cox proportional hazard regression was performed. Results: 889 patients were included in the study, performed at a referral center in cancer of a teaching hospital in Northeastern Brazil. The independent risk factors for death identified by CGA were: Mini exam of mental state (MMSE) as a continuous variable (HR 1.04 95% CI 1.00-1.07), Geriatric depression scale (GDS-15) ≥ 10 (HR 1 , 50 IC95% 1.10-2.07), Karnofsky Functional Performance Scale (KPS) < 50 (HR 1.57 IC95% 1.02-2.42), Katz Index ≤4 (HR 2.58 IC95% 1.68-3.97) and the Mininutrition assessment (MAN-SF) < 12 (HR 2.96 IC95% 2.00-4.39), with higher risk for early death amongst patients with abnormalities detected by the scales performed at admission (log rank < 0.001). Conclusions: Comprehensive geriatric assessment is an important tool to identify fragility in elderly cancer patients. Some of its scales should be incorporated into clinical practice, as they are simple and significant prognostic markers and identify patients with a higher risk of death in the first twelve months.

A practical clinical score to predict early death after a comprehensive geriatric assessment (CGA) in elderly cancer patients: A prospective cohort study with 608 patients.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e21537-e21537
Author(s):  
Jurema Telles O Lima ◽  
Anke Bergmann ◽  
Maria Julia Gonçalves Mello ◽  
Zilda Cavalcanti ◽  
Mirella Rebello Bezerra ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cohort Study ◽  
Cancer Patients ◽  
Geriatric Assessment ◽  
Comprehensive Geriatric Assessment ◽  
Prospective Cohort ◽  
Early Death ◽  
Clinical Score ◽  
Elderly Cancer Patients

e21537 Background: Components of the comprehensive geriatric assessment (CGA) correlate with risk of early mortality in elderly cancer patients (ECP). However, its complexity and the time required for its administration. The aim of this study was to determine the impact of each CGA domain on overall survival(OS) and to first step for the development of a prognostic scoring system to stratify ECP. Methods: a prospective cohort study. Participants with a recent diagnosis of cancer were from eight hospitals and one cancer center in Brazil and were recruited during their first medical appointment at the outpatient oncologic clinic. A basal CGA was done before the care decision (ADL, Charlson Comorbidity Index- CCI, Karnofsky Performance status – KPS, GDS15, IPAQ, MMSE, MNA, MNA-SF, PS, PPS, Polipharmacy, QLQc30, TUG). During the follow up of six months, information about the treatments performed and early death was collected. OS was estimated using the Kaplan–Meier method, and survival curves were compared using the Log rank test for categorical variables. A multivariate Cox proportional hazards model was used to select early death risk factors. A clinical score considering the number of risk variables was created. Results: From 2015-2016, 608 ECP, mean age 71.9 (SD ±7.4; range 60-96), 50.7% male, were enrolled. 100 (16.4%) ECP died in less than six months of follow-up. In our multivariate model, controlled by age, site of cancer and cancer stage, the remaining significant risk factors were malnutrition/nonutrition determined by MNA (HR 3.3, 95%CI 1.81-5.99, p < 0.001), KPS < 50% (HR 2.44, CI 1.56-3.81, p < 0.001) and CCI > 2 (HR 1.6, CI 1.09-2.52, p = 0.018). The risk for early death according to the number of risk variables: three (HR 12.99, CI 5.69-29.60, p < 0.001), two (HR 5.65, CI 2.61-12.24, p < 0.001) or one (HR 2.7, CI 1.28-5.87, p = 0.009). Conclusions: a practical clinical score using three instruments of the CGA (MNA, KPS and CCI) can predict independent the risk for an early death in ECP. The development of a practical system for risk scoring, incorporating few clinical prognostic factors, helps to stratify patients into risk groups and to plan a personalized care.

scholarly journals Developing Machine Learning Algorithms for the Prediction of Early Death in Elderly Cancer Patients: Usability Study (Preprint)

2018 ◽  
Author(s):  
Gabrielle Ribeiro Sena ◽  
Tiago Pessoa Ferreira Lima ◽  
Maria Julia Gonçalves Mello ◽  
Luiz Claudio Santos Thuler ◽  
Jurema Telles Oliveira Lima
Keyword(s):  
Machine Learning ◽  
Cancer Patients ◽  
Geriatric Assessment ◽  
Short Form ◽  
Geriatric Oncology ◽  
Depression Scale ◽  
Mini Nutritional Assessment ◽  
Early Death ◽  
Machine Learning Algorithms ◽  
Elderly Cancer Patients

BACKGROUND The importance of classifying cancer patients into high- or low-risk groups has led many research teams, from the biomedical and bioinformatics fields, to study the application of machine learning (ML) algorithms. The International Society of Geriatric Oncology recommends the use of the comprehensive geriatric assessment (CGA), a multidisciplinary tool to evaluate health domains, for the follow-up of elderly cancer patients. However, no applications of ML have been proposed using CGA to classify elderly cancer patients. OBJECTIVE The aim of this study was to propose and develop predictive models, using ML and CGA, to estimate the risk of early death in elderly cancer patients. METHODS The ability of ML algorithms to predict early mortality in a cohort involving 608 elderly cancer patients was evaluated. The CGA was conducted during admission by a multidisciplinary team and included the following questionnaires: mini-mental state examination (MMSE), geriatric depression scale-short form, international physical activity questionnaire-short form, timed up and go, Katz index of independence in activities of daily living, Charlson comorbidity index, Karnofsky performance scale (KPS), polypharmacy, and mini nutritional assessment-short form (MNA-SF). The 10-fold cross-validation algorithm was used to evaluate all possible combinations of these questionnaires to estimate the risk of early death, considered when occurring within 6 months of diagnosis, in a variety of ML classifiers, including Naive Bayes (NB), decision tree algorithm J48 (J48), and multilayer perceptron (MLP). On each fold of evaluation, tiebreaking is handled by choosing the smallest set of questionnaires. RESULTS It was possible to select CGA questionnaire subsets with high predictive capacity for early death, which were either statistically similar (NB) or higher (J48 and MLP) when compared with the use of all questionnaires investigated. These results show that CGA questionnaire selection can improve accuracy rates and decrease the time spent to evaluate elderly cancer patients. CONCLUSIONS A simplified predictive model aiming to estimate the risk of early death in elderly cancer patients is proposed herein, minimally composed by the MNA-SF and KPS. We strongly recommend that these questionnaires be incorporated into regular geriatric assessment of older patients with cancer.

Predictive factors of early death after a comprehensive geriatric assessment in elderly cancer patients.

2015 ◽  
Vol 33 (15_suppl) ◽  
pp. e20530-e20530
Author(s):  
Beatriz Jimenez-Munarriz ◽  
Rosario Madero ◽  
Ana M. Jimenez Gordo ◽  
MJ Molina-Garrido ◽  
Juana Saldana ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Geriatric Assessment ◽  
Predictive Factors ◽  
Comprehensive Geriatric Assessment ◽  
Early Death ◽  
Elderly Cancer Patients

scholarly journals Developing Machine Learning Algorithms for the Prediction of Early Death in Elderly Cancer Patients: Usability Study

JMIR Cancer ◽  
10.2196/12163 ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. e12163 ◽  
Author(s):  
Gabrielle Ribeiro Sena ◽  
Tiago Pessoa Ferreira Lima ◽  
Maria Julia Gonçalves Mello ◽  
Luiz Claudio Santos Thuler ◽  
Jurema Telles Oliveira Lima
Keyword(s):  
Machine Learning ◽  
Cancer Patients ◽  
Geriatric Assessment ◽  
Short Form ◽  
Geriatric Oncology ◽  
Depression Scale ◽  
Mini Nutritional Assessment ◽  
Early Death ◽  
Machine Learning Algorithms ◽  
Elderly Cancer Patients

Background The importance of classifying cancer patients into high- or low-risk groups has led many research teams, from the biomedical and bioinformatics fields, to study the application of machine learning (ML) algorithms. The International Society of Geriatric Oncology recommends the use of the comprehensive geriatric assessment (CGA), a multidisciplinary tool to evaluate health domains, for the follow-up of elderly cancer patients. However, no applications of ML have been proposed using CGA to classify elderly cancer patients. Objective The aim of this study was to propose and develop predictive models, using ML and CGA, to estimate the risk of early death in elderly cancer patients. Methods The ability of ML algorithms to predict early mortality in a cohort involving 608 elderly cancer patients was evaluated. The CGA was conducted during admission by a multidisciplinary team and included the following questionnaires: mini-mental state examination (MMSE), geriatric depression scale-short form, international physical activity questionnaire-short form, timed up and go, Katz index of independence in activities of daily living, Charlson comorbidity index, Karnofsky performance scale (KPS), polypharmacy, and mini nutritional assessment-short form (MNA-SF). The 10-fold cross-validation algorithm was used to evaluate all possible combinations of these questionnaires to estimate the risk of early death, considered when occurring within 6 months of diagnosis, in a variety of ML classifiers, including Naive Bayes (NB), decision tree algorithm J48 (J48), and multilayer perceptron (MLP). On each fold of evaluation, tiebreaking is handled by choosing the smallest set of questionnaires. Results It was possible to select CGA questionnaire subsets with high predictive capacity for early death, which were either statistically similar (NB) or higher (J48 and MLP) when compared with the use of all questionnaires investigated. These results show that CGA questionnaire selection can improve accuracy rates and decrease the time spent to evaluate elderly cancer patients. Conclusions A simplified predictive model aiming to estimate the risk of early death in elderly cancer patients is proposed herein, minimally composed by the MNA-SF and KPS. We strongly recommend that these questionnaires be incorporated into regular geriatric assessment of older patients with cancer.

Comprehensive Geriatric Assessment Adds Information to Eastern Cooperative Oncology Group Performance Status in Elderly Cancer Patients: An Italian Group for Geriatric Oncology Study

2002 ◽  
Vol 20 (2) ◽  
pp. 494-502 ◽  
Author(s):  
Lazzaro Repetto ◽  
Lucia Fratino ◽  
Riccardo A. Audisio ◽  
Antonella Venturino ◽  
Walter Gianni ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Geriatric Assessment ◽  
Comprehensive Geriatric Assessment ◽  
Daily Living ◽  
Group Performance ◽  
Performance Status ◽  
Eastern Cooperative Oncology Group ◽  
Geriatric Oncology ◽  
Oncology Group ◽  
Elderly Cancer Patients

PURPOSE: To appraise the performance of Comprehensive Geriatric Assessment (CGA) in elderly cancer patients (≥ 65 years) and to evaluate whether it could add further information with respect to the Eastern Cooperative Oncology Group performance status (PS). PATIENTS AND METHODS: We studied 363 elderly cancer patients (195 males, 168 females; median age, 72 years) with solid (n = 271) or hematologic (n = 92) tumors. In addition to PS, their physical function was assessed by means of the activity of daily living (ADL) and instrumental activities of daily living (IADL) scales. Comorbidities were categorized according to Satariano’s index. The association between PS, comorbidity, and the items of the CGA was assessed by means of logistic regression analysis. RESULTS: These 363 elderly cancer patients had a good functional and mental status: 74% had a good PS (ie, lower than 2), 86% were ADL-independent, and 52% were IADL-independent. Forty-one percent of patients had one or more comorbid conditions. Of the patients with a good PS, 13.0% had two or more comorbidities; 9.3% and 37.7% had ADL or IADL limitations, respectively. By multivariate analysis, elderly cancer patients who were ADL-dependent or IADL-dependent had a nearly two-fold higher probability of having an elevated Satariano’s index than independent patients. A strong association emerged between PS and CGA, with a nearly five-fold increased probability of having a poor PS (ie, ≥ 2) recorded in patients dependent for ADL or IADL. CONCLUSION: The CGA adds substantial information on the functional assessment of elderly cancer patients, including patients with a good PS. The role of PS as unique marker of functional status needs to be reappraised among elderly cancer patients.

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