Prognostication in advanced cancer: update and directions for future research

Hui, David; Paiva, Carlos Eduardo; Fabbro, Egidio G. Del; Steer, Christopher; Naberhuis, Jane; Wetering, M. D. van de; Fernández-Ortega, Paz; Morita, Tatsuya; Suh, Sang Yeon; Bruera, Éduardo; Mori, Masanori

doi:10.1007/s00520-019-04727-y

Cited by 141 publications

(127 citation statements)

References 61 publications

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“…A total score over 6 predicts a survival of less than 3 weeks [37,38]. The PPI may be sufficient to evaluate prognosis in cases where laboratory values are not available [39]. Limitations of the PPI include the difficulty of accurately diagnosing delirium and its low negative predictive value.…”

Section: Palliative Prognostic Index (Ppi)mentioning

confidence: 99%

The efficacy of specialised rehabilitation using the Op-reha Guide for cancer patients in palliative care units: protocol of a multicentre, randomised controlled trial (JORTC-RHB02)

et al. 2020

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Background Although rehabilitation is recommended for terminal cancer patients, the specific components and methods of such programs are poorly documented. No studies to date have examined the effectiveness of rehabilitation for terminal cancer patients. This study aims to evaluate the efficacy of a new intervention for rehabilitation therapists, using the Op-reha Guide (Guide to Optimal and Patient-Centred Rehabilitation Practice for Patients in Palliative Care Units [PCUs]) in rehabilitation practice. This guide consists of recommended actions and attitudes for rehabilitation therapists and aims to optimise therapists’ actions according to the patient’s needs and condition. It shares goals with terminal cancer patients to maintain their activities of daily living (ADL). Methods This study uses a multicentre, prospective, randomised controlled trial (RCT) design with two parallel groups in PCUs where specialised rehabilitation will be routinely performed for terminal cancer patients by rehabilitation therapists. Participants will be randomised (1:1) to intervention (the Op-reha Guide) and control groups (usual rehabilitation). We will then conduct an observational study in PCUs that do not perform specialised rehabilitation for terminal cancer patients; this will be considered the usual care group, and the efficacy of usual rehabilitation will be quantitatively evaluated. Inclusion criteria are hospitalisation in PCU, European Cooperative Oncology Group Performance Status of 2 or 3, and clinical estimation of life expectancy of 3 weeks or more. Patients with severe symptom burden will be excluded. We hypothesise that the Op-reha Guide will be more effective in maintaining the ADL of terminal cancer patients hospitalised in PCUs than usual rehabilitation. The primary endpoint is defined as the change in (total) modified Barthel Index from baseline to Day 22. Quality of life will be a secondary endpoint. In total, 135 patients will be recruited from 16 Japanese sites between July 2019 and December 2021. Discussion This will be the first trial to evaluate the efficacy of specialised rehabilitation for terminal cancer patients hospitalised in PCUs, and will contribute to the evidence on the efficacy of implementing rehabilitation for terminal cancer patients. Trial registration UMIN-CTR, UMIN000037298 R000042525 (date of registration 7 July 2019).

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Section: Palliative Prognostic Index (Ppi)mentioning

confidence: 99%

The efficacy of specialised rehabilitation using the Op-reha Guide for cancer patients in palliative care units: protocol of a multicentre, randomised controlled trial (JORTC-RHB02)

et al. 2020

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“…The usefulness of this algorithm can be determined by applying to a large number of palliative patients in future prospective studies. As suggested by the literature, our study also contributes to the validation of ANN for future prognostication ( 43 ). We think that the ANN model is an important statistical method for survival prediction that can include a considerable number of parameters and provides the opportunity to practice in very large patient populations.…”

Section: Resultsmentioning

confidence: 59%

Comparison of Artificial Neural Networks and Logistic Regression for 30-days Survival Prediction of Cancer Patients

2020

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Introduction: A machine learning technique that imitates neural system and brain can provide better than traditional methods like logistic regression for survival prediction and create an algorithm by determining influential factors. Aim: To determine the influential factors on survival time of palliative care cancer patients and to compare two statistical methods for better prediction of survival. Methods: One-year data is gathered from the patients that we followed in the palliative care clinic of our hospital (2017-2018) (n = 189). All data were retrospectively evaluated. After descriptive statistics, we used Pearson and Spearman correlations for parametric and non-parametric variables. The Artificial Neural Networks (ANN) and logistic regression model were applied to parameters which have a significant correlation with short survival. Results: Significantly correlated variables with short survival were Palliative Performance Scale (PPS), Edmonton Symptom Assessment System (ESAS), Karnofsky Performance Scale (KPS), brain, liver, and distant metastasis, hemogram parameters, cero-reactive protein (CRP) and albumin (ALB). ANN model showed 89.3% prediction accuracy while the logistic regression model showed 73.0%. ANN model achieved a better AUC value of 0.86 than logistic regression model (0.76). Discussion: There are several prognostic evaluation tools such as PPS, KPS, CRP, albumin, leukocytes, neutrophil were reported several studies as survival-related parameters in logistic regression models, also. Many studies compare ANN with logistic regression. When we evaluated these parameters totally, we observed the same relations with survival then we used the same parameters in the ANN model. The effectivity of the survival prediction models can be improved with the use of ANN. Conclusion: ANN provides a more accurate estimation than logistic regression. ANN model is an important statistical method for survival prediction of cancer patients.

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“…It may be that other objective measurements of "real life" performance status will more consistently reflect ECOG-PS, such as activity trackers (e.g., Fitbit) [33][34][35]. With reference to the mGPS there has been in recent years extensive validation of its use in patients with advanced care, and routine assessment is now advocated [36,37]. Of the present criteria considered, it is the only one that is completely objective as it relies on two routine, laboratory-derived values.…”

Section: Discussionmentioning

confidence: 99%

The Relationship between ECOG-PS, mGPS, BMI/WL Grade and Body Composition and Physical Function in Patients with Advanced Cancer

Dolan

Daly

Simmons

et al. 2020

Cancers

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Cancer remains one of the leading causes of mortality worldwide and the associated reduction in physical function has a marked impact on both quality of life and survival. The aim of the present study was to examine the relationship between Eastern Cooperative Oncology Group-Performance status (ECOG-PS), modified Glasgow Prognostic Score (mGPS), Body Mass Index/Weight Loss grade (BMI/WL grade), and Computerised Tomography (CT)-derived body composition measurement and physical function in patients with advanced cancer. Nine sites contributed prospective data on patient demographics, ECOG-PS, mGPS, physical function tests, and CT-derived body composition. Categorical variables were analysed using χ2 test for linear-by-linear association, or χ2 test for 2-by-2 tables. Associations were analysed using binary logistic regression. A total of 523 cancer patients (266 males, 257 females) were included in the final analysis and most had metastatic disease (83.2%). The median overall survival was 5.6 months. On multivariate binary logistic regression analysis, a high ECOG-PS remained independently associated with a low skeletal muscle index (p < 0.001), low skeletal muscle density (p < 0.05), and timed up and go test failure (p < 0.001). A high mGPS remained independently associated with a low skeletal muscle density (p < 0.05) and hand grip strength test failure (p < 0.01). A high BMI/WL grade remained independently associated with a low subcutaneous fat index (p < 0.05), low visceral obesity (p < 0.01), and low skeletal muscle density (p < 0.05). In conclusion, a high ECOG-PS and a high mGPS as outlined in the ECOG-PS/mGPS framework were consistently associated with poorer body composition and physical function in patients with advanced cancer.

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Prognostication in advanced cancer: update and directions for future research

Cited by 141 publications

References 61 publications

The efficacy of specialised rehabilitation using the Op-reha Guide for cancer patients in palliative care units: protocol of a multicentre, randomised controlled trial (JORTC-RHB02)

The efficacy of specialised rehabilitation using the Op-reha Guide for cancer patients in palliative care units: protocol of a multicentre, randomised controlled trial (JORTC-RHB02)

Comparison of Artificial Neural Networks and Logistic Regression for 30-days Survival Prediction of Cancer Patients

The Relationship between ECOG-PS, mGPS, BMI/WL Grade and Body Composition and Physical Function in Patients with Advanced Cancer

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