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      Development and validation of a prediction model of poor performance status and severe symptoms over time in cancer patients (PROVIEW+)

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          Abstract

          Background:

          Predictive cancer tools focus on survival; none predict severe symptoms.

          Aim:

          To develop and validate a model that predicts the risk for having low performance status and severe symptoms in cancer patients.

          Design:

          Retrospective, population-based, predictive study

          Setting/Participants:

          We linked administrative data from cancer patients from 2008 to 2015 in Ontario, Canada. Patients were randomly selected for model derivation (60%) and validation (40%). Using the derivation cohort, we developed a multivariable logistic regression model to predict the risk of an outcome at 6 months following diagnosis and recalculated after each of four annual survivor marks. Model performance was assessed using discrimination and calibration plots. Outcomes included low performance status (i.e. 10–30 on Palliative Performance Scale), severe pain, dyspnea, well-being, and depression (i.e. 7–10 on Edmonton Symptom Assessment System).

          Results:

          We identified 255,494 cancer patients (57% female; median age of 64; common cancers were breast (24%); and lung (13%)). At diagnosis, the predicted risk of having low performance status, severe pain, well-being, dyspnea, and depression in 6-months is 1%, 3%, 6%, 13%, and 4%, respectively for the reference case (i.e. male, lung cancer, stage I, no symptoms); the corresponding discrimination for each outcome model had high AUCs of 0.807, 0.713, 0.709, 0.790, and 0.723, respectively. Generally these covariates increased the outcome risk by >10% across all models: lung disease, dementia, diabetes; radiation treatment; hospital admission; pain; depression; transitional performance status; issues with appetite; or homecare.

          Conclusions:

          The model accurately predicted changing cancer risk for low performance status and severe symptoms over time.

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          Most cited references39

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          Assessing the performance of prediction models: a framework for traditional and novel measures.

          The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.
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            Early palliative care for patients with metastatic non-small-cell lung cancer.

            Patients with metastatic non-small-cell lung cancer have a substantial symptom burden and may receive aggressive care at the end of life. We examined the effect of introducing palliative care early after diagnosis on patient-reported outcomes and end-of-life care among ambulatory patients with newly diagnosed disease. We randomly assigned patients with newly diagnosed metastatic non-small-cell lung cancer to receive either early palliative care integrated with standard oncologic care or standard oncologic care alone. Quality of life and mood were assessed at baseline and at 12 weeks with the use of the Functional Assessment of Cancer Therapy-Lung (FACT-L) scale and the Hospital Anxiety and Depression Scale, respectively. The primary outcome was the change in the quality of life at 12 weeks. Data on end-of-life care were collected from electronic medical records. Of the 151 patients who underwent randomization, 27 died by 12 weeks and 107 (86% of the remaining patients) completed assessments. Patients assigned to early palliative care had a better quality of life than did patients assigned to standard care (mean score on the FACT-L scale [in which scores range from 0 to 136, with higher scores indicating better quality of life], 98.0 vs. 91.5; P=0.03). In addition, fewer patients in the palliative care group than in the standard care group had depressive symptoms (16% vs. 38%, P=0.01). Despite the fact that fewer patients in the early palliative care group than in the standard care group received aggressive end-of-life care (33% vs. 54%, P=0.05), median survival was longer among patients receiving early palliative care (11.6 months vs. 8.9 months, P=0.02). Among patients with metastatic non-small-cell lung cancer, early palliative care led to significant improvements in both quality of life and mood. As compared with patients receiving standard care, patients receiving early palliative care had less aggressive care at the end of life but longer survival. (Funded by an American Society of Clinical Oncology Career Development Award and philanthropic gifts; ClinicalTrials.gov number, NCT01038271.)
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              Early palliative care for patients with advanced cancer: a cluster-randomised controlled trial.

              Patients with advanced cancer have reduced quality of life, which tends to worsen towards the end of life. We assessed the effect of early palliative care in patients with advanced cancer on several aspects of quality of life. The study took place at the Princess Margaret Cancer Centre (Toronto, ON, Canada), between Dec 1, 2006, and Feb 28, 2011. 24 medical oncology clinics were cluster randomised (in a 1:1 ratio, using a computer-generated sequence, stratified by clinic size and tumour site [four lung, eight gastrointestinal, four genitourinary, six breast, two gynaecological]), to consultation and follow-up (at least monthly) by a palliative care team or to standard cancer care. Complete masking of interventions was not possible; however, patients provided written informed consent to participate in their own study group, without being informed of the existence of another group. Eligible patients had advanced cancer, European Cooperative Oncology Group performance status of 0-2, and a clinical prognosis of 6-24 months. Quality of life (Functional Assessment of Chronic Illness Therapy--Spiritual Well-Being [FACIT-Sp] scale and Quality of Life at the End of Life [QUAL-E] scale), symptom severity (Edmonton Symptom Assessment System [ESAS]), satisfaction with care (FAMCARE-P16), and problems with medical interactions (Cancer Rehabilitation Evaluation System Medical Interaction Subscale [CARES-MIS]) were measured at baseline and monthly for 4 months. The primary outcome was change score for FACIT-Sp at 3 months. Secondary endpoints included change score for FACIT-Sp at 4 months and change scores for other scales at 3 and 4 months. This trial is registered with ClinicalTrials.gov, number NCT01248624. 461 patients completed baseline measures (228 intervention, 233 control); 393 completed at least one follow-up assessment. At 3-months, there was a non-significant difference in change score for FACIT-Sp between intervention and control groups (3·56 points [95% CI -0·27 to 7·40], p=0·07), a significant difference in QUAL-E (2·25 [0·01 to 4·49], p=0·05) and FAMCARE-P16 (3·79 [1·74 to 5·85], p=0·0003), and no difference in ESAS (-1·70 [-5·26 to 1·87], p=0·33) or CARES-MIS (-0·66 [-2·25 to 0·94], p=0·40). At 4 months, there were significant differences in change scores for all outcomes except CARES-MIS. All differences favoured the intervention group. Although the difference in quality of life was non-significant at the primary endpoint, this trial shows promising findings that support early palliative care for patients with advanced cancer. Canadian Cancer Society, Ontario Ministry of Health and Long Term Care. Copyright © 2014 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                Palliat Med
                Palliat Med
                PMJ
                sppmj
                Palliative Medicine
                SAGE Publications (Sage UK: London, England )
                0269-2163
                1477-030X
                15 June 2021
                October 2021
                : 35
                : 9 , Special Issue: Big Data in Palliative and End-of-Life Care
                : 1713-1723
                Affiliations
                [1 ]Department of Oncology, McMaster University, Hamilton, ON, Canada
                [2 ]Institute for Clinical Evaluative Sciences, Toronto, ON, Canada
                [3 ]Division of Palliative Care, Department of Medicine, Ottawa Hospital Research Institute, Ottawa, ON, Canada
                [4 ]Bruyère Research Institute, Ottawa, ON, Canada
                [5 ]Department of Oncology, University of Calgary, Calgary, AB, Canada
                [6 ]Tom Baker Cancer Centre, Alberta Health Services, Calgary, AB, Canada
                [7 ]Department of Kinesiology and Physical Education and Department of Health Sciences, Wilfrid Laurier University, Waterloo, ON, Canada
                [8 ]Division of Palliative Care, Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
                [9 ]School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
                [10 ]Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
                Author notes
                [*]Hsien Seow, Department of Oncology, McMaster University, Juravinski Cancer Centre, 699 Concession Street Rm 4-229, Hamilton, ON L8V 5C2, Canada. Email: seowh@ 123456mcmaster.ca
                Author information
                https://orcid.org/0000-0001-6701-1714
                https://orcid.org/0000-0002-4409-0795
                https://orcid.org/0000-0001-6059-5366
                Article
                10.1177_02692163211019302
                10.1177/02692163211019302
                8532207
                34128429
                d5df5c95-418f-489a-8c63-43533ed18852
                © The Author(s) 2021

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                Funding
                Funded by: canadian institutes of health research, FundRef https://doi.org/10.13039/501100000024;
                Award ID: 379009
                Funded by: canadian institutes of health research, FundRef https://doi.org/10.13039/501100000024;
                Award ID: 383402
                Categories
                Original Articles
                Custom metadata
                ts1

                Anesthesiology & Pain management
                cancer,prognosis,palliative care,logistic model,adl,depression,dyspnea,pain
                Anesthesiology & Pain management
                cancer, prognosis, palliative care, logistic model, adl, depression, dyspnea, pain

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