Prospective Comparison of Prognostic Scores in Palliative Care Cancer Populations

Maltoni, Marco; Scarpi, Emanuela; Pittureri, Cristina; Martini, Francesca; Montanari, Luigi; Amaducci, Elena; Derni, Stefania; Fabbri, L.; Rosati, Marta; Amadori, Dino; Nanni, Oriana

doi:10.1634/theoncologist.2011-0397

Cited by 123 publications

(145 citation statements)

References 74 publications

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“…The authors concluded that all four prognostic scores accurately predicted survival. All prognostic scores, except CPS, had a more than 70% positive prediction rate of a less than 21-days survival (Maltoni et al, 2012).…”

Section: Introductionmentioning

confidence: 91%

“…PPI is scored using presentations of clinical symptoms only (performance status, dyspnea, oral intake, edema and delirium) without CPS or laboratory tests (Morita et al, 1999). Both scores had been validated in various hospice settings with acceptable sensitivity and specificity for end-of-life prediction (Maltoni et al, , 2001(Maltoni et al, , 2012Caraceni et al, 2000;Glare et al, 2001;Stone et al, 2008;Stiel et al, 2010;Yoong et al, 2010;Alshemmari et al, 2012). One study from Germany compared PaP score, PPI and CPS in terminally ill patients.…”

Section: Introductionmentioning

confidence: 99%

“…All medical staffs involved in hospice care can use PPI to predict life expectancy in terminally ill cancer patients. PPI had been validated in smaller patient numbers in hospice wards and hospice consulting services in Japan and western countries (Stone et al, 2008;Stiel et al, 2010;Maltoni et al, 2012). However, the value of PPI in Taiwanese terminal cancer patients, especially in the hospice consultation setting, has not been determined.…”

Section: Introductionmentioning

confidence: 99%

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Validation of a Palliative Prognostic Index to Predict Life Expectancy for Terminally Ill Cancer Patients in a Hospice Consultation Setting in Taiwan

Cheng¹,

Kao²,

Hung³

et al. 2012

Asian Pacific Journal of Cancer Prevention

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Background: The aim of our study was to assess the practical utility of the palliative prognostic index (PPI) as a prognostic tool used by nurse specialists in a hospice consultation setting in Taiwan The sensitivity and specificity for the poor prognosis group was 66% and 71%; the positive predictive value and negative predictive value were 81% and 52%, respectively, to predict patient death within 21 days (area under the curve of the receiver operating characteristic was 0.68). Conclusions: Assessment by PPI can accurately predict survival of terminal cancer patients receiving hospice consultation care. PPI is a simple tool and can be administered by nurse members of hospice consultation teams.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Validation of a Palliative Prognostic Index to Predict Life Expectancy for Terminally Ill Cancer Patients in a Hospice Consultation Setting in Taiwan

Cheng¹,

Kao²,

Hung³

et al. 2012

Asian Pacific Journal of Cancer Prevention

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“…These prognostic parameters have been extensively studied and in some cases prognostic scoring systems have been developed (11). However, limitations of prognostic markers are acknowledged, such as subjective measures (symptoms, clinical prediction, performance status) or assessing multiple parameters.…”

Section: Introductionmentioning

confidence: 99%

Prognostic Factors in Patients with Advanced Cancer: A Comparison of Clinicopathological Factors and the Development of an Inflammation-Based Prognostic System

Laird

Kaasa

McMillan

et al. 2013

Clinical Cancer Research

181

160

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Purpose: In advanced cancer, oncological treatment is influenced by performance status (PS); however, this has limitations. Biomarkers of systemic inflammation may have prognostic value in advanced cancer. The study compares key factors in prognosis (performance status, patient-reported outcomes; PRO) with an inflammation-based score (Glasgow Prognostic Score, mGPS). A new method of prognosis in advanced cancer (combining performance status and mGPS) is tested and then validated.Experimental Design: Two international biobanks of patients with advanced cancer were analyzed. Key prognostic factors [performance status, PROs (European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire C-30), and mGPS (using C-reactive protein and albumin concentrations)] were examined. The relationship between these and survival was examined using Kaplan-Meier and Cox regression methods, in a test sample before independent validation.Results: Data were available on 1,825 patients (test) and 631 patients (validation). Median survival ranged from 3.2 months (test) to 7.03 months (validation). On multivariate analysis, performance status (HR 1.62-2.77) and mGPS (HR 1.51-2.27) were independently associated with, and were the strongest predictors of survival (P < 0.01). Survival at 3 months varied from 82% (mGPS 0) to 39% (mGPS 2) and from 75% (performance status 0-1) to 14% (performance status 4). When used together, survival ranged from 88% (mGPS 0, PS 0-1) to 10% (mGPS 2, performance status 4), P < 0.001.Conclusion: A systemic inflammation-based score, mGPS, and performance status predict survival in advanced cancer. The mGPS is similar to performance status in terms of prognostic power. Used together, performance status and mGPS act synergistically improving prognostic accuracy. This new method may be of considerable value in the management of patients with advanced cancer.

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“…There are some comparative studies [9,43,67], and the largest is the J-ProVal study reported by Baba et al…”

Section: Direct Comparison Of Diferent Prognostic Models In the Same mentioning

confidence: 99%

Prognosis Prediction Models and their Clinical Utility in Palliative Care

Uneno¹,

Kanai²

2017

Highlights on Several Underestimated Topics in Palliative Care

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Prognosis prediction is a clinically relevant issue to facilitate optimal decision-making for both physicians and patients with cancer. Many previous studies revealed that prognosis prediction based on the physician's intuition and/or clinical experience is inaccurate and often optimistic, which means that there is a tendency to overestimate patient survival in daily clinical practice. In recent decades, many eforts have been made to develop prognosis prediction models which aid physicians to make more accurate prognosis prediction. In this chapter, we review the representative prognosis prediction models in palliative care and related studies. In addition, we refer to several prognosis prediction models developed by unique methods (for instance, case-crossover design or machine learning). Finally, we focus on the possible clinical utility of prognosis prediction models. In fact, no previous studies have clearly demonstrated whether the application of such prognosis prediction models truly beneits patient care in daily clinical practice.Therefore, we will discuss how the application of prognosis prediction models could beneit patients under palliative care.

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Prospective Comparison of Prognostic Scores in Palliative Care Cancer Populations

Cited by 123 publications

References 74 publications

Validation of a Palliative Prognostic Index to Predict Life Expectancy for Terminally Ill Cancer Patients in a Hospice Consultation Setting in Taiwan

Validation of a Palliative Prognostic Index to Predict Life Expectancy for Terminally Ill Cancer Patients in a Hospice Consultation Setting in Taiwan

Prognostic Factors in Patients with Advanced Cancer: A Comparison of Clinicopathological Factors and the Development of an Inflammation-Based Prognostic System

Prognosis Prediction Models and their Clinical Utility in Palliative Care

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