The application of the palliative prognostic index, charlson comorbidity index, and Glasgow prognostic score in predicting the life expectancy of patients with hematologic malignancies under palliative care

Chou, Wen‐Chi; Kao, Chen-Yi; Wang, Po‐Nan; Chang, Hong-Yeh; Wang, Hung‐Ming; Chang, Pei‐Hung; Yeh, Kun‐Yun; Hung, Yu‐Shin

doi:10.1186/s12904-015-0011-5

Cited by 33 publications

(28 citation statements)

References 24 publications

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“…The accuracy of prognostication was even improved by concurrent use of the Glasgow Prognostic Score and the Charlson Comorbidity Index [39], but their impact as prognosticator to identify patients in need of SPC remains to be verified.…”

Section: Potential Indications For Palliative Care Needmentioning

confidence: 99%

Palliative Care in Patients with Hematological Malignancies

Oechsle¹

2018

Oncol Res Treat

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Patients with hematologic malignancies (HM) nowadays rarely receive palliative or hospice care, and studies on potential effects of integrated palliative care are rare. This narrative review provides a comprehensive overview on their current end-of-life care, first data on integrated specialist palliative care (SPC) and potential barriers. Symptom burden and distress in patients with HM seem to be comparable to other cancer patients, but their performance status and specific symptoms are even worse. Prolongation of life usually remains the main focus until the last days or weeks of life. Their chance to die in hospital is twice as high, but their chance to receive specialist palliative or hospice care is only half of that of other cancer patients. Prospective studies indicate a high acceptability and positive effects of integrated SPC, e.g. clarification of treatment goals, stabilization of quality of life, decreased depression, anxiety, symptom burden, and post-traumatic stress disorders. Interdisciplinary cooperation, timely discussions about SPC referral, and indicators to ‘flag' patients in need for SPC are important, but they are largely missing. In conclusion, further studies have to evaluate factors identifying patients with HM in need for SPC. Further models of early integrated palliative care should be evaluated in prospective studies and established in daily clinical practice.

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Section: Potential Indications For Palliative Care Needmentioning

confidence: 99%

Palliative Care in Patients with Hematological Malignancies

Oechsle¹

2018

Oncol Res Treat

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“…This could be attributed to the Chou et al. () and Ohno et al. () studies having shorter time frames of survival in their populations (16 days and 6.4 weeks respectively) than was the aim of this study, to identify risk of deteriorating and dying within 3–6 months.…”

Section: Discussionmentioning

confidence: 85%

“…A recent systematic review of prognostic factors present in the final 3 months of life revealed that the body of knowledge is predominantly focused on people who are admitted to the intensive care unit, or are treated aggressively (Button et al, 2017b). Few studies have explored prognostic factors or prognostic tools to inform transitioning to end-of-life care for people with a haematological malignancy (Button et al, 2017a;Chou et al, 2015;Corbett, Johnstone, McCracken Trauer, & Spruyt, 2013;Kripp et al, 2014;Ohno, Abe, Sasaki, & Okuhiro, 2017).…”

Section: Introductionmentioning

confidence: 99%

Harnessing the power of clinician judgement. Identifying risk of deteriorating and dying in people with a haematological malignancy: A Delphi study

Button

Gavin

Chan

et al. 2018

Journal of Advanced Nursing

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Aim To provide expert consensus on the clinical indicators that signal a person with a haematological malignancy is at high risk of deteriorating and dying. Background Identification of people who are at risk of deteriorating and dying is essential to facilitate patient autonomy, appropriate treatment decisions, and effective end‐of‐life care. Design A three‐step modified Delphi approach. Methods The study was conducted over 6 months (September 2015–March 2016) to gather opinion from an international panel of experts (N = 27) on the clinical indicators that signal a person with a haematological malignancy is at high risk of deteriorating and dying. The first round was informed by a systematic review of prognostic factors present in the final months of life for people with a haematological malignancy. Consensus was achieved if 70% of responses fell within two points on a seven‐point Likert‐type scale. Findings Consensus was achieved on the following 11 clinical indicators: (a) advancing age; (b) declining performances status; (c) presence of co‐morbidities; (d) disease status; (e) persistent infections (bacterial and viral); (f) fungal infections; (g) severe graft versus host disease; (h) requiring high care; (i) signs of frailty; (j) treatment limitations; and (k) anorexia and/or weight loss. Consensus was also achieved on associated themes and statements for each indicator. Conclusion The findings of this study indicate that subjective clinician‐assessed indicators that are contextually relevant to the nature of haematological malignancies are markers of risk. This study has provided valuable preliminary findings on the topic and will inform future research.

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“…Validation studies of the PPI were performed in Japan, Taiwan, Kuwait, Ireland, the United Kingdom, and Australia [39][40][41][42][43][44][45][46]. Morita et al revealed that the PPI signiicantly reduced overestimation of survival compared to the CPS [39].…”

Section: Risk Groupsmentioning

confidence: 99%

“…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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The application of the palliative prognostic index, charlson comorbidity index, and Glasgow prognostic score in predicting the life expectancy of patients with hematologic malignancies under palliative care

Cited by 33 publications

References 24 publications

Palliative Care in Patients with Hematological Malignancies

Palliative Care in Patients with Hematological Malignancies

Harnessing the power of clinician judgement. Identifying risk of deteriorating and dying in people with a haematological malignancy: A Delphi study

Prognosis Prediction Models and their Clinical Utility in Palliative Care

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