Palliative performance scale and survival among outpatients with advanced cancer

Myers, Jeff; Kim, Audrey; Flanagan, J.; Selby, Debbie

doi:10.1007/s00520-014-2440-8

Cited by 39 publications

(26 citation statements)

References 20 publications

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“…We also found that the higher Palliative Performance Scale, chemotherapy, and radiotherapy were among the factors strongly correlated with longer survival of the patients, which was consistent with previous reports [41–44]. Moreover, the patients that visited the palliative care clinic or had a home visit tended to survive longer, and the patients that needed hospital admission lived a shorter length of time in our study.…”

Section: Discussionsupporting

confidence: 92%

The association between different opioid doses and the survival of advanced cancer patients receiving palliative care

2016

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BackgroundConcerns that opioids may hasten death can be a cause of the physicians’ reluctance to prescribe opioids, leading to inadequate symptom palliation. Our aim was to find if there was an association between different opioid doses and the survival of the cancer patients that participated in our palliative care program.MethodsA retrospective study was conducted at Ramathibodi Hospital, Bangkok between January 2013 and December 2015. All of the cancer patients that were referred to palliative care teams by their primary physicians were included in the study. The study data included the patients’ demographics, disease status, comorbidities, functional status, type of services, cancer treatments, date of consultation, and the date of the patient’s death or last follow-up. The information concerning opioid use was collected by reviewing the medical records and this was converted to an oral morphine equivalent (OME), following a standard ratio. The time-varying covariate in the Cox regression analysis was applied in order to determine the association between different doses of opioids and patient survival.ResultsA total of 317 cancer patients were included in the study. The median (IQR) of the OME among our patients was 6.43 mg/day (0.53, 27.36). The univariate Cox regression analysis did not show any association between different opioid doses (OME ≤ 30 mg/day and > 30 mg/day) and the patients’ survival (p = 0.52). The PPS levels (p < 0.01), palliative care clinic visits (HR 0.32, 95%CI 0.24–0.43), home visits (HR 0.75, 95%CI 0.57–0.99), chemotherapy (HR 0.32, 95%CI 0.22–0.46), and radiotherapy (HR 0.53, 95%CI 0.36–0.78) were identified as factors that increased the probability of survival.ConclusionsOur study has demonstrated that different opioid doses in advanced cancer patients are not associated with shortened survival period.

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Section: Discussionsupporting

confidence: 92%

The association between different opioid doses and the survival of advanced cancer patients receiving palliative care

2016

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“…We took this approach because follow-ups were not spaced evenly in the original trial design, and data were highly likely to be missing not-at-random due to patient decline and death. Functional status has been used to explore ACL (5) and track symptom burden (13) in palliative care patients; it has also been found to be a significant predictor of survival in these populations (14–16). By evaluating ACL based on performance status (AKPS), we maintained our sample size (n=244; 1,210 encounters).…”

Section: Methodsmentioning

confidence: 99%

Anticholinergic Drug Burden in Noncancer Versus Cancer Patients Near the End of Life

Kamal

Wolf

Samsa

et al. 2016

Journal of Pain and Symptom Management

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Context Anticholinergic drugs can cause several side effects, impairing cognition and quality of life (QOL). Cancer patients are often exposed to increasing cumulative anticholinergic load (ACL) as they approach death, but this burden has not been examined in patients with non-malignant diseases. Objectives To determine ACL and its impact in non-cancer versus cancer palliative care patients. Methods We performed a secondary analysis of 244 subjects enrolled in a randomized controlled trial. ACL was quantified with the Anticholinergic Drug Scale. We used multivariable regression to calculate the effect of ACL on key outcomes, including drowsiness, fatigue, and QOL. Patients were stratified by diagnosis, and drugs were grouped as symptom management (SM) or disease management (DM). Results Overall ACL in cancer and non-cancer patients was not significantly different (2.6 vs. 2.4; P=0.23). SM drugs caused greater anticholinergic exposure than DM drugs in both cancer and non-cancer patients (2.3 vs. 0.5, and 1.5 vs. 1.3, respectively; both P<0.05), however DM drugs exposed non-cancer patients to relatively more ACL than cancer patients (1.2 vs. 0.6, P<0.0001). ACL was associated with worse fatigue (OR, 1.08; CI, 1.002–1.17) and worse QOL (OR, 0.89; CI, 0.80–0.98). Conclusions ACL is associated with worse fatigue and QOL, and may not differ significantly between cancer and non-cancer patients nearing end of life. SM drugs are more responsible for ACL in cancer and non-cancer patients, though DM drugs contribute significantly to ACL in the latter group. We recommend more attention to reducing anticholinergic use in all patients with life-limiting illness.

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“…Performance status [22][23][24], dyspnea [25,26], malnutrition [27,28], appetite/weight loss [29], and delirium [30,31] are well-known clinical factors. Recently, novel prognostic factors have been proposed.…”

Section: Prognostic Factorsmentioning

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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Palliative performance scale and survival among outpatients with advanced cancer

Abstract: Given the ongoing challenge of inaccurate survival estimates, this data suggests what may be of greatest clinical utility is to use specific PPS ratings as triggers for key care discussions among patients with incurable and progressive cancer.

Cited by 39 publications

References 20 publications

The association between different opioid doses and the survival of advanced cancer patients receiving palliative care

The association between different opioid doses and the survival of advanced cancer patients receiving palliative care

Anticholinergic Drug Burden in Noncancer Versus Cancer Patients Near the End of Life

Prognosis Prediction Models and their Clinical Utility in Palliative Care

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