Survival prediction for advanced cancer patients in the real world: A comparison of the Palliative Prognostic Score, Delirium-Palliative Prognostic Score, Palliative Prognostic Index and modified Prognosis in Palliative Care Study predictor model

Baba, Mika; Maeda, Isseki; Morita, Tatsuya; Inoue, Satoshi; Ikenaga, Masakazu; Matsumoto, Yoshihisa; Sekine, Ryuichi; Yamaguchi, Takashi; Hirohashi, Takeshi; Tajima, Tsukasa; Tatara, Ryohei; Watanabe, Hiroaki; Otani, Hiroyuki; Takigawa, Chizuko; Matsuda, Yoshinobu; Nagaoka, Hiroka; Mori, Masanori; Tei, Yo; Hiramoto, Shuji; Suga, Akihiko; Kinoshita, Hiroya

doi:10.1016/j.ejca.2015.04.025

Cited by 103 publications

(121 citation statements)

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“…The Palliative Prognostic Score (PaP score) represents one of the most validated prognostic models in the advanced cancer setting [8–12]. It consists of CPS and 5 clinical and laboratory variables (i.e.…”

Section: Introductionmentioning

confidence: 99%

Clinician prediction of survival versus the Palliative Prognostic Score: Which approach is more accurate?

Hui

Park

Liu

et al. 2016

European Journal of Cancer

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Background Clinician prediction of survival (CPS) has low accuracy in the advanced cancer setting, raising the need for prediction models such as the palliative prognostic (PaP) score that includes a transformed CPS (PaP-CPS) and 5 clinical/laboratory variables (PaP-without CPS). However, it is unclear if the PaP score is more accurate than PaP-CPS, and whether PaP-CPS helps to improve the accuracy of PaP score. We compared the accuracy among PaP-CPS, PaP-without CPS and PaP-total score in patients with advanced cancer. Patients and Methods In this prospective study, PaP score was documented in hospitalization patients seen by palliative care. We compared the discrimination of PaP-CPS versus PaP-total and PaP-without CPS versus PaP-total using 4 indices: concordance statistics, area under the receiver-operating characteristics curve (AUC), net reclassification index and integrated discrimination improvement for 30-day survival and 100-day survival. Results 216 patients were enrolled with a median survival of 109 days (95% confidence interval [CI] 71–133 days). The AUC for 30-day survival was 0.57 (95%CI 0.47–0.67) for PaP-CPS, 0.78 (95%CI 0.7–0.87) for PaP-without CPS, and 0.73 (95%CI 0.64–0.82) for PaP-total score. PaP-total was significantly more accurate than PaP-CPS according to all 4 indices for both 30-day and 100-day survival (P<0.001). PaP-without CPS was significantly more accurate than PaP-total for 30-day survival (P<0.05). Conclusion We found that PaP-score was more accurate than CPS, and the addition of CPS to the prognostic model reduced its accuracy. This study highlights the limitations of clinical gestalt and the need to use objective prognostic factors and models for survival prediction.

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Section: Introductionmentioning

confidence: 99%

Clinician prediction of survival versus the Palliative Prognostic Score: Which approach is more accurate?

Hui

Park

Liu

et al. 2016

European Journal of Cancer

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“…Concerning predictive value, the PPI showed a signiicantly lower C-index than the PaP score and the D-PaP score in almost all setings. The modiied PiPS model showed equivalent or superior accuracy to the PaP score and the D-PaP score in all setings [10]. The authors concluded that the "PPI is simple and highly feasible, and seems to be suitable for routine clinical use for situations where rough estimates of prognosis are suicient and/or patients do not want invasive procedures.…”

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

confidence: 89%

“…Baba et al tested ive diferent prognosis models: the PaP score, the D-PaP score, the PPI model, the PiPS-A model, and the PiPS-B model [10]. Concerning feasibility, prognostic models without laboratory tests (the PPI model and the PiPS-A model) showed more than 90% feasibility in all palliative care setings, including home care services.…”

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

confidence: 99%

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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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“…64 Estimates of the prevalence of delirium in patients with cancer range widely from 10% to 50% among inpatients undergoing cancer surgery 65–69 and 20% to 90% among patients with advanced cancer receiving palliative care. 70–73 Without routine evaluation for delirium in outpatient cancer clinics, it is difficult to estimate the true prevalence of delirium among patients with cancer.…”

Section: Gap 4: Patients With Cancer Are Not Routinely Screened Fomentioning

confidence: 99%

Understanding cognition in older patients with cancer

Karuturi

Wong

Hsu

et al. 2016

Journal of Geriatric Oncology

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Cancer and neurocognitive disorders, such as dementia and delirium, are common and serious diseases in the elderly that are accompanied by high degree of morbidity and mortality. Furthermore, evidence supports the under-diagnosis of both dementia and delirium in older adults. Complex questions exist regarding the interaction of dementia and delirium with cancer, beginning with guidelines on how best measure disease severity, the optimal screening test for either disorder, the appropriate level of intervention in the setting of abnormal findings, and strategies aimed at preventing the development or progression of either process. Ethical concerns emerge in the research setting, pertaining to the detection of cognitive dysfunction in participants, validity of consent, disclosure of abnormal results if screening is pursued, and recommended level of intervention by investigators. Furthermore, understanding the ways in which comorbid cognitive dysfunction and cancer impact both cancer and non-cancer-related outcomes is essential in guiding treatment decisions. In the following article, we will discuss what is presently known of the interactions of pre-existing cognitive impairment and delirium with cancer. We will also discuss identified deficits in our knowledge base, and propose ways in which innovative research may address these gaps.

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Survival prediction for advanced cancer patients in the real world: A comparison of the Palliative Prognostic Score, Delirium-Palliative Prognostic Score, Palliative Prognostic Index and modified Prognosis in Palliative Care Study predictor model

Cited by 103 publications

References 32 publications

Clinician prediction of survival versus the Palliative Prognostic Score: Which approach is more accurate?

Clinician prediction of survival versus the Palliative Prognostic Score: Which approach is more accurate?

Prognosis Prediction Models and their Clinical Utility in Palliative Care

Understanding cognition in older patients with cancer

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