Validation of the Simplified Palliative Prognostic Index Using a Single Item From the Communication Capacity Scale

Hamano, Jun; Morita, Tatsuya; Ozawa, Taketoshi; Shishido, Hideki; Kawahara, Manabu; Aoki, Shigeru; Demizu, Akira; Goshima, Masahiro; Goto, Keiji; Gyoda, Yasuaki; Hashimoto, Kenkichi; Otomo, Sen; Sekimoto, Masako; Shibata, Takemi; Sugimoto, Yuka; Matsunaga, Mikako; Takeda, Yuichiro; Nagayama, Jun; Kinoshita, Hiroya

doi:10.1016/j.jpainsymman.2015.04.021

Cited by 15 publications

(42 citation statements)

References 12 publications

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“…Four different delirium diagnostic reference standards were used to validate the accuracy of delirium screening tools in the included studies. Most studies ( n = 14) used one delirium diagnostic reference standard, 36 –40,42 –50 while three studies used two different sets of diagnostic criteria. 35,41,51 Gold standard DSM criteria were used across 10 different studies and reflected contemporaneous use of the edition.…”

Section: Resultsmentioning

confidence: 99%

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Delirium screening tools validated in the context of palliative care: A systematic review

et al. 2021

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Background: Delirium is a distressing neuropsychiatric disorder affecting patients in palliative care. Although many delirium screening tools exist, their utility, and validation within palliative care settings has not undergone systematic review. Aim: To systematically review studies that validate delirium screening tools conducted in palliative care settings. Design: Systematic review with narrative synthesis (PROSPERO ID: CRD42019125481). A risk of bias assessment via Quality Assessment Tool for Diagnostic Accuracy Studies-2 was performed. Data sources: Five electronic databases were systematically searched (January 1, 1982–May 3, 2020). Quantitative studies validating a screening tool in adult palliative care patient populations were included. Studies involving alcohol withdrawal, critical or perioperative care were excluded. Results: Dual-reviewer screening of 3749 unique titles and abstracts identified 95 studies for full-text review and of these, 17 studies of 14 screening tools were included ( n = 3496 patients). Data analyses revealed substantial heterogeneity in patient demographics and variability in screening and diagnostic practices that limited generalizability between study populations and care settings. A risk of bias assessment revealed methodological and reporting deficits, with only 3/17 studies at low risk of bias. Conclusions: The processes of selecting a delirium screening tool and determining optimal screening practices in palliative care are complex. One tool is unlikely to fit the needs of the entire palliative care population across all palliative care settings. Further research should be directed at evaluating and/or adapting screening tools and practices to fit the needs of specific palliative care settings and populations.

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Section: Resultsmentioning

confidence: 99%

“…36,37,39,42 Finally, with respect to flow and timing, inconsistent administration of the index test or reference standard to all patients was the largest source of bias ( n = 6). 39,40,42,43,47,48…”

Section: Discussionmentioning

confidence: 99%

Delirium screening tools validated in the context of palliative care: A systematic review

et al. 2021

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“…This study was a secondary analysis of the Japan Prognostic Assessment Tools Validation study, which was a multicenter, prospective cohort study performed to investigate the feasibility and accuracy of existing prognostic tools. The methodology of the Japan Prognostic Assessment Tools Validation study has been reported in detail in elsewhere . It was conducted at 58 palliative care services in Japan from September 2012 through April 2014.…”

Section: Methodsmentioning

confidence: 99%

“…The methodology of the Japan Prognostic Assessment Tools Validation study has been reported in detail in elsewhere. [15][16][17][18][19] It was conducted at 58 palliative care services in Japan from September 2012 through April 2014. The participating services included 19 hospital palliative care teams, 16 palliative care units, and 23 home-based palliative care services.…”

Section: Methodsmentioning

confidence: 99%

Multicenter cohort study on the survival time of cancer patients dying at home or in a hospital: Does place matter?

Hamano

Yamaguchi

Maeda

et al. 2016

Cancer

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and involved 58 specialist palliative care services. RESULTS: Among the 2426 patients recruited, 2069 patients were analyzed for this study: 1582 receiving hospital-based palliative care and 487 receiving home-based palliative care. A total of 1607 patients actually died in a hospital, and 462 patients died at home. The survival of patients who died at home was significantly longer than the survival of patients who died in a hospital in the days' prognosis group (estimated median survival time, 13 days [95% confidence interval (CI), 10. Original Article hazards analysis revealed that the place of death had a significant influence on the survival time in both unadjusted (hazard ratio [HR], 0.86; 95% CI, 0.78-0.96; P <.01) and adjusted models (HR, 0.87; 95% CI, 0.77-0.97; P 5.01). CONCLUSIONS: In comparison with cancer patients who died in a hospital, cancer patients who died at home had similar or longer survival. Cancer 2016;122:1453-60.

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“…The one substitutes the Communication Capacity Scale (CCS) for delirium to the required items of PPI [47]. The other adds a new item about the activities of daily living changes to the PPI; however, this did not signiicantly improve its…”

Section: Risk Groupsmentioning

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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Validation of the Simplified Palliative Prognostic Index Using a Single Item From the Communication Capacity Scale

Abstract: The simplified PPI showed essentially the same predictive value as the original PPI and is an alternative when clinicians have difficulties in diagnosing delirium.

Cited by 15 publications

References 12 publications

Delirium screening tools validated in the context of palliative care: A systematic review

Delirium screening tools validated in the context of palliative care: A systematic review

Multicenter cohort study on the survival time of cancer patients dying at home or in a hospital: Does place matter?

Prognosis Prediction Models and their Clinical Utility in Palliative Care

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