Construction of a new, objective prognostic score for terminally ill cancer patients: a multicenter study

Suh, Sang Yeon; Choi, Youn Seon; Shim, Jae Yong; Kim, Young Sung; Yeom, Chang Hwan; Kim, Dae-Young; Park, Shin Ae; Kim, Sooa; Seo, Ji Yeon; Kim, Su Hyun; Kim, Daegyeun; Choi, Sung Eun; Ahn, Hong Yup

doi:10.1007/s00520-009-0639-x

Cited by 78 publications

(79 citation statements)

References 30 publications

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“…Therefore, prognostic factors are essential for the survival prediction. Comparable to other studies, we also observed a negative influence of anaemia [37,38,39], leukocytosis [35,40,41,42], low albumin [35,43,44], hypercalcaemia [45], levels of LDH [40,46] and γGT [47,48]. Anorexia and confusion/cognitive impairment also had a negative impact on the survival time of patients, as in other similar studies [49,50,51,52].…”

Section: Discussionsupporting

confidence: 71%

Characteristics, Treatment and Prognostic Factors of Patients with Gynaecological Malignancies Treated in a Palliative Care Unit at a University Hospital

et al. 2013

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Background: Limited clinical data have been published on patients suffering from advanced gynaecological malignancies treated in palliative care units, and little is known about prognostic factors. Methods: In a retrospective study, the data of 225 patients with breast, ovarian and cervical cancer treated in the palliative care unit of a university hospital between 1998 and 2009 were assembled. Clinical aspects and baseline symptoms, laboratory parameters, the clinical course, and outcome were evaluated. Results: 225 patients (497 cases; cancer diagnoses: breast 79%, ovarian 13%, and cervix 8%) were included in the analysis. The main symptoms were weakness/fatigue (71%), pain (65%), anorexia/nausea (62%), and dyspnea (46%). Pain control was achieved in 85% of all cases, satisfying control of other symptoms in 80%. The median overall survival (OS) was 59 days. 53% of the patients died at the palliative care unit. In the Cox proportional hazards model, 8 parameters indicated an unfavourable outcome: anorexia/nausea, disordered mental status, elevated lactate dehydrogenase, γ-glutamyltransferase, leukocyte count, hypoalbuminaemia, anaemia and hypercalcaemia. Based on these parameters 3 risk groups were defined: low risk (0-2 factors), intermediate risk (3-5 factors), and high risk (6-8 factors). Median survival for high-risk group was 13 days, for intermediate group 61 days, and for low-risk patients 554 days (p < 0.0001). Conclusion: Weakness/fatigue, pain and anorexia were the main symptoms leading to the hospitalisation of patients with gynaecological malignancies. Symptom and pain control was accomplished in 80% of cases. 8 parameters were identified as indicating a poor outcome, and patients showing at least 6 or more of these factors had a very limited prognosis. Although studied retrospectively, these results may be helpful for individual treatment decisions in patients with advanced gynaecological malignancies. Prospective data and the introduction of documentation systems could help to gain more powerful knowledge about the quality of palliative care.

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Section: Discussionsupporting

confidence: 71%

Characteristics, Treatment and Prognostic Factors of Patients with Gynaecological Malignancies Treated in a Palliative Care Unit at a University Hospital

et al. 2013

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“…12 The overall accuracy of the 3-week survival was 75.5% when the cutoff score was set at 3.0. 12 Factors included in the OPS were investigated by reviewing the patients' medical records and identified using laboratory data obtained on the day of admission. Laboratory data obtained within 1 week of the day of transfer were used for patients who were initially admitted to other parts of the hospital.…”

Section: Methodsmentioning

confidence: 99%

Retrospective Assessment of Objective Prognostic Score in Terminally Ill Korean Patients With Cancer

Yoon

Jung

Kim

et al. 2013

Am J Hosp Palliat Care

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We conducted a retrospective study by reviewing the medical records of 104 patients to assess the usefulness of the objective prognostic score (OPS) in an independent population of Korea. The median survival time (±standard error) of the high OPS group (≥3) was 9.0 ± 1.31 days and that of the low OPS group (<3) was 26.0 ± 3.3 days. The former was significantly shorter than the latter (P < .001). Only delirium (hazard ratio 1.751, P = .032) was related to shorter survival time independent of the OPS. This study demonstrates that the OPS is a valid and useful prognostic tool for predicting survival in terminally ill Korean patients with cancer, and that inclusion of delirium into the OPS may improve its prognostic value.

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“…In addition to the abovementioned representative prognostic models, other prognostic models are proposed, including the indicator of poor prognosis [68], the Vitamin B12/CRP index [69,70], the terminal cancer prognostic score [71], the Chuang prognostic score [72,73], the prognostic 7-day survival formula [74], the Chinese prognostic scale [75], the computerassisted model [76], the Japan palliative oncology study-prognostic index [77], the objective prognostic score [78,79], the prognostic nomogram for terminally ill cancer patients [80], and the symptom-based predictive tool [81]. Further validation studies or comparative studies among those models are warranted.…”

Section: Other Prognosis Prediction Modelsmentioning

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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Construction of a new, objective prognostic score for terminally ill cancer patients: a multicenter study

Cited by 78 publications

References 30 publications

Characteristics, Treatment and Prognostic Factors of Patients with Gynaecological Malignancies Treated in a Palliative Care Unit at a University Hospital

Characteristics, Treatment and Prognostic Factors of Patients with Gynaecological Malignancies Treated in a Palliative Care Unit at a University Hospital

Retrospective Assessment of Objective Prognostic Score in Terminally Ill Korean Patients With Cancer

Prognosis Prediction Models and their Clinical Utility in Palliative Care

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