Combination of Initial Palliative Prognostic Index and Score Change Provides a Better Prognostic Value for Terminally Ill Cancer Patients: A Six-Year Observational Cohort Study

Kao, Chen-Yi; Hung, Yu‐Shin; Wang, Hung‐Ming; Chen, Jen‐Shi; Chin, Tsu-Ling; Lu, Ching-Yi; Chi, C.‐C.; Yeh, Ya-Chi; Yang, Jin-Mei; Yen, Jung-Hsuan; Chou, Wen‐Chi

doi:10.1016/j.jpainsymman.2013.12.246

Cited by 31 publications

(34 citation statements)

References 28 publications

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“…The median survival rate was 53 days with an improvement in PPI score, 36 days with a stable PPI score, and 22 days with PPI deterioration over the 1-week period. 79 The C-statistic for 30-day survival was 0.63 for a baseline PPI score, 0.64 for a PPI change score, and 0.71 for the combined baseline and change in PPI. 79 When only patients with a higher baseline PPI (ie, > 6) were included, the C-statistics for 30-, 60-, and 90-day survival rates were 0.66, 0.64, and 0.63 for the baseline PPI, respectively, and 0.72, 0.76, and 0.79 for the magnitude of PPI change between baseline and day 8.…”

Section: Actuarial Estimation Of Survivalmentioning

confidence: 94%

“…79 The C-statistic for 30-day survival was 0.63 for a baseline PPI score, 0.64 for a PPI change score, and 0.71 for the combined baseline and change in PPI. 79 When only patients with a higher baseline PPI (ie, > 6) were included, the C-statistics for 30-, 60-, and 90-day survival rates were 0.66, 0.64, and 0.63 for the baseline PPI, respectively, and 0.72, 0.76, and 0.79 for the magnitude of PPI change between baseline and day 8. 80 The corresponding accuracy rates were 61%, 57%, and 55% for baseline PPI, and 71%, 79%, and 83% for PPI change, respectively.…”

Section: Actuarial Estimation Of Survivalmentioning

confidence: 94%

“…In a study of 2,392 patients with advanced cancer, Kao et al 79 found that the median PPI increased from 6 on day 1 to 7 on day 8 ( P <.001). The median survival rate was 53 days with an improvement in PPI score, 36 days with a stable PPI score, and 22 days with PPI deterioration over the 1-week period.…”

Section: Actuarial Estimation Of Survivalmentioning

confidence: 98%

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Prognostication of Survival in Patients with Advanced Cancer: Predicting the Unpredictable?

2015

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Background Prognosis is a key driver of clinical decision-making. However, available prognostication tools have limited accuracy and variable levels of validation. Methods Principles of survival prediction and literature on clinician prediction of survival, prognostic factors, and prognostic models were reviewed, with a focus on patients with advanced cancer and a survival rate of a few months or less. Results The 4 principles of survival prediction are (a) prognostication is a process instead of an event, (b) prognostic factors may evolve over the course of the disease, (c) prognostic accuracy for a given prognostic factor/tool varies by the definition of accuracy, the patient population, and the time frame of prediction, and (d) the exact timing of death cannot be predicted with certainty. Clinician prediction of survival rate is the most commonly used approach to formulate prognosis. However, clinicians often overestimate survival rates with the temporal question. Other clinician prediction of survival approaches, such as surprise and probabilistic questions, have higher rates of accuracy. Established prognostic factors in the advanced cancer setting include decreased performance status, delirium, dysphagia, cancer anorexia–cachexia, dyspnea, inflammation, and malnutrition. Novel prognostic factors, such as phase angle, may improve rates of accuracy. Many prognostic models are available, including the Palliative Prognostic Score, the Palliative Prognostic Index, and the Glasgow Prognostic Score. Conclusions Despite the uncertainty in survival prediction, existing prognostic tools can facilitate clinical decision-making by providing approximated time frames (months, weeks, or days). Future research should focus on clarifying and comparing the rates of accuracy for existing prognostic tools, identifying and validating novel prognostic factors, and linking prognostication to decision-making.

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Section: Actuarial Estimation Of Survivalmentioning

confidence: 94%

Section: Actuarial Estimation Of Survivalmentioning

confidence: 94%

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Prognostication of Survival in Patients with Advanced Cancer: Predicting the Unpredictable?

2015

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“…The majority of them were originally developed to estimate the prognosis of patients with days to weeks of survival. [19][20][21][22][23][24] To the best of our knowledge, no other prognostic tool has been designed to target outpatients when they are first referred to PC. From the available prognostic tools, the modified Glasgow Prognostic Score (mGPS), 25 phase angle, 26 and performance status scales 27 were externally validated in populations of cancer patients with median survival times longer than 2-3 months.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a prognostic nomogram for ambulatory patients with advanced cancer

Paiva

Pantano

et al. 2018

Cancer Medicine

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Predicting survival of advanced cancer patients (ACPs) is a difficult task. We aimed at developing and testing a new prognostic tool in ACPs when they were first referred to palliative care (PC). A total of 497 patients were analyzed in this study (development sample, n = 221; validation sample, n = 276). From 35 initial putative prognostic variables, 14 of them were selected for multivariable Cox regression analyses; the most accurate final model was identified by backward variable elimination. Parameters were built into a nomogram to estimate the probability of patient survival at 30, 90, and 180 days. Calibration and discrimination properties of the Barretos Prognostic Nomogram (BPN) were evaluated in the validation phase of the study. The BPN was composed of 5 parameters: sex, presence of distant metastasis, Karnofsky Performance Status (KPS), white blood cell (WBC) count, and serum albumin concentration. The C‐index was 0.71. The values of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve were 0.84, 0.74, and 0.74 at 30, 90, and 180 days, respectively. There were good calibration results according to the Hosmer‐Lemeshow test. The median survival times were 313, 129, and 37 days for the BPN scores <25th percentile (<125), 25th to 75th percentile (125‐175), and >75th percentile (>175), respectively (P < .001). The BPN is a new prognostic tool with adequate calibration and discrimination properties. It is now available to assist oncologists and palliative care physicians in estimating the survival of adult patients with advanced solid tumors.

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“…Some scholars hold the view that a oneshot PPI assessment might not be accurate enough as a prognostic tool because patients' clinical features changed dynamically during the end-of-life trajectory. [47][48][49] Arai et al [49] reported a retrospective cohort study that reassessment of the PPI was necessary because of the change in the PPI as an important and independent factor associated with the survival of advanced cancer patients. Another previous study reported by Kao et al [48] showed that the combination of initial PPI and score change was more accurate to predict the actual prognosis.…”

Section: Accuracy Of the Three Prognostic Toolsmentioning

confidence: 99%

Validation of the Palliative Prognostic Index, Performance Status–based Palliative Prognostic Index and Chinese Prognostic Scale in a Home Palliative Care Setting for Patients With Advanced Cancer in China

Zhou

Cao

et al. 2020

Preprint

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Background:Thepredictive value of theprognostic tool for patients with advanced cancer isuncertainin mainland China, especially in the home-based palliative care (HPC) setting. This study aimed to compare the accuracy of the Palliative Prognostic Index (PPI), the Performance Status–Based Palliative Prognostic Index (PS-PPI), and the Chinese Prognosis Scale (ChPS) for patients with advanced cancer in the HPC setting in mainland China.Methods:Patients with advanced cancer admitted to the hospice center of Yuebei People’s Hospital between January 2014 and December 2018 were retrospectively calculated the scores according to the three prognostic tools. The Kaplan-Meier method was used to compare survival times among different risk groups. Receiver operating characteristic curve analysis was used to assess the predictive value. The accuracy of 21-, 42- and 90-day survival was compared among the three prognostic tools.Results: A total of 1863 patients were included. Survival time among the risk groups of all prognostic tools was significantly different from each other except for the PPI. The AUROC of the ChPS was significantly higher than that of the PPI and PS-PPI for 7-, 14, 21-, 42-, 90-, 120-, 150- and 180-day survival (P <0.05). The AUROC of the PPI and PS-PPI were not significantly different from each other (P >0.05).Conclusions: The ChPS is more suitable than the PPI and PS-PPI for advanced cancer patients in the HPC setting. More researches are needed to verify the predictive value of the ChPS, PPI, and PS-PPI in the HPC setting in the future.

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Combination of Initial Palliative Prognostic Index and Score Change Provides a Better Prognostic Value for Terminally Ill Cancer Patients: A Six-Year Observational Cohort Study

Abstract: Combination of initial PPI and score change is more useful than initial PPI for identifying patients with poor outcomes in good prognostic groups and patients with better outcomes in poor prognostic groups.

Cited by 31 publications

References 28 publications

Prognostication of Survival in Patients with Advanced Cancer: Predicting the Unpredictable?

Prognostication of Survival in Patients with Advanced Cancer: Predicting the Unpredictable?

Development and validation of a prognostic nomogram for ambulatory patients with advanced cancer

Validation of the Palliative Prognostic Index, Performance Status–based Palliative Prognostic Index and Chinese Prognostic Scale in a Home Palliative Care Setting for Patients With Advanced Cancer in China

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