A New Palliative Prognostic Score

Pirovano, M; Maltoni, Marco; Nanni, Oriana; Marinari, Mauro; Indelli, Monica; Zaninetta, Giovanni; Petrella, Vincenzo; Barni, Sandro; Zecca, Ernesto; Scarpi, Emanuela; Labianca, Roberto; Amadori, Dino; Luporini, G

doi:10.1016/s0885-3924(98)00145-6

Cited by 373 publications

(92 citation statements)

References 41 publications

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“…Future research, to potentially reduce the heterogeneity and increase accuracy, could be to incorporate a validated prognostic tool [53], using agreed “clinically relevant” prognostic categories. Examples of such tools include the Prognosis in Palliative Care predictor models[63] and the Palliative Prognostic score [66]. Alternatively, a recent review article [67], highlights that treatment plans and decisions can be made without such a weighted focus on survival estimates.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review of Predictions of Survival in Palliative Care: How Accurate Are Clinicians and Who Are the Experts?

et al. 2016

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BackgroundPrognostic accuracy in palliative care is valued by patients, carers, and healthcare professionals. Previous reviews suggest clinicians are inaccurate at survival estimates, but have only reported the accuracy of estimates on patients with a cancer diagnosis.ObjectivesTo examine the accuracy of clinicians’ estimates of survival and to determine if any clinical profession is better at doing so than another.Data SourcesMEDLINE, Embase, CINAHL, and the Cochrane Database of Systematic Reviews and Trials. All databases were searched from the start of the database up to June 2015. Reference lists of eligible articles were also checked.Eligibility CriteriaInclusion criteria: patients over 18, palliative population and setting, quantifiable estimate based on real patients, full publication written in English. Exclusion criteria: if the estimate was following an intervention, such as surgery, or the patient was artificially ventilated or in intensive care.Study Appraisal and Synthesis MethodsA quality assessment was completed with the QUIPS tool. Data on the reported accuracy of estimates and information about the clinicians were extracted. Studies were grouped by type of estimate: categorical (the clinician had a predetermined list of outcomes to choose from), continuous (open-ended estimate), or probabilistic (likelihood of surviving a particular time frame).Results4,642 records were identified; 42 studies fully met the review criteria. Wide variation was shown with categorical estimates (range 23% to 78%) and continuous estimates ranged between an underestimate of 86 days to an overestimate of 93 days. The four papers which used probabilistic estimates tended to show greater accuracy (c-statistics of 0.74–0.78). Information available about the clinicians providing the estimates was limited. Overall, there was no clear “expert” subgroup of clinicians identified.LimitationsHigh heterogeneity limited the analyses possible and prevented an overall accuracy being reported. Data were extracted using a standardised tool, by one reviewer, which could have introduced bias. Devising search terms for prognostic studies is challenging. Every attempt was made to devise search terms that were sufficiently sensitive to detect all prognostic studies; however, it remains possible that some studies were not identified.ConclusionStudies of prognostic accuracy in palliative care are heterogeneous, but the evidence suggests that clinicians’ predictions are frequently inaccurate. No sub-group of clinicians was consistently shown to be more accurate than any other.Implications of Key FindingsFurther research is needed to understand how clinical predictions are formulated and how their accuracy can be improved.

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Section: Discussionmentioning

confidence: 99%

A Systematic Review of Predictions of Survival in Palliative Care: How Accurate Are Clinicians and Who Are the Experts?

et al. 2016

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“…For example, the Palliative Prognostic Index (PPI) is designed for predicting survival in either 3 or 6 weeks [20] and Palliative Prognostic Score Scale (PaP) was developed for 30-day survival prediction [21]. Nevertheless, about one-fifth of our patients did not survive beyond one week.…”

Section: Discussionmentioning

confidence: 99%

Predicting 7-Day Survival Using Heart Rate Variability in Hospice Patients with Non-Lung Cancers

Chiang

Kuo²,

et al. 2013

PLoS ONE

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BackgroundA simple and accurate survival prediction tool can facilitate decision making processes for hospice patients with advanced cancers. The objectives of this study were to explore the association of cardiac autonomic functions and survival in patients with advanced cancer and to evaluate the prognostic value of heart rate variability (HRV) in 7-day survival prediction.MethodsA prospective study was conducted on 138 patients with advanced cancer recruited from the hospice ward of a regional hospital in southern Taiwan. Information on functional status and symptom burden of the patients was recorded. Frequency-domain HRV was obtained for the evaluation of cardiac autonomic functions at admission. The end point of the study was defined as the survival status at day 7 after admission to the hospice ward. Multivariate logistic regression analyses were performed to evaluate the independent associations between HRV indices and survival of 7 days or less.ResultsThe median survival time of the patients was 20 days (95% CI, 17–28 days). Results from the multivariate logistic regression analysis indicated that the natural logarithm-transformed high-frequency power (lnHFP) of a value less than 2 (OR = 3.8, p = 0.008) and ECOG performance status of 3 or 4 (OR = 3.4, p = 0.023) were significantly associated with a higher risk of survival of 7 days or less. Receiver operating characteristic (ROC) curve analysis revealed that the area under the curve was 0.71 (95% CI, 0.61–0.81).ConclusionsIn hospice patients with non-lung cancers, an lnHPF value below 2 at hospice admission was significantly associated with survival of 7 days or less. HRV might be used as a non-invasive and objective tool to facilitate medical decision making by improving the accuracy in survival prediction.

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“…This was then transformed into PaP score categories following the original scoring system: >12 weeks = 0 point; 11–12 weeks = 2 points; 7–10 weeks = 2.5 points; 5–6 weeks = 4.5 points; 3–4 weeks = 6 points; and ≤2 weeks = 8.5 points) [8, 9]. …”

Section: Methodsmentioning

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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A New Palliative Prognostic Score

Cited by 373 publications

References 41 publications

A Systematic Review of Predictions of Survival in Palliative Care: How Accurate Are Clinicians and Who Are the Experts?

A Systematic Review of Predictions of Survival in Palliative Care: How Accurate Are Clinicians and Who Are the Experts?

Predicting 7-Day Survival Using Heart Rate Variability in Hospice Patients with Non-Lung Cancers

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

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