Comparing the performance of the palliative prognostic (PaP) score with clinical predictions of survival: A systematic review

Spiritual Care for Cancer Patients at the End-of-Life

BACKGROUND

Spiritual care for patients at the end of life is an important element in their holistic care. The aim of this study is to assess the opinions of cancer patients with limited prognosis about the importance of faith in fighting illness and the factors contributing to a better adjustment to illness and to their self-reconciliation and spiritual well-being.

MATERIAL AND METHODS

This study used a specially designed questionnaire for cancer patients with limited prognosis. The 30 respondents were patients with an estimated prognosis of less than 1 month, cared for in a unit with palliative and home care beds.

RESULTS

The patients emphasized the importance of family as a supporter in the fight against disease (90%), followed by faith (66.7%) and a care team (63.3%). The most common concerns expressed were related to the course of their disease, family distress, fear of death, and the Russian-Ukrainian war.

CONCLUSION

Family and faith represent important factors in supporting and caring for a patient at the end-of-life. Patients who felt spiritually at peace and were supported in their faith by family and a priest had a better spiritual state.

Palliative prognostic scores for survival prediction of cancer patients: a systematic review and meta-analysis

BACKGROUND

The palliative prognostic score is the most widely validated prognostic tool for cancer survival prediction, with modified versions available. A systematic evaluation of palliative prognostic score tools is lacking. This systematic review and meta-analysis aimed to evaluate the performance and prognostic utility of palliative prognostic score, delirium-palliative prognostic score, and palliative prognostic score without clinician prediction in predicting 30-day survival of cancer patients and to compare their performance.

METHODS

Six databases were searched for peer-reviewed studies and grey literature published from inception to June 2, 2023. English studies must assess palliative prognostic score, delirium-palliative prognostic score, or palliative prognostic score without clinician-predicted survival for 30-day survival in adults aged 18 years and older with any stage or type of cancer. Outcomes were pooled using the random effects model or summarized narratively when meta-analysis was not possible.

RESULTS

A total of 39 studies (n = 10 617 patients) were included. Palliative prognostic score is an accurate prognostic tool (pooled area under the curve [AUC] = 0.82, 95% confidence interval [CI] = 0.79 to 0.84) and outperforms palliative prognostic score without clinician-predicted survival (pooled AUC = 0.74, 95% CI = 0.71 to 0.78), suggesting that the original palliative prognostic score should be preferred. The meta-analysis found palliative prognostic score and delirium-palliative prognostic score performance to be comparable. Most studies reported survival probabilities corresponding to the palliative prognostic score risk groups, and higher risk groups were statistically significantly associated with shorter survival.

CONCLUSIONS

Palliative prognostic score is a validated prognostic tool for cancer patients that can enhance clinicians' confidence and accuracy in predicting survival. Future studies should investigate if accuracy differs depending on clinician characteristics. Reporting of validation studies must be improved, as most studies were at high risk of bias, primarily because calibration was not assessed.

An Analysis of Clinical and Systemic Factors Associated with Palliative Radiotherapy Delivery and Completion at the End of Life in Alberta, Canada

Radiotherapy (RT) is often utilized for symptom control at the end of life. Palliative RT (pRT) may not be taken to completion by patients, thus decreasing clinical benefits and adversely impacting resource allocation. We determined rates of incomplete pRT and examined predictors of non-completion using an electronic questionnaire. Methods: A questionnaire was embedded within the RT electronic prescribing system for all five cancer centers of Alberta, Canada, between 2017 and 2020. Prescribing radiation oncologists (ROs) were tasked with completing the questionnaire. Treatment variables were collected for 2040 patients prescribed pRT. Details on pRT courses delivered and completed were used to determine rates of incomplete RT. Electronic medical records of a subset of 367 patients randomly selected from the 2040 patients were then analyzed to examine for association of non-completion of RT with patient, disease, and therapy-related factors. Results: Overall, 10% of patients did not complete pRT. The rate of single fractions prescribed as a proportion of all RT fractions increased from 18% (pre-2017: pre-study era) to 29% (2017-2020: study era) (p < 0.0001). After conducting multivariate analysis on the overall group, multiple lifetime malignancies (OR:0.64) or increasing the number of pRT fractions (OR:0.08-0.17) were associated with non-completion. Being selected for stereotactic RT (OR:3.75) or survival > 30 days post-RT prescription (OR:2.20-5.02) were associated with greater rates of RT completion. The ROs' estimates of life expectancy at the time of RT prescription were not predictive of RT completion. In the multivariate analysis of the 367-patient subset, the presence of hepatic metastases (OR 2.59), survival 30-59 days (OR 6.61) and survival 90+ days (OR 8.18) post-RT prescription were associated with pRT completion. Only increasing pRT fractionation (OR:0.05-0.2) was associated with non-completion. Conclusion: One in ten patients prescribed pRT did not complete their treatment course. Decreasing pRT fractionation and improving prognostication in patients near the end of life may decrease rates of incomplete RT courses.

Clinical Features of Patients With Hematological Malignancies Treated at the Palliative Care Unit

Background

In Japan, the number of patients with aggressive hematological malignancies (PHMs) admitted at the palliative care unit (PCU) in their end-of-life (EOL) stage was fewer than that of patients with solid tumors due to several reasons. The assessment of patient characteristics and the methods of survival prediction among PHMs in the EOL stage are warranted.

Objectives

This study aimed to identify the current medical status and the method of survival prediction among PHMs treated at the PCU.

Setting/Subjects/Measurements

We retrospectively analyzed the clinical data of 25 PHMs treated at our PCU between January 2017 and December 2020. The association between survival time and the palliative prognostic score (PAP) and palliative prognostic index (PPI) was analyzed.

Results

The average age of the PHMs was higher than that of patients with lung cancer as a control. The median survival time of the PHMs was shorter than the control group. Most PHMs could not receive standard chemotherapy, and the most common cause of death was disease-related organ failure. Significant associations were observed between the survival time and each PAP/PPI value in patients with malignant lymphoma, but not in those with leukemia.

Conclusion

The PHMs in the PCU had a lower median survival time than the control group. These results were induced by the result of patient selection to avoid treatment-related severe toxicity. The survival prediction using the PAP and PPI was less accurate in patients with leukemia.

Performance of the Palliative Prognostic Index for cancer patients: A systematic review and meta-analysis

BACKGROUND

Clinician predicted survival for cancer patients is often inaccurate, and prognostic tools may be helpful, such as the Palliative Prognostic Index (PPI). The PPI development study reported that when PPI score is greater than 6, it predicted survival of less than 3 weeks with a sensitivity of 83% and specificity of 85%. When PPI score is greater than 4, it predicts survival of less than 6 weeks with a sensitivity of 79% and specificity of 77%. However, subsequent PPI validation studies have evaluated various thresholds and survival durations, and it is unclear which is most appropriate for use in clinical practice. With the development of numerous prognostic tools, it is also unclear which is most accurate and feasible for use in multiple care settings.

AIM

We evaluated PPI model performance in predicting survival of adult cancer patients based on different thresholds and survival durations and compared it to other prognostic tools.

DESIGN

This systematic review and meta-analysis was registered in PROSPERO (CRD42022302679). We calculated the pooled sensitivity and specificity of each threshold using bivariate random-effects meta-analysis and pooled diagnostic odds ratio of each survival duration using hierarchical summary receiver operating characteristic model. Meta-regression and subgroup analysis were used to compare PPI performance with clinician predicted survival and other prognostic tools. Findings which could not be included in meta-analyses were summarised narratively.

DATA SOURCES

PubMed, ScienceDirect, Web of Science, CINAHL, ProQuest and Google Scholar were searched for articles published from inception till 7 January 2022. Both retrospective and prospective observational studies evaluating PPI performance in predicting survival of adult cancer patients in any setting were included. The Prediction Model Risk of Bias Assessment Tool was used for quality appraisal.

RESULTS

Thirty-nine studies evaluating PPI performance in predicting survival of adult cancer patients were included (n = 19,714 patients). Across meta-analyses of 12 PPI score thresholds and survival durations, we found that PPI was most accurate for predicting survival of <3 weeks and <6 weeks. Survival prediction of <3 weeks was most accurate when PPI score>6 (pooled sensitivity = 0.68, 95% CI 0.60-0.75, specificity = 0.80, 95% CI 0.75-0.85). Survival prediction of <6 weeks was most accurate when PPI score>4 (pooled sensitivity = 0.72, 95% CI 0.65-0.78, specificity = 0.74, 95% CI 0.66-0.80). Comparative meta-analyses found that PPI performed similarly to Delirium-Palliative Prognostic Score and Palliative Prognostic Score in predicting <3-week survival, but less accurately in <30-day survival prediction. However, Delirium-Palliative Prognostic Score and Palliative Prognostic Score only provide <30-day survival probabilities, and it is uncertain how this would be helpful for patients and clinicians. PPI also performed similarly to clinician predicted survival in predicting <30-day survival. However, these findings should be interpreted with caution as limited studies were available for comparative meta-analyses. Risk of bias was high for all studies, mainly due to poor reporting of statistical analyses. while there were low applicability concerns for most (38/39) studies.

CONCLUSIONS

PPI score>6 should be used for <3-week survival prediction, and PPI score>4 for <6-week survival. PPI is easily scored and does not require invasive tests, and thus would be easily implemented in multiple care settings. Given the acceptable accuracy of PPI in predicting <3- and <6-week survival and its objective nature, it could be used to cross-check clinician predicted survival especially when clinicians have doubts about their own judgement, or when clinician estimates seem to be less reliable. Future studies should adhere to the reporting guidelines and provide comprehensive analyses of PPI model performance.

Characteristics of Untreated Cancer Patients Admitted to an Acute Supportive/Palliative Care Unit

BACKGRUND

The characteristics of patients who had never received anticancer treatments at admission of an acute supportive palliative care unit (ASPCU) have never been explored.

MEASURES

From a consecutive sample of 422 advanced cancer patients, 62 patients with no previous anticancer therapy were selected and compared with a random sample of patients who had received anticancer treatments. Age, gender, primary tumor, Karnofsky status, characteristics of admission, the level of education, economic status, awareness of disease, the presence of cachexia, and comorbidities and palliative prognostic score, symptom intensity, opioid drugs used at admission, reasons for admission to APSCU were recorded in both groups. At time of discharge, ESAS and analgesic drugs used were recorded again. Discharge modalities were also recorded. One month after the end of recruitment period (the last patient enrollment), a follow-up was performed by phone contacts with relatives to assess survival at three months after discharge.

OUTCOMES

Patients without previous anticancer therapy (14.7%) were mainly admitted to ASPCU for a low Karnofsky level and high symptom burden, often waiting for or needing a histological diagnosis to make a decision for the next therapeutic steps. This group of patients were older (P<0.0005), more frequently males (P=0.007), and had more comorbidities (P<0.0005) in comparison with treated patients. Twenty-four per cent of these patients started chemotherapy subsequently. Treatment-naive patients had a higher level of symptom burden, which was less responsive to a comprehensive palliative and more frequently died within three months in comparison with treated patients.

DISCUSSION

Treatment-naive patients showed a higher level of symptom burden, which was less responsive to a comprehensive palliative treatment. In addition they more frequently died within three months in comparison with treated patients.

Accuracy of clinical predictions of prognosis at the end-of-life: evidence from routinely collected data in urgent care records

BACKGROUND

The accuracy of prognostication has important implications for patients, families, and health services since it may be linked to clinical decision-making, patient experience and outcomes and resource allocation. Study aim is to evaluate the accuracy of temporal predictions of survival in patients with cancer, dementia, heart, or respiratory disease.

METHODS

Accuracy of clinical prediction was evaluated using retrospective, observational cohort study of 98,187 individuals with a Coordinate My Care record, the Electronic Palliative Care Coordination System serving London, 2010-2020. The survival times of patients were summarised using median and interquartile ranges. Kaplan Meier survival curves were created to describe and compare survival across prognostic categories and disease trajectories. The extent of agreement between estimated and actual prognosis was quantified using linear weighted Kappa statistic.

RESULTS

Overall, 3% were predicted to live "days"; 13% "weeks"; 28% "months"; and 56% "year/years". The agreement between estimated and actual prognosis using linear weighted Kappa statistic was highest for patients with dementia/frailty (0.75) and cancer (0.73). Clinicians' estimates were able to discriminate (log-rank p < 0.001) between groups of patients with differing survival prospects. Across all disease groups, the accuracy of survival estimates was high for patients who were likely to live for fewer than 14 days (74% accuracy) or for more than one year (83% accuracy), but less accurate at predicting survival of "weeks" or "months" (32% accuracy).

CONCLUSION

Clinicians are good at identifying individuals who will die imminently and those who will live for much longer. The accuracy of prognostication for these time frames differs across major disease categories, but remains acceptable even in non-cancer patients, including patients with dementia. Advance Care Planning and timely access to palliative care based on individual patient needs may be beneficial for those where there is significant prognostic uncertainty; those who are neither imminently dying nor expected to live for "years".

Prognostic evaluation in patients with advanced cancer in the last months of life: ESMO Clinical Practice Guideline

•This ESMO Clinical Practice Guideline provides key recommendations for using prognostic estimates in advanced cancer. •The guideline covers recommendations for patients with cancer and an expected survival of months or less. •An algorithm for use of clinical predictions, prognostic factors and multivariable risk prediction models is presented. •The author group encompasses a multidisciplinary group of experts from different institutions in Europe, USA and Asia. •Recommendations are based on available scientific data and the authors’ collective expert opinion.

Prediction of Survival in Patients with Advanced Cancer: A Narrative Review and Future Research Priorities

This paper aimed to summarize the current situation of prognostication for patients with an expected survival of weeks or months, and to clarify future research priorities. Prognostic information is essential for patients, their families, and medical professionals to make end-of-life decisions. The clinician's prediction of survival is often used, but this may be inaccurate and optimistic. Many prognostic tools, such as the Palliative Performance Scale, Palliative Prognostic Index, Palliative Prognostic Score, and Prognosis in Palliative Care Study, have been developed and validated to reduce the inaccuracy of the clinician's prediction of survival. To date, there is no consensus on the most appropriate method of comparing tools that use different formats to predict survival. Therefore, the feasibility of using prognostic scales in clinical practice and the information wanted by the end users can determine the appropriate prognostic tool to use. We propose four major themes for further prognostication research: (1) functional prognosis, (2) outcomes of prognostic communication, (3) artificial intelligence, and (4) education for clinicians.

Palliative care outpatients in a German comprehensive cancer center-identifying indicators for early and late referral

PURPOSE

Despite that early integration of palliative care is recommended in advanced cancer patients, referrals to outpatient specialised palliative care (SPC) frequently occur late. Well-defined referral criteria are still missing. We analysed indicators associated with early (ER) and late referral (LR) to SPC of an high volume outpatient unit of a comprehensive cancer center.

METHODS

Characteristics, laboratory parameters and symptom burden of 281 patients at first SPC referral were analysed. Timing of referral was categorized as early, intermediate and late (> 12, 3-12 and < 3 months before death). Ordinal logistic regression analysis was used to identify factors related to referral timing. Kruskal-Wallis test was used to determine symptom severity and laboratory parameter in each referral category.

RESULTS

LRs (50.7%) had worse scores of weakness, loss of appetite, drowsiness, assistance of daily living (all p < 0.001) and organisation of care (p < 0.01) in contrast to ERs. The mean symptom sum score was significantly higher in LRs than ERs (13.03 vs. 16.08; p < 0.01). Parameters indicative of poor prognosis, such as elevated LDH, CRP and neutrophil-to-lymphocyte ratio (NLR) (p < 0.01) as well as the presence of ascites (p < 0.05), were significantly higher (all p < 0.001) in LRs. In univariable analyses, psychological distress (p < 0.05) and female gender (p < 0.05) were independently associated with an ER.

CONCLUSION

A symptom sum score and parameters of poor prognosis like NLR or LDH might be useful to integrate into palliative care screening tools.

Prognostication in palliative radiotherapy—ProPaRT: Accuracy of prognostic scores

Background

Prognostication can be used within a tailored decision-making process to achieve a more personalized approach to the care of patients with cancer. This prospective observational study evaluated the accuracy of the Palliative Prognostic score (PaP score) to predict survival in patients identified by oncologists as candidates for palliative radiotherapy (PRT). We also studied interrater variability for the clinical prediction of survival and PaP scores and assessed the accuracy of the Survival Prediction Score (SPS) and TEACHH score.

Materials and methods

Consecutive patients were enrolled at first access to our Radiotherapy and Palliative Care Outpatient Clinic. The discriminating ability of the prognostic models was assessed using Harrell’s C index, and the corresponding 95% confidence intervals (95% CI) were obtained by bootstrapping.

Results

In total, 255 patients with metastatic cancer were evaluated, and 123 (48.2%) were selected for PRT, all of whom completed treatment without interruption. Then, 10.6% of the irradiated patients who died underwent treatment within the last 30 days of life. The PaP score showed an accuracy of 74.8 (95% CI, 69.5–80.1) for radiation oncologist (RO) and 80.7 (95% CI, 75.9–85.5) for palliative care physician (PCP) in predicting 30-day survival. The accuracy of TEACHH was 76.1 (95% CI, 70.9–81.3) and 64.7 (95% CI, 58.8–70.6) for RO and PCP, respectively, and the accuracy of SPS was 70 (95% CI, 64.4–75.6) and 72.8 (95% CI, 67.3–78.3).

Conclusion

Accurate prognostication can identify candidates for low-fraction PRT during the last days of life who are more likely to complete the planned treatment without interruption.

All the scores showed good discriminating capacity; the PaP had the higher accuracy, especially when used in a multidisciplinary way.