Survival prediction for advanced cancer patients in the real world: A comparison of the Palliative Prognostic Score, Delirium-Palliative Prognostic Score, Palliative Prognostic Index and modified Prognosis in Palliative Care Study predictor model

Successful Management of Terminal Delirium With Transdermal Blonanserin Patch in a Terminally Ill Cancer Patient

Delirium is a distressing condition in terminally ill cancer patients, often treated with antipsychotics. Administering them orally, subcutaneously, or intravenously can be challenging in severely agitated patients. Transdermal antipsychotic patches offer an alternative, but their use for terminal delirium remains underexplored. We present the case of a 73-year-old man with advanced diffuse large B cell lymphoma who developed severe mixed delirium during third-line chemotherapy. Nonpharmacological interventions and oral risperidone plus intravenous haloperidol failed to improve his condition. Subsequently, a transdermal blonanserin patch was applied, resulting in the resolution of hallucinations on day 1 and agitation on day 3, allowing improved communication. The patch was easily applied daily without notable adverse events. However, he deteriorated a week later with an estimated survival of days, ultimately requiring continuous midazolam for refractory agitation. This case underscores the potential of transdermal blonanserin patches for delirium in terminally ill cancer patients, emphasizing the need for future prospective studies.

Outcomes of prognostication in people living with advanced cancer: A qualitative study to inform a Core Outcome Set

BACKGROUND

Studies of prognostication in advanced cancer use a wide range of outcomes and outcome measures, making it difficult to compare these studies and their findings. Core Outcome Sets facilitate comparability and standardisation between studies and would benefit future prognostic research. This qualitative study, the second step in a wider study developing such a Core Outcome Set, aimed to explore the perceptions and experiences of patients with advanced cancer, informal caregivers, and clinicians regarding the potential outcomes to assess the impact of prognostication.

METHODS

We conducted semi-structured interviews with patients living with advanced cancer (n = 8), informal caregivers (n = 10), and clinicians (n = 10) recruited from palliative care services across three sites in London, United Kingdom. Interviews were conducted in-person, via telephone, or video conferencing, and were audio-recorded. Data were analysed using framework analysis. Findings were compared with outcomes identified in a previously published systematic review.

RESULTS

We identified 33 outcomes, 16 of which were not previously reported in the literature. We grouped these outcomes into 10 domains, nine from the COMET taxonomy, plus a tenth domain (spiritual/religious/existential functioning/wellbeing) which we added further to the previous systematic review. These findings highlighted discrepancies between the priorities of existing research and those of stakeholders. Novel outcomes highlight the more personal and emotional impacts of prognostication, whilst other outcomes confirm the relevance of survival length, depression, anxiety, pain, hope dynamics, emotional distress, and the quality of patient-clinician relationships for assessing the impact of prognostication.

CONCLUSIONS

This study offers valuable insights into outcomes which matter to key stakeholders, particularly patients and informal caregivers, highlights discrepancies between their priorities and those identified in previous studies, and underscores the need for a patient-centred approach in research and clinical practice in prognostication in advanced cancer. This work will contribute to developing a Core Outcome Set for assessing the impact of prognostication in advanced cancer.

Comparison of Simplified Palliative Prognostic Index and Palliative Performance Scale in Patients with Advanced Cancer in a Home Palliative Care Setting

Objective: The Palliative Performance Scale (PPS) has been reported to be as accurate as Palliative Prognostic Index (PPI). PPS is a component of the simplified PPI (sPPI). It is unknown whether PPS is as accurate as sPPI. This study aimed to compare the prognostic performance of the PPS and sPPI in patients with advanced cancer in a home palliative care setting in South Korea. Methods: This was a secondary analysis of a prospective cohort study that included Korean patients with advanced cancer who received home-based palliative care. We used the medical records maintained by specialized palliative care nurses. We computed the prognostic performance of PPS and sPPI using the area under the receiver operating characteristic curve (AUROC) and calibration plots for the 3- and 6-week survival. Results: A total of 80 patients were included, with a median overall survival of 47.0 days. The AUROCs of PPS were 0.71 and 0.69 at the 3- and 6-week survival predictions, respectively. The AUROCs of sPPI were 0.87 and 0.73 at the 3- and 6-week survival predictions, respectively. The calibration plot demonstrated satisfactory agreement across all score ranges for both the PPS and sPPI. Conclusions: This study showed that the sPPI assessed by nurses was more accurate than the PPS in a home palliative care setting in predicting the 3-week survival in patients with advanced cancer. The PPS can be used for a quick assessment.

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.

Usefulness of the Palliative Prognostic Index in Predicting Prognosis when Considering the Transition from Hospital to Home Care in Patients with Terminal Stage Cancer

BACKGROUND

No accurate prognostic tool is available for patients with cancer who spend their final days at home. In this study, we examined whether performance status (PS) and the palliative prognostic index (PPI), a well-known prognostic tool in palliative care units, could be used to predict prognosis in the home care setting at the time of intervention by home physicians.

SUBJECTS AND METHODS

Using medical records, we conducted a retrospective analysis of 132 patients who were referred to the Home Clinic Naginoki for home care for terminal stages of carcinoma in situ. Based on the status at the time of the first visit, the PPI-Low group was defined as those scoring six or below and the PPI-High group as those scoring greater than six.

RESULTS

The PPI-high group had a significantly poorer prognosis within 21 days than the PPI-low group (21-day-OS; Low 71.4% vs. High 13.2%; p<0.001). The Eastern Cooperative Oncology Group (ECOG) PS alone predicted better prognosis in the group with PS of one or two (21-day survival 90.1%), and the PPI score further significantly stratified the prognosis for patients with PS three or four, with a trend toward poor prognosis (p ≤ 0.005).

CONCLUSION

ECOG PS 1 or 2 has a favorable prognosis and that using PPI in ECOG PS 3 or 4 leads to a more accurate prognosis prediction. PPI evaluated during the hospital-based treatment of patients with terminal cancer can also be used to predict prognosis if the patient is transitioned to a home care environment.

What outcomes do studies use to measure the impact of prognostication on people with advanced cancer? Findings from a systematic review of quantitative and qualitative studies

BACKGROUND

Studies evaluating the impact of prognostication in advanced cancer patients vary in the outcomes they measure, and there is a lack of consensus about which outcomes are most important.

AIM

To identify outcomes previously reported in prognostic research with people with advanced cancer, as a first step towards constructing a core outcome set for prognostic impact studies.

DESIGN

A systematic review was conducted and analysed in two subsets: one qualitative and one quantitative. (PROSPERO ID: CRD42022320117; 29/03/2022).

DATA SOURCES

Six databases were searched from inception to September 2022. We extracted data describing (1) outcomes used to measure the impact of prognostication and (2) patients' and informal caregivers' experiences and perceptions of prognostication in advanced cancer. We classified findings using the Core Outcome Measures in Effectiveness Trials (COMET) initiative taxonomy, along with a narrative description. We appraised retrieved studies for quality, but quality was not a basis for exclusion.

RESULTS

We identified 42 eligible studies: 32 quantitative, 6 qualitative, 4 mixed methods. We extracted 70 outcomes of prognostication in advanced cancer and organised them into 12 domains: (1) survival; (2) psychiatric outcomes; (3) general outcomes; (4) spiritual/religious/existential functioning/wellbeing, (5) emotional functioning/wellbeing; (6) cognitive functioning; (7) social functioning; (8) global quality of life; (9) delivery of care; (10) perceived health status; (11) personal circumstances; and (12) hospital/hospice use.

CONCLUSION

Outcome reporting and measurement varied markedly across the studies. A standardised approach to outcome reporting in studies of prognosis is necessary to enhance data synthesis, improve clinical practice and better align with stakeholders' priorities.

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.

Survival Prediction of Patients Who Were Terminally Ill Using the EORTC QLQ-C15-PAL Scores and Laboratory Test Values

Background

Prognostics for patients with cancer is especially important for the supportive care of those who are terminally ill. We previously found that symptom scores as patient-reported outcomes (PROs)-such as dyspnea and fatigue scores-some biochemical parameters, the palliative performance scale (PPS) scores, and symptom clusters were useful prognostic factors; however, the predictability of a prognosis based on these factors remains unclear.

Objective

To identify appropriate three-week survival predictive factor(s), in terms of performance, in patients who were terminally ill.

Design

We collected symptom scores as PROs using the Japanese version of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 15 Palliative Care (EORTC QLQ-C15-PAL).

Setting/Subjects

We used data from terminally ill patients with cancer who were hospitalized at the palliative care unit of the Higashisumiyoshi-Morimoto Hospital (Osaka, Japan) from June 2018 to December 2019 (n = 130), as well as additional data obtained from the same clinical study from January to March 2020 (n = 31).

Measurements

To evaluate predictive performance, indices such as sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy were calculated.

Results

We found that the presence of a symptom cluster showed high sensitivity but low specificity and that a higher PPS value (>30) showed high specificity but low sensitivity, suggesting that these factors could provide relevant information for survival prognosis (less than or equal to three weeks).

Conclusion

Symptom clusters obtained from patients is important for effective supportive care of those who are terminally ill.

Validation of Modified Models of Objective Prognostic Score in Patients With Advanced Cancer

Background: The objective prognostic score (OPS) needs to be modified to reflect practical palliative care circumstances. Objectives: We aimed to validate modified models of OPS with few or no laboratory tests for patients with advanced cancer. Design: An observational study was performed. Setting/Subjects: A secondary analysis of an international, multicenter cohort study of patients in East Asia was performed. The subjects were inpatients with advanced cancer in the palliative care unit. Measurements: We developed two modified OPS (mOPS) models to predict two-week survival: mOPS-A consisted of two symptoms, two objective signs, and three laboratory results, while mOPS-B consisted of three symptoms, two signs, and no laboratory data. We compared the accuracy of the prognostic models using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Calibration plots for two-week survival and net reclassification indices (NRIs) were compared for the two models. Survival differences between higher and lower score groups of each model were identified by the log-rank test. Results: We included a total of 1796 subjects having median survival of 19.0 days. We found that mOPS-A had higher specificity (0.805-0.836) and higher AUROCs (0.791-0.797). In contrast, mOPS-B showed higher sensitivity (0.721-0.725) and acceptable AUROCs (0.740-0.751) for prediction of two-week survival. Two mOPSs showed good concordance in calibration plots. Considering NRIs, replacing the original OPS with mOPSs improved overall reclassification (absolute NRI: 0.47-4.15%). Higher score groups of mOPS-A and mOPS-B showed poorer survival than those of lower score groups (p < 0.001). Conclusions: mOPSs used reduced laboratory data and had relatively good accuracy for predicting survival in advanced cancer patients receiving palliative care.

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.

Cancer patients with COVID-19: does prior nutritional risk associated with cancer indicate a poor prognosis for COVID-19?

OBJECTIVE

To verify whether the presence of related nutritional risk indicators prior to COVID-19 diagnosis is associated with poor survival in patients with cancer.

METHODS

We retrospectively analyzed the data of hospitalized cancer patients who tested positive for COVID-19 between March 2020 and February 2021. Nutritional risk was defined as the presence of one of the following characteristics: body mass index <20kg/m 2 , scored Patient-generated Subjective Global Assessment ≥9 points or classification B, albumin level <3.5g/dL, and C-reactive protein level ≥10mg/L, evaluated between 7 and 60 days prior to the date of patient inclusion. The endpoint measure was all-cause mortality within 30 days of COVID-19 diagnosis.

RESULTS

A total of 253 patients were included, most of whom were elderly (62.4%) and female (63.6%). Overall, 45.4% of the patients were at nutritional risk. Survival was significantly lower in patients at nutritional risk (8 days; interquartile range [IQR]: 3-29) than in patients not at nutritional risk (16 days; IQR: 6-30) (p<0.001). The presence of prior nutritional risk was associated with increased 30-day mortality (HR: 1.42; 95%CI: 1.03-1.94), regardless of age, gender, tumor site or stage, and other risk factors, and the model had good discrimination accuracy (concordance statistic: 0.744).

CONCLUSION

The presence of prior nutritional risk indicators is related to poor prognosis in patients with cancer and COVID-19, emphasizing the importance of nutritional care, notably during this pandemic.

Demystifying Prognosis : Understanding the Science and Art of Prognostication

The science of prognostication is emerging as a vital part of providing goal concordant patient care. Historically, modern medicine has tended to shy away from approaching prognostication as a core clinical skill, and prognosis as something to be shared directly with the patient. In recent years however, the medical field's shift towards a focus on patient autonomy and more openness in matters regarding end of life has propelled the study of prognostication into a more essential component of patient centered care. This calls for more emphasis on teaching the science of prognosis and the skill of prognostication as a core part of modern medical education. The following chapter aims to delve into the science of prognostication, explore the methods of formulating a prognosis, and discuss issues surrounding the communication of prognosis.

Exploring the Potential Use of Wearable Devices as a Prognostic Tool among Patients in Hospice Care

Background: Smartphones and wearable devices have become a part and parcel of the healthcare industry. The use of wearable technology has already proved its potentials in improving healthcare research, clinical work, and patient care. The real time data allows the care providers to monitor the patients' symptoms remotely, prioritize the patients' visits, assist in decision-making, and carry out advanced care planning. Objectives: The primary objective of our study was to investigate the potential use of wearable devices as a prognosis tool among patients in hospice care and palliative care, and the secondary objective was to examine the association between wearable devices and clinical data in the context of patient outcomes, such as discharge and deceased at various time intervals. Methods: We employed a prospective observational research approach to continuously monitor the hand movements of the selected 68 patients between December 2019 and June 2022 via an actigraphy device at hospice or palliative care ward of Taipei Medical University Hospital (TMUH) in Taiwan. Results: The results revealed that the patients with higher scores in the Karnofsky Performance Status (KPS), and Palliative Performance Scale (PPS) tended to live at discharge, while Palliative Prognostic Score (PaP) and Palliative prognostic Index (PPI) also shared the similar trend. In addition, the results also confirmed that all these evaluating tools only suggested rough rather than accurate and definite prediction. The outcomes (May be Discharge (MBD) or expired) were positively correlated with accumulated angle and spin values, i.e., the patients who survived had higher angle and spin values as compared to those who died/expired. Conclusion: The outcomes had higher correlation with angle value compared to spin and ACT. The correlation value increased within the first 48 h and then began to decline. We recommend rigorous prospective observational studies/randomized control trials with many participants for the investigations in the future.

Risk factors of sudden unexpected death in patients with advanced cancer near the end of life

BACKGROUND

The definition of sudden unexpected death (SUD) in patients with advanced cancer near the end of life (EOL) was unclear.

METHODS

This study was conducted as a single-center retrospective analysis. We analyzed 1,282 patients who died of advanced cancer from August 2011 to August 2019 retrospectively. We divided into patients who died within 24 h after the acute change of general condition or others and analyzed risk factors by a multiple logistics method. The reason for SUD was found, the reason is detected by using an electronic medical record retrospectively. The risk factors in SUD were analyzed using age, sex, EOL symptom and treatment, the primary site of cancer, metastatic site of cancer, comorbidly, chemotherapy, and Eastern Cooperative Oncology Group Performance Status. The primary endpoint was to identify the frequency and risk factors of SUD in patients with advanced cancer near the EOL.

RESULTS

As a background, the median age is 73 years old, 690 males, 592 females, 227 gastroesophageal cancers, 250 biliary pancreatic cancers, 54 hepatocellular carcinomas, 189 colorectal cancer, 251 lung cancers, 71 breast cancers, 58 urological malignancies, 60 gynecological malignancies, 47 head and neck cancer, 31 hematological malignancies, and 22 sarcomas. The number of patients who died suddenly was 93 (7.2%) at EOL. In a multivariate analysis, Age (ORs 0.619), sex (ORs 1.700), patients with EOL delirium (ORs 0.483), nausea and vomiting (ORs 2.263), 1L or more infusion (ORs 3.479), EOL opioids (ORs 0.465), EOL sedations (ORs 0.339), and with cardiac comorbidity (ORs 0.345) were independent risk factors.

CONCLUSIONS

The frequency of patients who died suddenly was 7.2% (n = 93) at EOL. Age, sex, EOL symptom, EOL treatment, and cardiac comorbidity were independent risk factors in patients with advanced cancer near the EOL. Information on these risk factors is useful to explaining their EOL in advance.

Are Prognostic Scores Better Than Clinician Judgment? A Prospective Study Using Three Models

CONTEXT

Several prognostic models such as the Palliative Performance Scale (PPS), Palliative Prognostic Index (PPI), Palliative Prognostic Score (PaP) have been developed to complement clinician's prediction of survival (CPS). However, few studies with large scales have been conducted to show which prognostic tool had better performance than CPS in patients with weeks of survival.

OBJECTIVES

We aimed to compare the prognostic performance of the PPS, PPI, PaP, and CPS in inpatients admitted to palliative care units (PCUs).

METHODS

This study was part of a multi-center prospective observational study involving patients admitted to PCUs in Japan. We computed their prognostic performance using the area under the receiver operating characteristics curve (AUROC) and calibration plots for seven, 14-, 30- and 60-day survival.

RESULTS

We included 1896 patients with a median overall survival of 19 days. The AUROC was 73% to 84% for 60-day and 30-day survival, 75% to 84% for 14-day survival, and 80% to 87% for seven-day survival. The calibration plot demonstrated satisfactory agreement between the observational and predictive probability for the four indices in all timeframes. Therefore, all four prognostic indices showed good performance. CPS and PaP consistently had significantly better performance than the PPS and PPI from one-week to two-month timeframes.

CONCLUSION

The PPS, PPI, PaP, and CPS had relatively good performance in patients admitted to PCUs with weeks of survival. CPS and PaP had significantly better performance than the PPS and PPI. CPS may be sufficient for experienced clinicians while PPS may help to improve prognostic confidence for inexperienced clinicians.

Comparison of Objective Prognostic Score and Palliative Prognostic Score performance in inpatients with advanced cancer in Japan and Korea

OBJECTIVE

Accurate prognostication is important for patients and their families to prepare for the end of life. Objective Prognostic Score (OPS) is an easy-to-use tool that does not require the clinicians' prediction of survival (CPS), whereas Palliative Prognostic Score (PaP) needs CPS. Thus, inexperienced clinicians may hesitate to use PaP. We aimed to evaluate the accuracy of OPS compared with PaP in inpatients in palliative care units (PCUs) in three East Asian countries.

METHOD

This study was a secondary analysis of a cross-cultural, multicenter cohort study. We enrolled inpatients with far-advanced cancer in PCUs in Japan, Korea, and Taiwan from 2017 to 2018. We calculated the area under the receiver operating characteristics (AUROC) curve to compare the accuracy of OPS and PaP.

RESULTS

A total of 1,628 inpatients in 33 PCUs in Japan and Korea were analyzed. OPS and PaP were calculated in 71.7% of the Japanese patients and 80.0% of the Korean patients. In Taiwan, PaP was calculated for 81.6% of the patients. The AUROC for 3-week survival was 0.74 for OPS in Japan, 0.68 for OPS in Korea, 0.80 for PaP in Japan, and 0.73 for PaP in Korea. The AUROC for 30-day survival was 0.70 for OPS in Japan, 0.71 for OPS in Korea, 0.79 for PaP in Japan, and 0.74 for PaP in Korea.

SIGNIFICANCE OF RESULTS

Both OPS and PaP showed good performance in Japan and Korea. Compared with PaP, OPS could be more useful for inexperienced physicians who hesitate to estimate CPS.

Successful Strategies and Areas of Improvement-Lessons Learned from Design and Conduction of a Randomized Placebo-Controlled Trial in Palliative Care, 'Palliative-D'

Clinical trials in palliative care are challenging to design and conduct. Burden on patients should be minimized, while gatekeeping by professionals and next-of kin needs to be avoided. Clinical deterioration due to disease progression affects attrition unrelated to intervention, and different care settings complicate comparisons and reduce the generalizability of the results. The aim of this review is to provide advice for colleagues planning to perform clinical trials in palliative care based on our own experiences from performing the Palliative-D study and by a thorough literature review on this topic. The Palliative-D study was a double-blind trial with 244 randomized patients comparing the effect of vitamin D₃ to placebo in patients with advanced or metastatic cancer in the palliative phase of their disease trajectory who were enrolled in specialized palliative home care teams. Endpoints were opioid and antibiotic use, fatigue, and QoL. Recruitment was successful, but attrition rates were higher than expected, and we did not reach targeted power. For the 150 patients who completed the study, the completeness of the data was exceptionally high. Rather than patient reported pain, we choose the difference in the mean change in opioid dose between groups after twelve weeks compared to baseline as the primary endpoint. In this paper we discuss challenges in palliative care research based on lessons learned from the "Palliative-D" trial regarding successful strategies as well as areas for improvement.

Comparison of the accuracy of clinicians' prediction of survival and Palliative Prognostic Score: an East Asian cross-cultural study

PURPOSE

No study has been conducted to compare the clinicians' prediction of survival (CPS) with Palliative Prognostic Scores (PaP) across countries. We aimed to compare the performance of the CPS in PaP (PaP-CPS), the PaP without the CPS, and the PaP total scores in patients with advanced cancer in three East Asian countries.

METHODS

We compared the discriminative accuracy of the three predictive models (the PaP-CPS [the score of the categorical CPS of PaP], the PaP without the CPS [sum of the scores of only the objective variables of PaP], and the PaP total score) in patients admitted to palliative care units (PCUs) in Japan, Korea, and Taiwan. We calculated the area under the receiver operating characteristic curve (AUROC) for 30-day survival to compare the discriminative accuracy of these three models.

RESULTS

We analyzed 2,072 patients from three countries. The AUROC for the PaP total scores was 0.84 in patients in Japan, 0.76 in Korea, and 0.79 in Taiwan. The AUROC of the PaP-CPS was 0.82 in patients in Japan, 0.75 in Korea, and 0.78 in Taiwan. The AUROC of the PaP without the CPS was 0.75 in patients in Japan, 0.66 in Korea, and 0.67 in Taiwan.

CONCLUSION

The PaP total scores and the PaP-CPS consistently showed similar discriminative accuracy in predicting 30-day survival in patients admitted to PCUs in Japan, Korea, and Taiwan. It may be sufficient for experienced clinicians to use the CPS alone for estimating the short-term survival (less than one month) of patients with far-advanced cancer. The PaP may help to improve prognostic confidence and further reduce subjective variations.

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

BACKGROUND

In patients with advanced cancer, prognosis is usually determined using clinicians' predictions of survival (CPS). The palliative prognostic (PaP) score is a prognostic algorithm that was developed to predict survival in patients with advanced cancer. The score categorises patients into three risk groups in accordance with their probability of surviving for 30 days. The relative accuracy of PaP and CPS is unclear.

DESIGN

This was a systematic review of MEDLINE, Embase, AMED, CINAHL Plus and the Cochrane Database of Systematic Reviews and Trials from inception up to June 2021. The inclusion criteria were studies in adults with advanced cancer reporting data on performance of both PaP and CPS. Data were extracted on accuracy of prognoses and where available on discrimination (area under the receiver operating characteristic curve or C-index) and/or diagnostic performance (sensitivity, specificity).

RESULTS

Eleven studies were included. One study reported a direct comparison between PaP risk groups and equivalent risk groups defined by CPS and found that PaP was as accurate as CPS. Five studies reported discrimination of PaP as a continuous total score (rather than using the previously validated risk categories) and reported C-statistics that ranged from 0.64 (95% confidence interval [CI] 0.54, 0.74) up to 0.90 (95% CI 0.87, 0.92). Other studies compared PaP against CPS using non-equivalent metrics (e.g. comparing probability estimates against length of survival estimates).

CONCLUSIONS

PaP risk categories and CPS are equally able to discriminate between patients with different survival probabilities. Total PaP scores show good discrimination between patients in accordance with their length of survival. The role of PaP in clinical practice still needs to be defined.

TRIAL REGISTRATION

PROSPERO (CRD42021241074, 5th March 2021).

Diagnostic models for impending death in terminally ill cancer patients: A multicenter cohort study

Background

Accurately predicting impending death is essential for clinicians to clarify goals of care. We aimed to develop diagnostic models to predict death ≤3 days in cancer patients.

Methods

In this multicenter cohort study, we consecutively enrolled advanced cancer patients admitted to 23 inpatient hospices in 2017. Fifteen clinical signs related to impending death were documented daily from the day when the Palliative Performance Scale (PPS) declined to ≤20–14 days later. We conducted recursive partitioning analysis using the entire data set and performed cross‐validation to develop the model (prediction of 3‐day impending death‐decision tree [P3did‐DT]). Then, we summed the number of systems (nervous/cardiovascular/respiratory/musculoskeletal), where any sign was present to underpin P3did score (range = 0–4).

Results

Data following PPS ≤20 were obtained from 1396 of 1896 inpatients (74%). The mean age was 73 ± 12 years, and 399 (29%) had gastrointestinal tract cancer. The P3did‐DT was based on three variables and had four terminal leaves: urine output (u/o) ≤200 ml/day and decreased response to verbal stimuli, u/o ≤200 ml/day and no decreased response to verbal stimuli, u/o >200 ml/day and Richmond Agitation‐Sedation Scale (RASS) ≤−2, and u/o >200 ml/day and RASS ≥−1. The 3‐day mortality rates were 80.3%, 53.3%, 39.9%, and 20.6%, respectively (accuracy = 68.3%). In addition, 79.6%, 62.9%, 47.2%, 32.8%, and 17.4% of patients with P3did scores of 4, 3, 2, 1, and 0, respectively, died ≤3 days.

Conclusion

We successfully developed diagnostic models for death ≤3 days. These may further help clinicians predict impending death and help patients/families prepare for their final days.

Validation of the scored Patient-Generated Subjective Global Assessment Short Form as a prognostic tool for patients with incurable cancer

BACKGROUND

The Patient-Generated Subjective Global Assessment Short Form (PG-SGA SF) is a standardized tool for assessing nutrition risk in patients with cancer. The aim of this study was to propose and validate a cutoff point for the PG-SGA SF related to the prognosis of patients with incurable cancer in exclusive palliative care.

METHODS

This is a prospective cohort study of patients with incurable cancer at the National Cancer Institute in Brazil. A total sample (n = 2,144) was randomly divided into groups: (1) training (n = 1,072), to determine the most accurate PG-SGA SF cutoff, and (2) validation (n = 1,072), to test the predictive accuracy of this cutoff point. The receiver operating characteristic curve was plotted to determine the best cutoff point of the PG-SGA SF related to death. Concordance statistics (C statistic) were used to test the predictive accuracy of the models. Kaplan-Meier curve and the Cox hazard model were used to verify a prognostic value of the cutoff point.

RESULTS

PG-SGA SF score ≥15 was found to be the best cutoff based on 90-day mortality with good accuracy discrimination (C statistic ≥ 0.74). Patients whose PG-SGA SF score was ≥15 had a shorter survival of 32 (interquartile range [IQR], 12-75) vs 83 days (IQR, 31-90) (p-value < .001) and higher risk of death (hazard ratio: 2.20; 95% CI, 1.64-2.95).

CONCLUSIONS

The proposed PG-SGA SF cutoff score is valid and, alongside its usefulness in nutrition triage, could provide prognostic value for patients with incurable cancer.

Validation of the Simplified Palliative Prognostic Index to Predict Survival for Advanced Cancer Patients in Home Hospice Setting

Background

The simplified Palliative Prognostic Index (sPPI) substitutes a single item from the Communication Capacity Scale (CCS) for the delirium item of the original PPI. This study aimed to examine the validity of the sPPI for patients with advanced cancer in a home-based hospice care setting.

Methods

This study included 75 patients with advanced cancer who received home-based hospice care. We used medical records maintained by professional hospice nurses who had visited the patients in their homes. Based on their sPPI score, patients were divided into three groups—A (<4), B (≥4 and <6), and C (≥6)—to compare survival. Further, we investigated the sPPI’s accuracy using the area under the receiver operating characteristic curve (AUC) and sensitivity and specificity for 3- and 6-week survival. We used three sPPIs including different substitutions for the delirium item (two methods using the CCS and one using the Korean Nursing Delirium Screening Scale).

Results

The median survival was 60–61 days for group A, 27–30 days for group B, and 12–16 days for group C. The difference in survival was significant (P<0.05). The AUC was 0.814–0.867 for 3-week survival and 0.736–0.779 for 6-week survival. For 3- and 6-week survival, prognostic prediction showed sensitivities of 76.2%–90.9% and 76.3%–86.8%, and specificities of 64.2%–88.7% and 51.4%–70.3%, respectively.

Conclusion

The sPPI, which is measured by professional hospice nurses, has acceptable validity to predict survival for patients with advanced cancer in a home hospice setting in South Korea.

Prognosis palliative care study, palliative prognostic index, palliative prognostic score and objective prognostic score in advanced cancer: a prospective comparison

BACKGROUND

Predicting how long a patient with far advanced cancer has to live is a significant part of hospice and palliative care. Various prognostic models have been developed, but have not been fully compared in South Korea.

OBJECTIVES

We aimed to compare the accuracy of the Prognosis in Palliative Care Study (PiPS), Palliative Prognostic Index (PPI), Palliative Prognostic Score (PaP) and Objective Prognostic Score (OPS) for patients with far advanced cancer in a palliative care unit in South Korea.

METHODS

This prospective study included patients with far advanced cancer who were admitted to a single palliative care unit at the National University Hospital. Variables for calculating the prognostic models were recorded by a palliative care physician. The survival rate was estimated using the Kaplan-Meier method. The sensitivity, specificity, positive predictive value and negative predictive value of each model were calculated.

RESULTS

A total of 160 patients participated. There was a significant difference in survival rates across all groups, each categorised through the five prognostic models. The overall accuracy (OA) of the prognostic models ranged between 54.5% and 77.6%. The OA of clinicians' predictions of survival ranged between 61.9% and 81.3%.

CONCLUSION

The PiPS, PPI, PaP and OPS were successfully validated in a palliative care unit of South Korea. There was no difference in accuracy between the prognostic models, and OA tended to be lower than in previous studies.

Accuracy of the Palliative Prognostic Score With or Without Clinicians' Prediction of Survival in Patients With Far Advanced Cancer

CONTEXT

Previous studies suggest that clinicians' prediction of survival (CPS) may have reduced the accuracy of objective indicators for prognostication in palliative care.

OBJECTIVES

We aimed to examine the accuracy of CPS alone, compared to the original Palliative Prognostic Score (PaP), and five clinical/laboratory variables of the PaP in patients with far advanced cancer.

METHODS

We compared the discriminative accuracy of three prediction models (the PaP-CPS [the score of the categorical CPS of PaP], PaP without CPS [sum of the scores of only the objective variables of PaP], and PaP total score) across 3 settings: inpatient palliative care consultation team, palliative care unit, and home palliative care. We computed the area under receiver operating characteristic curve (AUROC) for 30-day survival and concordance index (C-index) to compare the discriminative accuracy of these three models.

RESULTS

We included a total of 1534 subjects with median survival of 34.0 days. The AUROC and C-index in the three settings were 0.816-0.896 and 0.732-0.799 for the PaP total score, 0.808-0.884 and 0.713-0.782 for the PaP-CPS, and 0.726-0.815 and 0.672-0.728 for the PaP without CPS, respectively. The PaP total score and PaP-CPS showed similar AUROCs and C-indices across the three settings. The PaP total score had significantly higher AUROCs and C-indices than the PaP without CPS across the three settings.

CONCLUSION

Overall, the PaP total score, PaP-CPS, and PaP without CPS showed good discriminative performances. However, the PaP total score and PaP-CPS were significantly more accurate than the PaP without CPS.

Palliative Prognostic Index accuracy of survival prediction in an inpatient palliative care service at a Brazilian tertiary hospital

PURPOSE

The Palliative Prognostic Index (PPI) was developed to improve survival prediction for advanced cancer patients. However, there is limited data about the PPI application in a real-world scenario. This study aimed to assess the accuracy of PPI > 6 in predicting survival of cancer inpatients.

METHODS

A prospective observational cohort in an inpatient palliative care service at a tertiary hospital in São Paulo-SP, Brazil, between May 2011 and December 2018.

RESULTS

We included 1,376 critically ill cancer inpatients. Patients were divided into three PPI subgroups: PPI ≤ 4, PPI 4-6, and PPI ≥ 6. Their respective medium overall survival values were 44 days (95% confidence interval [CI] 35.52-52.47), 20 days (95% CI 15.40-24.59), and 8 days (95% CI 7.02-8.98), (p < 0.001). PPI ≥ 6 predicted survival of <3 weeks with a positive predictive value (PPV) of 72% and an negative predictive value (NPV) of 68% (sensitivity 67%, specificity 72%). PPI > 4 predicted survival of <6 weeks with a PPV of 88% and an NPV of 36% (sensitivity 74%, specificity 59%). When PPI was <4, the mortality rate over 3 weeks was 39% with a relative risk (RR) of 0.15 (95% CI 0.11-0.20; p < 0.001), and the 6-week mortality rate was 63% with a RR of 0.18 (95% CI 0.13-0.25; p < 0.001) compared to PPI ≥ 4.

CONCLUSIONS

PPI was a good discriminator of survival among critically ill cancer inpatients and could assist in hospital discharge decision. PPI may help healthcare policymakers and professionals in offering high-quality palliative care to patients.

Prognostic tools for survival prediction in advanced cancer patients: A systematic review

Survival prediction for palliative cancer patients by physicians is often optimistic. Patients with a very short life expectancy (<4 weeks) may not benefit from radiation therapy (RT), as the time to maximal symptom relief after treatment can take 4-6 weeks. We aimed to identify a prognostic tool (or tools) to predict survival of less than 4 weeks and less than 3 months in patients with advanced cancer to guide the choice of radiation dose and fractionation. We searched Embase, Medline (EBSCOhost) and CINAHL (EBSCOhost) clinical databases for literature published between January 2008 and June 2018. Seventeen studies met the inclusion criteria and were included in the review. Prediction accuracy at less than 4 weeks and less than 3 months were compared across the prognostic tools. Reporting of prediction accuracy among the different studies was not consistent: the Palliative Prognostic Score (PaP), Palliative Prognostic Index (PPI) and Number of Risk Factors (NRF) best-predicted survival duration of less than 4 weeks. The PPI, performance status with Palliative Prognostic Index (PS-PPI), NRF and Survival Prediction Score (SPS) may predict 3-month survival. We recommend PPI and PaP tools to assess the likelihood of a patient surviving less than 4 weeks. If predicted to survive longer and RT is justified, the NRF tool could be used to determine survival probability less than 3 months which can then help clinicians select dose and fractionation. Future research is needed to verify the reliability of survival prediction using these prognostic tools in a radiation oncology setting.

Prognostic models of survival in patients with advanced incurable cancer: the PiPS2 observational study

BACKGROUND

The Prognosis in Palliative care Study (PiPS) prognostic survival models predict survival in patients with incurable cancer. PiPS-A (Prognosis in Palliative care Study - All), which involved clinical observations only, and PiPS-B (Prognosis in Palliative care Study - Blood), which additionally required blood test results, consist of 14- and 56-day models that combine to create survival risk categories: 'days', 'weeks' and 'months+'.

OBJECTIVES

The primary objectives were to compare PIPS-B risk categories against agreed multiprofessional estimates of survival and to validate PiPS-A and PiPS-B. The secondary objectives were to validate other prognostic models, to assess the acceptability of the models to patients, carers and health-care professionals and to identify barriers to and facilitators of clinical use.

DESIGN

This was a national, multicentre, prospective, observational, cohort study with a nested qualitative substudy using interviews with patients, carers and health-care professionals.

SETTING

Community, hospital and hospice palliative care services across England and Wales.

PARTICIPANTS

For the validation study, the participants were adults with incurable cancer, with or without capacity to consent, who had been recently referred to palliative care services and had sufficient English language. For the qualitative substudy, a subset of participants in the validation study took part, along with informal carers, patients who declined to participate in the main study and health-care professionals.

MAIN OUTCOME MEASURES

For the validation study, the primary outcomes were survival, clinical prediction of survival and PiPS-B risk category predictions. The secondary outcomes were predictions of PiPS-A and other prognostic models. For the qualitative substudy, the main outcomes were participants' views about prognostication and the use of prognostic models.

RESULTS

For the validation study, 1833 participants were recruited. PiPS-B risk categories were as accurate as agreed multiprofessional estimates of survival (61%; p = 0.851). Discrimination of the PiPS-B 14-day model (c-statistic 0.837, 95% confidence interval 0.810 to 0.863) and the PiPS-B 56-day model (c-statistic 0.810, 95% confidence interval 0.788 to 0.832) was excellent. The PiPS-B 14-day model showed some overfitting (calibration in the large -0.202, 95% confidence interval -0.364 to -0.039; calibration slope 0.840, 95% confidence interval 0.730 to 0.950). The PiPS-B 56-day model was well-calibrated (calibration in the large 0.152, 95% confidence interval 0.030 to 0.273; calibration slope 0.914, 95% confidence interval 0.808 to 1.02). PiPS-A risk categories were less accurate than agreed multiprofessional estimates of survival (p < 0.001). The PiPS-A 14-day model (c-statistic 0.825, 95% confidence interval 0.803 to 0.848; calibration in the large -0.037, 95% confidence interval -0.168 to 0.095; calibration slope 0.981, 95% confidence interval 0.872 to 1.09) and the PiPS-A 56-day model (c-statistic 0.776, 95% confidence interval 0.755 to 0.797; calibration in the large 0.109, 95% confidence interval 0.002 to 0.215; calibration slope 0.946, 95% confidence interval 0.842 to 1.05) had excellent or reasonably good discrimination and calibration. Other prognostic models were also validated. Where comparisons were possible, the other prognostic models performed less well than PiPS-B. For the qualitative substudy, 32 health-care professionals, 29 patients and 20 carers were interviewed. The majority of patients and carers expressed a desire for prognostic information and said that PiPS could be helpful. Health-care professionals said that PiPS was user friendly and may be helpful for decision-making and care-planning. The need for a blood test for PiPS-B was considered a limitation.

LIMITATIONS

The results may not be generalisable to other populations.

CONCLUSIONS

PiPS-B risk categories are as accurate as agreed multiprofessional estimates of survival. PiPS-A categories are less accurate. Patients, carers and health-care professionals regard PiPS as potentially helpful in clinical practice.

FUTURE WORK

A study to evaluate the impact of introducing PiPS into routine clinical practice is needed.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN13688211.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 28. See the NIHR Journals Library website for further project information.

Prognostic tools or clinical predictions: Which are better in palliative care?

PURPOSE

The Palliative Prognostic (PaP) score; Palliative Prognostic Index (PPI); Feliu Prognostic Nomogram (FPN) and Palliative Performance Scale (PPS) have all been proposed as prognostic tools for palliative cancer care. However, clinical judgement remains the principal way by which palliative care professionals determine prognoses and it is important that the performance of prognostic tools is compared against clinical predictions of survival (CPS).

METHODS

This was a multi-centre, cohort validation study of prognostic tools. Study participants were adults with advanced cancer receiving palliative care, with or without capacity to consent. Key prognostic data were collected at baseline, shortly after referral to palliative care services. CPS were obtained independently from a doctor and a nurse.

RESULTS

Prognostic data were collected on 1833 participants. All prognostic tools showed acceptable discrimination and calibration, but none showed superiority to CPS. Both PaP and CPS were equally able to accurately categorise patients according to their risk of dying within 30 days. There was no difference in performance between CPS and FPN at stratifying patients according to their risk of dying at 15, 30 or 60 days. PPI was significantly (p<0.001) worse than CPS at predicting which patients would survive for 3 or 6 weeks. PPS and CPS were both able to discriminate palliative care patients into multiple iso-prognostic groups.

CONCLUSIONS

Although four commonly used prognostic algorithms for palliative care generally showed good discrimination and calibration, none of them demonstrated superiority to CPS. Prognostic tools which are less accurate than CPS are of no clinical use. However, prognostic tools which perform similarly to CPS may have other advantages to recommend them for use in clinical practice (e.g. being more objective, more reproducible, acting as a second opinion or as an educational tool). Future studies should therefore assess the impact of prognostic tools on clinical practice and decision-making.

30-Day Mortality Following Palliative Radiotherapy

Purpose

30-day mortality (30-DM) is a parameter with widespread use as an indicator of avoidance of harm used in medicine. Our objective is to determine the 30-DM followed by palliative radiation therapy (RT) in our department and to identify potential prognosis factors.

Material/Methods

We conducted a retrospective cohort study including patients treated with palliative RT in our center during 2018 and 2019. Data related to clinical and treatment characteristics were collected.

Results

We treated 708 patients to whom 992 palliative irradiations were delivered. The most frequent primary tumor sites were lung (31%), breast (14.8%), and gastrointestinal (14.8%). Bone was the predominant location of the treatment (56%), and the use of single doses was the preferred treatment schedule (34.4%). The 30-DM was 17.5%. For those who died in the first month the median survival was 17 days. Factors with a significant impact on 30-DM were: male gender (p < 0.0001); Eastern Cooperative Oncology Group (ECOG) Performance Status (PS) of 2–3 (p = 0.0001); visceral metastases (p = 0.0353); lung, gastrointestinal or urinary tract primary tumors (p = 0.016); and single dose RT (p = <0.0001). In the multivariate analysis, male gender, ECOG PS 2–3, gastrointestinal and lung cancer were found to be independent factors related to 30-DM.

Conclusion

Our 30-DM is similar to previous studies. We have found four clinical factors related to 30-DM of which ECOG was the most strongly associated. This data may help to identify terminally ill patients with poor prognosis in order to avoid unnecessary treatments.

Early and Midtreatment Mortality in Palliative Radiotherapy: Emphasizing Patient Selection in High-Quality End-of-Life Care

BACKGROUND

Palliative radiotherapy (RT) is effective, but some patients die during treatment or too soon afterward to experience benefit. This study investigates end-of-life RT patterns to inform shared decision-making and facilitate treatment consistent with palliative goals.

MATERIALS AND METHODS

All patients who died ≤6 months after initiating palliative RT at an academic cancer center between 2015 and 2018 were identified. Associations with time-to-death, early mortality (≤30 days), and midtreatment mortality were analyzed.

RESULTS

In total, 1,620 patients died ≤6 months from palliative RT initiation, including 574 (34%) deaths at ≤30 days and 222 (14%) midtreatment. Median survival was 43 days from RT start (95% CI, 41-45) and varied by site (P<.001), ranging from 36 (head and neck) to 53 days (dermal/soft tissue). On multivariable analysis, earlier time-to-death was associated with osseous (hazard ratio [HR], 1.33; P<.001) and head and neck (HR, 1.45; P<.001) sites, multiple RT courses ≤6 months (HR, 1.65; P<.001), and multisite treatments (HR, 1.40; P=.008), whereas stereotactic technique (HR, 0.77; P<.001) and more recent treatment year (HR, 0.82; P<.001) were associated with longer survival. No difference in time to death was noted among patients prescribed conventional RT in 1 to 10 versus >10 fractions (median, 40 vs 47 days; P=.272), although the latter entailed longer courses. The 30-day mortality group included 335 (58%) inpatients, who were 27% more likely to die midtreatment (P=.031). On multivariable analysis, midtreatment mortality among these inpatients was associated with thoracic (odds ratio [OR], 2.95; P=.002) and central nervous system (CNS; OR, 2.44; P=.002) indications, >5-fraction courses (OR, 3.27; P<.001), and performance status of 3 to 4 (OR, 1.63; P=.050). Conversely, palliative/supportive care consultation was associated with decreased midtreatment mortality (OR, 0.60; P=.045).

CONCLUSIONS

Earlier referrals and hypofractionated courses (≤5-10 treatments) should be routinely considered for palliative RT indications, given the short life expectancies of patients at this stage in their disease course. Providers should exercise caution for emergent thoracic and CNS indications among inpatients with poor prognoses due to high midtreatment mortality.

Oral dryness and moisture degree at the lingual but not buccal mucosa predict prognosis in end-of-life cancer patients

PURPOSE

The aim of this study is to investigate the association of oral dryness with overall survival and determine the threshold points of moisture degree for predicting 7-day survival in palliative care patients.

METHODS

A total of 147 consecutive palliative care patients were included between January 2017 and November 2018. Oral dryness at the lingual and buccal mucosa was measured using an oral moisture-checking device. Overall survival was compared between patients with and without oral dryness using Kaplan-Meier curves with a log-rank test. Prediction accuracy was evaluated by the receiver operating characteristic (ROC) curve and the area under the curve (AUC).

RESULTS

Median survival (95% confidence interval) in patients with oral dryness at the lingual mucosa was shorter than that in patients without oral dryness (17 [11-24] days vs. 28 [22-37] days, log-rank test, p <0.001), but not at the buccal mucosa. Time-dependent ROC revealed that the AUCs for 7-, 14-, 21-, and 28-day survival predictions were 0.72, 0.68, 0.61, and 0.59 with a cutoff value of 19.2%, respectively. The prevalence of performance status (PS) 4 and oxygen administration in the 7-day death group were higher than those in the non-7-day death group. A stratified analysis indicated that moisture degree <19.2% showed fair predictive performance with an AUC of 0.74 and 0.74, in the case of PS ≤3 or without oxygen administration.

CONCLUSION

Oral dryness was associated with increased risk of mortality in palliative care patients. Moisture degree <19.2% at the lingual mucosa predicted less than 7-day survival.

"3-Day Surprise Question" to predict prognosis of advanced cancer patients with impending death: Multicenter prospective observational study

Background

The study aimed to clarify the efficacy of the “3‐Day Surprise Question (3DSQ)” in predicting the prognosis for advanced cancer patients with impending death.

Patients and Methods

This study was a part of multicenter prospective observational study which investigated the dying process in advanced cancer patients in Japan. For patients with a Palliative Performance Scale ≤20, the 3DSQ “Would I be surprised if this patient died in the next 3 days?” was answered by their physicians. In addition to the sensitivity and specificity of the 3DSQ, the characteristics of patients who survived longer than expected were examined via multivariate analysis.

Results

Among the 1896 patients enrolled, 1411 were evaluated. Among 1179 (83.6%) patients who were classified into the “Not surprised” group, 636 patients died within 3 days. Among 232 (16.4%) patients of “Yes surprised” group, 194 patients lived longer than 3 days. The sensitivity, specificity, positive predictive value, and negative predictive value of the 3DSQ were 94.3% (95% confidence interval [CI]: 92.7% to 95.8%), 26.3% (95% CI: 24.8% to 27.6%), 53.9% (95% CI: 53.0% to 54.7%), and 83.6% (95% CI: 78.7% to 87.7%), respectively. Multivariate analysis showed palpable radial artery, absent respiration with mandibular movement, SpO₂ ≥ 90%, opioid administration, and no continuous deep sedation as characteristics of patients who lived longer than expected.

Conclusions

The 3‐Day Surprise Question can be a useful screening tool to identify advanced cancer patients with impending death.

Comparison of the prevalence and associated factors of hyperactive delirium in advanced cancer patients between inpatient palliative care and palliative home care

Background

Hyperactive delirium is known to increase family distress and the burden on health care providers. We compared the prevalence and associated factors of agitated delirium in advanced cancer patients between inpatient palliative care and palliative home care on admission and at 3 days before death.

Methods

This was a post hoc exploratory analysis of two multicenter, prospective cohort studies of advanced cancer patients, which were performed at 23 palliative care units (PCUs) between Jan and Dec 2017, and on 45 palliative home care services between July and Dec 2017.

Results

In total, 2998 patients were enrolled and 2829 were analyzed in this study: 1883 patients in PCUs and 947 patients in palliative home care. The prevalence of agitated delirium between PCUs and palliative home care was 5.2% (95% CI: 4.2% ‐ 6.3%) vs. 1.4% (0.7% ‐ 2.3%) on admission (p < 0.001) and 7.6% (6.4% ‐ 8.9%) vs. 5.4% (4.0% ‐ 7.0%) 3 days before death (p < 0.001). However, multivariate logistic regression analysis revealed that the place of care was not significantly associated with the prevalence of agitated delirium at 3 days before death after adjusting for prognostic factors, physical risk factors, and symptoms.

Conclusions

There was no significant difference in the prevalence of agitated delirium at 3 days before death between inpatient palliative care and palliative home care after adjusting for the patient background, prognostic factors, symptoms, and treatment.

Pharmacist's Intervention Considering the Prognosis for a Terminal Cancer Patient: A Case Report

Prognostic prediction has been reported to affect the decision of doctors and non-physician health care providers such as nurses, social workers, pastors, and hospice volunteers on the selection of appropriate medical interventions. This was a case of a 65-year-old woman who presented with a poor oral intake. The patient had a history of sigmoid colon cancer with abdominal wall metastasis and peritoneal dissemination. On the day of admission, nausea, anorexia, and malaise were noted, requiring immediate intervention. The patient’s prognosis was predicted using the Palliative Prognostic Index. The pharmacist suggested the use of dexamethasone tablets in order to alleviate the patient’s symptoms. Indeed, the administration of dexamethasone alleviated the symptoms of nausea, loss of appetite, and malaise. To the best of our knowledge, this is the first case report to demonstrate that prognosis prediction is important not only for other medical staff but also for pharmacists when deciding the need to initiate a treatment and continue such treatment, and when providing pharmacist interventions.

Palliative Care for Idiopathic Pulmonary Fibrosis Patients: Pulmonary Physicians' View

CONTEXT

Although idiopathic pulmonary fibrosis (IPF) has worse outcomes compared with most malignancies, patients with IPF receive poor access to optimal palliative care.

OBJECTIVES

This study aimed to characterize the practice of pulmonologists regarding palliative care and end-of-life communication for patients with IPF and identify perceived difficulties and barriers thereto.

METHODS

Self-administered questionnaires were sent by mail to representative pulmonologists from Shizuoka prefecture, Japan. Physician-reported practice, difficulties, timing of end-of-life communication, and barriers related to palliative care were investigated.

RESULTS

Among the 135 participants, 130 (96%) completed the questionnaire. Most of the participants reported that patients with IPF complained of dyspnea and cough. However, less morphine was prescribed for IPF than for lung cancer. The participants experienced greater difficulty in providing palliative care for IPF than for lung cancer. Moreover, actual end-of-life discussions in patients with IPF were conducted later than the physician-perceived ideal timing. Among the barriers identified, few established treatment and difficulty in predicting prognosis (odds ratio [OR] 2.0; P = 0.04), discrepancies in understanding and care goals among patients, family, and medical staff (OR 2.2; P = 0.03), and inadequate communication about goal of care (OR 2.3; P = 0.003) were significantly associated with the physician-perceived difficulties in providing palliative care for patients with IPF.

CONCLUSION

Pulmonologists experienced greater difficulty in providing palliative care to patients with IPF than to those with lung cancer. Clinical studies on the optimal palliative care for patients with IPF are urgently required.

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

BACKGROUND

The predictive value of the prognostic tool for patients with advanced cancer is uncertain in 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.

Long-term prognostic counselling in people with multiple sclerosis using an online analytical processing tool

BACKGROUND

Prognostic counselling is a sensitive issue in medicine and especially so in MS due to the highly heterogeneous disease course. However, people with MS (pwMS) seek prognostic information. The web-based 'Evidence-Based Decision Support Tool in Multiple Sclerosis' (EBDiMS) uses data of 717 patients from the London/Ontario cohort to calculate personalized long-term prognostic information.

OBJECTIVE

The aim of this study was to investigate the feasibility and effect of long-term prognostic counselling in pwMS using EBDiMS.

METHODS

Ninety consecutive pwMS were provided with personalized estimations of expected time to reach Expanded Disability Status Scale (EDSS) scores of 6 and 8 and time to conversion to secondary-progressive MS. Participants gave estimates on their own putative prognosis and rated the tool's acceptability on six-step Likert-type scales.

RESULTS

Participants rated EBDiMS as highly understandable, interesting and relevant for patient-physician encounters, coping and therapy decisions. Although it provoked a certain degree of worry in some participants, 95% would recommend using the tool. Participants' own prognosis estimates did not change significantly following EBDiMS.

CONCLUSION

Long-term prognostic counselling using an online tool has been shown to be feasible in a clinical setting. EBDiMS provides pwMS with relevant, easy-to-understand, long-term prognostic information without causing relevant anxiety.

Dealing with prognostic uncertainty: the role of prognostic models and websites for patients with advanced cancer

PURPOSE OF REVIEW

To provide an updated overview of prognostic models in advanced cancer and highlight the role of prognostic calculators.

RECENT FINDINGS

In the advanced cancer setting, many important healthcare decisions are driven by a patient's prognosis. However, there is much uncertainty in formulating prognosis, particularly in the era of novel cancer therapeutics. Multiple prognostic models have been validated for patients seen by palliative care and have a life expectancy of a few months or less, such as the Palliative Performance Scale, Palliative Prognostic Score, Palliative Prognostic Index, Objective Prognostic Score, and Prognosis in Palliative Care Study Predictor. However, these models are seldom used in clinical practice because of challenges related to limited accuracy when applied individually and difficulties with model selection, computation, and interpretation. Online prognostic calculators emerge as tools to facilitate knowledge translation by overcoming the above challenges. For example, www.predictsurvival.com provides the output for seven prognostic indexes simultaneously based on 11 variables.

SUMMARY

Prognostic models and prognostic websites are currently available to augment prognostication in the advanced cancer setting. Further studies are needed to examine their impact on prognostic accuracy, confidence, and clinical outcomes.

Association Between Heart Rate and Reversibility of the Symptom, Refractoriness to Palliative Treatment, and Survival in Dyspneic Cancer Patients

CONTEXT

Dyspnea is one of the most distressing symptoms for terminally ill cancer patients and a predictor of poor prognosis. Identification of simple clinical signs, such as heart rate, indicating clinical course of each patient is of value.

OBJECTIVES

To explore the potential association between heart rate and reversibility of the symptom, treatment response to palliative intervention, and survival in terminally ill cancer patients with dyspnea at rest.

METHODS

This is a secondary analysis of a multicenter prospective cohort study of patients with advanced cancer to validate multiple prognostic tools. In the patients with dyspnea at rest at the baseline, we examined a potential association between heart rate and the reversibility of dyspnea and refractoriness to palliative treatment using logistic regression analysis. Survivals were compared using the Cox proportional hazards model among four groups with different levels of the heart rate (≤74, 75-84, 85-97, and ≥98).

RESULTS

A total of 2298 patients were enrolled, and 418 patients (18%) had dyspnea at rest. Reversibility of dyspnea was significantly higher in the patients with lower heart rate (P for trend = 0.008), and the refractoriness to palliative treatment tended to be higher in the patients with higher heart rate (P for trend = 0.101). The median survival for each heart rate quartile groups was significantly higher in the lower heart rate group (24 vs. 21 vs. 14 vs. 9 days; heart rate ≤74, 75-84, 85-97, and ≥98, respectively; log-rank P < 0.001).

CONCLUSION

Heart rate may help clinicians to make the prediction of the patient's clinical course more accurate.

Predicting survival in patients with advanced cancer in the last weeks of life: How accurate are prognostic models compared to clinicians' estimates?

BACKGROUND

It is unclear if validated prognostic scores such as the Palliative Performance Scale, Palliative Prognostic Index, and Palliative Prognostic Score are more accurate than clinician prediction of survival in patients admitted to an acute palliative care unit with only days of survival.

AIM

We compared the prognostic accuracy of Palliative Performance Scale, Palliative Prognostic Index, Palliative Prognostic Score, and clinician prediction of survival in this setting.

DESIGN

This is a pre-planned secondary analysis of a prospective study.

SETTING/PARTICIPANTS

We assessed Palliative Performance Scale, Palliative Prognostic Index, Palliative Prognostic Score, and clinician prediction of survival at baseline. We computed their prognostic accuracy using the Concordance index and area under the receiver operating characteristics curve for 7-, 14-, and 30-day survival.

RESULTS

A total of 204 patients were included with a median overall survival of 10 days (95% confidence interval: 8-11 days). The Concordance index for Palliative Performance Scale, Palliative Prognostic Index, Palliative Prognostic Score, and clinician prediction of survival were 0.74, 0.71, 0.70, and 0.75, respectively. The areas under the curve for these approaches were 0.82-0.87 for 30-day survival, 0.75-0.80 for 14-day survival, and 0.74-0.81 for 7-day survival. The four prognostic approaches had similar accuracies, with the exception of 7-day survival in which clinician prediction of survival was significantly more accurate than Palliative Prognostic Score (difference: 7%) and Palliative Prognostic Index (difference: 8%).

CONCLUSION

In patients with advanced cancer with days of survival, clinician prediction of survival and Palliative Performance Scale alone were as accurate as Palliative Prognostic Score and Palliative Prognostic Index. These four approaches may be useful for prognostication in acute palliative care units. Our findings highlight how patient population may impact the accuracy of prognostic scores.

The Impending Epidemic of Cardiovascular Diseases in Patients With Cancer in Japan

Onco-cardiology, a new academic field, aims to improve the quality of life and prognosis of cancer patients and survivors with cardiovascular diseases (CVD). With the aging of the population, an epidemic of cancer with CVD is emerging in developed countries. Cancer and CVD share risk factors, pathophysiology, treatments, and preventive and rehabilitative measures. A multidisciplinary team-based approach is needed to support cancer treatment to maximize its effectiveness and minimize its cardiotoxic potential. Basic and clinical onco-cardiology are already being practiced harmoniously. However, systematization in academia and clinical practice and accumulation of evidence have just started. In this review, we present the epidemiology, common risk factors between cancer and CVD, future epidemic of CVD in patients with cancer, and the necessity for an onco-cardiological approach to managing the burden of CVD in cancer patients and survivors.

Utility of Prognostic Prediction Models in the Terminal Stage of Gastrointestinal Cancer

PURPOSE

For patients receiving palliative care, information about prognosis is important to help them set priorities and expectations for care and to assist clinicians in decision-making. The purpose of this study was to investigate prognostic models applicable to the terminal stage of gastrointestinal cancer, especially in terms of accuracy of prediction regarding 3-week survival.

METHODS

We validated retrospectively the accuracy of a prognosis prediction model for 354 end-stage gastrointestinal cancer patients who underwent palliative care at our hospital. Using receiver operating characteristic analysis and the area under the curve (AUC), we selected the cut-off value for 3-week survival and evaluated the predictive ability using sensitivity, specificity, positive predictive value, negative predictive value, and accurate diagnosis rate.

RESULTS

In our analysis of various models, Palliative Prognostic Index (PPI) and Biological Prognostic Score (BPS) version 3 showed excellent predictive performance with AUCs of 0.85 and 0.83, respectively, and accurate diagnosis rates of 80.0 and 79.0, respectively. BPS version 2 showed fair predictive performance with an AUC of 0.76 and an accurate diagnosis rate of 72.0. Using these models, stratification of prognostic prediction was possible.

CONCLUSIONS

PPI and BPS were found to be accurate prediction models for short-term survival of terminal gastrointestinal cancer patients.

"How Long Have I Got?"-A Prospective Cohort Study Comparing Validated Prognostic Factors for Use in Patients with Advanced Cancer

BACKGROUND

The optimal prognostic factors in patients with advanced cancer are not known, as a comparison of these is lacking. The aim of the present study was to determine the optimal prognostic factors by comparing validated factors.

MATERIALS AND METHODS

A multicenter, prospective observational cohort study recruited patients over 18 years with advanced cancer. The following were assessed: clinician-predicted survival (CPS), Eastern Cooperative Oncology Group performance status (ECOG-PS), patient reported outcome measures (anorexia, cognitive impairment, dyspnea, global health), metastatic disease, weight loss, modified Glasgow Prognostic Score (mGPS) based on C-reactive protein and albumin, lactate dehydrogenase (LDH), and white (WCC), neutrophil (NC), and lymphocyte cell counts. Survival at 1 and 3 months was assessed using area under the receiver operating curve and logistic regression analysis.

RESULTS

Data were available on 478 patients, and the median survival was 4.27 (1.86-7.03) months. On univariate analysis, the following factors predicted death at 1 and 3 months: CPS, ECOG-PS, mGPS, WCC, NC (all p < .001), dyspnea, global health (both p ≤ .001), cognitive impairment, anorexia, LDH (all p < .01), and weight loss (p < .05). On multivariate analysis ECOG-PS, mGPS, and NC were independent predictors of survival at 1 and 3 months (all p < .01).

CONCLUSION

The simple combination of ECOG-PS and mGPS is an important novel prognostic framework which can alert clinicians to patients with good performance status who are at increased risk of having a higher symptom burden and dying at 3 months. From the recent literature it is likely that this framework will also be useful in referral for early palliative care with 6-24 months survival.

IMPLICATIONS FOR PRACTICE

This large cohort study examined all validated prognostic factors in a head-to-head comparison and demonstrated the superior prognostic value of the Eastern Cooperative Oncology Group performance status (ECOG-PS)/modified Glasgow Prognostic Score (mGPS) combination over other prognostic factors. This combination is simple, accurate, and also relates to quality of life. It may be useful in identifying patients who may benefit from early referral to palliative care. It is proposed ECOG-PS/mGPS as the new prognostic domain in patients with advanced cancer.

Prognostication in advanced cancer: update and directions for future research

The objective of this review is to provide an update on prognostication in patients with advanced cancer and to discuss future directions for research in this field. Accurate prognostication of survival for patients with advanced cancer is vital, as patient life expectancy informs many important personal and clinical decisions. The most common prognostic approach is clinician prediction of survival (CPS) using temporal, surprise, or probabilistic questions. The surprise and probabilistic questions may be more accurate than the temporal approach, partly by limiting the time frame of prediction. Prognostic models such as the Glasgow Prognostic Score (GPS), Palliative Performance Scale (PPS), Palliative Prognostic Score (PaP), Palliative Prognostic Index (PPI), or Prognosis in Palliative Care Study (PiPS) predictor model may augment CPS. However, care must be taken to select the appropriate tool since prognostic accuracy varies by patient population, setting, and time frame of prediction. In addition to life expectancy, patients and caregivers often desire that expected treatment outcomes and bodily changes be communicated to them in a sensible manner at an appropriate time. We propose the following 10 major themes for future prognostication research: (1) enhancing prognostic accuracy, (2) improving reliability and reproducibility of prognosis, (3) identifying the appropriate prognostic tool for a given setting, (4) predicting the risks and benefits of cancer therapies, (5) predicting survival for pediatric populations, (6) translating prognostic knowledge into practice, (7) understanding the impact of prognostic uncertainty, (8) communicating prognosis, (9) clarifying outcomes associated with delivery of prognostic information, and (10) standardizing prognostic terminology.