Survival prediction for terminally ill cancer patients: revision of the palliative prognostic score with incorporation of delirium

Early and simultaneous palliative care in cancer patients: an overview

PURPOSE

The timing of access to palliative care is still a debated topic. We aim to investigate recent evidence on early and simultaneous palliative care to better understand what the most appropriate timing for integration and its impact on the quality of life of cancer patients and their carers might be.

METHODS

This is an overview of systematic reviews published between November 2020 and February 2024. Three search strings were launched on the PubMed, CINAHL, Cochrane Library and Scopus databases. Results were screened according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement.

RESULTS

A total of 17/1052 articles were selected. The following categories emerged and were described: need to define a timing for integration; definition of specific criteria for referral; access to palliative care; benefits of early and simultaneous palliative care; needs of patients and carers and their preferences. No specific timing for referral to palliative care emerged: one article suggested within 8 weeks of diagnosis, while another identified multiple time points. The term "supportive care" appears to facilitate early referral to palliative care services.

CONCLUSION

There is ample evidence that early and simultaneous access to palliative care ensures a better care pathway. Although it is not possible to standardise referral times, communication between professionals, patients and carers, shared and advance care planning are key elements in the identification of palliative care needs and their early integration. Much research is still needed to ensure equitable and appropriate access to palliative care.

Prognostic models for survival predictions in advanced cancer patients: a systematic review and meta-analysis

BACKGROUND

Prognostication of survival among patients with advanced cancer is essential for palliative care (PC) planning. The implementation of a clinical point-of-care prognostic model may inform clinicians and facilitate decision-making. While early PC referral yields better clinical outcomes, actual referral time differs by clinical contexts and accessible. To summarize the various prognostic models that may cater to these needs, we conducted a systematic review and meta-analysis.

METHODS

A systematic literature search was conducted in Ovid Medline, Embase, CINAHL Ultimate, and Scopus to identify eligible studies focusing on incurable solid tumors, validation of prognostic models, and measurement of predictive performances. Model characteristics and performances were summarized in tables. Prediction model study Risk Of Bias Assessment Tool (PROBAST) was adopted for risk of bias assessment. Meta-analysis of individual models, where appropriate, was performed by pooling C-index.

RESULTS

35 studies covering 35 types of prognostic models were included. Palliative Prognostic Index (PPI), Palliative Prognostic Score (PaP), and Objective Prognostic Score (OPS) were most frequently identified models. The pooled C-statistic of PPI for 30-day survival prediction was 0.68 (95% CI: 0.62-0.73, n = 6). The pooled C-statistic of PaP for 30-day survival prediction was 0.76 (95% CI: 0.70-0.80, n = 11), while that for 21-day survival prediction was 0.80 (0.71-0.86, n = 4). The pooled C-statistic of OPS for 30-days survival prediction was 0.69 (95% CI: 0.65-0.72, n = 3). All included studies had high risk of bias.

CONCLUSION

PaP appears to perform better but further validation and implementation studies were needed for confirmation.

Organisation and management of multi-professional care for cancer patients at end-of-life: state-of-the-art from a survey to community and hospital-based professionals

Providing timely and satisficing End-of-Life care (EOLC) is a priority for healthcare systems since aging population and chronic diseases are boosting the global demand for care at end-of-life (EOL). In OECD countries the access to EOLC is insufficient. In Italy, the average rate of cancer patients assisted by the palliative care (PC) network at EOL was 28% in 2021, with high variability in the country. Among the Italian regions offering the best coverages, Tuscany has a rate of about 40%, but intraregional variation is marked as well. The study aims to explore the delivery of EOLC to adult cancer patients in public facilities in the Tuscany region through survey data collection among professionals. Two online surveys were delivered to Directors of community-based PC Functional-Units (FUs) and Directors of hospital-based medical-oncology units. All FU Directors responded to the survey (n = 14), and a response rate of 96% was achieved from hospital-unit Directors (n = 27). The results highlight the availability of numerous dedicated services, but reveal heterogeneity among and within organisations, including variations in the professionals involved, pathways, and tools adopted. Care continuity is supported by institutionalized collaboration between hospital and community settings, but hindered by fragmented care processes and heterogeneous transition pathways. Late referral to PC is perceived as a major constraint to EOLC. Developing structured pathways for patient transition to end-stage PC is crucial, and practices/processes should be uniformly implemented to ensure equity. Multi-professional care should be facilitated through tailored supporting tools. Both hospital-unit and FU Directors suggest developing shared pathways between organisations/professionals (82% and 80% respectively) and digital information sharing (61% and 80% respectively). Hospital and community-based professionals have similar perceptions about the concerns and challenges to EOLC provision in the region, but community-based professionals are more sensitive to the importance of improving communication on PC to the public and early discussing EOLC with caregivers. This finding suggests the need of enhancing hospital personnel's awareness about these issues. Professional training and the capacity to assess patients' needs and preferences should be improved. The identified needs can inform future research and interventions to improve the quality and outcomes of EOLC for cancer patients.

Is a Combination of Six Clinical Tests Useful as a Measure to Predict Short-Term Prognosis in Terminal Cancer Patients? A Prospective Observational Study in a Japanese Palliative Care Unit

Background

To address the need for short-term prognostic methods using objective measures, we developed a method to predict a 2- or 3-week prognosis using only six clinical tests (known as the WPCBAL score). However, the method has not yet been directly compared with globally accepted prognostic methods.

Objectives

This study aimed to clarify the usefulness of the WPCBAL score by comparing it with other prediction methods.

Setting/Subjects

A prospective observational study was conducted with patients admitted to the palliative care unit of a Municipal Hospital in Japan between November 2017 and May 2021.

Measurements

The primary endpoint was each prediction method's accuracy-the WPCBAL score, Glasgow Prognostic Score (GPS), Palliative Prognostic Index (PPI), Palliative Prognostic Score (PaP), Delirium-Palliative Prognostic Score (D-PaP), and Prognosis in Palliative Care Study predictor models (PiPS-A, PiPS-B)-in predicting a prognosis at 2 or 3 weeks. The secondary endpoints were sensitivity, specificity, positive and negative predictive values, area under the receiver operating characteristic curve, and each prediction method's feasibility rate.

Results

In total, 181 patients were included in this study. For the 3-week prognosis, the PaP had the highest accuracy (0.746), followed by the D-PaP (0.735), WPCBAL (0.696), PPI (0.652), and GPS (0.575). For the 2-week prognosis, the PiPS-B had the highest accuracy (0.702), followed by the WPCBAL (0.696) and PiPS-A (0.641).

Conclusions

The WPCBAL score's accuracy in predicting a 2- or 3-week prognosis was comparable to that of commonly used prognostic methods, thus suggesting its usefulness as a short-term prognostic method.

The impact of a web-based prognostic calculator on prognostic confidence in outpatient palliative care

PURPOSE

Clinicians are often uncertain about their prognostic estimates, which may impede prognostic communication and clinical decision-making. We assessed the impact of a web-based prognostic calculator on physicians' prognostic confidence.

METHODS

In this prospective study, palliative care physicians estimated the prognosis of patients with advanced cancer in an outpatient clinic using the temporal, surprise, and probabilistic approaches for 6 m, 3 m, 2 m, 1 m, 2 w, 1 w, and 3 d. They then reviewed information from www.predictsurvival.com , which calculated survival estimates from seven validated prognostic scores, including the Palliative Prognostic Score, Palliative Prognostic Index, and Palliative Performance Status, and again provided their prognostic estimates after calculator use. The primary outcome was prognostic confidence in temporal CPS (0-10 numeric rating scale, 0 = not confident, 10 = most confident).

RESULTS

Twenty palliative care physicians estimated prognoses for 217 patients. The mean (standard deviation) prognostic confidence significantly increased from 5.59 (1.68) before to 6.94 (1.39) after calculator use (p < 0.001). A significantly greater proportion of physicians reported feeling confident enough in their prognosis to share it with patients (44% vs. 74%, p < 0.001) and formulate care recommendations (80% vs. 94%, p < 0.001) after calculator use. Prognostic accuracy did not differ significantly before or after calculator use, ranging from 55-100%, 29-98%, and 48-100% for the temporal, surprise, and probabilistic approaches, respectively.

CONCLUSION

This web-based prognostic calculator was associated with increased prognostic confidence and willingness to discuss prognosis. Further research is needed to examine how prognostic tools may augment prognostic discussions and clinical decision-making.

Comparison of Prognostic Abilities of Palliative Prognostic Index, Laboratory Prognostic Score, and Palliative Prognostic Score

CONTEXT

Few studies have compared the prognostic value of scoring systems based on physical and blood parameters in terminally ill patients with cancer.

OBJECTIVES

This study evaluated the prognostic abilities of Palliative Prognostic Index (PPI), Laboratory Prognostic Score (LPS), and Palliative Prognostic Score (PaP).

METHODS

We included 989 terminally ill patients with cancer who consulted for admission to our palliative care unit. We compared the discriminative abilities of PPI, LPS, and PaP for 7-, 14-, 30-, 60-, and 90-day mortality. Additionally, we compared the estimated median survival of PPI, LPS, and PaP with the actual survival (AS). The prediction accuracy was considered adequate if the ratio of estimated median survival in days to AS in days fell within the range of 0.66 to 1.33, optimistic when it exceeds 1.33, and pessimistic when it falls below 0.66.

RESULTS

The accuracies for 7-, 14-, 30-, 60-, and 90-day mortality were superior for PPI, LPS, LPS, PaP, and PaP (72%, 73%, 71%, 80%, and 82%), respectively, although the discriminative abilities for 7-, 14-, 30-, 60-, and 90-day mortality were similar among the three scoring systems. The prediction accuracy of survival (PAS) was similar among the three scoring systems with adequate, optimistic, and pessimistic rates of 36%-41%, 20%-46%, and 16%-38%, respectively. PAS was superior in actual survival for 14-59 days.

CONCLUSIONS

The prognostic abilities of PPI, LPS, and PaP were comparable. The most adequate estimation occurred for patients with AS for 14-59 days. A more accurate prognostic model is needed for patients with longer survival.

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.

Long-term mortality of critically ill patients with cancer and delirium who survived to discharge: a retrospective cohort study

PURPOSE

Delirium is common in critically ill patients and has been associated with lower short-term survival; however, its association with long-term survival has been scarcely evaluated and few studies have shown divergent results.

METHODS

We conducted a retrospective cohort study of adult patients with cancer admitted to the intensive care unit (ICU) and discharged from hospital from January 2015 to December 2018. We considered delirium present if the Confusion Assessment Method for Intensive Care Unit (CAM-ICU) result was positive. We assessed the association between delirium during ICU stay and long-term mortality (up to three years after discharge). We also assessed the association between delirium type (hypoactive, hyperactive, and mixed) with long-term mortality.

RESULTS

We included 3,079 patients. Of these, 430 (14%) were considered delirious at some point during their ICU stay. Delirium was associated with one-year mortality after hospital discharge (hazard ratio [HR], 1.58; 95% confidence interval [CI], 1.36 to 1.83) after adjustment for potential confounders, but not with one to three year-mortality (HR, 0.92; 95% CI, 0.61 to 1.39). Hypoactive and mixed delirium were associated with one-year mortality (HR, 1.77; 95% CI, 1.46 to 2.14 and HR, 1.56; 95% CI, 1.21 to 2.00, respectively), but none of the delirium motor types was associated with one to three-year mortality.

CONCLUSIONS

We observed that delirium during ICU stay was associated with increased one-year mortality, but was not with mortality after one year. This association was observed in hypoactive and mixed delirium types but not with hyperactive delirium.

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.

Development and external validation of a predictive multivariable model for last-weeks survival of advanced cancer patients in the palliative home care setting (PACS)

PURPOSE

Various prognostic indexes have been proposed to improve physicians' ability to predict survival time in advanced cancer patients, admitted to palliative care (PC) with a survival probably to a few weeks of life, but no optimal score has been identified. The study aims therefore to develop and externally validate a new multivariable predictive model in this setting.

METHODS

We developed a model to predict short-term overall survival in cancer patients on the basis of clinical factors collected at PC admission. The model was developed on 1020 cancer patients prospectively enrolled to home palliative care at VIDAS Milan, Italy, between May 2018 and February 2020 and followed-up to June 2020, and validated in two separate samples of 544 home care and 247 hospice patients.

RESULTS

Among 68 clinical factors considered, five predictors were included in the predictive model, i.e., rattle, heart rate, anorexia, liver failure, and the Karnofsky performance status. Patient's survival probability at 5, 15, 30 and 45 days was estimated. The predictive model showed a good calibration and moderate discrimination (area under the receiver operating characteristic curve between 0.72 and 0.79) in the home care validation set, but model calibration was suboptimal in hospice patients.

CONCLUSIONS

The new multivariable predictive model for palliative cancer patients' survival (PACS model) includes clinical parameters routinely at patient's admission to PC and can be easily used to facilitate immediate and appropriate short-term clinical decisions for PC cancer patients in the home setting.

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.

To Treat or Not to Treat? Dilemmas when Deciding on Antineoplastic Treatment in Patients With Far Advanced Cancers

Patients with advanced cancers and their oncologists are often faced with difficult treatment decisions, especially when there are borderline situations of expected benefit or increased risk of complications. In this narrative review, we will explore the decision-making process for patients with advanced cancers and provide insights on how to approach this complex task, while didactically dividing the oncologist's assessments according to a mnemonic rule of the ABCDE of therapeutic decision-making. Part A (advanced cancer) recalls that the rule is to be used specifically for advanced cancers. Parts B (potential benefits) and C (clinical conditions and risks) represents the traditional risk vs benefit scale. In Part D, we discuss ways to identify and understand patients' desires, values, preferences, and beliefs. The prognostic estimation, from Part E, may function as an "adjust" for the antineoplastic treatment decision-making. Treatment decisions need to be conducted by skilled oncologists, in a patient-centered care, aiming to promote valuable oncology with lower rates of aggressive care.

Development and Validation of the PaP Score Nomogram for Terminally Ill Cancer Patients

The validated Palliative Prognostic (PaP) score predicts survival in terminally ill cancer patients, assigning patients to three different risk groups according to a 30-day survival probability: group A, >70%; group B, 30−70%; and group C, <30%. We aimed to develop and validate a PaP nomogram to provide individualized prediction of survival at 15, 30 and 60 days. Three cohorts of consecutive terminally ill cancer patients were used: one (n = 519) for nomogram development and internal validation, and a second (n = 451) and third (n = 549) for external validation. Multivariate analyses included dyspnea, anorexia, Karnofsky performance status, clinical prediction of survival, total white blood count and lymphocyte percentage. The predictive accuracy of the nomogram was determined by Harrell’s concordance index (95% CI), and calibration plots were generated. The nomogram had a concordance index of 0.74 (0.72−0.75) and showed good calibration. The internal validation showed no departures from ideal prediction. The accuracy of the nomogram at 15, 30 and 60 days was 74% (70−77), 89% (85−92) and 72% (68−76) in the external validation cohorts, respectively. The PaP nomogram predicts the individualized estimate of survival and could greatly facilitate clinical care decision-making at the end of life.

Delirium in the Neonate

Delirium is likely present in the neonatal intensive care unit and has been largely unrecognized. There are several risk factors for delirium including illness severity, neurosedative exposure, and environmental disruptions that put infants at risk for delirium. Regular use of scoring systems should be considered to improve delirium detection. When identified, initial steps in management should include resolving underlying causes and implementation of standard nonpharmacologic measures. Mounting pediatric evidence suggests that the atypical antipsychotics, as well as the α-2 agonists, may be additionally beneficial in treating delirium as well as improving the ability to wean off other neurosedative medications.

Development and internal validation of laboratory prognostic score to predict 14-day mortality in terminally ill patients with gastrointestinal malignancy

BACKGROUND

Few studies have developed an easy scoring system for the short-term survival of patients with gastrointestinal (GI) malignancy.

METHODS

A total of 816 terminally ill patients with GI malignancy were admitted to our palliative care unit. They were randomly divided into the investigation (n = 490) and validation (n = 326) groups. A total of 19 laboratory blood parameters were analyzed. Receiver-operating characteristic analysis was performed for each blood factor, and the area under the curve was calculated to determine the predictive value for 14-day survival after the blood test. Multivariable logistic regression analysis was performed to identify significant independent prognostic factors for 14-day mortality. To develop a scoring system for 14-day mortality, the laboratory prognostic score for gastrointestinal malignancy (GI-LPS) was calculated using the sum of indices of the independent prognostic factors.

RESULTS

Multivariable analysis showed that 5 of 19 indices, namely total bilirubin ≥ 2.1 mg/dL, blood urea nitrogen ≥ 28 mg/dL, eosinophil percentage ≤ 0.5%, neutrophil-to-lymphocyte ratio ≥ 9.2, and platelet count ≤ 194 × 10³/μL, were significant independent factors of 14-day survival. GI-LPS showed acceptable accuracy for 14-day mortality in the investigation and validation groups. GI-LPS 3 (including any three factors) predicted death within 14 days, with a sensitivity of 56-58%, a specificity of 82-87%, a positive predictive value of 48-50%, and a negative predictive value of 87-90%.

CONCLUSIONS

GI-LPS showed an acceptable ability to predict 14-day survival and can provide additional information to conventional prognostic scores.

The quality of palliative sedation in end-stage disease: audit from a department of oncology and haematology

PURPOSE

Palliative sedation (PS) plays a critical role to give suffering relief from refractory symptoms at the end of life. Our audit aimed to assess and improve quality of PS at the Department of Oncology and Hematology of University Hospital of Modena, to verify the adherence to international guidelines, the cooperation among members of care team, focusing with attention on family's perception of this delicate situation.

METHODS

From December 2016 to June 2019, data of patients undergoing PS in the Department were collected by an electronic folder tool, "Sedation Tool" (ST), that recorded clinical and PS informations, D-PaP, Rudkin score, and family's perception.

RESULTS

In total, 245 patients were enrolled. Eighty-two percent had a Karnofsky Performance Status 10-20%. The most common cancer types were lung and gastro-intestinal carcinomas (27% and 21% respectively). Refractory symptoms observed were confusion and agitation (76%), dyspnea (39%), pain (15%), delirium (10%), and psychological distress (5%). Midazolam was the drug of choice for PS. Most of patients had Rudkin score 5 after 24 h and 33% had terminal event within a period of 24 h from the beginning of PS. During PS, most of patient's relatives reported peacefulness (65%), agitation/impatience in 6% of cases, and concern for suffering (16%).

CONCLUSION

PS is used in case of worsening general conditions at the end-stage disease to relieve refractory symptoms with dignity. The ST can become a simple instrument to evaluate and improve PS quality, providing more attention on the impact of PS on relatives to then possibly develop new supportive procedures for patients and their families.

The prevalence, associated factors, clinical impact, and state of diagnosis of delirium in palliative care patients

PURPOSE

The aim of this study is to establish the prevalence, associated factors, and clinical impact of delirium in newly referred palliative care patients and the percentage of delirium diagnoses missed by primary medical teams.

METHODS

Newly referred palliative care patients were evaluated and were reviewed for possible associated factors of delirium. Univariable and multivariable analysis were used to identify associated factors. Median overall survival and survival curves were analyzed. The percentage of missed diagnosis in IPD patients was identified.

RESULTS

We included 350 palliative care patients. Nearly all patients had cancer diagnosis (96.6%). The overall prevalence of delirium was 44.0%. The independent associated factors of delirium were age ≥ 63 years (adjusted odds ratio [aOR], 7.0; 95% CI, 2.2-22.9), palliative performance scale ≤ 20% (aOR, 54.5; 95% CI, 13.1-228.0), brain metastasis (aOR, 15.6; 95% CI, 3.7-66.7), urinary tract infection (aOR, 18.8; 95% CI, 4.7-75.5), sepsis (aOR, 59.0; 95% CI, 4.4-797.8), hyponatremia (aOR, 8.8; 95% CI, 2.6-29.8), and hypercalcemia (not applicable). Interestingly, opioids and benzodiazepines were not associated with delirium. Delirious patients had significantly shorter survival (median survival 11 days). Delirium diagnoses were missed for 76.1%.

CONCLUSION

Nearly half of the palliative care patients had delirium, which was associated with noticeably short survivals. We identified the independent factors associated with the delirium. Despite having a remarkably high prevalence rate and being a well-known poor prognostic factor, there was still a very high rate of missed delirium diagnoses. Effective, routine, delirium screening of palliative care patients needs to be emphasized.

Assessment of Changes in Symptoms Is Feasible and Prognostic in the Last Weeks of Life: An International Multicenter Cohort Study

Background: Symptoms are not typically part of established various prognostic factors and scoring systems but are among the most frequently assessed issues in patient care. Objectives: To evaluate that, changes in symptoms can provide additional useful prognostic information. Design: A secondary analysis of an international cohort study in Japan, Korea, and Taiwan. Setting/Subjects: Subjects were adult patients with advanced cancer (n = 2074) who were admitted to 37 palliative care units (PCUs) in 3 countries from January 2017 to September 2018. Measurements: Symptoms (dyspnea, fatigue, dry mouth, and drowsiness) were assessed at admission and one-week later. Dyspnea was assessed by the presence of resting and exertional dyspnea, whereas other symptoms were assessed using the Integrated Palliative care Outcome Scales (IPOS) (range 0-4). For analysis, we grouped patients by symptom change, as either Improved, Stable, or Worsened (by having at least a one increment decrease, no change, or at least a one increment increase, respectively). Results: Worsened groups had the shortest survival (median survival 15-21 days) compared with those with Improved (median survival 23-31 days) and Stable symptoms (median survival 27-29 days) across all four symptoms (dyspnea, fatigue, dry mouth, and drowsiness). Survival differences were statistically significantly different across all three groups for all symptoms (all p < 0.001). Interestingly, Improved symptoms were associated with similar survival compared with Stable groups, with no statistical differences. Conclusions: Worsened symptoms at one week after admission were useful predictors of survival for patients with advanced cancer in PCUs during the final weeks of life. Longitudinal assessments are needed to reflect passage of time as well as impact of treatments.

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.

"The surprise questions" using variable time frames in hospitalized patients with advanced cancer

OBJECTIVE

Several studies supported the usefulness of "the surprise question" in terms of 1-year mortality of patients. "The surprise question" requires a "Yes" or "No" answer to the question "Would I be surprised if this patient died in [specific time frame]." However, the 1-year time frame is often too long for advanced cancer patients seen by palliative care personnel. "The surprise question" with shorter time frames is needed for decision making. We examined the accuracy of "the surprise question" for 7-day, 21-day, and 42-day survival in hospitalized patients admitted to palliative care units (PCUs).

METHOD

This was a prospective multicenter cohort study of 130 adult patients with advanced cancer admitted to 7 hospital-based PCUs in South Korea. The accuracy of "the surprise question" was compared with that of the temporal question for clinician's prediction of survival.

RESULTS

We analyzed 130 inpatients who died in PCUs during the study period. The median survival was 21.0 days. The sensitivity, specificity, and overall accuracy for the 7-day "the surprise question" were 46.7, 88.7, and 83.9%, respectively. The sensitivity, specificity, and overall accuracy for the 7-day temporal question were 6.7, 98.3, and 87.7%, respectively. The c-indices of the 7-day "the surprise question" and 7-day temporal question were 0.662 (95% CI: 0.539-0.785) and 0.521 (95% CI: 0.464-0.579), respectively. The c-indices of the 42-day "the surprise question" and 42-day temporal question were 0.554 (95% CI: 0.509-0.599) and 0.616 (95% CI: 0.569-0.663), respectively.

SIGNIFICANCE OF RESULTS

Surprisingly, "the surprise questions" and temporal questions had similar accuracies. The high specificities for the 7-day "the surprise question" and 7- and 21-day temporal question suggest they may be useful to rule in death if positive.

Laboratory prognostic score for predicting 14-day mortality in terminally ill patients with gynecologic malignancy

BACKGROUND

There are few studies developing a scoring system for short-term survival of patients with gynecologic malignancy.

METHODS

Seventy-three terminally ill patients with gynecologic malignancy who were admitted to our palliative care unit (PCU) from June 2009 to February 2018 were included. We accumulated routine blood data within 3 months before PCU discharge. Receiver-operating characteristic analysis was performed on each blood factor, and area under the curve (AUC) was calculated to determine the predictive value for 14-day survival after the blood test. Multivariable logistic regression analysis was performed to identify significant independent prognostic factors of 14-day mortality. To develop a scoring system for 14-day mortality, laboratory prognostic score for gynecologic malignancy (G-LPS) was calculated using the sum of indices of the independent prognostic factors.

RESULTS

Multivariable analysis showed that 6 of 24 indices, namely, C-reactive protein ≥ 13.3 mg/dL, total bilirubin ≥ 1.1 mg/dL, sodium < 131 mEq/L, blood urea nitrogen ≥ 28 mg/dL, white blood cell count ≥ 17.7 × 103/μL, and eosinophil level < 0.2%, were significant independent factors of 14-day survival. G-LPS was obtained from the sum of the six indices. The AUC was 0.7977 at the optimal cut-off value of G-LPS 3. G-LPS 3 predicted death within 14 days with a sensitivity of 72% and a specificity of 79%.

CONCLUSIONS

Six of the 24 laboratory indices were identified as independent prognostic factors of 14-day mortality in terminally ill patients with gynecologic malignancy. G-LPS showed acceptable ability of predicting 14-day survival.

External Validation of the Bone Metastases Ensemble Trees for Survival (BMETS) Machine Learning Model to Predict Survival in Patients With Symptomatic Bone Metastases

PURPOSE

The Bone Metastases Ensemble Trees for Survival (BMETS) model uses a machine learning algorithm to estimate survival time following consultation for palliative radiation therapy for symptomatic bone metastases (SBM). BMETS was developed at a tertiary-care, academic medical center, but its validity and stability when applied to external data sets are unknown.

PATIENTS AND METHODS

Patients treated with palliative radiation therapy for SBM from May 2013 to May 2016 at two hospital-based community radiation oncology clinics were included, and medical records were retrospectively reviewed to collect model covariates and survival time. The Kaplan-Meier method was used to estimate overall survival from consultation to death or last follow-up. Model discrimination was estimated using time-dependent area under the curve (tAUC), which was calculated using survival predictions from BMETS based on the initial training data set.

RESULTS

A total of 216 sites of SBM were treated in 182 patients. Most common histologies were breast (27%), lung (23%), and prostate (23%). Compared with the BMETS training set, the external validation population was older (mean age, 67 v 62 years; P < .001), had more primary breast (27% v 19%; P = .03) and prostate cancer (20% v 12%; P = .01), and survived longer (median, 10.7 v 6.4 months). When the BMETS model was applied to the external data set, tAUC values at 3, 6, and 12 months were 0.82, 0.77, and 0.77, respectively. When refit with data from the combined training and external validation sets, tAUC remained > 0.79.

CONCLUSION

BMETS maintained high discriminative ability when applied to an external validation set and when refit with new data, supporting its generalizability, stability, and the feasibility of dynamic modeling.

A non-lab nomogram of survival prediction in home hospice care patients with gastrointestinal cancer

BACKGROUND

Patients suffering from gastrointestinal cancer comprise a large group receiving home hospice care in China, however, little is known about the prediction of their survival time. This study aimed to develop a gastrointestinal cancer-specific non-lab nomogram predicting survival time in home-based hospice.

METHODS

We retrospectively studied the patients with gastrointestinal cancer from a home-based hospice between 2008 and 2018. General baseline characteristics, disease-related characteristics, and related assessment scale scores were collected from the case records. The data were randomly split into a training set (75%) for developing a predictive nomogram and a testing set (25%) for validation. A non-lab nomogram predicting the 30-day and 60-day survival probability was created using the least absolute shrinkage and selection operator (LASSO) Cox regression. We evaluated the performance of our predictive model by means of the area under receiver operating characteristic curve (AUC) and calibration curve.

RESULTS

A total of 1618 patients were included and divided into two sets: 1214 patients (110 censored) as training dataset and 404 patients (33 censored) as testing dataset. The median survival time for overall included patients was 35 days (IQR, 17-66). The 5 most significant prognostic variables were identified to construct the nomogram among all 28 initial variables, including Karnofsky Performance Status (KPS), abdominal distention, edema, quality of life (QOL), and duration of pain. In training dataset validation, the AUC at 30 days and 60 days were 0.723 (95% CI, 0.694-0.753) and 0.733 (95% CI, 0.702-0.763), respectively. Similarly, the AUC value was 0.724 (0.673-0.774) at 30 days and 0.725 (0.672-0.778) at 60 days in the testing dataset validation. Further, the calibration curves revealed good agreement between the nomogram predictions and actual observations in both the training and testing dataset.

CONCLUSION

This non-lab nomogram may be a useful clinical tool. It needs prospective multicenter validation as well as testing with Chinese clinicians in charge of hospice patients with gastrointestinal cancer to assess acceptability and usability.

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.

Beyond Performance Status

Oncologists should recognise the need to move beyond the Eastern Cooperative Oncology Group Performance Status (ECOG PS) score. ECOG PS is a longstanding and ubiquitous feature of oncology. It was evolved 40 years ago as an adaption of the 70-year-old Karnofsky performance score. It is short, easily understood and part of the global language of oncology. The wide prevalence of the ECOG PS attests to its proven utility and worth to help triage patient treatment. The ECOG PS is problematic. It is a unidimensional functional score. It is mostly physician assessed, subjective and therefore open to bias. It fails to account for multimorbidity, frailty or cognition. Too often the PS is recorded only once in wilful ignorance of a patient's changing physical state. As modern oncology offers an ever-widening array of therapies that are ‘personalised’ to tumour genotype, modern oncologists must strive to better define patient phenotype. Using a wider range of scoring and assessment tools, oncologists can identify deficits that may be reversed or steps taken to mitigate detrimental effects of treatment. These tools can function well to identify those patients who would benefit from comprehensive assessment. This overview identifies the strengths of ECOG PS but highlights the weaknesses and where these are supported by other measures. A strong recommendation is made here to move to routine use of the Clinical Frailty Score to start to triage patients and most appropriately design treatments and rehabilitation interventions.

CCAE: Cross-field categorical attributes embedding for cancer clinical endpoint prediction

Patients with advanced cancer are burdened physically and psychologically, so there is an urgent need to pay more attention to their health-related quality of life (HRQOL). With an expected clinical endpoint prediction, over-treatment can be effectively eliminated by the means of palliative care at the right time. This paper develops a deep learning based approach for cancer clinical endpoint prediction based on patient's electronic health records (EHR). Due to the pervasive existence of categorical information in EHR, it brings unavoidably obstacles to the effective numerical learning algorithms. To address this issue, we propose a novel cross-field categorical attributes embedding (CCAE) model to learn a vectorized representation for cancer patients in attribute-level by orders, in which the strong semantic coupling among categorical variables are well exploited. By transforming the order-dependency modeling into a sequence learning task in an ingenious way, recurrent neural network is adopted to capture the semantic relevance among multi-order representations. Experimental results from the SEER-Medicare EHR dataset have illustrated that the proposed model can achieve competitive prediction performance compared with other baselines.

Prognosticating for Adult Patients With Advanced Incurable Cancer: a Needed Oncologist Skill

Patients with advanced cancer and their families commonly seek information about prognosis to aid decision-making in medical (e.g. surrounding treatment), psychological (e.g. saying goodbye), and social (e.g. getting affairs in order) domains. Oncologists therefore have a responsibility to identify and address these requests by formulating and sensitively communicating information about prognosis. Current evidence suggests that clinician predictions are correlated with actual survival but tend to be overestimations. In an attempt to cultivate prognostic skills, it is recommended that clinicians practice formulating and recording subjective estimates of prognosis in advanced cancer patient’s medical notes. When possible, a multi-professional prognostic estimate should be sought as these may be more accurate than individual predictions alone. Clinicians may consider auditing the accuracy of their predictions periodically and using feedback from this process to improve their prognostic skills.

Clinicians may also consider using validated prognostic tools to complement their clinical judgements. However, there is currently only limited evidence about the comparative accuracy of different prognostic tools or the extent to which these measures are superior to clinical judgement. Oncologists and palliative care physicians should ensure that they receive adequate training in advanced communication skills, which builds upon their pre-existing skills, to sensitively deliver information on prognosis. In particular, clinicians should acknowledge their own prognostic uncertainty and should emphasise the supportive care that can continue to be provided after stopping cancer-directed therapies.

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.

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.

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.

Symptom expression in advanced cancer patients admitted to hospice or home care with and without delirium

The aim of this study is to investigate the relationship between delirium and symptom expression in advanced cancer patients admitted to palliative care services. This is a secondary analysis of a consecutive sample of advanced cancer patients who were admitted to home care and hospices, and prospectively assessed for a period of 10 months. The Edmonton Symptom Assessment Scale (ESAS) and the MDAS (Memorial Delirium Assessment Scale) were measured at admission (T0) and after seven days of home care or hospice care (T7). Of the eight hundred and forty-eight patients screened in the period, 585 were not considered in the analysis for various reasons. The mean age was 72.1 years (SD 13.7), and 146 patients were males (55.5%). The mean Karnofsky status recorded at T0 is 34.1 (SD = 6.69). The mean duration palliative care assistance is 38.4 days (SD = 48, range 2-220). Of 263 patients who had a MDAS available at T0, 110 patients (41.8%) had a diagnosis of delirium. Of them, 167 patients had complete data regarding MDAS measurement, either at T0 and T7. A larger number of patients (n 167, 63.5%) had delirium after a week of palliative care. Patients with delirium are likely to be older, to have a lower Karnofsky level at T0, and to be home care patients. At T0, weakness, nausea, drowsiness, lack of appetite, and well-being are associated with delirium. At T7, weakness, poor appetite, and poor well-being are significantly associated with delirium. 27% of patients who had a normal cognitive status at T0 developed delirium at T7. In patients with delirium, an improvement in the cognitive status corresponds to a significant improvement in weakness, depression, and appetite. Conversely, the occurrence of delirium in patients who had a normal cognitive status at admission significantly increases the level depression, while the level of weakness and appetite decrease. Symptom expression is amplified in patients with delirium admitted to home care or hospices, while patients without delirium can be more responsive to palliative treatments with a significant decrease in intensity of ESAS items.

Laboratory prognostic score for predicting 30-day mortality in terminally ill cancer patients

Conventional prognostic scores for terminally ill cancer patients may have less objectivity because they include subjective or categorical variables that do not consider intensity or severity. The aim of this study was to identify prognostic factors for 30-day mortality from routine blood examination of terminally ill cancer patients. A total of 1308 study patients in a hospice setting were divided into investigation (n=761) and validation (n=547) groups. Twenty laboratory blood parameters were analyzed. Multivariate analysis revealed that ten variables (C-reactive protein ≥5.4 mg/dL, serum albumin <2.8 g/dL, blood urea nitrogen ≥21 mg/dL, white blood cell count ≥8.600 × 103/μL, eosinophil percentage <0.8%, neutrophil-to-lymphocyte ratio ≥11.1, hemoglobin level ≥ 13.2 g/dL, mean corpuscular volume ≥ 93.7 fl, red cell distribution width ≥ 16, and platelet count < 159 × 103/μL) were significant independent prognostic factors for 30-day survival. The laboratory prognostic score (LPS) was calculated by the sum of blood indices among the ten variables. The LPS showed acceptable accuracy for 30-day mortality in the investigation and validation groups. LPS 5 (including any five factors) predicted death within 30 days, with a sensitivity of 85%, a specificity of 55%, a positive predictive value of 72%, and a negative predictive value of 74%. The predictive value of LPS was comparable to those of conventional prognostic scores, which include signs and symptoms. The LPS can provide additional information to conventional prognostic scores.

Consensus document of the Italian Association of Medical Oncology and the Italian Society of Palliative Care on early palliative care

A consensus document on early palliative care was produced by a committed Working Group of the Italian Society of Medical Oncology and the Italian Society of Palliative Care to improve the early integration of palliative care in medical oncology and to stimulate and guide the choices of those who daily face the problems of advanced stage cancer patients. The simultaneous administration of antineoplastic treatments and early palliative care was shown to be beneficial in metastatic cancer pathway outcomes. Patients who could benefit from early palliative care are those with an advanced cancer at presentation, a compromised PS for cancer, and/or morbidities, and who are too frail to receive treatment. According to the Bruera practice models, in which the combination of cancer management with early palliative care can be offered, three groups of patients needing simultaneous care were identified and three different models of the delivery of palliative care were proposed. In patients with good prognosis and low need of simultaneous care, the solo practice model and the request for consultations were suggested, while in patients with poor prognosis disease with high need of simultaneous care and in conditions with high need of simultaneous care, regardless of cancer prognosis, the integrated care approach should be offered. Palliative care consultation services are seldom accessible in the majority of Italian hospitals; thus the application of various practice models depends on available resources, and a shared care model with the structures of palliative care operating in the area is often required.

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

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

A review of systemic anticancer therapy in disease palliation

BACKGROUND

Systemic anticancer therapy (SACT) is a collective term to describe the growing number of differing therapies used in malignancy to achieve palliation. Improving symptoms, quality of life (QOL) and where possible quantity of life are the goals of these treatments.

SOURCES OF DATA

A comprehensive literature review was undertaken using Medline, Embase and the Cochrane database.

AREAS OF AGREEMENT

The use of palliative SACT can lead to increases in symptom control, QOL and survival. The breadth of treatable cancers has increased along with the number of therapeutic options.

AREAS OF CONTROVERSY

The increasing use of SACT in the last weeks of life and the lack of consistency about the terms Supportive Care/Best Supportive Care in the trial setting.

GROWING POINTS

Integration between oncology and other palliative services leads to better outcomes.

AREAS TIMELY FOR DEVELOPING RESEARCH

Improved prognostication tools to elucidate which patients will benefit from SACT.

Advance Directives, Hospitalization, and Survival Among Advanced Cancer Patients with Delirium Presenting to the Emergency Department: A Prospective Study

BACKGROUND

To improve the management of advanced cancer patients with delirium in an emergency department (ED) setting, we compared outcomes between patients with delirium positively diagnosed by both the Confusion Assessment Method (CAM) and Memorial Delirium Assessment Scale (MDAS), or group A (n = 22); by the MDAS only, or group B (n = 22); and by neither CAM nor MDAS, or group C (n = 199).

MATERIALS AND METHODS

In an oncologic ED, we assessed 243 randomly selected advanced cancer patients for delirium using the CAM and the MDAS and for presence of advance directives. Outcomes extracted from patients' medical records included hospital and intensive care unit admission rate and overall survival (OS).

RESULTS

Hospitalization rates were 82%, 77%, and 49% for groups A, B, and C, respectively (p = .0013). Intensive care unit rates were 18%, 14%, and 2% for groups A, B, and C, respectively (p = .0004). Percentages with advance directives were 52%, 27%, and 43% for groups A, B, and C, respectively (p = .2247). Median OS was 1.23 months (95% confidence interval [CI] 0.46-3.55) for group A, 4.70 months (95% CI 0.89-7.85) for group B, and 10.45 months (95% CI 7.46-14.82) for group C. Overall survival did not differ significantly between groups A and B (p = .6392), but OS in group C exceeded those of the other groups (p < .0001 each).

CONCLUSION

Delirium assessed by either CAM or MDAS was associated with worse survival and more hospitalization in patients with advanced cancer in an oncologic ED. Many advanced cancer patients with delirium in ED lack advance directives. Delirium should be assessed regularly and should trigger discussion of goals of care and advance directives.

IMPLICATIONS FOR PRACTICE

Delirium is a devastating condition among advanced cancer patients. Early diagnosis in the emergency department (ED) should improve management of this life-threatening condition. However, delirium is frequently missed by ED clinicians, and the outcome of patients with delirium is unknown. This study finds that delirium assessed by the Confusion Assessment Method or the Memorial Delirium Assessment Scale is associated with poor survival and more hospitalization among advanced cancer patients visiting the ED of a major cancer center, many of whom lack advance directives. Therefore, delirium in ED patients with cancer should trigger discussion about advance directives.

Development and validation of a set of six adaptable prognosis prediction (SAP) models based on time-series real-world big data analysis for patients with cancer receiving chemotherapy: A multicenter case crossover study

Background

We aimed to develop an adaptable prognosis prediction model that could be applied at any time point during the treatment course for patients with cancer receiving chemotherapy, by applying time-series real-world big data.

Methods

Between April 2004 and September 2014, 4,997 patients with cancer who had received systemic chemotherapy were registered in a prospective cohort database at the Kyoto University Hospital. Of these, 2,693 patients with a death record were eligible for inclusion and divided into training (n = 1,341) and test (n = 1,352) cohorts. In total, 3,471,521 laboratory data at 115,738 time points, representing 40 laboratory items [e.g., white blood cell counts and albumin (Alb) levels] that were monitored for 1 year before the death event were applied for constructing prognosis prediction models. All possible prediction models comprising three different items from 40 laboratory items (₄₀C₃ = 9,880) were generated in the training cohort, and the model selection was performed in the test cohort. The fitness of the selected models was externally validated in the validation cohort from three independent settings.

Results

A prognosis prediction model utilizing Alb, lactate dehydrogenase, and neutrophils was selected based on a strong ability to predict death events within 1–6 months and a set of six prediction models corresponding to 1,2, 3, 4, 5, and 6 months was developed. The area under the curve (AUC) ranged from 0.852 for the 1 month model to 0.713 for the 6 month model. External validation supported the performance of these models.

Conclusion

By applying time-series real-world big data, we successfully developed a set of six adaptable prognosis prediction models for patients with cancer receiving chemotherapy.

Delirium assessed by Memorial Delirium Assessment Scale in advanced cancer patients admitted to an acute palliative/supportive care unit

BACKGROUND

Delirium is often unrecognized in cancer patients. The aim of this study was to investigate the prevalence of delirium assessed by the Memorial Delirium Assessment Scale (MDAS) and possible associated factors on admission to an acute palliative/supportive care unit (APSCU). The secondary outcome was to assess changes in MDAS and symptom burden at time of discharge.

METHODS

A consecutive sample of advanced cancer patients who were admitted to an APSCU was prospectively assessed for a period of 10 months. Patient demographics, including age, gender, primary diagnosis, Karnofsky status, stage of disease, and educational level were collected. The Edmonton Symptom Assessment Scale (ESAS) and the MDAS were measured at hospital admission and discharge.

RESULTS

A total of 314 patients were surveyed. Of 292 patients with MDAS available at T0, 74 (25.3%) and 24 (8.2%) had a MDAS of 7-12 and ≥13, respectively. At discharge, there was a significant decrease in the number of patients with a MDAS ≥7/30. Higher values of MDAS were associated with age (p = .028), a lower Karnofsky status (p < .0005), gender (male, p = .04), low level of education (p = .002), less awareness of disease (p < .0005), more indications for end-of-life care admission (p < .0005) or other symptoms (p = .026), hospital stay (p = .038) and death (p < .0005). Significant decreases in ESAS were observed in all patients independently of MDAS values (p < .0005).

CONCLUSION

Delirium is highly prevalent in patients admitted to APSCU, characterized by a low mortality due to early referral. Comprehensive assessment and treatment may allow a decrease in the level of cognitive disorders and symptom burden.

Differences in Symptom Burden Among Cancer Patients With Different Stages of Cachexia

CONTEXT

Cancer patients with cachexia may suffer from significant burden of symptoms and it can severely impair patients' quality of life. However, only few studies have targeted the symptom burden in cancer cachexia patients, and whether the symptom burden differed in different cachexia stages is still unclear.

OBJECTIVES

The aims of this study were to evaluate the symptom burden in cancer cachexia patients and to compare the severity and occurrence rates of symptoms among cancer patients with non-cachexia, pre-cachexia, cachexia, and refractory cachexia.

METHODS

Advanced cancer patients (n = 306) were included in this cross-sectional study. Patients were divided into four groups, based on the cachexia stages of the international consensus. The M.D. Anderson Symptom Inventory added with eight more cachexia-specific symptoms were evaluated in our patients. Differences in symptom severity and occurrence rates among the four groups were compared using one-way ANOVA or Kruskal-Wallis test analyses.

RESULTS

Lack of appetite, disturbed sleep, fatigue, lack of energy, and distress were the symptoms with highest occurrence rates and severity scores in all four groups and were exacerbated by the severity of cachexia stages. After confounders were adjusted for, significant differences were seen in symptoms of pain, fatigue, disturbed sleep, remembering problems, lack of appetite, dry mouth, vomiting, numbness, feeling dizzy, early satiety, lack of energy, tastes/smell changes, and diarrhea.

CONCLUSION

This study identified higher symptom burden in cancer patients with cachexia and it increased with the stages of cachexia, which emphasized the importance of screening in multiple co-occurring symptoms for cachexia patients.

Prognostic Tools in Patients With Advanced Cancer: A Systematic Review

PURPOSE

In 2005, the European Association for Palliative Care made recommendations for prognostic markers in advanced cancer. Since then, prognostic tools have been developed, evolved, and validated. The aim of this systematic review was to examine the progress in the development and validation of prognostic tools.

METHODS

Medline, Embase Classic and Embase were searched. Eligible studies met the following criteria: patients with incurable cancer, >18 years, original studies, population n ≥100, and published after 2003. Descriptive and quantitative statistical analyses were performed.

RESULTS

Forty-nine studies were eligible, assessing seven prognostic tools across different care settings, primary cancer types, and statistically assessed survival prediction. The Palliative Performance Scale was the most studied (n = 21,082), comprising six parameters (six subjective), was externally validated, and predicted survival. The Palliative Prognostic Score composed of six parameters (four subjective and two objective), the Palliative Prognostic Index composed of nine parameters (nine subjective), and the Glasgow Prognostic Score composed of two parameters (two objective) and were all externally validated in more than 2000 patients with advanced cancer and predicted survival.

CONCLUSION

Various prognostic tools have been validated but vary in their complexity, subjectivity, and therefore clinical utility. The Glasgow Prognostic Score would seem the most favorable as it uses only two parameters (both objective) and has prognostic value complementary to the gold standard measure, which is performance status. Further studies comparing all proved prognostic markers in a single cohort of patients with advanced cancer are needed to determine the optimal prognostic tool.

Delirium and Mortality in Critically Ill Children: Epidemiology and Outcomes of Pediatric Delirium

OBJECTIVES

Delirium occurs frequently in adults and is an independent predictor of mortality. However, the epidemiology and outcomes of pediatric delirium are not well-characterized. The primary objectives of this study were to describe the frequency of delirium in critically ill children, its duration, associated risk factors, and effect on in-hospital outcomes, including mortality. Secondary objectives included determination of delirium subtype, and effect of delirium on duration of mechanical ventilation, and length of hospital stay.

DESIGN

Prospective, longitudinal cohort study.

SETTING

Urban academic tertiary care PICU.

PATIENTS

All consecutive admissions from September 2014 through August 2015.

INTERVENTIONS

Children were screened for delirium twice daily throughout their ICU stay.

MEASUREMENTS AND MAIN RESULTS

Of 1,547 consecutive patients, delirium was diagnosed in 267 (17%) and lasted a median of 2 days (interquartile range, 1-5). Seventy-eight percent of children with delirium developed it within the first 3 PICU days. Most cases of delirium were of the hypoactive (46%) and mixed (45%) subtypes; only 8% of delirium episodes were characterized as hyperactive delirium. In multivariable analysis, independent predictors of delirium included age less than or equal to 2 years old, developmental delay, severity of illness, prior coma, mechanical ventilation, and receipt of benzodiazepines and anticholinergics. PICU length of stay was increased in children with delirium (adjusted relative length of stay, 2.3; CI = 2.1-2.5; p < 0.001), as was duration of mechanical ventilation (median, 4 vs 1 d; p < 0.001). Delirium was a strong and independent predictor of mortality (adjusted odds ratio, 4.39; CI = 1.96-9.99; p < 0.001).

CONCLUSIONS

Delirium occurs frequently in critically ill children and is independently associated with mortality. Some in-hospital risk factors for delirium development are modifiable. Interventional studies are needed to determine best practices to limit delirium exposure in at-risk children.

Adding items that assess changes in activities of daily living does not improve the predictive accuracy of the Palliative Prognostic Index

BACKGROUND

Changes in activities of daily living in cancer patients may predict their survival. The Palliative Prognostic Index is a useful tool to evaluate cancer patients, and adding an item about activities of daily living changes might improve its predictive value.

AIM

To clarify whether adding an item about activities of daily living changes improves the accuracy of Palliative Prognostic Index.

DESIGN

Multicenter prospective cohort study.

SETTING

A total of 58 palliative care services in Japan.

PARTICIPANTS

Patients aged >20 years diagnosed with locally extensive or metastatic cancer (including hematological neoplasms) who had been admitted to palliative care units, were receiving care by hospital-based palliative care teams, or were receiving home-based palliative care. Palliative care physicians recorded clinical variables at the first assessment and followed up patients 6 months later.

RESULTS

A total of 2425 subjects were recruited and 2343 of these had analyzable data. The C-statistic of the original Palliative Prognostic Index was 0.801, and those of modified Palliative Prognostic Indices ranged from 0.793 to 0.805 at 3 weeks. For 6-week survival predictions, the C-statistic of the original Palliative Prognostic Index was 0.802, and those of modified Palliative Prognostic Indices ranged from 0.791 to 0.799. The weighted kappa of the original Palliative Prognostic Index was 0.510, and those of modified Palliative Prognostic Indices ranged from 0.484 to 0.508.

CONCLUSION

Adding items about activities of daily living changes to the Palliative Prognostic Index did not improve prognostic value in advanced cancer patients.