Prognostic factors in advanced cancer patients: evidence-based clinical recommendations--a study by the Steering Committee of the European Association for Palliative Care

The Role of Nursing Diagnoses in Enhancing Prognostic Accuracy in Home-Based Cancer Care: Insights From a Retrospective Cohort Study

AIMS

To (1) describe the characteristics of patients with advanced cancer receiving home-based care, (2) identify the nursing diagnoses associated with 6-month mortality and (3) explore the predictive power of nursing diagnoses on 6-month mortality for patients with an advanced cancer diagnosis.

BACKGROUND

Nursing diagnoses have been shown to capture the complexity of patients' experiences and the specific nursing care related to patients' responses to illness, including increased mortality risk. However, there is a lack of studies investigating the relationship between nursing diagnoses and mortality among cancer patients receiving home-based care.

DESIGN

Retrospective cohort study.

METHODS

Between July 2021 and June 2023, patients with advanced cancer were consecutively admitted to a home-based care service. Medical data, prognostic indexes and nursing assessment data, including nursing diagnoses from NANDA International, assigned during the first home visit, were extracted from patient health records. Survival analysis was performed over the first 6 months using the Kaplan-Meier method and Cox proportional hazards model.

RESULTS

Among 344 enrolled patients, the most frequent nursing diagnoses were chronic pain and constipation. The 45.9% of patients died at home within 6 months after discharge. Multivariate Cox regression identified a Palliative Prognostic Index ≥ 5, palliative status, terminal phase of illness and two nursing diagnoses-imbalanced nutrition: less than body requirements and death anxiety-as significant predictors of 6-month mortality.

CONCLUSIONS

Survival in advanced cancer patients receiving home care was primarily predicted by the terminal phase of illness, Palliative Prognostic Index, palliative status and two specific nursing diagnoses: imbalanced nutrition: less than body requirements and death anxiety.

REPORTING METHOD

The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines were adopted in this study.

PATIENT OR PUBLIC CONTRIBUTION

No Patient or Public Contribution.

Prognostication As an Interactionally Delicate Matter: A Conversation Analytic Study of Hospice Multidisciplinary Team Meetings

Prognostication has been found to be a delicate matter in interactions between palliative care professionals and patients. Studies have investigated how these discussions are managed and how speakers orient to their delicate nature. However, the degree to which prognostication is a delicate matter in discussions between palliative care professionals themselves has yet to be investigated. This study explored how hospice multidisciplinary team (MDT) members oriented to the delicacy of prognostication during their meetings. Video-recordings of 24 hospice MDT meetings were transcribed and analyzed using Conversation Analysis. In-depth analysis of the interactions showed how prognostic discussions were oriented to as delicate. This was displayed through markers such as pauses and self-repair organization including cutting off words and restarts, and through accounts accompanying the prognosis. In this way, it was seen that prognostication was not necessarily straightforward. This was further evidenced when prognostic requests were problematic to respond to. It is noteworthy that prognostic discussions are delicate during hospice MDT meetings. Potential reasons may reach further than the taboo of death and lie within prognostic uncertainty and accountability. Research is warranted to explore what causes this delicacy and whether specific support is needed for hospice staff.

Initiating Prognostic Talk During Hospice Multidisciplinary Team Meetings: A Conversation Analytic Study

Objective: Guidelines recommend that patients' prognoses should be discussed by the palliative care multidisciplinary team. However, there is a lack of evidence on how multidisciplinary teams carry out prognostic discussions, and especially how prognostic talk is initiated during team meetings. This study explored how prognostic talk is initiated and responded to during meetings of a hospice multidisciplinary team. Methods: Video-recordings of 24 inpatient multidisciplinary team meetings in a UK hospice were collected from May to December 2021. A total of 65 multidisciplinary team members participated in the meetings. Recordings were transcribed and analysed using Conversation Analysis. Results: Prognostic talk was initiated during multidisciplinary team members' patient case presentations. Case presentations followed a certain template, and prognoses could be initiated as responses to template items such as the patient's Phase of Illness and Karnofsky's Performance Status score and the patient's main diagnosis and issues. Prognoses also occurred as accounts for a lack of template item responses. Beyond the patient case presentation, prognostic talk was initiated in relation to discharge planning. Prognoses appeared with sequences of assessments that accounted for them. When a prognosis was provided, it received confirming minimal responses from other team members. Conclusions: Patients' prognoses were embedded into other care discussions during meetings of a hospice multidisciplinary team. These findings can be used to inform the development of clinical guidelines and interventions aiming at improving multidisciplinary team discussions around prognosis in the future.

A laboratory-less nomogram predicting survival rates for hospice patients with advanced cancer

INTRODUCTION

Cancer is the leading cause of death globally(1). According to the WHO's 2020 Global Cancer Report(2), China represented 23.7% of new cancer cases and 30.2% of cancer-related deaths worldwide in 2020. From 2015 to 2020, cancer cases made up 18.4% of the global total. Recent statistics show that in China, malignant tumors accounted for 23.91% of all deaths, with both incidence and mortality rates on the rise. Hospice patients in China often lack the measurement of laboratory indicators, which poses difficulties in their survival prediction. This is because almost all current survival prediction models include laboratory parameters. This study established a lab-free prediction model with an accuracy of approximately 73%-75% to predict the survival rates of patients at 30 days, 45 days, and 60 days. An online version has also been developed for wide applications.

MATERIALS AND METHODS

We conducted a retrospective analysis of data from patients who received hospice care between January 2008 and December 2018. A total of 4,229 patients were divided into a training set (70%) and a test set (30%). The training group was used to develop the nomogram and a web-based calculator using the least absolute shrinkage and selection operator (LASSO) technique. The test group was used to validate the nomogram, using metrics such as the area under the receiver operating characteristic curve, calibration curve, and decision curve analysis.

RESULTS

Our analysis included 4,299 patients, with 3,163 in the training group and 1,066 in the test group. Using the LASSO algorithm, we identified eight predictors, namely quality of life, Karnofsky performance score, gender, pain duration, anorexia, abdominal distention, tachypnea, and edema. A nomogram with an online version was constructed to predict survival rates at 30, 45, and 60 days for hospice patients with advanced cancer. In the test set, the area under the curve (AUC) values were 0.7538, 0.7342, and 0.7324 for 30-day, 45-day, and 60-day survival, respectively. The nomogram demonstrated excellent calibration, and the decision curve analysis (DCA) showed a significant clinical net benefit.

CONCLUSION

This study developed a laboratory-free nomogram and a web-based calculator for accurately predicting survival in hospice patients with terminal cancer.

Prognostic utility of Palliative Prognostic Index in advanced cancer: A systematic review and meta-analysis

OBJECTIVES

To evaluate the prognostic utility of Palliative Prognostic Index (PPI) scores in predicting the death of adults with advanced cancer.

METHODS

A systematic review and meta-analysis were conducted. Six databases were searched for articles published from inception till 16 February 2024. Observational studies reporting time-to-event outcomes of PPI scores used in any setting, timing and score cutoffs were eligible. Participants were adults with advanced cancer residing in any setting. Random effects meta-analysis was used to pool hazard, risk, or odds ratios. Findings were narratively synthesized when meta-analysis was not possible.

RESULTS

Twenty-three studies (n = 11,235 patients) were included. All meta-analyses found that higher PPI scores or risk categories were significantly associated with death and, similarly, in most narratively synthesized studies. PPI > 6 vs PPI ≤ 4 (pooled adjusted HR = 5.42, 95% confidence intervals [CI] 2.01-14.59, p = 0.0009; pooled unadjusted HR = 5.05, 95% CI 4.10-6.17, p < 0.00001), 4 < PPI ≤ 6 vs PPI ≤ 4 (pooled adjusted HR = 2.04, 95% CI 1.30-3.21, p = 0.002), PPI ≥ 6 vs PPI < 6 (pooled adjusted HR = 2.52, 95% CI 1.39-4.58, p = 0.005), PPI ≤ 4 vs PPI > 6 for predicting inpatient death (unadjusted RR = 3.48, 95% CI 2.46-4.91, p < 0.00001), and PPI as a continuous variable (pooled unadjusted HR = 1.30, 95% CI 1.22-1.38, p < 0.00001) were significant predictors for mortality. Changes in PPI scores may also be useful as a prognostic factor.

SIGNIFICANCE OF RESULTS

A higher PPI score is likely an independent prognostic factor for an increased risk of death, but more research is needed to validate the risk groups as defined by the original development study. Meta-analysis results need to be interpreted cautiously, as only 2-4 studies were included in each analysis. Clinicians and researchers may find this useful for guiding decision-making regarding the suitability of curative and/or palliative treatments and clinical trial design.

Low serum creatinine as well as high serum creatinine is associated with prognosis of patients with cancer in end-of-life

BACKGROUND

The prognosis of end-of-life patients is challenging, and clinicians have attempted to predict survival more accurately. High serum creatinine (sCr) levels are associated with lower survival rates in patients with various cancers; however, low sCr levels are commonly expected in patients with terminal cancer because of muscle wasting and malnutrition. Therefore, we investigated the prevalence of low and high sCr levels and their association with survival duration in patients with terminal cancer in a palliative care unit.

METHODS

We analyzed the medical records of 280 patients admitted to a palliative care unit. Patients were divided into low (<0.5 mg/dL), normal (0.5-1.2 mg/dL), and high (>1.2 mg/dL) sCr groups. Kaplan-Meier survival curves using sCr levels were plotted and compared using the log-rank test. Using stepwise selection, a multivariable Cox proportional hazards model was used to identify the significant prognostic factors.

RESULTS

The median survival durations in the high-, low-, and normal-sCr groups were 9.57 days, 22.26 days, and 27.51 days, respectively. Multivariable Cox proportional hazard model identified that males (hazard ratio [HR], 1.81; 95% confidence interval [CI], 1.16-2.85), poor performance status (HR, 3.43; 95% CI, 1.12-10.54), total parenteral nutrition use (HR, 1.84; 95% CI, 1.09-3.1), high sCr (HR, 2.74; 95% CI, 1.52-4.94), and low sCr (HR, 1.22; 95% CI, 1.07-1.43) were significantly associated with a shorter survival time.

CONCLUSION

Low and high serum creatinine levels were significantly associated with poor survival in patients with cancer at the end-of-life stage. Therefore, readily available and simple biomarkers may help plan advanced care in palliative care settings.

Prognostic factors and development of the practical prediction score for 7-day mortality of palliative patients in the emergency department

Background End-of-life (EOL) care aims to facilitate good death and fulfill the patient's final desires. Despite the known benefits of palliative consultations, emergency physicians encounter challenges in decision-making due to limited palliative resources. Previous studies primarily focus on non-emergency settings; thus, we aimed to develop a practical prediction score for 7-day mortality for palliative patients in the emergency department (ED). Methods This retrospective cohort study, conducted at a tertiary-level university hospital in Thailand between June 2022 and May 2023, evaluated adult palliative patients who declined life-sustaining treatments. Variables associated with 7-day mortality were analyzed using univariable and multivariable logistic regression. Results Of 499 ED visits, 152 patients (30.46%) died within seven days. Six predictors were identified: solid malignancy, respiratory with mandibular movement, systolic blood pressure < 100 mmHg, lymphopenia, thrombocytopenia, and a blood urea nitrogen to creatinine ratio > 20. These factors were used to develop the Ramathibodi End-of-life (RAMA-EOL) score, demonstrating an AuROC curve of 80.46% (95% CI: 76.15-84.77). Patients with a score ≥ 3 had a positive likelihood ratio of 2.92 (95% CI: 2.38-3.57) for 7-day mortality. Conclusion The RAMA-EOL score reliably predicts 7-day mortality in palliative ED patients, facilitating timely palliative care interventions for high-risk individuals.

Development and Validation of a Survival Prediction Model for Patients With Pancreatic Cancer

INTRODUCTION

Patients with pancreatic ductal adenocarcinoma (PDAC) face challenging treatment decisions following their diagnosis. We developed and validated a survival prognostication model using routinely available clinical information, patient-reported symptoms, performance status, and initial cancer-directed treatment.

METHODS

This retrospective cohort study included patients with PDAC from 2007 to 2020 using linked administrative databases in Ontario, Canada. Patients were randomly selected for model development (75%) and validation (25%). Using the development cohort, a multivariable Cox proportional hazards regression with backward stepwise variable selection was used to predict the probability of survival. Model performance was assessed on the validation cohort using the concordance index and calibration plots.

RESULTS

There were 17,450 patients (49% female) with a median age of 72 years (interquartile range 63-81) and a mean survival time of 9 months. In the derivation cohort, 1,469 patients (11%) had early stage, 4,202 (32%) had advanced stage disease, and 7,417 (57%) had unknown stage. The following factors were associated with an increased risk of death by more than 10%: tumor in the tail of the pancreas; advanced stage; hospitalization 3 months before diagnosis; congestive heart failure or dementia; low, moderate, or high pain score; moderate or high appetite score; high dyspnea and tiredness score; and a performance status score of 60-70 or lower. The calibration plot indicated good agreement with a C-index of 0.76.

DISCUSSION

This model accurately predicted one-year survival for PDAC using clinical factors, symptoms, and performance status. This model may foster shared decision making for patients and their providers.

Factors influencing the survival time of patients with advanced cancer at the end of life: a retrospective study

BACKGROUND

Predicting the survival time of patients at the end of life can provide more accurate treatment and care programs for patients. The purpose of this study was to investigate the factors impacting 14-day survival at the end of life.

METHOD

This was a retrospective study. Patients with advanced cancer admitted to the Department of Palliative Medicine in a tertiary hospital in China in 2021 were included and classified into group A (survival time ≤ 14 days) or group B (survival time > 14 days). Patient demographic characteristics, palliative performance scale (PPS) scores, Barthel index scores, Fracture Risk Assessment Scale (FRAIL) scale scores, clinical features and laboratory test results were extracted from medical records. Univariable and multivariable logistic regression analyses were used to identify predictors of death within 14 days. Survival time was compared between frail and nonfrail patients.

RESULTS

A total of 261 patients were included (122 in group A and 139 in group B), with a median survival time of 17 (13.04, 20.96) days. There were significant differences in age, FRAIL score, PPS, Barthel index, dyspnea, edema, C-reactive protein and white blood cell count between the two groups. According to the multivariable logistic regression analysis, the PPS could predict the risk of death within 14 days (OR = 6.818, 95% CI = 3.944-11.785, p < 0.001). The median survival time was 48 (33.71, 62.29) days in the nonfrail group (n = 34) and 15 (12.46, 17.54) days in the frail group (n = 227) (p < 0.001).

CONCLUSIONS

A lower PPS increases the risk of 14-day mortality in patients at the end of life. Frailty may shorten the survival time of patients at the end of life.

Cross-cultural adaptation of the Karnofsky Performance Status instrument to Brazilian Portuguese

INTRODUCTION

The Karnofsky Performance Status (KPS) is one of the most widely used tools for assessing the prognosis of oncology patients, providing an estimate of treatment efficiency and survival. Despite this, it is commonly used in free translations without validation. The objective of the present study was to perform the cross-cultural adaptation of the KPS instrument to Brazilian Portuguese (KPS-BR) through the stages of conceptual, semantic, operational, measurement, and functional equivalences.

METHODS

To assess consistency, we used Cronbach's alpha and kappa coefficients. The Chi-square test was performed to evaluate the association between scores and the number of deaths. The relationship with survival and mortality was explored with Kaplan-Meier curves.

RESULTS

A total of 316 patients participated in the study. The internal consistency analysis resulted in a Cronbach's alpha coefficient of 0.9265. For the inter-rater analysis, the correlation coefficient was 1, as was the kappa coefficient, indicating perfect agreement between observers. The correlation coefficient between the KPS-BR scale in the test-retest was 0.8631. We observed a 100% death rate at KPS-BR scale score 20 and a gradual decrease as the KPS-BR scale score increases up to KPS-BR 40 (p<0.0001). Estimation of survival using the Kaplan-Meier method demonstrated an association between KPS-BR scale scores and survival (p<0.0001).

CONCLUSION

The KPS-BR scale showed reliability and validity for the prognostic assessment of cancer patients, demonstrating a correlation with survival.

Optimal Machine Learning Models for Developing Prognostic Predictions in Patients With Advanced Cancer

CONTEXT

Accurate prognosis prediction for cancer patients in palliative care is critical for clinical decision-making and personalized care. Traditional statistical models have been complemented by machine learning approaches; however, their comparative effectiveness remains underexplored.

OBJECTIVES

To assess the prognostic accuracy of statistical and machine learning models in predicting 30-day survival in patients with advanced cancer using objective data, such as the result of the blood test.

METHODS

A secondary analysis of the Japan-Prognostic Assessment Tools Validation (J-ProVal) study was performed from September 2012 to April 2014. We used data from 58 palliative care services in Japan and enrolled 915 patients. Four models, fractional polynomial (FP) regression, Kernel Fisher discriminant analysis (KFDA), Kernel support vector machine (KSVM), and XGBoost, were compared using 17 objective clinical characteristics. Models were evaluated with the area under the receiver operating characteristic curve (AUC) as the primary metric.

RESULTS

The KSVM model demonstrated the highest predictive accuracy (AUC: 0.834), outperforming the FP model (AUC: 0.799). XGBoost showed comparatively lower performance; however, it was likely limited by the size of the dataset.

CONCLUSIONS

Machine learning, particularly KSVM, has high predictive accuracy in palliative care when sufficient data are available. However, our findings suggest that traditional statistical models offer advantages in stability and interpretability, underscoring the importance of tailored model selection based on data characteristics.

Treatment Terminations During Radiation Therapy: A 10-Year Experience

PURPOSE

Patients undergoing radiation therapy may terminate treatment for any number of reasons. The incidence of treatment termination (TT) during radiation therapy has not been studied. Herein, we present a cohort of TT at a large multicenter radiation oncology department over 10 years.

METHODS AND MATERIALS

TTs between January 2013 and January 2023 were prospectively analyzed as part of an ongoing departmental quality and safety program. TT was defined as any premature discontinuation of therapy after initiating radiation planning. The rate of TT was calculated as a percentage of all patients starting radiation planning. All cases were presented at monthly morbidity and mortality conferences with a root cause reviewed.

RESULTS

A total of 1448 TTs were identified out of 31,199 planned courses of care (4.6%). Six hundred eighty-six (47.4%) involved patients treated with curative intent, whereas 753 (52.0%) were treated with palliative intent, and 9 (0.6%) were treated for benign disease. The rate of TT decreased from 8.49% in 2013 to 3.02% in 2022, with rates decreasing yearly. The most common disease sites for TT were central nervous system (21.7%), head and neck (19.3%), thorax (17.5%), and bone (14.2%). The most common causes of TT were hospice and/or patient expiration (35.9%), patient choice unrelated to toxicity (35.2%), and clinician choice unrelated to toxicity (11.5%).

CONCLUSIONS

This 10-year prospective review of TTs identified a year-over-year decrease in TTs as a percentage of planned patients. This decrease may be associated with the addition of root cause reviews for TTs and discussions monthly at morbidity and mortality rounds, coupled with departmental upstream quality initiatives implemented over time. Understanding the reasons behind TTs may help decrease preventable TTs. Although some TTs may be unavoidable, open discourse and quality improvement changes effectively reduce TT incidents over time.

Complication avoidance, rehabilitation, pain therapy and palliative care for patients with metastatic spine tumors: WFNS spine committee recommendations

OBJECTIVE

This review aims to formulate the most current, evidence-based recommendations regarding complication avoidance, rehabilitation, pain therapy and palliative care for patients with metastatic spine tumors.

METHODS

A systematic literature search in PubMed and MEDLINE, and was performed from 2013 to 2023 using the search terms "complications" + "spine metastases", "spine metastases" + + "rehabilitation", "spine metastases" + "pain therapy" + "palliative care". Screening criteria resulted in 35, 15 and 56 studies respectively that were analyzed. Using the Delphi method and two rounds of voting at two separate international meetings, nine members of the WFNS (World Federation of Neurosurgical Societies) Spine Committee generated nine final consensus statements.

RESULTS

Preoperative assessment for complications following surgery in patients with metastatic spine tumors should include estimation of Karnofsky score, site of primary tumor, number of spinal and visceral metastasis, ASA score and preoperative Hb (Hemoglobin) value. Complication risk factors are age > 65 years, preoperative ASA score of 3 and 4 and greater operative blood loss. Pain management using WHO analgesic concept and early mobilization are needed, starting with non-opioids, weak opioids followed by strong opioids. Morphine is the first choice for moderate to severe pain whereas IV-PCA may be used for severe breakthrough pain with monitoring. Use of bisphosphonates is considered in cases of non-localized pain and not accessible radiation therapy.

CONCLUSIONS

These nine final consensus statements provide current, evidence-based guidelines on complication avoidance, rehabilitation, pain therapy and palliative care for patients with spinal metastases.

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.

Development of a predictive model for patients with bone metastases referred to palliative radiotherapy: Secondary analysis of a multicenter study (the PRAIS trial)

BACKGROUND

The decision to administer palliative radiotherapy (RT) to patients with bone metastases (BMs), as well as the selection of treatment protocols (dose, fractionation), requires an accurate assessment of survival expectancy. In this study, we aimed to develop three predictive models (PMs) to estimate short-, intermediate-, and long-term overall survival (OS) for patients in this clinical setting.

MATERIALS AND METHODS

This study constitutes a sub-analysis of the PRAIS trial, a longitudinal observational study collecting data from patients referred to participating centers to receive palliative RT for cancer-induced bone pain. Our analysis encompassed 567 patients from the PRAIS trial database. The primary objectives were to ascertain the correlation between clinical and laboratory parameters with the OS rates at three distinct time points (short: 3 weeks; intermediate: 24 weeks; prolonged: 52 weeks) and to construct PMs for prognosis. We employed machine learning techniques, comprising the following steps: (i) identification of reliable prognostic variables and training; (ii) validation and testing of the model using the selected variables. The selection of variables was accomplished using the LASSO method (Least Absolute Shrinkage and Selection Operator). The model performance was assessed using receiver operator characteristic curves (ROC) and the area under the curve (AUC).

RESULTS

Our analysis demonstrated a significant impact of clinical parameters (primary tumor site, presence of non-bone metastases, steroids and opioid intake, food intake, and body mass index) and laboratory parameters (interleukin 8 [IL-8], chloride levels, C-reactive protein, white blood cell count, and lymphocyte count) on OS. Notably, different factors were associated with the different times for OS with only IL-8 included both in the PMs for short- and long-term OS. The AUC values for ROC curves for 3-week, 24-week, and 52-week OS were 0.901, 0.767, and 0.806, respectively.

CONCLUSIONS

We successfully developed three PMs for OS based on easily accessible clinical and laboratory parameters for patients referred to palliative RT for painful BMs. While our findings are promising, it is important to recognize that this was an exploratory trial. The implementation of these tools into clinical practice warrants further investigation and confirmation through subsequent studies with separate databases.

A Prospective Longitudinal Study to Demonstrate the Utility of the Palliative Prognostic Index in Forecasting the Short-term Survival of Patients with Advanced Cancer in India

Objectives

In this study, our primary objectives were to validate the palliative prognostic index (PPI) tool in the context of palliative care for patients with advanced cancer. Specifically, we aimed to assess the accuracy of the PPI in predicting actual survival in these patients through prospective validation.

Materials and Methods

To achieve our objectives, we enrolled a cohort of 227 advanced cancer patients receiving palliative care. The study population comprised 132 (58.1%) men and 95 (41.9%) women, with a median age of 52 years (Range: 20-81). Among them, 56 (24.7%) underwent chemotherapy, and 26 (11.5%) underwent palliative radiotherapy. We utilised the PPI score to categorise patients into three prognostic groups: (a) PPI score <4 indicating likely survival of more than 6 weeks; (b) PPI score 4-6 indicating likely survival shorter than 6 weeks; and (c) PPI score >6 indicating likely survival <3 weeks.

Results

Through our analysis, we found that the PPI demonstrated limited predictive capabilities, particularly for short-term survival (<3 weeks). The PPI's performance metrics included a positive predictive value of 45.24%, a negative predictive value of 100%, a sensitivity of 100.00% and a specificity of 88.94%.

Conclusion

In conclusion, our study establishes the limited reliability of the PPI in predicting short-term survival (<3 weeks) among patients in palliative care with advanced cancer. These findings underscore the PPI's potential as a valuable tool for healthcare professionals, aiding in the development of treatment plans and facilitating discussions on end-of-life care options with patients and their families. In addition, the PPI may assist healthcare professionals in identifying individuals who could benefit from more aggressive interventions or those approaching the end of life, thereby guiding the provision of additional support and care.

From Pixels to Prognosis: A Survey on AI-Driven Cancer Patient Survival Prediction Using Digital Histology Images

Survival analysis is an integral part of medical statistics that is extensively utilized to establish prognostic indices for mortality or disease recurrence, assess treatment efficacy, and tailor effective treatment plans. The identification of prognostic biomarkers capable of predicting patient survival is a primary objective in the field of cancer research. With the recent integration of digital histology images into routine clinical practice, a plethora of Artificial Intelligence (AI)-based methods for digital pathology has emerged in scholarly literature, facilitating patient survival prediction. These methods have demonstrated remarkable proficiency in analyzing and interpreting whole slide images, yielding results comparable to those of expert pathologists. The complexity of AI-driven techniques is magnified by the distinctive characteristics of digital histology images, including their gigapixel size and diverse tissue appearances. Consequently, advanced patch-based methods are employed to effectively extract features that correlate with patient survival. These computational methods significantly enhance survival prediction accuracy and augment prognostic capabilities in cancer patients. The review discusses the methodologies employed in the literature, their performance metrics, ongoing challenges, and potential solutions for future advancements. This paper explains survival analysis and feature extraction methods for analyzing cancer patients. It also compiles essential acronyms related to cancer precision medicine. Furthermore, it is noteworthy that this is the inaugural review paper in the field. The target audience for this interdisciplinary review comprises AI practitioners, medical statisticians, and progressive oncologists who are enthusiastic about translating AI-driven solutions into clinical practice. We expect this comprehensive review article to guide future research directions in the field of cancer research.

How accurate is clinical prognostication by oncologists during routine practice in a general hospital and can it be improved by a specific prognosis training programme: a prospective interventional study

OBJECTIVES

Oncologists need competence in clinical prognostication to deliver appropriate care to patients with cancer. Most studies on prognostication have been restricted to patients in palliative care settings. This paper investigates (1) the prognostic accuracy of physicians regarding a broad cohort of patients with cancer with a median life expectancy of >2 years and (2) whether a prognosis training can improve prognostication.

DESIGN

Prospective single-centre study comprising 3 phases, each lasting 1 month.

SETTING

Large teaching hospital, department of oncology and haematology, Germany.

PARTICIPANTS

18 physicians with a professional experience from entry level to 34 years. 736 patients with oncological and malignant haematological diseases.

INTERVENTIONS

Baseline prognostication abilities were recorded during an 'untrained' phase 1. As an intervention, a specific prognosis-training programme was implemented prior to phases 2 and 3. In phase 3, physicians had to provide additional estimates with the inclusion of electronic prognostic tools.

OUTCOME MEASURES

Prognostic estimates (PE) were collected using 'standard' surprise question (SQ), 'probabilistic' SQ (both for short-term prognostication up to 6 months) and clinician prediction of survival (CPS) (for long-term prognostication). Estimated prognoses were compared with observed survival. Phase 1 was compared with phases 2 and 3.

RESULTS

We included 2427 PE for SQ, 1506 for CPS and 800 for probabilistic SQ. Median OS was 2.5 years. SQ accuracy improved significantly (p<0.001) from 72.6% in phase 1 to 84.3% in phase 3. Probabilistic SQ in phase 3 showed 83.1% accuracy. CPS accuracy was 25.9% and could not be significantly improved. (Electronic) prognostic tools-used alone-performed significantly worse (p<0.0005) than physicians and-used by the clinicians-did not improve their performance.

CONCLUSION

A specific prognosis-training programme could improve short-term and intermediate-term prognostication. Improvement of long-term prognostication was not possible. Inexperienced residents as well as experienced oncologists benefited from training.

Health-related Quality of Life in Patients with Previously Treated Advanced Urothelial Carcinoma from EV-301: A Phase 3 Trial of Enfortumab Vedotin Versus Chemotherapy

BACKGROUND AND OBJECTIVE

In comparison to chemotherapy, enfortumab vedotin (EV) prolonged overall survival in patients with previously treated advanced urothelial carcinoma in EV-301. The objective of the present study was to assess patient experiences of EV versus chemotherapy using patient-reported outcome (PRO) analysis of health-related quality of life (HRQoL).

METHODS

For patients in the phase 3 EV-301 trial randomized to EV or chemotherapy we assessed responses to the validated European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (QLQ-C30) at baseline, weekly for the first 12 wk, and then every 12 wk until discontinuation. We analyzed the QLQ-C30 change from baseline to week 12, the confirmed improvement rate, and the time to improvement or deterioration.

KEY FINDINGS AND LIMITATIONS

Baseline PRO compliance rates were 91% for the EV arm (n = 301) and 89% for the chemotherapy arm (n = 307); the corresponding average rates from baseline to week 12 were 70% and 67%. Patients receiving EV versus chemotherapy had reduced pain (difference in change from baseline to week 12: -5.7, 95% confidence interval [CI] -10.8 to -0.7; p = 0.027) and worsening appetite loss (7.3, 95% CI 0.90-13.69; p = 0.026). Larger proportions of patients in the EV arm reported HRQoL improvement from baseline than in the chemotherapy arm; the odds of a confirmed improvement across ten QLQ-C30 function/symptom scales were 1.67 to 2.76 times higher for EV than for chemotherapy. Patients in the EV arm had a shorter time to first confirmed improvement in global health status (GHS)/QoL, fatigue, pain, and physical, role, emotional, and social functioning (all p < 0.05). EV delayed the time to first confirmed deterioration in GHS/QoL (p = 0.027), but worsening appetite loss occurred earlier (p = 0.009) in comparison to chemotherapy.

CONCLUSIONS AND CLINICAL IMPLICATIONS

HRQoL with EV was maintained, and deterioration in HRQoL was delayed with EV in comparison to chemotherapy. Better results with EV were reported for some scales, with the greatest difference observed for pain. These findings reinforce the EV safety and efficacy outcomes and benefits observed in EV-301.

PATIENT SUMMARY

Patients with previously treated advanced cancer of the urinary tract receiving the drug enfortumab vedotin maintained their HRQoL in comparison to patients treated with chemotherapy. The EV-301 trial is registered on ClinicalTrials.gov as NCT03474107 and on EudraCT as 2017-003344-21.

Explainable Machine Learning Model to Predict Overall Survival in Patients Treated With Palliative Radiotherapy for Bone Metastases

PURPOSE

The estimation of prognosis and life expectancy is critical in the care of patients with advanced cancer. To aid clinical decision making, we build a prognostic strategy combining a machine learning (ML) model with explainable artificial intelligence to predict 1-year survival after palliative radiotherapy (RT) for bone metastasis.

MATERIALS AND METHODS

Data collected in the multicentric PRAIS trial were extracted for 574 eligible adults diagnosed with metastatic cancer. The primary end point was the overall survival (OS) at 1 year (1-year OS) after the start of RT. Candidate covariate predictors consisted of 13 clinical and tumor-related pre-RT patient characteristics, seven dosimetric and treatment-related variables, and 45 pre-RT laboratory variables. ML models were developed and internally validated using the Python package. The effectiveness of each model was evaluated in terms of discrimination. A Shapley Additive Explanations (SHAP) explainability analysis to infer the global and local feature importance and to understand the reasons for correct and misclassified predictions was performed.

RESULTS

The best-performing model for the classification of 1-year OS was the extreme gradient boosting algorithm, with AUC and F1-score values equal to 0.805 and 0.802, respectively. The SHAP technique revealed that higher chance of 1-year survival is associated with low values of interleukin-8, higher values of hemoglobin and lymphocyte count, and the nonuse of steroids.

CONCLUSION

An explainable ML approach can provide a reliable prediction of 1-year survival after RT in patients with advanced cancer. The implementation of SHAP analysis provides an intelligible explanation of individualized risk prediction, enabling oncologists to identify the best strategy for patient stratification and treatment selection.

Time estimates in prognostic discussions: A conversation analytic study of hospice multidisciplinary team meetings

BACKGROUND

Recommendations state that multidisciplinary team expertise should be utilised for more accurate survival predictions. How the multidisciplinary team discusses prognoses during meetings and how they reference time, is yet to be explored.

AIM

To explore how temporality is conveyed in relation to patients' prognoses during hospice multidisciplinary team meetings.

DESIGN

Video-recordings of 24 hospice multidisciplinary team meetings were transcribed and analysed using Conversation Analysis.

SETTING/PARTICIPANTS

A total of 65 staff participating in multidisciplinary team meetings in a UK hospice from May to December 2021.

RESULTS

Team members conveyed temporality in three different ways. (i) Staff stated that a patient was dying as part of the patient's current health status. These formulations did not include a time reference per se but described the patient's current situation (as dying) instead. (ii) Staff used specific time period references where another specific reference had been provided previously that somehow constrained the timeframe. In these cases, the prognosis would conflict with other proposed care plans. (iii) Staff members used unspecific time period references where the reference appeared vague and there was greater uncertainty about when the patient was expected to die.

CONCLUSIONS

Unspecific time period references are sufficient for achieving meaningful prognostic talk in multidisciplinary teams. In-depth discussion and accurate prediction of patient prognoses are not deemed a priority nor a necessity of these meetings. Providing precise predictions may be too difficult due to uncertainty and accountability. The lack of staff pursuing more specific time references implies shared knowledge between staff and a context-specific use of prognostic estimates.

A Systematic Review of Prognostic Factors in Patients with Cancer Receiving Palliative Radiotherapy: Evidence-Based Recommendations

(1) Background: Prognostication in patients with cancer receiving palliative radiotherapy remains a challenge. To improve the process, we aim to identify prognostic factors in this population from the literature and offer evidence-based recommendations on prognostication in patients undergoing palliative radiotherapy for non-curable or advanced cancers. (2) Methods: A systematic review was performed on the medical literature from 2005 to 2023 to extract papers on the prognosis of palliative radiotherapy patients with advanced cancer. The initial selection was performed by at least two authors to determine study relevance to the target area. Studies were then classified based on type and evidence quality to determine final recommendations. (3) Results: The literature search returned 57 papers to be evaluated. Clinical and biological prognostic factors were identified from these papers to improve clinical decision making or construct prognostic models. Twenty prognostic models were identified for clinical use. There is moderate evidence supporting (i) evidence-based factors (patient, clinical, disease, and lab) in guiding decision making around palliative radiation; (ii) that certain biological factors are of importance; (iii) prognostication models in patients with advanced cancer; and that (iv) SBRT or re-irradiation use can be guided by predictions of survival by prognostic scores or clinicians. Patients with more favorable prognoses are generally better suited to SBRT or re-irradiation, and the use of prognostic models can aid in this decision making. (4) Conclusions: This evaluation has identified several factors or tools to aid in prognosis and clinical decision making. Future studies should aim to further validate these tools and factors in a clinical setting, including the leveraging of electronic medical records for data availability. To increase our understanding of how causal factors interact with palliative radiotherapy, future studies should also examine and include prediction of response to radiation as an outcome.

Qualitative analysis of expressions used in the end-of-life discussions and their associated factors

OBJECTIVES

Discussing end-of-life (EOL) issues with patients remains challenging for health professionals. Physicians may use various expressions, including euphemistic ones, when disclosing the prognosis to their patients to reduce their psychological impact. However, the actual expressions of EOL disclosure in clinical practice are unclear. This study aims to investigate the expressions used in EOL disclosures and explore their associated factors.

METHODS

A retrospective chart review was conducted enrolling all the patients who died in a university-affiliated hospital. Expressions used in the EOL disclosure were qualitatively analyzed. The patients' participation rate and length from the discussion to death were investigated.

RESULTS

EOL disclosures were observed in 341 of 358 patients. The expressions used by the physicians were categorized into 4 groups; Group 1: Clear presentation of life expectancy (n = 106; 31.1%), Group 2: Euphemistic presentation of life expectancy (n = 24; 7.0%), Group 3: Presentation of risk of sudden death (n = 147; 43.1%), Group 4: No mention on life expectancy (n = 64; 18.8%). The proportion of male patients was higher in Group 2 (79%) and lower in Group 4 (56%). Patients with cancer accounted for approximately 70% of Groups 1 and 4, but only approximately 30% of Group 3. The patient participation rate was highest in Group4 (84.4%), followed by Group 2 (50.0%). The median time from EOL disclosure to death was longer in Groups 1 and 4 (26 and 29.5 days, respectively), compared to Groups 2 and 3 (18.5 and 16 days, respectively).

SIGNIFICANCE OF RESULTS

A variety of expressions are used in EOL disclosure. Patterns of communication are influenced by patients' gender and type of illness (cancer or noncancer). Euphemisms do not seem to facilitate timely disclosure of life expectancy or patient participation. For health professionals, not only devising the expressions to alleviate their patients' distress when breaking bad news but also considering the communication process and patient background are essential.

The value of performance status in predicting venous thromboembolism in lung cancer patients treated with immune checkpoint inhibitors

INTRODUCTION

The incidence of venous thromboembolism (VTE) is notably high in lung cancer patients, particularly among those treated with immune checkpoint inhibitors (ICIs). Previous studies have focused on the relationship between Eastern Cooperative Oncology Group (ECOG) Performance Status (PS) and VTE risk in immune checkpoint inhibitor therapy, but available evidence is inconsistent.

METHODS

The clinical data of lung cancer patients treated with ICIs were collected and analyzed from West China Hospital between January 2018 and March 2022. ECOG PS score was measured on admission. The primary outcome was the incidence of VTE, encompassing both deep vein thrombosis (DVT) and pulmonary embolism (PE). Multivariate logistic regression analysis was conducted to calculate the odds ratio (OR) and 95% confidence interval (95% CI).

RESULTS

A total of 1115 lung cancer patients receiving ICIs were eligible for this study, VTE developed in 105 (9.4%) during the 12-month follow-up, of which 95 (8.5%) had DVT,14 (1.3%) had definite PE. Poor ECOG PS (PS ≥ 2) was associated with an increased risk for VTE (OR = 5.405, 95% CI = 3.067-9.525, P < 0.001), DVT (OR = 4.669, 95% CI = 2.588-8.427, P < 0.001) and PE (OR = 8.413, 95% CI = 2.565-27.600, P < 0.001) after multivariable adjustment in the study cohort.

CONCLUSION

VTE occurred in 9.4% of lung cancer patients treated with ICIs, and poor performance status was associated with an increased risk of VTE.

Aggressive anticancer treatment in the last 2 weeks of life

BACKGROUND

There is a concern that terminally ill cancer patients may be aggressively treated due to the rapidly growing possibilities of anticancer treatment. The aim of this study was to evaluate the use of anticancer treatment at the end of life (EoL).

MATERIALS AND METHODS

This retrospective study included adult patients with advanced solid cancers who were treated at the Institute of Oncology Ljubljana and died of cancer between January 2015 and December 2019. A multiple logistic regression model was used to assess an association between the aggressiveness of anticancer treatment (i.e. systemic therapy, radiotherapy and surgery) in the last 2 weeks of life and year of death, age at death, sex, prognosis of cancer and enrolment into the specialist palliative care (SPC).

RESULTS

We included 1736 patients in our analysis. Overall, 13.7% of patients were enrolled into the SPC and 14.4% received anticancer treatment in the last 2 weeks of life. The odds of receiving anticancer treatment significantly increased over time [odds ratio (OR) 1.15, 95% confidence interval (CI) 1.04-1.27]. There was an increased use of novel systemic therapy (e.g. small-molecule targeted therapy and immunotherapy) at the EoL. Older patients had significantly lower odds to receive anticancer treatment in the last 2 weeks of life as compared to younger patients (OR 0.96, 95% CI 0.95-0.98). As compared to patients receiving only a standard oncology care, those also enrolled into the SPC had significantly lower odds for anticancer treatment in the last 2 weeks of life (OR 0.22, 95% CI 0.12-0.43).

CONCLUSIONS

Terminally ill cancer patients have increased odds for receiving anticancer treatment, especially novel systemic therapies, in the last 2 weeks of life. Younger patients and those not enrolled into the SPC are at particular risk for anticancer treatment at the EoL.

Audit of 30-day mortality following palliative radiotherapy: are we able to improve patient care at the end of life?

Background

Several measurements defining the expected 30-day mortality (30-DM) to use in audit of radiation oncology departments have been proposed. However, its external validity is limited because of the lack of data from non-English speaking countries. This study assessed 30-DM in patients treated with palliative radiotherapy (PRT) in a Chilean-reference radiotherapy centre and explored if there had been tailored treatment at the end of life.

Materials and methods

Retrospective data collection was carried out for all patients treated at our institution between 1st January 2018 and 31st December 2021. Individual factors were modelled first to check for univariate association with 30-DM, those variables with a significance level of < 0.05 were considered for the final multivariable model.

Results

3,357 patients were included. The most common primary malignancies were breast (22%) and lung (16.1%). The most common treatment sites were bone (47.7%) and brain (12.2%). Overall, 30-DM was 14.7%, this rate was higher in patients treated for brain metastases (25.7%) and thoracic palliation (22.1%). 30-DM was associated with poor performance status (p < 0.01), lung and esophageal-gastric cancer (p = 0.04 and p = 0.02, respectively), metastases other than bone (p < 0.01), brain metastases (p < 0.01) and private health insurance (p <0.01).

Conclusions

In patients treated for brain metastasis and thoracic palliation 30-DM was higher than suggested benchmarks. Moreover, in these groups long courses of PRT were often performed. Audit data should be useful for planning interventions that improve selection of patients and prompting review of policies for indication and fractionation schedules of PRT.

The Incidence and Management of Cancer-Related Anorexia During Treatment with Vascular Endothelial Growth Factor Receptor-Tyrosine Kinase Inhibitors

Cancer-related anorexia is a common complication and frequently occurs in cancer patients treated with vascular endothelial growth factor receptor-tyrosine kinase inhibitors (VEGFR-TKIs). Anorexia contributes to malnutrition, body weight loss, and cachexia in affected patients. Furthermore, patients who experience anorexia have worse outcomes than those who maintain their appetite, highlighting the importance of managing anorexia and related symptoms. However, as the causes of anorexia are both diverse and interconnected, there have been challenges in evaluating and implementing effective interventions. In this review, we described the contributing factors to cancer-related anorexia and reviewed recent literature for the frequency of anorexia symptoms in patients treated with VEGFR-TKIs. Additionally, we evaluated the evidence for current interventions and the potential benefits of multimodal and multidisciplinary approaches to care. The frequency of anorexia symptoms in patients who received VEGFR-TKIs ranged from 14%-58% for all-grade anorexia and 0%-6% for grade 3 or 4 anorexia. While many of the interventions for cancer-related anorexia have minimal benefit or adverse events, recent advances in our understanding of cancer-related anorexia suggest that multimodal therapy with multidisciplinary care is a promising avenue of investigation. Several studies currently underway are anticipated to further assess the effectiveness of multimodal approaches.

Thirty-day mortality in hospitalised patients with lung cancer: incidence and predictors

OBJECTIVES

Patients with lung cancer experience high rates of hospitalisation, mainly due to the high risk of complications that emerge during the natural history of the disease. We designed a retrospective, single-centre, observational study aimed at defining the clinical predictors of 30-day mortality in hospitalised patients with lung cancer.

METHODS

Clinical records from the first admission of patients with lung cancer to the oncology ward of the University Hospital of Parma from 1 January 2017 to 1 January 2022 were collected.

RESULTS

251 consecutive patients were enrolled at the time of data cut-off. In the univariate analysis, baseline clinical predictors of 30-day mortality were Eastern Cooperative Oncology Group performance status (ECOG PS) (≥2 vs 0-1: 27.5% vs 14.8%, p=0.028), high Blaylock Risk Assessment Screening Score (BRASS) (high vs intermediate-low: 34.3% vs 11.9%, p<0.001), presence of pain (yes vs no: 24.4% vs 11.7%, p=0.009), number of metastatic sites (≥3 vs <3: 26.5% vs 13.4%, p=0.017) and presence of bone metastases (yes vs no: 29.0% vs 10.8%, p=0.001). In the multivariate analysis, high BRASS remained significantly associated with increased 30-day mortality (high vs intermediate-low; OR 2.87, 95% CI 1.21 to 6.78, p=0.016).

CONCLUSION

Our results suggest that baseline poor ECOG PS, high BRASS, presence of pain, high tumour burden and presence of bone metastases could be used as clinical predictors of 30-day mortality in hospitalised patients with lung cancer. In particular, the BRASS scale should be used as a simple tool to predict 30-day mortality in hospitalised patients with lung cancer.

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.

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.

Utilization of End-of-Life Care Rooms by Patients Who Died in a Single Hospice Unit at a National University Hospital in South Korea

PURPOSE

For the dignity of patients nearing the end of their lives, it is essential to provide end-of-life (EoL) care in a separate, dedicated space. This study investigated the utilization of specialized rooms for dying patients within a hospice unit.

METHODS

This retrospective study examined patients who died in a single hospice unit between January 1, 2017, and December 31, 2021. Utilizing medical records, we analyzed the circumstances surrounding death, the employment of specialized rooms for terminally ill patients, and the characteristics of those who received EoL care in a shared room.

RESULTS

During the 1,825-day survey period, deaths occurred on 632 days, and 799 patients died. Of these patients, 496 (62.1%) received EoL care in a dedicated room. The average duration of using this dedicated space was 1.08 days. Meanwhile, 188 patients (23.5%) died in a shared room. Logistic regression analysis revealed that a longer stay in the hospice unit was associated with a lower risk of receiving EoL care in a shared room (odds ratio [OR]=0.98, 95% confidence interval [CI] 0.97~0.99; P=0.002). Furthermore, a higher number of deaths on the day a patient died was associated with a greater risk of receiving EoL care in a shared room (OR=1.66, 95% CI 1.33~2.08; P<0.001).

CONCLUSION

To ensure that more patients receive EoL care for an adequate duration in a private setting, additional research is necessary to increase the number of dedicated rooms and incorporate them into the hospice unit at an early stage.

Long-Term Effects of a Ketogenic Diet for Cancer

A ketogenic diet has been proposed as a potential supportive therapy for cancer patients, although its long-term influence on survival rates remain controversial. In our previous report, we presented promising results for 37 of 55 patients with advanced cancer enrolled between 2013 and 2018 who remained on a ketogenic diet for at least 3 months. We followed all 55 patients until March 2023 and analyzed the data up to March 2022. For the 37 patients with previously reported promising results, the median follow-up period was 25 (range of 3-104) months and 28 patients died. The median overall survival (OS) in this subset of 37 patients was 25.1 months and the 5-year survival rate was 23.9%. We also evaluated the association between the duration of the ketogenic diet and outcome in all 55 patients, except for 2 patients with insufficient data. The patients were divided into two groups: those who followed the diet for ≥12 months (n = 21) and those who followed it for <12 months (n = 32). The median duration of the ketogenic diet was 37 (range of 12-99) months for the ≥12 months group and 3 (range of 0-11) months for the <12 months group. During the follow-up period, 41 patients died (10/21 in the ≥12 months group and 31/32 in the <12 months group). The median OS was 19.9 months (55.1 months in the ≥12 months group and 12 months in the <12 months group). Following the inverse probability of treatment weighting to align the background factors of the two groups and make them comparable, the adjusted log-rank test showed a significantly better OS rate in the group that continued the ketogenic diet for a longer period (p < 0.001, adjusted log-rank test). These results indicate that a longer continuation of the ketogenic diet improved the prognosis of advanced cancer patients.

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.

Clinicians' Prediction of Survival and Prognostic Confidence in Patients with Advanced Cancer in Three East Asian Countries

Background: Little is known about accuracy and confidence of clinicians' prediction of survival (CPS) in East-Asian countries. Objective: We aimed to examine accuracy of CPS for 7-, 21-, and 42-day survival in palliative inpatients and its association with prognostic confidence. Design: An international prospective cohort study in Japan (JP), Korea (KR), and Taiwan (TW). Setting/Subjects: Subjects were inpatients with advanced cancer admitted to 37 palliative care units in three countries. Measurements: Discrimination of CPS was investigated through sensitivity, specificity, overall accuracy, and area under the receiver operating characteristics curves (AUROCs) according to 7-, 21-, and 42-day survival. The accuracies of CPS were compared with those of Performance Status-based Palliative Prognostic Index (PS-PPI). Clinicians were instructed to rate confidence level on a 0-10-point scale. Results: A total of 2571 patients were analyzed. The specificity was highest at 93.2-100.0% for the 7-day CPS, and sensitivity was highest at 71.5-86.8% for the 42-day CPS. The AUROCs of the seven-day CPS were 0.88, 0.94, and 0.89, while those of PS-PPI were 0.77, 0.69, and 0.69 for JP, KR, and TW, respectively. As for 42-day prediction, sensitivities of PS-PPI were higher than those of CPS. Clinicians' confidence was strongly associated with the accuracy of prediction in all three countries (all p-values <0.01). Conclusions: CPS accuracies were highest (0.88-0.94) for the seven-day survival prediction. CPS was more accurate than PS-PPI in all timeframe prediction except 42-day prediction in KR. Prognostic confidence was significantly associated with the accuracy of CPS.

An Algorithm to Classify Real-World Ambulatory Status From a Wearable Device Using Multimodal and Demographically Diverse Data: Validation Study

BACKGROUND

Measuring the amount of physical activity and its patterns using wearable sensor technology in real-world settings can provide critical insights into health status.

OBJECTIVE

This study's aim was to develop and evaluate the analytical validity and transdemographic generalizability of an algorithm that classifies binary ambulatory status (yes or no) on the accelerometer signal from wrist-worn biometric monitoring technology.

METHODS

Biometric monitoring technology algorithm validation traditionally relies on large numbers of self-reported labels or on periods of high-resolution monitoring with reference devices. We used both methods on data collected from 2 distinct studies for algorithm training and testing, one with precise ground-truth labels from a reference device (n=75) and the second with participant-reported ground-truth labels from a more diverse, larger sample (n=1691); in total, we collected data from 16.7 million 10-second epochs. We trained a neural network on a combined data set and measured performance in multiple held-out testing data sets, overall and in demographically stratified subgroups.

RESULTS

The algorithm was accurate at classifying ambulatory status in 10-second epochs (area under the curve 0.938; 95% CI 0.921-0.958) and on daily aggregate metrics (daily mean absolute percentage error 18%; 95% CI 15%-20%) without significant performance differences across subgroups.

CONCLUSIONS

Our algorithm can accurately classify ambulatory status with a wrist-worn device in real-world settings with generalizability across demographic subgroups. The validated algorithm can effectively quantify users' walking activity and help researchers gain insights on users' health status.

Experience and perspectives of end-of-life care discussion and physician orders for life-sustaining treatment of Korea (POLST-K): a cross-sectional study

BACKGROUND

This study aimed to identify the healthcare providers' experience and perspectives toward end-of-life care decisions focusing on end-of-life discussion and physician's order of life-sustaining treatment documentation in Korea which are major parts of the Life-Sustaining Treatment Act.

METHODS

A cross-sectional survey was conducted using a questionnaire developed by the authors. A total of 474 subjects-94 attending physicians, 87 resident physicians, and 293 nurses-participated in the survey, and the data analysis was performed in terms of frequency, percentage, mean and standard deviation using the SPSS 24.0 program.

RESULTS

Study results showed that respondents were aware of terminal illness and physician's order of life-sustaining treatment in Korea well enough except for some details. Physicians reported uncertainty in terminal state diagnosis and disease trajectory as the most challenging. Study participants regarded factors (related to relationships and communications) on the healthcare providers' side as the major impediment to end-of-life discussion. Study respondents suggested that simplification of the process and more staff are required to facilitate end-of-life discussion and documentation.

CONCLUSION

Based on the study results, adequate education and training for better end-of-life discussion are required for future practice. Also, a simple and clear procedure for completing a physician's order of life-sustaining treatment in Korea should be prepared and legal and ethical advice would be required. Since the enactment of the Life-Sustaining Treatment Act, several revisions already have been made including disease categories, thus continuous education to update and support clinicians is also called for.

Prognostication in Advanced Cancer by Combining Actigraphy-Derived Rest-Activity and Sleep Parameters with Routine Clinical Data: An Exploratory Machine Learning Study

Survival prediction is integral to oncology and palliative care, yet robust prognostic models remain elusive. We assessed the feasibility of combining actigraphy, sleep diary data, and routine clinical parameters to prognosticate. Fifty adult outpatients with advanced cancer and estimated prognosis of <1 year were recruited. Patients were required to wear an Actiwatch® (wrist actigraph) for 8 days, and complete a sleep diary. Univariate and regularised multivariate regression methods were used to identify predictors from 66 variables and construct predictive models of survival. A total of 49 patients completed the study, and 34 patients died within 1 year. Forty-two patients had disrupted rest-activity rhythms (dichotomy index (I < O ≤ 97.5%) but I < O did not have prognostic value in univariate analyses. The Lasso regularised derived algorithm was optimal and able to differentiate participants with shorter/longer survival (log rank p < 0.0001). Predictors associated with increased survival time were: time of awakening sleep efficiency, subjective sleep quality, clinician’s estimate of survival and global health status score, and haemoglobin. A shorter survival time was associated with self-reported sleep disturbance, neutrophil count, serum urea, creatinine, and C-reactive protein. Applying machine learning to actigraphy and sleep data combined with routine clinical data is a promising approach for the development of prognostic tools.

Systemic Opioids for Dyspnea in Cancer Patients: A Real-world Observational Study

CONTEXT

Although Systemic opioids are recommended as a pharmacological treatment for cancer-related dyspnea, their effectiveness and safety needs to be investigated in a real-world context OBJECTIVES: To evaluate the effectiveness and safety of systemic regular opioids for dyspnea in cancer patients, in the real-world palliative care practice.

METHODS

This was a multicenter prospective observational study. We consecutively enrolled adult cancer patients starting regular opioids (morphine, oxycodone, hydromorphone, or fentanyl) for dyspnea from 12 palliative care services across Japan. We evaluated dyspnea intensity using the Numerical Rating Scale (NRS) and Integrated Palliative Outcome Scale (IPOS) every 24 hours until 72 hours after starting opioids (T1-T3). We also evaluated common opioid-related adverse events (AEs) and other severe AEs.

RESULTS

We enrolled 402 cancer patients. The proportion of responders was 68.8% (95%confidence intervals (CI): 0.63-0.74) at T1, 75.7% (95%CI: 0.70-0.81) at T2, and 82.1% (95%CI: 0.76-0.87) at T3. The mean differences in dyspnea NRS from baseline were 1.73 (95%CI: 1.46-1.99) at T1, 1.99 (95%CI: 1.71-2.28) at T2, and 2.47 (95%CI:2.13-2.82) at T3. The most common treatment-emergent AE was somnolence with an incidence of the severe form of approximately 10% throughout the study period. In the multivariate analysis, baseline dyspnea NRS ≥6 had a positive correlation with dyspnea relief by systemic regular opioids, while liver metastasis, clinician-predicted survival days, and opioid tolerance had a negative correlation.

CONCLUSION

Regular systemic opioids were effective for dyspnea in real-world cancer patients.

Prognostic value of the nutritional risk index in patients with newly diagnosed multiple myeloma

The nutritional risk index (NRI), which is based on weight and albumin levels, is closely associated with the prognosis of many cancers. However, its prognostic value has not been investigated in patients with newly diagnosed multiple myeloma (NDMM). We aimed to assess the association between the NRI and survival outcomes in patients with NDMM. We retrospectively collected and analyzed clinical and laboratory data from patients with NDMM between 2005 and 2019 at our center. Patients were stratified into the high NRI (> 89) and low NRI (≤ 89) groups for prognostic analysis. The NRI and other variables were also explored to evaluate their prognostic value for overall survival (OS). A total of 638 patients diagnosed with NDMM were retrospectively included. Patients in the high NRI group had a significantly better median OS than those in the low NRI group (64 months vs 43 months, p < 0.001). In the multivariate analysis, a high NRI was shown to be an independent prognostic factor for OS (hazard ratio, 0.758; 95% confidence interval, 0.587-0.977; p = 0.033). Age, performance status, transplant status, and lactate dehydrogenase level were also independent prognostic factors for OS. In conclusion, our study demonstrates that the NRI is a simple and useful predictor of survival outcomes in patients with NDMM.

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.

An online randomised controlled trial of prognosticating imminent death in advanced cancer patients: Clinicians give greater weight to advice from a prognostic algorithm than from another clinician with a different profession

BACKGROUND

A second opinion or a prognostic algorithm may increase prognostic accuracy. This study assessed the level to which clinicians integrate advice perceived to be coming from another clinician or a prognostic algorithm into their prognostic estimates, and how participant characteristics and nature of advice received affect this.

METHODS

An online double-blind randomised controlled trial was conducted. Palliative doctors, nurses and other types of healthcare professionals were randomised into study arms differing by perceived source of advice (algorithm or another clinician). In fact, the advice was the same in both arms (emanating from the PiPS-B14 prognostic model). Each participant reviewed five patient summaries. For each summary, participants: (1) provided an initial probability estimate of two-week survival (0% 'certain death'-100% 'certain survival'); (2) received advice (another estimate); (3) provided a final estimate. Weight of Advice (WOA) was calculated for each summary (0 '100% advice discounting' - 1 '0% discounting') and multilevel linear regression analyses were conducted.

CLINICAL TRIAL REGISTRATION NUMBER

NCT04568629.

RESULTS

A total of 283 clinicians were included in the analysis. Clinicians integrated advice from the algorithm more than advice from another clinician (WOA difference = -0.12 [95% CI -0.18, -0.07], p < 0.001). There was no interaction between study arm and participant profession, years of palliative care or overall experience. Advice of intermediate strength (75%) was given a lower WOA (0.31) than advice received at either the 50% (WOA 0.40) or 90% level (WOA 0.43). The overall interaction between strength of advice and study arm on WOA was significant (p < 0.001).

CONCLUSION

Clinicians adjusted their prognostic estimates more when advice was perceived to come from a prognostic algorithm than from another clinician. Research is needed to understand how clinicians make prognostic decisions and how algorithms are used in clinical practice.

Outcome of Hospitalized Cancer Patients with Hypernatremia: A Retrospective Case-Control Study

Hypernatremia (>145 mmol/L) is a relatively rare event, and the data regarding its role in the outcome of inpatients on an oncology ward are weak. The aim of this study was to describe the prevalence, prognosis, and outcome of hospitalized cancer patients with hypernatremia. We performed a retrospective case-control study of data obtained from inpatients with a solid tumor at the St. Claraspital, Basel, Switzerland, who were admitted between 2017 and 2020. The primary endpoint was overall survival. Hypernatremia was found in 93 (3.16%) of 2945 inpatients bearing cancer or lymphoma. From 991 eligible normonatremic control patients, 93 were matched according to diagnosis, age, and sex. The median overall survival time (OS) of patients with hypernatremia was 1.5 months compared to 11.7 months of the normonatremic controls (HR 2.69, 95% CI 1.85-3.90, p < 0.0001). OS of patients with irreversible compared to reversible hypernatremia was significantly shorter (23 versus 88 days, HR 4.0, 95% CI 2.04-7.70, p < 0.0001). The length of hospital stay was significantly longer for the hypernatremic than for the normonatremic group (p < 0.0001). Significantly more patients with hypernatremia died in the hospital (30.1% versus 8.6%, p < 0.001). These results suggest hypernatremia to be associated with an unfavorable outcome and a very short OS.

Malnutrition among Cancer Patients in a Tertiary Care Centre: A Descriptive Cross-sectional Study

Introduction

Malnutrition is one of the most frequent disorders among cancer patients. It is seen in 50-90% of cancer patients. This high prevalence of malnutrition is very concerning as it is associated with reduced effective treatment, functional status, quality of life and survival. The aim of the study was to find out the prevalence of malnutrition among cancer patients in a tertiary care centre.

Methods

A descriptive cross-sectional study was conducted among 95 cancer patients in the Department of Clinical Oncology of a tertiary care centre from 25 January 2022 to 25 July 2022. Ethical approval was obtained from the Institutional Review Committee (Reference number: 1192/2078/79). Convenience sampling was done. Patients were screened using Patient-Generated Subjective Global Assessment for malnutrition. Point estimate and 95% Confidence Interval were calculated.

Results

Among 95 cancer patients, 22 (23.15%) (15.10-32.90, 95% Confidence Interval) were malnourished.

Conclusions

The prevalence of malnutrition was found to be lower than in other studies done in similar settings. Nutritional assessment and support should be an integral part of care for gastrointestinal cancer.

Keywords

malnourishment; nutritional deficiency; screening.

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.

Effect of heated humidified high-flow nasal cannula (HFNC) oxygen therapy in dyspnea patients with advanced cancer, a randomized controlled clinical trial

PURPOSE

Heated humidified high-flow nasal cannula (HFNC) oxygen therapy is one of the most important oxygen therapy methods, which are commonly applied to relieve dyspnea in advanced cancer patients. Our study aims to observe the efficacy and safety of HFNC oxygen therapy on dyspnea patients with advanced cancer and explore the clinical application.

METHODS

Sixty subjects with advanced cancer requiring oxygen therapy from a grade 3, class A hospital in China were recruited and randomized (1:1) to traditional nasal catheter oxygen therapy or HFNC. Primary outcomes were dyspnea, oral dryness, and sleep condition, which were recorded after 72-h treatment. Secondary outcomes were heart rate (HR), respiration rate (RR), S_pO₂, P_aO₂, and P_aCO₂, which were recorded after 2, 6, 24, and 72 h treatment.

RESULTS

Seventy-two hours after treatment, there were significant improvements in all primary outcomes (P < 0.001). P_aO₂ and RR were statistically changed 2 h after HFNC treatment (P < 0.001). P_aCO₂ and HR were statistically changed 24 h after HFNC treatment (P < 0.001).

CONCLUSION

HFNC oxygen therapy has good effect, high safety, and is easy to be accepted by dyspnea patients with advanced cancer. It can be used as the first choice of oxygen therapy for these patients and has broad clinical prospects.

TRIAL REGISTRATION

This work was retrospectively registered in the Chinese Clinical Trials Registry (ChiCTR2100049582) on August 4, 2021.

Prognostication in palliative radiotherapy—ProPaRT: Accuracy of prognostic scores

Background

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

Materials and methods

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

Results

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

Conclusion

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

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

Association of Antipsychotic Dose With Survival of Advanced Cancer Patients With Delirium

CONTEXT

Delirium is common in patients with advanced cancer, and antipsychotics are widely used for its management.

OBJECTIVES

We aimed to explore the association of the antipsychotic dose with survival of terminally ill cancer patients with delirium.

METHODS

A secondary analysis of a multicenter prospective observational study was conducted. We enrolled adult advanced cancer patients who developed delirium and received antipsychotics at 14 palliative care units in Japan between September 2015 and May 2016. Hazard ratios of survival after starting antipsychotics between groups with different oral chlorpromazine equivalent doses: low: <100 mg, moderate: 100-200 mg, high: ≥200 mg, were calculated with adjustment for potential confounders using Cox regression. The antipsychotic dose-specific mortality risk was estimated with smooth splines.

RESULTS

Of 453 patients enrolled, 422 patients were analyzed. The median antipsychotic dose was 92.6 mg: low-dose (N = 231), moderate-dose (122), and high-dose (69). The median survival of all patients was 11 days. Compared with the low-dose group, the high-dose group showed a significantly shorter survival (HR: 1.46, 95%CI: 1.08-1.98). Smooth splines demonstrated that HR continuously increased as the antipsychotic dose increased. In patients treated with atypical antipsychotics, the high-dose group showed a significantly shorter survival than the low-dose group (HR: 2.86), while in patients treated with typical antipsychotics, survival was not significantly different (0.99).

CONCLUSIONS

Higher doses of antipsychotics were associated with increased mortality in terminally ill cancer patients with delirium. To minimize the potential mortality risk, antipsychotics should be started at low doses and titrated carefully.

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.