Predictive model for survival in patients with advanced cancer

Evaluating the Utility of the Surprise Question Among General Physicians for Appropriate Palliative Care Indication in Brazil

Objectives

This study aimed to assess the agreement between established tools, such as the Palliative Performance Scale (PPS) and Brazilian version of the Supportive and Palliative Care Indicators Tool (SPICT-BR), and the subjective assessment of palliative care (PC) need using the Surprise Question (SQ) administered by resident physicians. This assessment was conducted among hospitalized patients, with and without cancer, to determine the efficacy of these tools in indicating the need for PC.

Methods

A six-month cross-sectional study in 2019 of medical records of patients hospitalized in a single center in IAMSPE-Brazil. The SPICT-BR and PPS were applied to the medical record data, and the SQ was posed to each resident physician. Comparisons for categorical data were made using the chi-square test, with p < 0.05 considered statistically significant.

Results

Of 203 patients evaluated, 57.6% were male and 81.2% were older adults (≥60 years). The mean age was 67.40 ± 9.72 years. Chronic disease was nonneoplastic in 78.32% of patients, and 56.65% had not been hospitalized in the preceding year. The PPS score was <70% in 69.4% of patients, and 51.2% met at least one SPICT-BR criterion. Among patients with cancer, 40.9% had over two positive SPICT-BR criteria; 97.5% of these patients received NO responses to SQ by residents (p < 0.0001). Similarly, 90.6% of patients with one SPICT-BR criterion received NO responses to SQ, with no significant difference between groups.

Conclusion

The SQ proved to be a valuable tool for PC indication, particularly when administered by untrained professionals. Consistent with SPICT-BR findings, our study highlights the SQ's role in facilitating early identification of patients in need of PC.

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.

Palliative radiotherapy and the introduction of a Rapid Access Palliative Clinic in a national radiation oncology network

BACKGROUND

Palliative radiotherapy (PRT) is commonly used to treat symptoms of advanced cancer. PRT has been associated with elevated 30-day mortality (30DM). A Rapid Access Palliative Clinic (RAPC) can streamline the treatment process for patients receiving treatment.

AIMS

We reviewed the PRT practices in a radiation oncology network in Ireland, and the implementation of a RAPC. Patient outcomes were assessed to inform future treatment decisions.

METHODS

A retrospective review of all patients who received PRT over 6 months in 2018 in St. Luke's Radiation Oncology Network (SLRON) was undertaken. We assessed 30DM rates, demographics and referral to specialist palliative care (SPC) services. Subsequently, a retrospective analysis was conducted of a RAPC which ran for 6 months from 2019 to 2020. We assessed treatment data and mortality.

RESULTS

Over 6 months, 645 patients commenced PRT in the SLRON. The 30DM for this cohort was 15.8% (n = 102), with most patients having lung primaries. Of the 30DM cohort, only 55% (n = 56) were referred to SPC services and only 26.4% (n = 27) had performance status recorded. Over 6 months, 40 patients attended 28 RAPCs. Of these, 88% (n = 35) received PRT. Single fraction therapy was utilised in 60% and 48% of patients underwent CT simulation and treatment on the same day. Ultimately, 75% of patients received SPC referral.

CONCLUSIONS

Referral rates to SPC services and documentation of performance status were low in our 30DM retrospective review cohort. The RAPC facilitated quick treatment turnaround, fewer hospital visits and referral to SPC services.

A Prospective Study Assessing the Efficacy and Toxicity of Stereotactic Body Radiation Therapy for Oligometastatic Bone Metastases

Purpose

Stereotactic body radiation therapy (SBRT) is a promising treatment for oligometastatic disease in bone because of its delivery of high dose to target tissue and minimal dose to surrounding tissue. The purpose of this study is to assess the efficacy and toxicity of this treatment in patients with previously unirradiated oligometastatic bony disease.

Methods and Materials

In this prospective phase II trial, patients with oligometastatic bone disease, defined as ≤3 active sites of disease, were treated with SBRT at Brigham and Women's Hospital/Dana Farber Cancer Center and Beth Israel Deaconess Medical Center between December 2016 and May 2019. SBRT dose and fractionation regimen were not protocol mandated. Local progression-free survival, progression-free survival, prostatic specific antigen progression, and overall survival were reported. Treatment-related toxicity was also reported.

Results

A total of 98 patients and 126 lesions arising from various tumor histologies were included in this study. The median age of patients enrolled was 72.8 years (80.6% male, 19.4% female). Median follow-up was 26.7 months. The most common histology was prostate cancer (68.4%, 67/98). The most common dose prescriptions were 27/30 Gy in 3 fractions (27.0%, 34/126), 30 Gy in 5 fractions (16.7%, 21/126), or 30/35 Gy in 5 fractions (16.7%, 21/126). Multiple doses per treatment regimen reflect dose painting employing the lower dose to the clinical target volume and higher dose to the gross tumor volume. Four patients (4.1%, 4/98) experienced local progression at 1 site for each patient (3.2%, 4/126). Among the entire cohort, 2-year local progression-free survival (including death without local progression) was 84.8%, 2-year progression-free survival (including deaths as well as local, distant, and prostatic specific antigen progression) was 47.5%, and 2-year overall survival was 87.3%. Twenty-six patients (26.5%, 26/98) developed treatment-related toxicities.

Conclusions

Our study supports existing literature in showing that SBRT is effective and tolerable in patients with oligometastatic bone disease. Larger phase III trials are necessary and reasonable to determine long-term efficacy and toxicities.

Thirty-day mortality after palliative radiotherapy in advanced cancer patients: Optimizing end-of-life care in Asia

INTRODUCTION

Evidence-based guidelines recommend hypofractionated palliative radiotherapy (PRT); nonetheless, many patients receive prolonged course of PRT. To identify patients with limited benefits from PRT in end-of-life care, we evaluated the pattern of PRT at an Asian institution and factors associated with 30-day mortality after PRT (30dM).

METHODS

We retrospectively reviewed 228 patients who died after PRT in Yonsei Wonju Severance Christian hospital between October 2014 and March 2022. The associations between clinical factors and survival were assessed using the Cox proportional hazards method. Survival was analysed using the existing models to evaluate their performance in our cohort.

RESULTS

The median PRT duration was 13 (IQR, 7-15) days. Only 11.4% of the patients were treated with hypofractionated radiotherapy. One-third of the patients (32.9%) could not complete PRT and 39 (17.1%) died during PRT. The 30dM was 31.6%. The median time from PRT to death was 17 (IQR, 11-23) days for the patients who died within 30 days. The number of involved organs (≤2 vs. >2; P < 0.001), albumin level (<3.3 vs. ≥3.3; P = 0.016), admission during PRT (P < 0.001), admission 3 months before PRT (P = 0.036) and ICU care during PRT (P < 0.001) were prognostic factors. A comparison of survival based on the existing models yielded unsatisfactory results in our cohort.

CONCLUSION

Almost one-third of the patients received PRT in the last 30 days of life. The use of hypofractionation for PRT was low in this Asian population. Further research is necessary to develop a predictive model of early mortality, allowing tailored end-of-life care for Asian patients.

Radiation Therapy in the Last Month of Life: Association With Aggressive Care at the End of Life

CONTEXT

Half of the patients with cancer who undergo radiation therapy do so with palliative intent.

OBJECTIVES

To determine the proportion of undergoing radiation in the last month of life, patient characteristics, cancer course, the type and duration of radiation, whether palliative care was involved, and the of radiation with aggressive cancer care metrics.

METHODS

One thousand seven hundred twenty-seven patients who died of cancer between January 1, 2018, and December 31, 2019, were included. Demographics, cancer stage, palliative care referral, advance directives, use of home health care, radiation timing, and survival were collected. Type of radiation, course, and intent were reviewed. Chi-square analysis was utilized for categorical variables, and Kruskal-Wallis tests for continuous variables. A stepwise selection was used to build a Cox proportional hazard model.

RESULTS

Two hundred thirty-three patients underwent radiation in the last month of life. Younger patients underwent radiation 67.3 years (SD 11.52) versus 69.2 years (SD 11.96). 42.6% had radiation within two weeks of death. The average fraction number was 5.5. Individuals undergoing radiation were more likely to start chemotherapy within the last 30 days of life, continue chemotherapy within two weeks of death, be admitted to the ICU, and have two or more hospitalizations or emergency room visits. Survival measured from the date of diagnosis was shorter for those undergoing radiation, 122 days (IQR 58-462) versus 474 days (IQR 225-1150). Palliative care consultations occurred later in those undergoing radiation therapy.

CONCLUSION

Radiation therapy in the last month of life occurs in younger patients with rapidly progressive cancer, who are subject to more aggressive cancer care, and have late palliative care consults.

Who should receive single-fraction palliative radiotherapy for gastric cancer bleeding?: An exploratory analysis of a multicenter prospective observational study (JROSG 17-3)

Purpose

Although the Palliative Prognostic Index (PPI) has been used to predict survival in various cancers, to our knowledge, no study has examined its applicability in gastric cancer. This study aimed to determine the baseline PPI cutoff value for recommending single-fraction radiotherapy in patients with bleeding gastric cancer.

Materials and methods

This was a secondary analysis of the Japanese Radiation Oncology Study Group (JROSG) 17-3, a multicenter prospective study of palliative radiotherapy for bleeding gastric cancer. Discrimination was evaluated using a time-dependent receiver operating characteristic curve, and the optimal cutoff value was determined using the Youden index. A calibration plot was used to assess the agreement between predicted and observed survival.

Results

We enrolled 55 patients in JROSG 17-3. The respective median survival times were 6.7, 2.8, and 1.0 months (p = 0.021) for patients with baseline PPI scores of ≤ 2, 2 < PPI ≤ 4, and PPI > 4. The areas under the curve for predicting death within 2, 3, 4, and 5 months were 0.813, 0.787, 0.775, and 0.721, respectively. The negative predictive value was highest when survival < 2 months was predicted and the Youden index was highest when the cutoff PPI value was 2. The calibration curve showed a reasonable agreement between the predicted and observed survival.

Conclusion

Baseline PPI is useful for estimating short-term prognosis in patients treated with palliative radiotherapy for gastric cancer bleeding. A cutoff PPI value of 2 for estimating survival ≤ 2 months should be used to recommend single-fraction radiotherapy.

Mortality during or shortly after Curative-Intent Radio-(Chemo-)Therapy over the last decade at a large comprehensive cancer center

Background and Introduction

Definitive surgical, oncological and radio-oncological treatment may result in significant morbidity and acute mortality. Mortality during or shortly after treatment in patients undergoing curative radio-(chemo)-therapy has not been studied systematically. We reviewed all curative radio-(chemo-)therapies at a large comprehensive cancer center over the last decade.

Materials and Methods

The institutional record was screened for patients who received curative-intent radio-(chemo-)therapy and deceased during or within 30 days after radiotherapy. Curative therapy was defined as prescribed dosage of EQD2 ≥ 50 Gy for radiotherapy alone and EQD2 ≥ 40 Gy for radiochemotherapies. Data on demographics, disease and treatment were assembled and assessed.

Results

Of 15,255 radiotherapy courses delivered at our center, 8,515 (56%) were performed with curative-intent. During or within 30 days after radio-(chemo-)therapy, 78 patients died (0.9% of all curative-intent courses). Median age of the deceased patients was 70 (IQR, 62-78) years, and 36% (28/78) were female. Median pre-therapeutic ECOG-PS was 1 (IQR, 0-2) and Charlson-Comorbidity-Index was 3+ (IQR, 2-3+). The most common primary malignancies were head and neck cancer (33/78; 42%) and central nervous system tumors (13/78; 17%). Peritherapeutic mortality varied by primary tumor, with the highest prevalence observed in head and neck and gastrointestinal cancer patients with 2.9% (33/1,144) and 2.4% (8/332), respectively. Among patients with known cause of death (34/78; 44%), tumor progression (12/34; 35%) and pulmonary complications/causes (11/34; 35%) were most common. On multivariable regression analysis, a worse ECOG-PS was associated with a relatively earlier peri-radiotherapeutic death (p = 0.014).

Conclusion

Mortality during or within 30 days of curative-intent radio-(chemo-)therapy was low, yet highest for head and neck (2.9%) and gastrointestinal tumor (2.4%) patients. Reasons for these findings include rapid tumor progression in some cancers, good patient selection, with ECOG-PS being most useful and predictive for avoiding early mortality. Future research should help refine predictors for peri-RT mortality.

Liver-specific metastases as an independent prognostic factor in cancer patients receiving hospice care in hospital

BACKGROUND

Survival prediction is important in cancer patients receiving hospice care. Palliative prognostic index (PPI) and palliative prognostic (PaP) scores have been used to predict survival in cancer patients. However, cancer primary site with metastatic status, enteral feeding tubes, Foley catheter, tracheostomy, and treatment interventions are not considered in aforementioned tools. The study aimed to investigate the cancer features and potential clinical factors other than PPI and PaP to predict patient survival.

METHODS

We conducted a retrospective study for cancer patients admitted to a hospice ward between January 2021 and December 2021. We examined the correlation of PPI and PaP scores with survival time since hospice ward admission. Multiple linear regression was used to test the potential clinical factors other than PPI and PaP for predicting survival.

RESULTS

A total of 160 patients were enrolled. The correlation coefficients for PPI and PaP scores with survival time were -0.305 and -0.352 (both p < 0.001), but the predictabilities were only marginal at 0.087 and 0.118, respectively. In multiple regression, liver metastasis was an independent poor prognostic factor as adjusted by PPI (β = -8.495, p = 0.013) or PaP score (β = -7.139, p = 0.034), while feeding gastrostomy or jejunostomy were found to prolong survival as adjusted by PPI (β = 24.461, p < 0.001) or PaP score (β = 27.419, p < 0.001).

CONCLUSIONS

Association between PPI and PaP with patient survival in cancer patients at their terminal stages is low. The presence of liver metastases is a poor survival factor independent of PPI and PaP score.

Early death after palliative radiation treatment: 30-, 35- and 40-day mortality data and statistically robust predictors

BACKGROUND

This study analyzed mortality after radiotherapy for bone metastases (287 courses). Endpoints such as treatment in the last month of life and death within 30, 35 and 40 days from start of radiotherapy were evaluated.

METHODS

Different baseline parameters including but not limited to blood test results and patterns of metastases were assessed for association with early death. After univariate analyses, multi-nominal logistic regression was employed.

RESULTS

Of 287 treatment courses, 42 (15%) took place in the last month of life. Mortality from start of radiotherapy was 13% (30-day), 15% (35-day) and 18% (40-day), respectively. We identified three significant predictors of 30-day mortality (performance status (≤ 50, 60-70, 80-100), weight loss of at least 10% within 6 months (yes/no), pleural effusion (present/absent)) and employed these to construct a predictive model with 5 strata and mortality rates of 0-75%. All predictors of 30-day mortality were also associated with both, 35- and 40-day mortality.

CONCLUSION

Early death was not limited to the first 30 days after start of radiotherapy. For different cut-off points, similar predictive factors emerged. A model based on three robust predictors was developed.

Prognostication for Patients Receiving Palliative Radiation Therapy

Estimation of patient prognosis plays a central role in guiding decision making for the palliative management of metastatic disease, and a number of statistical models have been developed to provide survival estimates for patients in this context. In this review, we discuss several well-validated survival prediction models for patients receiving palliative radiotherapy to sites outside of the brain. Key considerations include the type of statistical model, model performance measures and validation procedures, studies' source populations, time points used for prognostication, and details of model output. We then briefly discuss underutilization of these models, the role of decision support aids, and the need to incorporate patient preference in shared decision making for patients with metastatic disease who are candidates for palliative radiotherapy.

The LabPS score: Inexpensive, Fast, and Site-agnostic Survival Prediction

OBJECTIVES

To provide a widely applicable, blood-biomarker-based and performance-status-based prognostic model, which predicts the survival of patients undergoing palliative non-brain radiotherapy. This model has already been examined in a cohort of patients treated for brain metastases and performed well.

METHODS

This was a retrospective single-institution analysis of 375 patients, managed with non-ablative radiotherapy to extracranial targets, such as bone, lung, or lymph nodes. Survival was stratified by LabPS score, a model including serum hemoglobin, platelets, albumin, C-reactive protein, lactate dehydrogenase, and performance status. Zero, 0.5, or 1 point was assigned and the final point sum calculated. A higher point sum indicates shorter survival.

RESULTS

The LabPS score predicted overall survival very well (median 0.6 to 26.5 mo, 3-month rate 0% to 100%, 1-year rate 0% to 89%), P =0.0001. However, the group with the poorest prognosis (4.5 points) was very small. Most patients with comparably short survival or radiotherapy administered in the last month of life had a lower point sum. Additional prognostic factors, such as liver metastases, opioid analgesic use, and/or corticosteroid medication, were identified.

CONCLUSIONS

If busy clinicians prefer a general prognostic model rather than a panel of separate diagnosis-specific/target-specific scores, they may consider validating the LabPS score in their own practice. In resource-constrained settings, inexpensive standard blood tests may be preferable over imaging-derived prognostic information. Just like other available scores, the LabPS cannot identify all patients with very short survival.

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.

Palliative appropriateness criteria: external validation of a new method to evaluate the suitability of palliative radiotherapy fractionation

BACKGROUND

Recently, the palliative appropriateness criteria (PAC) score, a novel metric to aid clinical decision-making between different palliative radiotherapy fractionation regimens, has been developed. It includes baseline parameters including but not limited to performance status. The researchers behind the PAC score analyzed the percent of remaining life (PRL) on treatment. The latter was accomplished by calculating the time between start and finish of palliative radiotherapy (minimum 1 day in case of a single-fraction regimen) and dividing it by overall survival in days from start of radiotherapy. The purpose of the present study was to validate this novel metric.

PATIENTS AND METHODS

The retrospective validation study included 219 patients (287 courses of palliative radiotherapy). The methods were identical to those employed in the score development study. The score was calculated by assigning 1 point each to several factors identified in the original study and using the online calculator provided by the PAC developers.

RESULTS

Median survival was 6 months and death within 30 days from start of radiotherapy was recorded in 13% of courses. PRL on treatment ranged from 1 to 23%, median 8%. Significant associations were confirmed between online-calculated PAC score, observed survival, and risk of death within 30 days from the start of radiotherapy. Patients with score 0 had distinctly better survival than all other groups. The score-predicted median risk of death within 30 days from start of radiotherapy was 22% in our cohort. A statistically significant correlation was found between predicted and observed risk (p < 0.001). The original and present study were not perfectly concordant regarding number and type of baseline parameters that should be included when calculating the PAC score.

CONCLUSION

This study supports the dual strategy of PRL and risk of early death calculation, with results stratified for fractionation regimen, in line with the original PAC score study. When considering multifraction regimens, the PAC score identifies patients who may benefit from shorter courses. Additional work is needed to answer open questions surrounding the underlying components of the score, because the original and validation study were only partially aligned.

Predicting survival in metastatic non-small cell lung cancer patients with poor ECOG-PS: A single-arm prospective study

BACKGROUND

Patients with advanced non-small cell lung cancer (NSCLC) are a heterogeneous population with short lifespan. We aimed to develop methods to better differentiate patients whose survival was >90 days.

METHODS

We evaluated 83 characteristics of 106 treatment-naïve, stage IV NSCLC patients with Eastern Cooperative Oncology Group Performance Status (ECOG-PS) >1. Automated machine learning was used to select a model and optimize hyperparameters. 100-fold bootstrapping was performed for dimensionality reduction for a second ("lite") model. Performance was measured by C-statistic and accuracy metrics in an out-of-sample validation cohort. The "lite" model was validated on a second independent, prospective cohort (N = 42). Network analysis (NA) was performed to evaluate the differences in centrality and connectivity of features.

RESULTS

The selected method was ExtraTrees Classifier, with C-statistic of 0.82 (p < 0.01) and accuracy of 0.81 (p = 0.01). The "lite" model had 16 variables and obtained C-statistic of 0.84 (p < 0.01) and accuracy of 0.75 (p = 0.039) in the first cohort, and C-statistic of 0.706 (p < 0.01) and accuracy of 0.714 (p < 0.01) in the second cohort. The networks of patients with lower survival were more interconnected. Features related to cachexia, inflammation, and quality of life had statistically different prestige scores in NA.

CONCLUSIONS

Machine learning can assist in the prognostic evaluation of advanced NSCLC. The model generated with a reduced number of features showed high accessibility and reasonable metrics. Features related to quality of life, cachexia, and performance status had increased correlation and importance scores, suggesting that they play a role at later disease stages, in line with the biological rationale already described.

Role of Traditional Chinese Medicine Syndrome Type, Gut Microbiome, and Host Immunity in Predicting Early and Advanced Stage Colorectal Cancer

OBJECTIVE

To investigate the role of Traditional Chinese Medicine (TCM) syndrome type, gut microbiome distribution, and host immunity function in predicting the early and advanced clinical stages of colorectal cancer (CRC).

METHODS

A cross-sectional case-control study was performed which included 48 early stage and 48 advanced patients with CRC enrolled from March 2018 to December 2020. 16S rRNA gene sequencing was performed to analyze the gut microbiomes of the patients, while T and B lymphocyte subsets in peripheral blood were assessed using flow cytometry. TCM syndrome type was measured using the spleen deficiency syndrome (SDS) scale.

RESULTS

The abundance levels of Prevotella, Escherichia-Shigella, and Faecalibacterium in the gut microbiota were significantly increased in the advanced group, while Bacteroides was significantly decreased. Phascolarctobacterium was detectable only in the early metaphase group, whereas Alistipes was detectable only in the advanced group. The lymphocyte (P = .006), T helper cell (TH) (P = .002), cytotoxic T cell (TC) (P = .003), double positive T cell (DPT) (P = .02), and total T counts (P = .001) were significantly higher in the early metaphase group than in the advanced metaphase group. Compared with patients with early stage CRC, the advanced group had a higher SDS score. After adjusting for clinical stage, Spearman's correlation analysis showed interactions among gut microbiome abundance, T cell level, and SDS score. Multivariate logistic analysis showed that after controlling for the SDS score, abundance of Alistipes and Faecalibacterium, and double negative T cell (DNT) level, DPT was significantly associated with a lower risk of advanced-stage disease (hazard ratio, 0.918; P = .022).

CONCLUSION

Our study suggested associations between clinical stage, SDS, gut microbiota, and T lymphocytes, which provided insights for a potential prediction model for the disease progression of CRC.

Palliative Appropriateness Criteria: A Pragmatic Method to Evaluate the Suitability of Palliative Radiotherapy Fractionation

Purpose: To describe a novel metric to aid clinical decision making between shorter versus longer palliative radiotherapy (PRT) regimens using objective patient factors. Materials and Methods: Patients receiving PRT at a single institution between 2014 and 2018 were reviewed. The time between PRT start and finish was calculated and divided by overall survival (in days from start of PRT) to generate the percent of remaining life (PRL). This value was compared across various clinical factors using the Kruskal-Wallis test. Factors identified with a significance level p < 0.01 were included in a novel Palliative Appropriateness Criteria Score (PACS) and were included in an online risk assessment tool to assist clinicians in patient-specific fractionation decisions. Results: Totally 1027 courses of PRT were analyzed. Median age was 64 years; Eastern Cooperative Oncology Group (ECOG) performance status was 3-4 in 22%. Primary malignancies included were lung (38%), breast (13.8%), prostate (9.3%), and other (39%). The indication for PRT was pain (61%), neurological (21%), or other (18%). Palliative regimens included 199 (19.4%) receiving single fraction, 176 (17.1%) receiving 2-5 fractions, and 652 (63.5%) receiving 10 fractions. Median follow-up was 83 days overall and 437 days for patients alive at last follow-up. Factors significantly associated with increased PRL (and included in the PACS) were male gender, ECOG 3-4, lung or "other" primary diagnosis (vs. breast or prostate), PRT indication (neurological dysfunction vs. pain/other), inpatient status, and extraosseous sites treatment. Death within 30 days was significantly associated with high-risk PACS categorization, regardless of fractionation scheme (p < 0.001). Conclusions: The PACS is a novel metric for evaluating the utility of PRT regimens to improve clinical decision making. Single fraction is associated with low PRL. When considering multifraction PRT regimens, the PACS identifies patients who may benefit from shorter courses of PRT and alternatively, low-risk patients for whom a more protracted course is reasonable. Prospective external validation is warranted.

Prognostic Score in Radiotherapy Practice for Palliative Treatments (PROPHET) Study for Bone Metastases: An Investigation Into the Clinical Effect on Treatment Prescription

Purpose

Bone metastases frequently occur during malignant disease. Palliative radiation therapy (PRT) is a crucial part of palliative care because it can relieve pain and improve patients' quality of life. Often, a clinician's survival estimation is too optimistic. Prognostic scores (PSs) can help clinicians tailor PRT indications to avoid over- or undertreatment. Although the PS is supposed to aid radiation oncologists (ROs) in palliative-care scenarios, it is unclear what type of support, and to what extent, could impact daily clinical practice.

Methods and Materials

A national-based investigation of the prescriptive decisions on simulated clinical cases was performed in Italy. Nine clinical cases from real-world clinical practice were selected for this study. Each case description contained complete information regarding the parameters defining the prognosis class according to the PS (in particular, the Mizumoto Prognostic Score, a validated PS available in literature and already applied in some clinical trials). Each case description contained complete information regarding the parameters defining the prognosis class according to the PS. ROs were interviewed through questionnaires, each comprising the same 3 questions per clinical case, asking (1) the prescription after detailing the clinical case features but not the PS prognostic class definition; (2) whether the RO wanted to change the prescription once the PS prognostic class definition was revealed; and (3) in case of a change of the prescription, a new prescriptive option. Three RO categories were defined: dedicated to PRT (RO-d), nondedicated to PRT (RO-nd), and resident in training (IT). Interviewed ROs were distributed among different regions of the country.

Results

Conversion rates, agreements, and prescription trends were investigated. The PS determined a statistically significant 11.12% of prescription conversion among ROs. The conversion was higher for the residents and significantly higher for worse prognostic scenario subgroups, respectively. The PS improved prescriptive agreement among ROs (particularly for worse-prognostic-scenario subgroups). Moreover, PS significantly increased standard prescriptive approaches (particularly for worse-clinical-case presentations).

Conclusions

To the best of our knowledge, the PROPHET study is the first to directly evaluate the potential clinical consequences of the regular application of any PS. According to the Prophet study, a prognostic score should be integrated into the clinical practice of palliative radiation therapy for bone metastasis and training programs in radiation oncology.

All-Cause Mortality Risk Prediction in Older Adults with Cancer: Practical Approaches and Limitations

PURPOSE OF REVIEW

The prediction of all-cause mortality is an important component of shared decision-making across the cancer care continuum, particularly in older adults with limited life expectancy, for whom there is an increased risk of over-diagnosis and treatment.

RECENT FINDINGS

Currently, several international societies recommend the use of all-cause mortality risk prediction tools when making decisions regarding screening and treatment in geriatric oncology. Here, we review some practical aspects of the utilization of those tools and dissect the characteristics of those most employed in geriatric oncology, highlighting both their advantages and their limitations.

Replacing performance status with a simple patient-reported outcome in palliative radiotherapy prognostic modelling

Background and purpose

Prognostication is key to determining care in advanced incurable cancer. Although performance status (PS) has been shown to be a strong prognostic predictor, inter-rater reliability is limited, restricting models to specialist settings. This study assessed the extent to which a simple patient-reported outcome measure (PROM), the EQ-5D, may replace PS for prognosis of patients with bone metastases.

Materials and methods

Data from 1,011 patients in the Dutch Bone Metastasis Study were used. Cox proportional hazards models were developed to investigate the prognostic value of models incorporating PS alone, the EQ-5D SC dimension alone, all EQ-5D dimensions and EQ-VAS, and finally all dimensions and PS. Three prognostic groups were identified and performance assessed using the Harrell's C-index and Altman-Royston index of separation.

Results

Replacing performance status (PS) with the self-care (SC) dimension of the EQ-5D provides similar model performance. In our SC-based model, three groups are identified with median survival of 86 days (95 % CI 76-101), 174 days (95 % CI 145-213), and 483 days (95 % CI 431-539). Whilst not statistically significantly different, the C-index was 0.706 for the PS-only model, 0.718 for SC-only and 0.717 in our full model, suggesting patient-report outcome models perform as well as that based on PS.

Conclusion

Prognostic performance was similar across all models. The SC model provides prognostic value similar to that of PS, particularly where a prognosis of<6 months is considered. Larger, more contemporaneous studies are needed to assess the extent to which PROMs may be of prognostic value, particularly where specialist assessment is less feasible.

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.

Evaluating High-Dimensional Machine Learning Models to Predict Hospital Mortality Among Older Patients With Cancer

PURPOSE

Older hospitalized cancer patients face high risks of hospital mortality. Improved risk stratification could help identify high-risk patients who may benefit from future interventions, although we lack validated tools to predict in-hospital mortality for patients with cancer. We evaluated the ability of a high-dimensional machine learning prediction model to predict inpatient mortality and compared the performance of this model to existing prediction indices.

METHODS

We identified patients with cancer older than 75 years from the National Emergency Department Sample between 2016 and 2018. We constructed a high-dimensional predictive model called Cancer Frailty Assessment Tool (cFAST), which used an extreme gradient boosting algorithm to predict in-hospital mortality. cFAST model inputs included patient demographic, hospital variables, and diagnosis codes. Model performance was assessed with an area under the curve (AUC) from receiver operating characteristic curves, with an AUC of 1.0 indicating perfect prediction. We compared model performance to existing indices including the Modified 5-Item Frailty Index, Charlson comorbidity index, and Hospital Frailty Risk Score.

RESULTS

We identified 2,723,330 weighted emergency department visits among older patients with cancer, of whom 144,653 (5.3%) died in the hospital. Our cFAST model included 240 features and demonstrated an AUC of 0.92. Comparator models including the Modified 5-Item Frailty Index, Charlson comorbidity index, and Hospital Frailty Risk Score achieved AUCs of 0.58, 0.62, and 0.71, respectively. Predictive features of the cFAST model included acute conditions (respiratory failure and shock), chronic conditions (lipidemia and hypertension), patient demographics (age and sex), and cancer and treatment characteristics (metastasis and palliative care).

CONCLUSION

High-dimensional machine learning models enabled accurate prediction of in-hospital mortality among older patients with cancer, outperforming existing prediction indices. These models show promise in identifying patients at risk of severe adverse outcomes, although additional validation and research studying clinical implementation of these tools is needed.

Prevalence and predictors for 72-h mortality after transfer to acute palliative care unit

Purpose

Accurate prediction of survival is important to facilitate clinical decision-making and improve quality of care at the end of life. While it is well documented that survival prediction poses a challenge for treating physicians, the need for clinically valuable predictive factors has not been met. This study aims to quantify the prevalence of patient transfer 72 h before death onto the acute palliative care unit in a tertiary care center in Switzerland, and to identify factors predictive of 72-h mortality.

Methods

All patients hospitalized between January and December 2020 on the acute palliative care unit of the Competence Center Palliative Care of the Department of Radiation Oncology at the University Hospital Zurich were assessed. Variables were retrieved from the electronic medical records. Univariable and multivariable logistic regressions were used to identify predictors of mortality.

Results

A total of 398 patients were screened, of which 188 were assessed. Every fifth patient spent less than 72 h on the acute palliative care unit before death. In multivariable logistic regression analysis, predictors for 72-h mortality after transfer were no prior palliative care consult (p = 0.011), no advance care directive (p = 0.044), lower performance status (p = 0.035), lower self-care index (p = 0.003), and lower blood albumin level (p = 0.026).

Conclusion

Late transfer to the acute palliative care unit is not uncommon, which can cause additional distress to patients and caretakers. Though clinically practical short-term survival predictors remain largely unidentified, early integration of palliative care should be practiced more regularly in patients with life-limiting illness.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00520-022-07075-6.

Validation and Application of the Chinese Version of the M. D. Anderson Symptom Inventory in Breast Cancer Patients

OBJECTIVE

To validate and use the Chinese Version of the M. D. Anderson Symptom Inventory (MDASI-C) to assess the symptom burden of breast cancer patients in China.

METHODS

A total of 342 breast cancer patients in China participated in this study. Their symptoms were investigated with the MDASI-C from November 2020 to February 2021, and the reliability and validity of this tool were evaluated, respectively. Cluster analysis and correlation analysis were also performed.

RESULTS

The Cronbach's alpha coefficient values of the symptom and interference items were 0.827 and 0.880, respectively. Construct validity revealed a four-factor structure. The Kaiser-Meyer-Olkin value was 0.760. The Karnofsky Performance Status, treatment phase, and cancer stage of the patients were grouped, and the differences of scores within the groups were significant. In addition, the employment status, education level, and age of the patients were significantly correlated with the symptoms. The correlation analysis of the education level of the patients showed that most of the symptoms and interference items were reduced as the education level was increased. The top three symptoms were disturbed sleep (3.10±2.52), difficulty remembering (2.54±2.30), and fatigue (2.24±2.13). The clinical and biochemical indicators such as body mass index and neutral granulocyte lymphocyte ratio had effects on many symptoms. As the patients' BMI increased, the patients' pain, disturbed sleep, and difficulty remembering were aggravated, and numbness was alleviated.

CONCLUSION

The MDASI-C is a reliable and effective assessment tool to evaluate patients with breast cancer in China. The symptoms are related to many clinical and biochemical indicators.

The cancer survival index-A prognostic score integrating psychosocial and biological factors in patients diagnosed with cancer or haematologic malignancies

Objective

We aimed to investigate whether (1) psychological and social indicators influence survival in patients diagnosed with cancer or haematologic malignancies when important biological aspects are controlled for, (2) psychological, social and biological indicators can be utilised to design one collated index for survival, usable in clinical practice to identify patients at risk of shorter survival and to improve personalised healthcare provision.

Methods

In this cross‐sectional study, 2263 patients with cancer or haematologic malignancies participated. We analysed 15 biological, psychological and social indicators as risk factors for survival with a Cox proportional hazards model. Indicators significantly associated with survival were combined to compute models for the identification of patient groups with different risks of death. The training sample contained 1122 patients. Validation samples included the remaining 1141 patients, the total sample, as well as groups with different cancer entities.

Results

Five indicators were found to significantly impact survival: Cancer site (HR: 3.56), metastatic disease (HR: 1.88), symptoms of depression (HR: 1.34), female sex (HR: 0.73) and anaemia (HR: 0.48). Combining these indicators to a model, we developed the Cancer Survival Index, identifying three distinct groups of patients with estimated survival times of 47.2 months, 141 months and 198.2 months (p < 0.001). Post hoc analysis of the influence of depression on survival showed a mediating effect of the following four factors, related to both depression and survival: previous psychiatric conditions, employment status, metastatic disease and haemoglobin levels.

Conclusions

Psychosocial and biological factors impact survival in various malignancies and can be utilised jointly to compute an index for estimating the survival of each patient individually—the Cancer Survival Index.

Prediction of post-radiotherapy survival for bone metastases: a comparison of the 3-variable number of risk factors model with the new Katagiri scoring system

We investigated patient survival after palliative radiotherapy for bone metastases while comparing the prognostic accuracies of the 3-variable number of risk factors (NRF) model and the new Katagiri scoring system (Katagiri score). Overall, 485 patients who received radiotherapy for bone metastases were grouped as per the NRF model (groups I, II and III) and Katagiri score (low-, intermediate- and high-risk). Survival was compared using the log-rank or log-rank trend test. Independent prognostic factors were identified using multivariate Cox regression analyses (MCRA). MCRA and receiver operating characteristic (ROC) curves were used to compare both models' accuracy. For the 376 evaluable patients, the overall survival (OS) rates decreased significantly in the higher-tier groups of both models (P < 0.001). All evaluated factors except 'previous chemotherapy status' differed significantly between groups. Both models exhibited independent predictive power (P < 0.001). Per NRF model, hazard ratios (HRs) were 1.44 (P = 0.099) and 2.944 (P < 0.001), respectively, for groups II and III, relative to group I. Per Katagiri score, HRs for intermediate- and high-risk groups were 4.02 (P < 0.001) and 7.09 (P < 0.001), respectively, relative to the low-risk group. Areas under the curve (AUC) for predicting 6-, 18- and 24-month mortality were significantly higher when using the Katagiri score (P = 0.036, 0.039 and 0.022). Both models predict survival. Prognostic accuracy of the Katagiri score is superior, especially in patients with long-term survival potential; however, in patients with short prognosis, no difference occurred between both models; simplicity and patient burden should also be considered.

When does early palliative care influence aggressive care at the end of life?

BACKGROUND

Early palliative care improves patient quality of life and influences cancer care. The time frame of early has not been established. Eight quality measures reflect aggressive care at the end of life. We retrospectively reviewed patients who died with cancer between January 1, 2018, through December 31, 2019, and compared the timing of palliative care consultation, advance directives (AD), and home palliative care with aggressive care at the end of life (ACEOL).

METHODS

Patients without ACEOL indicators were compared to patients with one or more than one indicator of ACEOL. The proportion of patients who received palliative care, completed AD, and the timing of palliative care and AD (less than 30 days, 30-90 days, and greater than 90 days prior to death) was compared for patients who had ACEOL versus those who did not. Chi-square analysis was used for categorical data, one-way ANOVA for continuous variables, and odds ratio (OR) with confidence intervals (CI) was reported as a measure of effect size. A p value ≤ 0.05 was considered significant.

RESULTS

1727 patients died, 46% were female, and the mean age was 69 (SD 11.91). Seventy-one percent had a palliative care consult, 26% completed AD, and 888 (51.4%) had at least one indicator of ACEOL. The most common indicator of ACEOL was new chemotherapy within 30 days of death, in 571 of 888 (64%) of patients experiencing ACEOL. ADs completed at any time reduced ACEOL (OR 0.80, 95%CI 0.64-0.99). Palliative care initiated at 30 days was associated with a greater risk of ACEOL (OR 5.32, 95% CI 3.94-7.18) and initiated between 30 and 90 days (OR 1.39, 95% CI 1.07-1.80) compared to no palliative care but was associated with reduced chemotherapy as an indicator of ACEOL when > 90 days (OR 0.46, 95% CI 0.38-0.57) before death.

DISCUSSION

Completed ADs were associated with reduced chemotherapy in the last 30 days of life and reduced ICU admissions. This may reflect goals of care and end-of-life discussions and transition of care to comfort measures. Palliative care paradoxically when initiated within 90 days before death was associated with greater ACEOL compared to no palliative care. This may be due to consultation late in the course of illness with a focus on crisis management in patients frequently utilizing the health care system. There is an associated reduction in the use of chemotherapy in the last 30 days of life if palliative care is consulted 90 days prior to death.

CONCLUSIONS

An initial palliative care consult greater than 90 days before death and ADs completed at any time during the disease trajectory was associated only with reduced chemotherapy in the last 30 days of life compared with no palliative care among the 7 ACEOL indicators. ADs were associated with reduced ICU admissions. Most palliative care consults occurred within 90 days of death and a palliative care consult within 90 days of death is not an optimal utilization of services.

Dual Use of the METSSS Model Predicting Survival After Palliative Radiotherapy: An Exploratory Analysis

Introduction: The recently published METSSS model, which was developed for prediction of survival after palliative radiotherapy, includes age, sex, cancer type, localization of distant metastases, Charlson-Deyo comorbidity score and radiotherapy site. Its ability to predict other relevant endpoints has not been studied yet. Therefore, this exploratory study analyzed the endpoints “unplanned termination of radiotherapy” and “treatment in the last 30 days of life” in the METSSS-defined risk groups (low/medium/high).

Methods: The risk group was assigned in the METSSS online calculator for our patient cohort with non-hematological malignancies treated between 2009 and 2014 during the first course of treatment (resembling details of the original METSSS study). All patients were treated with classical palliative dose/fractionation regimes such as five fractions of 4 Gy, 10 fractions of 3 Gy or 13 fractions of 3 Gy. No stereotactic high-dose radiation was utilized. Given that single-fraction radiotherapy cannot be discontinued, patients treated with 8 Gy x1 for uncomplicated painful bone metastases were excluded. Both completed and discontinued multi-fraction radiotherapy courses (at least two fractions intended) were included.

Results: The study included 290 patients, 19 of whom failed to complete their prescribed course of palliative radiotherapy (7%). Thirty-nine (13%) were irradiated in the last 30 days of life. Only one patient was classified as low-risk according to the METSSS model (medium: 15, high: 274). Only Eastern Cooperative Oncology Group (ECOG) performance status (PS) was significantly associated with incomplete treatment. All 16 patients with low/medium METSSS risk scores completed their prescribed course of radiotherapy, compared to the 93% completion rate in the high-risk group, p=0.41. With regard to treatment in the last 30 days of life, ECOG PS, metastases to brain, liver and lung, and the number of prescribed fractions were statistically significant. One patient with a low/medium METSSS risk score was treated in the last 30 days of life (6%), compared to 14% in the high-risk group, p=0.49.

Conclusion: Unexpected imbalances in the METSSS risk group size resulted in lower statistical power than anticipated. Patients with low/medium METSSS risk scores performed numerically better. However, other predictive factors, especially ECOG PS, which is not part of the METSSS model, maybe more relevant. Further efforts towards the application of the model beyond its original objective cannot be recommended.

Predictors of Mortality in Patients with Advanced Cancer-A Systematic Review and Meta-Analysis

To timely initiate advance care planning in patients with advanced cancer, physicians should identify patients with limited life expectancy. We aimed to identify predictors of mortality. To identify the relevant literature, we searched Embase, MEDLINE, Cochrane Central, Web of Science, and PubMed databases between January 2000-April 2020. Identified studies were assessed on risk-of-bias with a modified QUIPS tool. The main outcomes were predictors and prediction models of mortality within a period of 3-24 months. We included predictors that were studied in ≥2 cancer types in a meta-analysis using a fixed or random-effects model and summarized the discriminative ability of models. We included 68 studies (ranging from 42 to 66,112 patients), of which 24 were low risk-of-bias, and 39 were included in the meta-analysis. Using a fixed-effects model, the predictors of mortality were: the surprise question, performance status, cognitive impairment, (sub)cutaneous metastases, body mass index, comorbidity, serum albumin, and hemoglobin. Using a random-effects model, predictors were: disease stage IV (hazard ratio [HR] 7.58; 95% confidence interval [CI] 4.00-14.36), lung cancer (HR 2.51; 95% CI 1.24-5.06), ECOG performance status 1+ (HR 2.03; 95% CI 1.44-2.86) and 2+ (HR 4.06; 95% CI 2.36-6.98), age (HR 1.20; 95% CI 1.05-1.38), male sex (HR 1.24; 95% CI 1.14-1.36), and Charlson comorbidity score 3+ (HR 1.60; 95% CI 1.11-2.32). Thirteen studies reported on prediction models consisting of different sets of predictors with mostly moderate discriminative ability. To conclude, we identified reasonably accurate non-tumor specific predictors of mortality. Those predictors could guide in developing a more accurate prediction model and in selecting patients for advance care planning.

Association of radiation dose intensity with overall survival in patients with distant metastases

Background

Patients with metastatic cancer referred to radiation oncology have diverse prognoses and there is significant interest in personalizing treatment. We hypothesized that patients selected for higher biologically equivalent doses have improved overall survival.

Methods

The study population consists of 355 consecutive adult patients with distant metastases treated by a single radiation oncologist from 2014 to 2018. The validated NEAT model was used to prospectively stratify patients into four distinct cohorts. Radiation dose intensity was standardized using the equivalent dose in 2 Gy fractions (EQD2) model with an α/β of 10. Radiation dose intensity on survival was assessed via Cox regression models and propensity score match pairing with Kaplan–Meier analysis.

Results

The median survival was 9.3 months and the median follow‐up for surviving patients was 18.3 months. The NEAT model cohorts indicated median survivals of 29.5, 11.8, 4.9, and 1.8 months. Patients receiving an EQD2 of ≥40 Gy had a median survival of 16.0 months versus 3.8 months for patients receiving an EQD2 of <40 Gy (p < 0.001). On multivariable analysis, performance status, primary tumor site, radiation dose intensity, albumin, liver metastases, and number of active tumors were all independent predictors of survival (p < 0.05 for all). Propensity score matching was performed for performance status, albumin, number of active tumors, primary tumor site, and liver metastasis, finding higher EQD2 to remain significantly associated with improved survival within the matched cohort (p = 0.004).

Conclusion

Higher radiation dose intensity was used in patients with better prognosis and was associated with improved survival for patients with metastatic disease.

[Ethical stakes in palliative care in radiation oncology]

In 2021, the Ethics Commission of the SFRO has chosen the issue of the practice of palliative care in radiotherapy oncology. Radiation oncology plays a central role in the care of patients with cancer in palliative phase. But behind the broad name of palliative radiotherapy, we actually find a large variety of situations involving diverse ethical issues. Radiation oncologists have the delicate task to take into account multiple factors throughout a complex decision-making process. While the question of the therapeutic indication and the technical choice allowing it to be implemented remains central, reflection cannot be limited to these decision-making and technical aspects alone. It is also a question of being able to create the conditions for a singularity focused care and to build an authentic care relationship, beyond technicity. It is through this daily ethical work, in close collaboration with patients, and under essential conditions of multidisciplinarity and multiprofessionalism, that our fundamental role as caregiver can be deployed.

Patterns of Specialty Palliative Care Utilization Among Patients Receiving Palliative Radiation Therapy

CONTEXT

Palliative radiation therapy (RT) is frequently used to ameliorate cancer-associated symptoms and improve quality of life.

OBJECTIVES

To examine how palliative care (PC) as a specialty is integrated at the time of RT consultation for patients with advanced cancer.

METHODS

We retrospectively reviewed 162 patients with metastatic cancer who received palliative RT at our institution (7/2017-2/2018). Fisher's exact test identified differences in incidence of receiving any specialty PC. Logistic regression analyses determined predictors of receiving PC.

RESULTS

Of the 74 patients (46%) who received any specialty PC, 24 (32%) initiated PC within four weeks of RT consultation. The most common reasons for specialty PC initiation were pain (64%) and goals of care/end-of-life care management (23%). Referrals to specialty PC were made by inpatient care teams (48.6%), medical oncologists (48.6%), radiation oncologists (1.4%), and self-referring patients (1.4%). Patients with pain at RT consultation had a higher incidence of receiving specialty PC (58.7% vs. 37.4%, P = 0.0097). There was a trend toward decreased PC among patients presenting with neurological symptoms (34.8% vs. 50%, P = 0.084). On multivariable analysis, receiving specialty PC significantly differed by race (non-white vs. white, odds ratio [OR] = 6.295 [95% CI 1.951-20.313], P = 0.002), cancer type (lung vs. other histology, OR = 0.174 [95% CI 0.071-0.426], P = 0.0006), and RT consultation setting (inpatient vs. outpatient, OR = 3.453 [95% CI 1.427-8.361], P = 0.006).

CONCLUSION

Fewer than half of patients receiving palliative RT utilized specialty PC. Initiatives are needed to increase PC, especially for patients with lung cancer and neurological symptoms, and to empower radiation oncologists to refer patients to specialty PC.

A Prognostic Model to Facilitate Palliative Care Referral in Oncology Outpatients

Purpose

We aimed to develop a prognostic model to assist palliative care referral at least 3 months before death in advanced cancer patients treated at an outpatient medical oncology clinic.

Materials and Methods

In this prospective cohort study, a total of 200 patients were enrolled at a tertiary cancer center in South Korea. The major eligibility criterion was an expected survival of less than a year as estimated by their oncologists. We analyzed the influences of known prognostic factors along with chemotherapy status, mid-arm circumference, and triceps skinfold thickness on survival time.

Results

The mean age of the patients was 64.5 years, 36% were female, and the median survival time was 7.6 months. In the multivariate analysis, we found six significant factors related to poor survival: a poor Eastern Cooperative Oncology Group (ECOG) performance status (≥ 2), not undergoing chemotherapy, anorexia, a low lymphocyte level (< 12%), a high lactate dehydrogenase (LDH) level (≥ 300 IU/L), and a low mid-arm circumference (< 23 cm). We developed a prognostic model (score, 0–8.0) to predict 3-month survival based on the multivariate analysis. Patients who scored ≥ 4.0 points had a short survival of less than 3 months (p < 0.001). The discriminating ability of the prognostic model using the area under the receiver operating characteristic curve was 0.88.

Conclusion

The prognostic model using ECOG performance status, chemotherapy status, anorexia, lymphocytes, LDH, and mid-arm circumference can predict 3-month survival in medical oncology outpatients. It can alert oncologists to refer patients to palliative care specialists before it is too late.

Efficacy and Survival after Palliative Radiotherapy for Malignant Pulmonary Obstruction

Introduction: The purpose of this study was to determine the efficacy of palliative radiotherapy (PRT) for patients with pulmonary obstruction from advanced malignancy and identify factors associated with lung re-expansion and survival. Materials and Methods: We reviewed all patients treated with PRT for malignant pulmonary obstruction (n = 108) at our institution between 2010 and 2018. Radiographic evidence of lung re-expansion was determined through review of follow-up CT or chest X-ray. Cumulative incidence of re-expansion and overall survival (OS) were estimated using competing risk methodology. Clinical characteristics were evaluated for association with re-expansion, OS, and early mortality. Treatment time to remaining life ratio (TT:RL) was evaluated as a novel metric for palliative treatment. Results: Eighty-one percent of patients had collapse of an entire lung lobe, 46% had Eastern Cooperative Oncology Group (ECOG) performance status 3-4, and 64% were inpatient at consultation. Eighty-four patients had follow-up imaging available, and 25 (23%) of all patients had lung re-expansion at median time of 35 days. Rates of death without re-expansion were 38% and 65% at 30 and 90 days, respectively. Median OS was 56 days. Death within 30 days of PRT occurred in 38%. Inpatients and larger tumors trended toward lower rates of re-expansion. Notable factors associated with OS were re-expansion, nonlung histology, tumor size, and performance status. Median TT:RL was 0.11 and significantly higher for subgroups: ECOG 3-4 (0.19), inpatients (0.16), patients with larger tumors (0.14), those unfit for systemic therapy (0.17), and with 10-fraction PRT (0.14). Conclusion: One-fourth of patients experienced re-expansion after PRT for malignant pulmonary obstruction. Survival is poor and a significant proportion of remaining life may be spent on treatment. Careful consideration of these clinical factors is recommended when considering PRT fractionation.

Risk Stratification for Imminent Risk of Death at the Time of Palliative Radiotherapy Consultation

This cohort study of patients with advanced cancer who received palliative radiotherapy within 30 days of death assesses models of prognostic criteria for providing radiotherapy at the end of life and compares outcomes with similar prognostic tools.

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

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

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

BACKGROUND

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

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

PURPOSE

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

PATIENTS AND METHODS

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

RESULTS

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

CONCLUSION

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

Characteristics and Predictors of Radiographic Local Failure in Patients With Spinal Metastases Treated With Palliative Conventional Radiation Therapy

Purpose

Although local control is an important issue for longer-term survivors of spinal metastases treated with conventional external beam radiation therapy (EBRT), the literature on radiographic local failure (LF) in these patients is sparse. To inform clinical decision-making, we evaluated rates, consequences, and predictors of radiographic LF in patients with spinal metastases managed with palliative conventional EBRT alone.

Methods and Materials

We retrospectively reviewed 296 patients with spinal metastases who received palliative EBRT at a single institution (2006-2013). Radiographic LF was defined as radiologic progression within the treatment field, with death considered a competing risk. Kaplan-Meier, cumulative incidence, and Cox regression analyses determined overall survival estimates, LF rates, and predictors of LF, respectively.

Results

There were 182 patients with follow-up computed tomography or magnetic resonance imaging; median overall survival for these patients was 7.7 months. Patients received a median of 30 Gy in 10 fractions to a median of 4 vertebral bodies. Overall, 74 of 182 patients (40.7%) experienced LF. The 6-, 12-, and 18-month LF rates were 26.5%, 33.1%, and 36.5%, respectively, while corresponding rates of death were 24.3%, 38.1%, and 45.9%. Median time to LF was 3.8 months. Of those with LF, 51.4% had new compression fractures, 39.2% were admitted for pain control, and 35.1% received reirradiation; median time from radiation therapy (RT) to each of these events was 3.0, 5.7, and 9.2 months, respectively. Independent predictors of LF included single-fraction RT (8 Gy) (hazard ratio [HR], 2.592; 95% confidence interval [CI], 1.437-4.675; P = .002), lung histology (HR, 3.568; 95% CI, 1.532-8.309; P = .003), and kidney histology (HR, 4.937; 95% CI, 1.529-15.935; P = .008).

Conclusions

Patients experienced a >30% rate of radiographic LF by 1 year after EBRT. Single-fraction RT and lung or kidney histology predicted LF. Given the high rates of LF for patients with favorable prognosis, assessing the risk of death versus LF is important for clinical decision-making.

Adoption of single fraction radiotherapy for uncomplicated bone metastases in a tertiary centre

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Single fraction radiotherapy is feasible for uncomplicated bone metastases.

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Four-week mortality was similar between single fraction and multiple fraction.

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Our paper has the highest rate of reported single fraction radiotherapy in literature.

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Re-irradiation were higher for single fraction radiotherapy in uncomplicated bone metastases.

Background

Single-fraction radiotherapy (SFRT) offers equal pain relief for uncomplicated painful bone metastases as compared to multiple-fraction radiotherapy (MFRT). Despite this evidence, the adoption of SFRT has been poor with published rates of SFRT for uncomplicated bone metastases ranging from <10% to 70%. We aimed to evaluate the adoption of SFRT and its evolution over time following the more formal endorsement of the international guidelines in our centre starting from 2013.

Materials and methods

We performed a retrospective review of fractionation schedules at our centre for painful uncomplicated bone metastases from January 2013 until December 2017. Only patients treated with 1 × 8 Gy (SFRT-group) or 10 × 3 Gy (MFRT-group) were included. We excluded other fractionation schedules, primary cancer of the bone and post-operative radiotherapy. Uncomplicated was defined as painful but not associated with impending fracture, existing fracture or existing neurological compression. Temporal trends in SFRT/MFRT usage and overall survival were investigated. We performed a lesion-based patterns of care analysis and a patient-based survival analysis. Mann-Whitney U and Chi-square test were used to assess differences between fractionation schedules and temporal trends in prescription, with Kaplan-Meier estimates used for survival analysis (p-value <0.05 considered significant).

Results

Overall, 352 patients and 594 uncomplicated bone metastases met inclusion criteria. Patient characteristics were comparable between SFRT and MFRT, except for age. Overall, SFRT was used in 92% of all metastases compared to 8% for MFRT. SFRT rates increased throughout the study period from 85% in 2013 to 95% in 2017 (p = 0.06). Re-irradiation rates were higher in patients treated with SFRT (14%) as compared to MFRT (4%) (p = 0.046). Four-week mortality and median overall survival did not differ significantly between SFRT and MFRT (17% vs 18%, p = 0.8 and 25 weeks vs 38 weeks, p = 0.97, respectively).

Conclusions

Adherence to the international guidelines for SFRT for uncomplicated bone metastasis was high and increased over time to 95%, which is the highest reported rate in literature.

Physician-Predicted Prognosis and Palliative Radiotherapy Treatment Utilization at the End of Life: An Audit of a Large Cancer Center Network

CONTEXT

At our institution, clinical pathways capture physicians' prognostication of patients being evaluated for palliative radiotherapy. We hypothesize a low utilization rate of long-course radiotherapy (LCRT) and stereotactic ablative radiotherapy (SAbR) among patients seen at the end of life, especially those with physician-predicted poor prognosis.

OBJECTIVE

To analyze utilization rates and predictors of LCRT and SAbR at the end of life.

METHODS

A retrospective review was conducted on patients who were evaluated for palliative radiotherapy between January 2017 and August 2019 and died within 90 days of consultation. Binary logistic regression was used to identify predictors for utilization of LCRT (≥10 fractions) and SAbR.

RESULTS

A total of 1608 patients were identified, of which 1038 patients (64.6%) were predicted to die within a year. Six hundred ninety-three patients (66.8%) out of 1038 were prescribed LCRT or SAbR. On a multivariate analysis, patients were less likely to be prescribed LCRT if treated at an academic site (odds ratio [OR], 0.30; 95% confidence interval [CI], 0.23-0.39; P < 0.01) and treated for bone metastases (OR, 0.08; 95% CI, 0.05-0.11; P < 0.01) or other nonbrain/nonbone metastases (OR, 0.19; 95% CI, 0.13-0.30; P < 0.01). SAbR was less likely to be prescribed among patients predicted to die within a year (OR, 0.09; 95% CI, 0.06-0.16; P < 0.01), treated for bone metastases (OR, 0.13; 95% CI, 0.07-0.22; P < 0.01), with poor performance status (OR, 0.51; 95% CI, 0.31-0.85; P = 0.01), and with a breast primary (OR, 0.35; 95% CI, 0.15-0.82; P = 0.02).

CONCLUSION

Although most patients were predicted to have a limited prognosis, LCRT and SAbR were commonly prescribed at the end of life.

Validation of the Palliative Prognostic Index, Performance Status-Based Palliative Prognostic Index and Chinese Prognostic Scale in a home palliative care setting for patients with advanced cancer in China

BACKGROUND

The predictive value of the prognostic tool for patients with advanced cancer is uncertain in mainland China, especially in the home-based palliative care (HPC) setting. This study aimed to compare the accuracy of the Palliative Prognostic Index (PPI), the Performance Status-Based Palliative Prognostic Index (PS-PPI), and the Chinese Prognosis Scale (ChPS) for patients with advanced cancer in the HPC setting in mainland China.

METHODS

Patients with advanced cancer admitted to the hospice center of Yuebei People's Hospital between January 2014 and December 2018 were retrospectively calculated the scores according to the three prognostic tools. The Kaplan-Meier method was used to compare survival times among different risk groups. Receiver operating characteristic curve analysis was used to assess the predictive value. The accuracy of 21-, 42- and 90-day survival was compared among the three prognostic tools.

RESULTS

A total of 1863 patients were included. Survival time among the risk groups of all prognostic tools was significantly different from each other except for the PPI. The AUROC of the ChPS was significantly higher than that of the PPI and PS-PPI for 7-, 14, 21-, 42-, 90-, 120-, 150- and 180-day survival (P < 0.05). The AUROC of the PPI and PS-PPI were not significantly different from each other (P > 0.05).

CONCLUSIONS

The ChPS is more suitable than the PPI and PS-PPI for advanced cancer patients in the HPC setting. More researches are needed to verify the predictive value of the ChPS, PPI, and PS-PPI in the HPC setting in the future.

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

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

External validation of life expectancy prognostic models in patients evaluated for palliative radiotherapy at the end-of-life

BACKGROUND

The TEACHH and Chow models were developed to predict life expectancy (LE) in patients evaluated for palliative radiotherapy (PRT). We sought to validate the TEACHH and Chow models in patients who died within 90 days of PRT consultation.

METHODS

A retrospective review was conducted on patients evaluated for PRT from 2017 to 2019 who died within 90 days of consultation. Data were collected for the TEACHH and Chow models; one point was assigned for each adverse factor. TEACHH model included: primary site of disease, ECOG performance status, age, prior palliative chemotherapy courses, hospitalization within the last 3 months, and presence of hepatic metastases; patients with 0-1, 2-4, and 5-6 adverse factors were categorized into groups (A, B, and C). The Chow model included non-breast primary, site of metastases other than bone only, and KPS; patients with 0-1, 2, or 3 adverse factors were categorized into groups (I, II, and III).

RESULTS

A total of 505 patients with a median overall survival of 2.1 months (IQR: 0.7-2.6) were identified. Based on the TEACHH model, 10 (2.0%), 387 (76.6%), and 108 (21.4%) patients were predicted to live >1 year, >3 months to ≤1 year, and ≤3 months, respectively. Utilizing the Chow model, 108 (21.4%), 250 (49.5%), and 147 (29.1%) patients were expected to live 15.0, 6.5, and 2.3 months, respectively.

CONCLUSION

Neither the TEACHH nor Chow model correctly predict prognosis in a patient population with a survival <3 months. A better predictive tool is required to identify patients with short LE.

Prognostic role of baseline neutrophil-to-lymphocyte ratio in metastatic solid tumors

High baseline neutrophil-to-lymphocyte ratio (NLR) has been associated with poor survival in a number of solid tumors, but has not been extensively investigated in the context of radiation oncology. Developing more robust models to predict survival would inform patient care for patients with metastatic solid tumors. The present study was undertaken to evaluate the effect of baseline NLR (using 4 as a cutoff) on survival in 320 consecutive patients with metastatic cancer who were referred to a single radiation oncologist between 2012 and 2015, with a median follow-up of 20.6 months. The median NLR was 4.4 (interquartile range, 2.8-7.2). Patients with a baseline NLR ≤4 had a median survival of 9.3 months compared to 4.1 months for NLR >4 (P<0.001). The number of active tumors, Eastern Cooperative Oncology Group performance status score, baseline albumin, primary tumor site, liver metastases and baseline NLR predicted overall survival on both univariate and multivariate analysis (P<0.05 for all). After adjusting for known prognostic factors for advanced solid tumors, baseline NLR >4 independently predicted adverse survival in this cohort.