The TEACHH model to predict life expectancy in patients presenting for palliative spine radiotherapy: external validation and comparison with alternate models

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.

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.

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.

The LabBM score is an excellent survival prediction tool in patients undergoing palliative radiotherapy

Background and aim

The prognostic assessment of patients referred for palliative radiotherapy can be conducted by site-specific scores. A quick assessment that would cover the whole spectrum could simplify the working day of clinicians who are not specialists for a particular disease site. This study evaluated a promising score, the LabBM (validated for brain metastases), in patients treated for other indications.

Materials and methods

The LabBM score was calculated in 375 patients by assigning 1 point each for C-reactive protein and lactate dehydrogenase above the upper limit of normal, and 0.5 points each for hemoglobin, platelets and albumin below the lower limit of normal. Uni- and multivariate analyses were performed.

Results

Median overall survival gradually decreased with increasing point sum (range 25.1-1.1 months). When grouped according to the original three-tiered model, excellent discrimination was found. Patients with 0-1 points had a median survival of 15.7 months. Those with 1.5-2 points had a median survival of 5.8 months. Finally, those with 2.5-3.5 points had a median survival of 3.2 months (all p-values ≤ 0.001).

Conclusion

The LabBM score, which is derived from inexpensive blood tests and easy to use, stratified patients into three very distinct prognostic groups and deserves further validation.

[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.

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.

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.

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.

Palliative Radiotherapy: Inpatients, Outpatients, and the Changing Role of Supportive Care in Radiation Oncology

Palliative radiotherapy is an effective treatment in alleviating many symptoms of advanced cancer. Short courses of radiotherapy provide rapid symptom relief and minimize impact on patients. Patients referred for palliative radiotherapy have many concerns beyond radiotherapy; often, these concerns are not fully addressed in traditional radiotherapy clinics. Discussions of prognosis, patient goals, and concerns are areas for improved collaboration. Innovative, dedicated palliative radiotherapy programs have developed over the past 20 years to provide holistic care to patients referred for palliative radiotherapy and have improved patient-focused outcomes. Advanced radiotherapy techniques may provide opportunities to further improve palliative radiotherapy outcomes.