Clinician- and Patient-Directed Communication Strategies for Patients With Cancer at High Mortality Risk: A Cluster Randomized Trial

Importance

Serious illness conversations (SICs) that elicit patients' values, goals, and care preferences reduce anxiety and depression and improve quality of life, but occur infrequently for patients with cancer. Behavioral economic implementation strategies (nudges) directed at clinicians and/or patients may increase SIC completion.

Objective

To test the independent and combined effects of clinician and patient nudges on SIC completion.

Design, Setting, and Participants

A 2 × 2 factorial, cluster randomized trial was conducted from September 7, 2021, to March 11, 2022, at oncology clinics across 4 hospitals and 6 community sites within a large academic health system in Pennsylvania and New Jersey among 163 medical and gynecologic oncology clinicians and 4450 patients with cancer at high risk of mortality (≥10% risk of 180-day mortality).

Interventions

Clinician clusters and patients were independently randomized to receive usual care vs nudges, resulting in 4 arms: (1) active control, operating for 2 years prior to trial start, consisting of clinician text message reminders to complete SICs for patients at high mortality risk; (2) clinician nudge only, consisting of active control plus weekly peer comparisons of clinician-level SIC completion rates; (3) patient nudge only, consisting of active control plus a preclinic electronic communication designed to prime patients for SICs; and (4) combined clinician and patient nudges.

Main Outcomes and Measures

The primary outcome was a documented SIC in the electronic health record within 6 months of a participant's first clinic visit after randomization. Analysis was performed on an intent-to-treat basis at the patient level.

Results

The study accrued 4450 patients (median age, 67 years [IQR, 59-75 years]; 2352 women [52.9%]) seen by 163 clinicians, randomized to active control (n = 1004), clinician nudge (n = 1179), patient nudge (n = 997), or combined nudges (n = 1270). Overall patient-level rates of 6-month SIC completion were 11.2% for the active control arm (112 of 1004), 11.5% for the clinician nudge arm (136 of 1179), 11.5% for the patient nudge arm (115 of 997), and 14.1% for the combined nudge arm (179 of 1270). Compared with active control, the combined nudges were associated with an increase in SIC rates (ratio of hazard ratios [rHR], 1.55 [95% CI, 1.00-2.40]; P = .049), whereas the clinician nudge (HR, 0.95 [95% CI, 0.64-1.41; P = .79) and patient nudge (HR, 0.99 [95% CI, 0.73-1.33]; P = .93) were not.

Conclusions and Relevance

In this cluster randomized trial, nudges combining clinician peer comparisons with patient priming questionnaires were associated with a marginal increase in documented SICs compared with an active control. Combining clinician- and patient-directed nudges may help to promote SICs in routine cancer care.

Trial Registration

ClinicalTrials.gov Identifier: NCT04867850.

Improving Cancer Survival Prediction via Graph Convolutional Neural Network Learning on Protein-Protein Interaction Networks

Cancer is one of the most challenging health problems worldwide. Accurate cancer survival prediction is vital for clinical decision making. Many deep learning methods have been proposed to understand the association between patients' genomic features and survival time. In most cases, the gene expression matrix is fed directly to the deep learning model. However, this approach completely ignores the interactions between biomolecules, and the resulting models can only learn the expression levels of genes to predict patient survival. In essence, the interaction between biomolecules is the key to determining the direction and function of biological processes. Proteins are the building blocks and principal undertakings of life activities, and as such, their complex interaction network is potentially informative for deep learning methods. Therefore, a more reliable approach is to have the neural network learn both gene expression data and protein interaction networks. We propose a new computational approach, termed CRESCENT, which is a protein-protein interaction (PPI) prior knowledge graph-based convolutional neural network (GCN) to improve cancer survival prediction. CRESCENT relies on the gene expression networks rather than gene expression levels to predict patient survival. The performance of CRESCENT is evaluated on a large-scale pan-cancer dataset consisting of 5991 patients from 16 different types of cancers. Extensive benchmarking experiments demonstrate that our proposed method is competitive in terms of the evaluation metric of the time-dependent concordance index( Ctd) when compared with several existing state-of-the-art approaches. Experiments also show that incorporating the network structure between genomic features effectively improves cancer survival prediction.

Performance of the Palliative Prognostic Index for cancer patients: A systematic review and meta-analysis

BACKGROUND

Clinician predicted survival for cancer patients is often inaccurate, and prognostic tools may be helpful, such as the Palliative Prognostic Index (PPI). The PPI development study reported that when PPI score is greater than 6, it predicted survival of less than 3 weeks with a sensitivity of 83% and specificity of 85%. When PPI score is greater than 4, it predicts survival of less than 6 weeks with a sensitivity of 79% and specificity of 77%. However, subsequent PPI validation studies have evaluated various thresholds and survival durations, and it is unclear which is most appropriate for use in clinical practice. With the development of numerous prognostic tools, it is also unclear which is most accurate and feasible for use in multiple care settings.

AIM

We evaluated PPI model performance in predicting survival of adult cancer patients based on different thresholds and survival durations and compared it to other prognostic tools.

DESIGN

This systematic review and meta-analysis was registered in PROSPERO (CRD42022302679). We calculated the pooled sensitivity and specificity of each threshold using bivariate random-effects meta-analysis and pooled diagnostic odds ratio of each survival duration using hierarchical summary receiver operating characteristic model. Meta-regression and subgroup analysis were used to compare PPI performance with clinician predicted survival and other prognostic tools. Findings which could not be included in meta-analyses were summarised narratively.

DATA SOURCES

PubMed, ScienceDirect, Web of Science, CINAHL, ProQuest and Google Scholar were searched for articles published from inception till 7 January 2022. Both retrospective and prospective observational studies evaluating PPI performance in predicting survival of adult cancer patients in any setting were included. The Prediction Model Risk of Bias Assessment Tool was used for quality appraisal.

RESULTS

Thirty-nine studies evaluating PPI performance in predicting survival of adult cancer patients were included (n = 19,714 patients). Across meta-analyses of 12 PPI score thresholds and survival durations, we found that PPI was most accurate for predicting survival of <3 weeks and <6 weeks. Survival prediction of <3 weeks was most accurate when PPI score>6 (pooled sensitivity = 0.68, 95% CI 0.60-0.75, specificity = 0.80, 95% CI 0.75-0.85). Survival prediction of <6 weeks was most accurate when PPI score>4 (pooled sensitivity = 0.72, 95% CI 0.65-0.78, specificity = 0.74, 95% CI 0.66-0.80). Comparative meta-analyses found that PPI performed similarly to Delirium-Palliative Prognostic Score and Palliative Prognostic Score in predicting <3-week survival, but less accurately in <30-day survival prediction. However, Delirium-Palliative Prognostic Score and Palliative Prognostic Score only provide <30-day survival probabilities, and it is uncertain how this would be helpful for patients and clinicians. PPI also performed similarly to clinician predicted survival in predicting <30-day survival. However, these findings should be interpreted with caution as limited studies were available for comparative meta-analyses. Risk of bias was high for all studies, mainly due to poor reporting of statistical analyses. while there were low applicability concerns for most (38/39) studies.

CONCLUSIONS

PPI score>6 should be used for <3-week survival prediction, and PPI score>4 for <6-week survival. PPI is easily scored and does not require invasive tests, and thus would be easily implemented in multiple care settings. Given the acceptable accuracy of PPI in predicting <3- and <6-week survival and its objective nature, it could be used to cross-check clinician predicted survival especially when clinicians have doubts about their own judgement, or when clinician estimates seem to be less reliable. Future studies should adhere to the reporting guidelines and provide comprehensive analyses of PPI model performance.

Factors and Barriers to Goals-of-Care Conversations for Patients With Cancer and Inpatient Mortality

PURPOSE

Conversations about personal values and goals of care (GOC) at the end of life are essential in caring for patients with advanced cancer. However, GOC conversations may be influenced by patient and oncologist factors during transitions of care.

METHODS

We electronically administered surveys to medical oncologists of inpatients who died from May 1, 2020, to May 31, 2021. Primary outcomes included oncologists' knowledge of inpatient death, anticipation of patient death, and recollection of GOC discussions. Secondary outcomes, including GOC documentation and advance directives (ADs), were collected retrospectively from electronic health records. Outcomes were analyzed for association with patient, oncologist, and patient-oncologist relationship factors.

RESULTS

For 75 patients who died, 104/158 (66%) surveys were completed by 40 inpatient and 64 outpatient oncologists. Eighty-one oncologists (77.9%) were aware of patients' deaths, 68 (65.4%) anticipated patients' deaths within 6 months, and 67 (64.4%) recalled having GOC discussions before or during the terminal hospitalization. Outpatient oncologists were more likely to report knowledge of patient death (P < .001), as were those with longer therapeutic relationships (P < .001). Inpatient oncologists were more likely to correctly anticipate patient death (P = .014). Secondary outcomes revealed 21.3% of patients had documented GOC discussions before admission and 33.3% had ADs; patients with a longer duration of cancer diagnosis were more likely to have ADs (P = .003). Oncologist-reported barriers to GOC included unrealistic expectations from patients or family (25%) and decreased patient participation because of clinical conditions (15%).

CONCLUSION

Most oncologists recalled having GOC discussions for patients with inpatient mortality, yet documentation of serious illness conversations remained suboptimal. Further studies are needed to examine barriers to GOC conversations and documentation during transitions of care and across health care settings.

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.

Oncologist Perceptions of Algorithm-Based Nudges to Prompt Early Serious Illness Communication: A Qualitative Study

Background: Early serious illness conversations (SICs) about goals of care and prognosis improve mood, quality of life, and end-of-life care quality. Algorithm-based behavioral nudges to oncologists increase the frequency and timeliness of such conversations. However, clinicians' perspectives on such nudges are unknown. Design: Qualitative study consisting of semistructured interviews among medical oncology clinicians who participated in a stepped-wedge cluster randomized trial of Conversation Connect, an algorithm-based intervention consisting of behavioral nudges to promote early SICs in the outpatient oncology setting. Results: Of 79 eligible oncology clinicians, 56 (71%) were approached to participate in interviews and 25 (45%) accepted. Key facilitators to algorithm-based nudges included prompting documentation of conversations, peer comparisons, performance reports, and validating norms around early conversations. Barriers included cancer-specific heterogeneity in algorithm performance and the frequency and tone of text messages. Areas of improvement included utilizing different information channels, identifying patients earlier in the disease trajectory, and incorporating patient-targeted messaging that emphasizes the value of early conversations. Conclusions: Oncology clinicians identified key facilitators and barriers to Conversation Connect. These insights inform future algorithm-based supportive care interventions in oncology. Controlled trial (NCT03984773).

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.

Clinician perspectives on machine learning prognostic algorithms in the routine care of patients with cancer: a qualitative study

PURPOSE

Oncologists may overestimate prognosis for patients with cancer, leading to delayed or missed conversations about patients' goals and subsequent low-quality end-of-life care. Machine learning algorithms may accurately predict mortality risk in cancer, but it is unclear how oncology clinicians would use such algorithms in practice.

METHODS

The purpose of this qualitative study was to assess oncology clinicians' perceptions on the utility and barriers of machine learning prognostic algorithms to prompt advance care planning. Participants included medical oncology physicians and advanced practice providers (APPs) practicing in tertiary and community practices within a large academic healthcare system. Transcripts were coded and analyzed inductively using NVivo software.

RESULTS

The study included 29 oncology clinicians (19 physicians, 10 APPs) across 6 practice sites (1 tertiary, 5 community) in the USA. Fourteen participants had previously had exposure to an automated machine learning-based prognostic algorithm as part of a pragmatic randomized trial. Clinicians believed that there was utility for algorithms in validating their own intuition about prognosis and prompting conversations about patient goals and preferences. However, this enthusiasm was tempered by concerns about algorithm accuracy, over-reliance on algorithm predictions, and the ethical implications around disclosure of an algorithm prediction. There was significant variation in tolerance for false positive vs. false negative predictions.

CONCLUSION

While oncologists believe there are applications for advanced prognostic algorithms in routine care of patients with cancer, they are concerned about algorithm accuracy, confirmation and automation biases, and ethical issues of prognostic disclosure.

Advance Directives in Oncology and Haematology: A Long Way to Go-A Narrative Review

Patients living with cancer often experience serious adverse events due to their condition or its treatments. Those events may lead to a critical care unit admission or even result in death. One of the most important but challenging parts of care is to build a care plan according to the patient’s wishes, meeting their goals and values. Advance directives (ADs) allow everyone to give their preferences in advance regarding life sustaining treatments, continuation, and withdrawal or withholding of treatments in case one is not able to speak their mind anymore. While the absence of ADs is associated with a greater probability of receiving unwanted intensive care around the end of their life, their existence correlates with the respect of the patient’s desires and their greater satisfaction. Although progress has been made to promote ADs’ completion, they are still scarcely used among cancer patients in many countries. Several limitations to their acceptance and use can be detected. Efforts should be made to provide tailored solutions for the identified hindrances. This narrative review aims to depict the situation of ADs in the oncology context, and to highlight the future areas of improvement.

Longitudinal patient-reported outcomes and survival among early-stage non-small cell lung cancer patients receiving stereotactic body radiotherapy

BACKGROUND AND PURPOSE

The study objective was to determine whether longitudinal changes in patient-reported outcomes (PROs) were associated with survival among early-stage, non-small cell lung cancer (NSCLC) patients undergoing stereotactic body radiation therapy (SBRT).

MATERIALS AND METHODS

Data were obtained from January 2015 through March 2020. We ran a joint probability model to assess the relationship between time-to-death, and longitudinal PRO measurements. PROs were measured through the Edmonton Symptom Assessment Scale (ESAS). We controlled for other covariates likely to affect symptom burden and survival including stage, tumor diameter, comorbidities, gender, race/ethnicity, relationship status, age, and smoking status.

RESULTS

The sample included 510 early-stage NSCLC patients undergoing SBRT. The median age was 73.8 (range: 46.3-94.6). The survival component of the joint model demonstrates that longitudinal changes in ESAS scores are significantly associated with worse survival (HR: 1.04; 95% CI: 1.02-1.05). This finding suggests a one-unit increase in ESAS score increased probability of death by 4%. Other factors significantly associated with worse survival included older age (HR: 1.04; 95% CI: 1.03-1.05), larger tumor diameter (HR: 1.21; 95% CI: 1.01-1.46), male gender (HR: 1.87; 95% CI: 1.36-2.57), and current smoking status (HR: 2.39; 95% CI: 1.25-4.56).

CONCLUSION

PROs are increasingly being collected as a part of routine care delivery to improve symptom management. Healthcare systems can integrate these data with other real-world data to predict patient outcomes, such as survival. Capturing longitudinal PROs-in addition to PROs at diagnosis-may add prognostic value for estimating survival among early-stage NSCLC patients undergoing SBRT.

Behavioral economic implementation strategies to improve serious illness communication between clinicians and high-risk patients with cancer: protocol for a cluster randomized pragmatic trial

BACKGROUND

Serious illness conversations (SICs) are an evidence-based approach to eliciting patients' values, goals, and care preferences that improve patient outcomes. However, most patients with cancer die without a documented SIC. Clinician-directed implementation strategies informed by behavioral economics ("nudges") that identify high-risk patients have shown promise in increasing SIC documentation among clinicians. It is unknown whether patient-directed nudges that normalize and prime patients towards SIC completion-either alone or in combination with clinician nudges that additionally compare performance relative to peers-may improve on this approach. Our objective is to test the effect of clinician- and patient-directed nudges as implementation strategies for increasing SIC completion among patients with cancer.

METHODS

We will conduct a 2 × 2 factorial, cluster randomized pragmatic trial to test the effect of nudges to clinicians, patients, or both, compared to usual care, on SIC completion. Participants will include 166 medical and gynecologic oncology clinicians practicing at ten sites within a large academic health system and their approximately 5500 patients at high risk of predicted 6-month mortality based on a validated machine-learning prognostic algorithm. Data will be obtained via the electronic medical record, clinician survey, and semi-structured interviews with clinicians and patients. The primary outcome will be time to SIC documentation among high-risk patients. Secondary outcomes will include time to SIC documentation among all patients (assessing spillover effects), palliative care referral among high-risk patients, and aggressive end-of-life care utilization (composite of chemotherapy within 14 days before death, hospitalization within 30 days before death, or admission to hospice within 3 days before death) among high-risk decedents. We will assess moderators of the effect of implementation strategies and conduct semi-structured interviews with a subset of clinicians and patients to assess contextual factors that shape the effectiveness of nudges with an eye towards health equity.

DISCUSSION

This will be the first pragmatic trial to evaluate clinician- and patient-directed nudges to promote SIC completion for patients with cancer. We expect the study to yield insights into the effectiveness of clinician and patient nudges as implementation strategies to improve SIC rates, and to uncover multilevel contextual factors that drive response to these strategies.

TRIAL REGISTRATION

ClinicalTrials.gov , NCT04867850 . Registered on April 30, 2021.

FUNDING

National Cancer Institute P50CA244690.

Automated model versus treating physician for predicting survival time of patients with metastatic cancer

OBJECTIVE

Being able to predict a patient's life expectancy can help doctors and patients prioritize treatments and supportive care. For predicting life expectancy, physicians have been shown to outperform traditional models that use only a few predictor variables. It is possible that a machine learning model that uses many predictor variables and diverse data sources from the electronic medical record can improve on physicians' performance. For patients with metastatic cancer, we compared accuracy of life expectancy predictions by the treating physician, a machine learning model, and a traditional model.

MATERIALS AND METHODS

A machine learning model was trained using 14 600 metastatic cancer patients' data to predict each patient's distribution of survival time. Data sources included note text, laboratory values, and vital signs. From 2015-2016, 899 patients receiving radiotherapy for metastatic cancer were enrolled in a study in which their radiation oncologist estimated life expectancy. Survival predictions were also made by the machine learning model and a traditional model using only performance status. Performance was assessed with area under the curve for 1-year survival and calibration plots.

RESULTS

The radiotherapy study included 1190 treatment courses in 899 patients. A total of 879 treatment courses in 685 patients were included in this analysis. Median overall survival was 11.7 months. Physicians, machine learning model, and traditional model had area under the curve for 1-year survival of 0.72 (95% CI 0.63-0.81), 0.77 (0.73-0.81), and 0.68 (0.65-0.71), respectively.

CONCLUSIONS

The machine learning model's predictions were more accurate than those of the treating physician or a traditional model.

Palliative Care Service Utilization and Advance Care Planning for Adult Glioblastoma Patients: A Systematic Review

Glioblastoma (GBM) has a median overall survival of 16-21 months. As patients with GBM suffer concurrently from terminal cancer and a disease with progressive neurocognitive decline, advance care planning (ACP) and palliative care (PC) are critical. We conducted a systematic review exploring published literature on the prevalence of ACP, end-of-life (EOL) services utilization (including PC services), and experiences among adults with GBM. We searched from database inception until 20 December 2020. Preferred reporting items for systematic reviews guidelines were followed. Included studies were assessed for quality using the Newcastle-Ottawa Scale. The 16 articles were all nonrandomized studies conducted in six countries with all but two published in 2014 or later. ACP documentation varied from 4-55%, PC referral was pursued in 39-40% of cases, and hospice referrals were made for 66-76% of patients. Hospitalizations frequently occurred at the EOL with 20-56% of patients spending over 25% of their overall survival time hospitalized. Many GBM patients do not pursue ACP or have access to PC. There is a dearth of focused and high-quality studies on ACP, PC, and hospice use among adults with GBM. Prospective studies that address these and additional aspects related to EOL care, such as healthcare costs and inpatient supportive care needs, are needed.

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.

The Importance of Prognostication: Impact of Prognostic Predictions, Disclosures, Awareness, and Acceptance on Patient Outcomes

In the advanced cancer setting, patients, families, and clinicians are often confronted with an uncertain future regarding treatment outcomes and survival. Greater certainty on what to expect can enhance decision-making for many personal and healthcare issues. Although 70-90% of patients with advanced cancer desire open and honest prognostic disclosure, a small proportion do not want to know. Approximately half of patients with advanced cancer have an inaccurate understanding of their illness, which could negatively impact their decision-making. In this review, we use a conceptual framework to highlight 5 key steps along the prognostic continuum, including (1) prognostic formulation, (2) prognostic disclosure, (3) prognostic awareness, (4) prognostic acceptance, and (5) prognosis-based decision-making. We shall summarize the impact of prognostic predictions, disclosure, awareness, and acceptance on various patient and caregiver outcomes, such as hope, trust, anxiety, depression, chemotherapy use, and care planning. Based on where the patient is at along the prognostic continuum, we propose 5 different subgroups (avoidance: "I don't want to know"; discordant, "I never wanted to know"; anxious, "I don't know what's happening"; concerned, "I don't like this"; acceptance, "I know how to plan ahead"). Although prognostication is not necessarily a linear process, recognizing where the patient is at cognitively and emotionally along the prognostic continuum may allow clinicians to provide personalized interventions, such as specialist palliative care and psychology referral, towards personalizing prognostic disclosure, enhancing prognostic awareness, increasing prognostic acceptance, and supporting decision-making and, ultimately, improving patient 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.

Integrating machine-generated mortality estimates and behavioral nudges to promote serious illness conversations for cancer patients: Design and methods for a stepped-wedge cluster randomized controlled trial

INTRODUCTION

Patients with cancer often receive care that is not aligned with their personal values and goals. Serious illness conversations (SICs) between clinicians and patients can help increase a patient's understanding of their prognosis, goals and values.

METHODS AND ANALYSIS

In this study, we describe the design of a stepped-wedge cluster randomized trial to evaluate the impact of an intervention that employs machine learning-based prognostic algorithms and behavioral nudges to prompt oncologists to have SICs with patients at high risk of short-term mortality. Data are collected on documented SICs, documented advance care planning discussions, and end-of-life care utilization (emergency room and inpatient admissions, chemotherapy and hospice utilization) for patients of all enrolled clinicians.

CONCLUSION

This trial represents a novel application of machine-generated mortality predictions combined with behavioral nudges in the routine care of outpatients with cancer. Findings from the trial may inform strategies to encourage early serious illness conversations and the application of mortality risk predictions in clinical settings.

TRIAL REGISTRATION

Clinicaltrials.gov Identifier: NCT03984773.

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.

Barriers to the composition and implementation of advance directives in oncology: a literature review

The advance directive (AD) is an important resource in oncology and all areas of medicine directly involved in the care of palliative patients. It provides people with the right to have their living wills honoured when they cannot respond by themselves. Despite their importance, ADs are still underused in most countries due to multiple factors. The objective of this review is to better categorise the barriers and difficulties that could impair the composition and implementation of ADs, allowing direct efforts against these obstacles. After the literature review, we believe that there would be five steps in the trajectory of an AD (discussion, composition, registration, access and implementation) and that all those steps can be affected by factors involving the health systems and professionals, the patient themselves and relatives or caregivers.

Artificial Intelligence in Radiation Oncology

The integration of artificial intelligence in the radiation oncologist's workflow has multiple applications and significant potential. From the initial patient encounter, artificial intelligence may aid in pretreatment disease outcome and toxicity prediction. It may subsequently aid in treatment planning, and enhanced dose optimization. Artificial intelligence may also optimize the quality assurance process and support a higher level of safety, quality, and efficiency of care. This article describes components of the radiation consultation, planning, and treatment process and how the thoughtful integration of artificial intelligence may improve shared decision making, planning efficiency, planning quality, patient safety, and patient outcomes.

Using Big Data and Predictive Analytics to Determine Patient Risk in Oncology

Big data and predictive analytics have immense potential to improve risk stratification, particularly in data-rich fields like oncology. This article reviews the literature published on use cases and challenges in applying predictive analytics to improve risk stratification in oncology. We characterized evidence-based use cases of predictive analytics in oncology into three distinct fields: (1) population health management, (2) radiomics, and (3) pathology. We then highlight promising future use cases of predictive analytics in clinical decision support and genomic risk stratification. We conclude by describing challenges in the future applications of big data in oncology, namely (1) difficulties in acquisition of comprehensive data and endpoints, (2) the lack of prospective validation of predictive tools, and (3) the risk of automating bias in observational datasets. If such challenges can be overcome, computational techniques for clinical risk stratification will in short order improve clinical risk stratification for patients with cancer.