Palliative medicine review: prognostication

Gap-App: A sex-distinct AI-based predictor for pancreatic ductal adenocarcinoma survival as a web application open to patients and physicians

In this study, using RNA-Seq gene expression data and advanced machine learning techniques, we identified distinct gene expression profiles between male and female pancreatic ductal adenocarcinoma (PDAC) patients. Building upon this insight, we developed sex-specific 3-year survival predictive models, which achieved accuracies of 88.47% for males and 88.94% for females, respectively. These models outperformed a single general model despite the smaller sample sizes, highlighting the value of sex-specific analysis. Based on these findings, we created Gap-App, a web application that enables the use of individual gene expression profiles combined with sex information for personalized survival predictions. Gap-App, the first online tool aiming to bridge the gap between complex genomic data and clinical application and facilitating more precise and individualized cancer care, marks a significant advancement in personalized prognosis. The study not only underscores the importance of acknowledging sex differences in personalized prognosis, but also sets the stage for the shift from traditional one-size-fits-all to more personalized and targeted medicine. The GAP-App service is freely available at www.gap-app.org.

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.

The "Surprise Question" for Prognostication in People With Parkinson's Disease and Related Disorders

CONTEXT

Parkinson's disease and related disorders (PDRD) are fatal neurodegenerative disorders characterized by a fluctuating course that can complicate prognostication. The "surprise question" (SQ: "Would you be surprised if your patient died in the next year?") has been used to identify patients with limited prognosis but has not been assessed in PDRD.

OBJECTIVES

To determine the validity of the SQ in predicting 12-month mortality in PDRD.

METHODS

Data was analyzed from 301 patients and 34 community-based neurologists who were participating in a clinical trial of outpatient palliative care for patients with PDRD. Clinicians answered the SQ for each patient at baseline. Descriptive statistics at baseline, chi-square tests of independence, 2 × 2 and 2 × 3 cross tables were used. Survival analysis compared SQ responses using Kaplan-Meier curves. Risk estimate analyses identified patient characteristics associated with clinicians' responses.

RESULTS

Mortality was 10.3% (N = 31) at 1 year. The sensitivity and specificity of the SQ was 80.7% and 58.9%, respectively with AUC = 0.70, positive predictive value of 18.4% and negative predictive value of 96.4%. Older age, atypical parkinsonism, and dementia were associated with responding "no" to the SQ.

CONCLUSION

The SQ is sensitive to 12-month mortality in PDRD, with a high negative predictive value. The SQ may be useful for identifying patients less likely to die within a year and may be useful for identifying patients with palliative care needs outside of end-of-life care. This latter use may assist in mobilizing early and timely referral to specialist palliative care.

Comparative Analysis of Palliative Care Needs Identified in Inpatient Rehabilitation and Skilled Nursing Facilities by Multidisciplinary Team Members

OBJECTIVE

Assess access to, need for, and beliefs surrounding specialized palliative care (PC).

DESIGN

Observational, comparative analysis needs assessment survey.

SETTING

Four inpatient rehabilitation facilities (IRFs) or skilled nursing facilities with long-term care (SNFs/LTC) that provide subacute rehabilitation within 1 tertiary care system.

PARTICIPANTS

Allied health professionals, physicians, nursing, case managers, social workers, spiritual care (n=198).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Frequency of patient needs, attitudes about current systems, individual beliefs, and barriers to PC. Confidence in management, communicating, and navigating primary PC competencies among clinical pathway employees.

RESULTS

Of 198 respondents, 37% said PC was available at their facility. Those in IRF reported higher frequencies of grief/unmet spiritual needs of patients compared with SNF/LTC (P≤.001). Conversely, SNF/LTC reported higher frequencies of agitation, poor appetite, and end-of-life care (P≤.003). Respondents in SNF/LTC felt more confident managing end-of-life care, explaining what hospice and PC are and appropriateness for referral to each, discussing advance directives, determining appropriate decision-makers, and navigating ethical decisions than in IRFs (P≤.007). SNF/LTC participants reported higher effectiveness of their current system involving PC and ease of hospice transition compared with IRFs (P≤.008). A majority agreed that PC does not take away patient hope, could prevent recurrent hospitalizations, improve symptom management, communication, and patient and family satisfaction. The most common reported barriers to PC consultation were (1) attitudes and beliefs of staff or patients and families, (2) system issues with access, cost, or prognosis communication, and (3) lack of understanding of PC role.

CONCLUSIONS

A gap exists in PC access in IRF and SNF/LTC despite patient needs and staff beliefs. Future studies should focus on identifying which patients should be referred to PC in the post-acute setting and what outcomes can be used as a guide to meet the needs of this growing area of practice.

What outcomes do studies use to measure the impact of prognostication on people with advanced cancer? Findings from a systematic review of quantitative and qualitative studies

BACKGROUND

Studies evaluating the impact of prognostication in advanced cancer patients vary in the outcomes they measure, and there is a lack of consensus about which outcomes are most important.

AIM

To identify outcomes previously reported in prognostic research with people with advanced cancer, as a first step towards constructing a core outcome set for prognostic impact studies.

DESIGN

A systematic review was conducted and analysed in two subsets: one qualitative and one quantitative. (PROSPERO ID: CRD42022320117; 29/03/2022).

DATA SOURCES

Six databases were searched from inception to September 2022. We extracted data describing (1) outcomes used to measure the impact of prognostication and (2) patients' and informal caregivers' experiences and perceptions of prognostication in advanced cancer. We classified findings using the Core Outcome Measures in Effectiveness Trials (COMET) initiative taxonomy, along with a narrative description. We appraised retrieved studies for quality, but quality was not a basis for exclusion.

RESULTS

We identified 42 eligible studies: 32 quantitative, 6 qualitative, 4 mixed methods. We extracted 70 outcomes of prognostication in advanced cancer and organised them into 12 domains: (1) survival; (2) psychiatric outcomes; (3) general outcomes; (4) spiritual/religious/existential functioning/wellbeing, (5) emotional functioning/wellbeing; (6) cognitive functioning; (7) social functioning; (8) global quality of life; (9) delivery of care; (10) perceived health status; (11) personal circumstances; and (12) hospital/hospice use.

CONCLUSION

Outcome reporting and measurement varied markedly across the studies. A standardised approach to outcome reporting in studies of prognosis is necessary to enhance data synthesis, improve clinical practice and better align with stakeholders' priorities.

Developing a Core Outcome Set for Prognostic Research in Palliative Cancer Care: Protocol for a Mixed Methods Study

BACKGROUND

Studies exploring the impact of receiving end-of-life prognoses in patients with advanced cancer use a variety of different measures to evaluate the outcomes, and thus report often conflicting findings. The standardization of outcomes reported in studies of prognostication in palliative cancer care could enable uniform assessment and reporting, as well as intertrial comparisons. A core outcome set promotes consistency in outcome selection and reporting among studies within a particular population. We aim to develop a set of core outcomes to be used to measure the impact of end-of-life prognostication in palliative cancer care.

OBJECTIVE

This protocol outlines the proposed methodology to develop a core outcome set for measuring the impact of end-of-life prognostication in palliative cancer care.

METHODS

We will adopt a mixed methods approach consisting of 3 phases using methodology recommended by the Core Outcome Measure in Effectiveness Trials (COMET) initiative. In phase I, we will conduct a systematic review to identify existing outcomes that prognostic studies have previously used, so as to inform the development of items and domains for the proposed core outcome set. Phase II will consist of semistructured interviews with patients with advanced cancer who are receiving palliative care, informal caregivers, and clinicians, to explore their perceptions and experiences of end-of-life prognostication. Outcomes identified in the interviews will be combined with those found in existing literature and taken forward to phase III, a Delphi survey, in which we will ask patients, informal caregivers, clinicians, and relevant researchers to rate these outcomes until consensus is achieved as to which are considered to be the most important for inclusion in the core outcome set. The resulting, prioritized outcomes will be discussed in a consensus meeting to agree and endorse the final core outcome set.

RESULTS

Ethical approval was received for this study in September 2022. As of July 2023, we have completed and published the systematic review (phase I) and have started recruitment for phase II. Data analysis for phase II has not yet started. We expect to complete the study by October 2024.

CONCLUSIONS

This protocol presents the stepwise approach that will be taken to develop a core outcome set for measuring the impact of end-of-life prognostication in palliative cancer care. The final core outcome set has the potential for translation into clinical practice, allowing for consistent evaluation of emerging prognostic algorithms and improving communication of end-of-life prognostication. This study will also potentially facilitate the design of future clinical trials of the impact of end-of-life prognostication in palliative care that are acceptable to key stakeholders.

TRIAL REGISTRATION

Core Outcome Measures in Effectiveness Trials 2136; https://www.comet-initiative.org/Studies/Details/2136.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/49774.

Survival Prediction of Patients Who Were Terminally Ill Using the EORTC QLQ-C15-PAL Scores and Laboratory Test Values

Background

Prognostics for patients with cancer is especially important for the supportive care of those who are terminally ill. We previously found that symptom scores as patient-reported outcomes (PROs)-such as dyspnea and fatigue scores-some biochemical parameters, the palliative performance scale (PPS) scores, and symptom clusters were useful prognostic factors; however, the predictability of a prognosis based on these factors remains unclear.

Objective

To identify appropriate three-week survival predictive factor(s), in terms of performance, in patients who were terminally ill.

Design

We collected symptom scores as PROs using the Japanese version of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 15 Palliative Care (EORTC QLQ-C15-PAL).

Setting/Subjects

We used data from terminally ill patients with cancer who were hospitalized at the palliative care unit of the Higashisumiyoshi-Morimoto Hospital (Osaka, Japan) from June 2018 to December 2019 (n = 130), as well as additional data obtained from the same clinical study from January to March 2020 (n = 31).

Measurements

To evaluate predictive performance, indices such as sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy were calculated.

Results

We found that the presence of a symptom cluster showed high sensitivity but low specificity and that a higher PPS value (>30) showed high specificity but low sensitivity, suggesting that these factors could provide relevant information for survival prognosis (less than or equal to three weeks).

Conclusion

Symptom clusters obtained from patients is important for effective supportive care of those who are terminally ill.

Validation of Modified Models of Objective Prognostic Score in Patients With Advanced Cancer

Background: The objective prognostic score (OPS) needs to be modified to reflect practical palliative care circumstances. Objectives: We aimed to validate modified models of OPS with few or no laboratory tests for patients with advanced cancer. Design: An observational study was performed. Setting/Subjects: A secondary analysis of an international, multicenter cohort study of patients in East Asia was performed. The subjects were inpatients with advanced cancer in the palliative care unit. Measurements: We developed two modified OPS (mOPS) models to predict two-week survival: mOPS-A consisted of two symptoms, two objective signs, and three laboratory results, while mOPS-B consisted of three symptoms, two signs, and no laboratory data. We compared the accuracy of the prognostic models using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Calibration plots for two-week survival and net reclassification indices (NRIs) were compared for the two models. Survival differences between higher and lower score groups of each model were identified by the log-rank test. Results: We included a total of 1796 subjects having median survival of 19.0 days. We found that mOPS-A had higher specificity (0.805-0.836) and higher AUROCs (0.791-0.797). In contrast, mOPS-B showed higher sensitivity (0.721-0.725) and acceptable AUROCs (0.740-0.751) for prediction of two-week survival. Two mOPSs showed good concordance in calibration plots. Considering NRIs, replacing the original OPS with mOPSs improved overall reclassification (absolute NRI: 0.47-4.15%). Higher score groups of mOPS-A and mOPS-B showed poorer survival than those of lower score groups (p < 0.001). Conclusions: mOPSs used reduced laboratory data and had relatively good accuracy for predicting survival in advanced cancer patients receiving palliative care.

Guidelines for goals of care discussions in patients with gynecologic cancer

This article represents a distillation of literature to provide guidance for goals of care discussions with patients who have gynecologic malignancies. As clinicians who provide surgical care, chemotherapy, and targeted therapeutics, gynecologic oncology clinicians are uniquely positioned to form longitudinal relationships with patients that can enable patient-centered decision making. In this review, we describe optimal timing, components, and best practices for goals of care discussions in gynecologic oncology.

Development of prediction models for one-year brain tumour survival using machine learning: a comparison of accuracy and interpretability

BACKGROUND AND OBJECTIVE

Prediction of survival in patients diagnosed with a brain tumour is challenging because of heterogeneous tumour behaviours and treatment response. Advances in machine learning have led to the development of clinical prognostic models, but due to the lack of model interpretability, integration into clinical practice is almost non-existent. In this retrospective study, we compare five classification models with varying degrees of interpretability for the prediction of brain tumour survival greater than one year following diagnosis.

METHODS

1028 patients aged ≥16 years with a brain tumour diagnosis between April 2012 and April 2020 were included in our study. Three intrinsically interpretable 'glass box' classifiers (Bayesian Rule Lists [BRL], Explainable Boosting Machine [EBM], and Logistic Regression [LR]), and two 'black box' classifiers (Random Forest [RF] and Support Vector Machine [SVM]) were trained on electronic patients records for the prediction of one-year survival. All models were evaluated using balanced accuracy (BAC), F1-score, sensitivity, specificity, and receiver operating characteristics. Black box model interpretability and misclassified predictions were quantified using SHapley Additive exPlanations (SHAP) values and model feature importance was evaluated by clinical experts.

RESULTS

The RF model achieved the highest BAC of 78.9%, closely followed by SVM (77.7%), LR (77.5%) and EBM (77.1%). Across all models, age, diagnosis (tumour type), functional features, and first treatment were top contributors to the prediction of one year survival. We used EBM and SHAP to explain model misclassifications and investigated the role of feature interactions in prognosis.

CONCLUSION

Interpretable models are a natural choice for the domain of predictive medicine. Intrinsically interpretable models, such as EBMs, may provide an advantage over traditional clinical assessment of brain tumour prognosis by weighting potential risk factors and their interactions that may be unknown to clinicians. An agreement between model predictions and clinical knowledge is essential for establishing trust in the models decision making process, as well as trust that the model will make accurate predictions when applied to new data.

Accuracy of clinical predictions of prognosis at the end-of-life: evidence from routinely collected data in urgent care records

BACKGROUND

The accuracy of prognostication has important implications for patients, families, and health services since it may be linked to clinical decision-making, patient experience and outcomes and resource allocation. Study aim is to evaluate the accuracy of temporal predictions of survival in patients with cancer, dementia, heart, or respiratory disease.

METHODS

Accuracy of clinical prediction was evaluated using retrospective, observational cohort study of 98,187 individuals with a Coordinate My Care record, the Electronic Palliative Care Coordination System serving London, 2010-2020. The survival times of patients were summarised using median and interquartile ranges. Kaplan Meier survival curves were created to describe and compare survival across prognostic categories and disease trajectories. The extent of agreement between estimated and actual prognosis was quantified using linear weighted Kappa statistic.

RESULTS

Overall, 3% were predicted to live "days"; 13% "weeks"; 28% "months"; and 56% "year/years". The agreement between estimated and actual prognosis using linear weighted Kappa statistic was highest for patients with dementia/frailty (0.75) and cancer (0.73). Clinicians' estimates were able to discriminate (log-rank p < 0.001) between groups of patients with differing survival prospects. Across all disease groups, the accuracy of survival estimates was high for patients who were likely to live for fewer than 14 days (74% accuracy) or for more than one year (83% accuracy), but less accurate at predicting survival of "weeks" or "months" (32% accuracy).

CONCLUSION

Clinicians are good at identifying individuals who will die imminently and those who will live for much longer. The accuracy of prognostication for these time frames differs across major disease categories, but remains acceptable even in non-cancer patients, including patients with dementia. Advance Care Planning and timely access to palliative care based on individual patient needs may be beneficial for those where there is significant prognostic uncertainty; those who are neither imminently dying nor expected to live for "years".

Development of a Clinical Prediction Model for 1-Year Mortality in Patients With Advanced Cancer

IMPORTANCE

To optimize palliative care in patients with cancer who are in their last year of life, timely and accurate prognostication is needed. However, available instruments for prognostication, such as the surprise question ("Would I be surprised if this patient died in the next year?") and various prediction models using clinical variables, are not well validated or lack discriminative ability.

OBJECTIVE

To develop and validate a prediction model to calculate the 1-year risk of death among patients with advanced cancer.

DESIGN, SETTING, AND PARTICIPANTS

This multicenter prospective prognostic study was performed in the general oncology inpatient and outpatient clinics of 6 hospitals in the Netherlands. A total of 867 patients were enrolled between June 2 and November 22, 2017, and followed up for 1 year. The primary analyses were performed from October 9 to 25, 2019, with the most recent analyses performed from June 19 to 22, 2022. Cox proportional hazards regression analysis was used to develop a prediction model including 3 categories of candidate predictors: clinician responses to the surprise question, patient clinical characteristics, and patient laboratory values. Data on race and ethnicity were not collected because most patients were expected to be of White race and Dutch ethnicity, and race and ethnicity were not considered as prognostic factors. The models' discriminative ability was assessed using internal-external validation by study hospital and measured using the C statistic. Patients 18 years and older with locally advanced or metastatic cancer were eligible. Patients with hematologic cancer were excluded.

MAIN OUTCOMES AND MEASURES

The risk of death by 1 year.

RESULTS

Among 867 patients, the median age was 66 years (IQR, 56-72 years), and 411 individuals (47.4%) were male. The 1-year mortality rate was 41.6% (361 patients). Three prediction models with increasing complexity were developed: (1) a simple model including the surprise question, (2) a clinical model including the surprise question and clinical characteristics (age, cancer type prognosis, visceral metastases, brain metastases, Eastern Cooperative Oncology Group performance status, weight loss, pain, and dyspnea), and (3) an extended model including the surprise question, clinical characteristics, and laboratory values (hemoglobin, C-reactive protein, and serum albumin). The pooled C statistic was 0.69 (95% CI, 0.67-0.71) for the simple model, 0.76 (95% CI, 0.73-0.78) for the clinical model, and 0.78 (95% CI, 0.76-0.80) for the extended model. A nomogram and web-based calculator were developed to support clinicians in adequately caring for patients with advanced cancer.

CONCLUSIONS AND RELEVANCE

In this study, a prediction model including the surprise question, clinical characteristics, and laboratory values had better discriminative ability in predicting death among patients with advanced cancer than models including the surprise question, clinical characteristics, or laboratory values alone. The nomogram and web-based calculator developed for this study can be used by clinicians to identify patients who may benefit from palliative care and advance care planning. Further exploration of the feasibility and external validity of the model is needed.

Mortality prediction upon hospital admission - the value of clinical assessment: A retrospective, matched cohort study

Accurate prediction of mortality upon hospital admission is of great value, both for the sake of patients and appropriate resources' allocation. A myriad of assessment tools exists for this purpose. The evidence relating to the comparative value of clinical assessment versus established indexes are scarce. We analyzed the accuracy of a senior physician's clinical assessment in a retrospective cohort of patients in a crude, general patients' population and later on a propensity matched patients' population. In one department of internal medicine in a tertiary hospital, of 9891 admitted patients, 973 (10%) were categorized as prone to death in a 6-months' duration by a senior physician. The risk of death was significantly higher for these patients [73.1% vs 14.1% mortality within 180 days; hazard ratio (HR) = 7.58; confidence intervals (CI) 7.02-8.19, P < .001]. After accounting for multiple, other patients' variables associated with increased risk of mortality, the correlation remained significant (HR = 3.25; CI 2.85-3.71, P < .001). We further performed a propensity matching analysis (a subgroup of 710 patients, subdivided to two groups with 355 patients each): survival rates were as low as 45% for patients categorized as prone to death compared to 78% in patients who weren't categorized as such (P < .001). Reliance on clinical evaluation, done by an experienced senior physician, is an appropriate tool for mortality prediction upon hospital admission, achieving high accuracy rates.

Fluorescence in situ hybridization detection of chromosome 7 and/or 17 polysomy as a prognostic marker for cholangiocarcinoma

Cholangiocarcinoma (CCA) is highly endemic in the Northeast Thailand. Recently, chromosome aberrations provided new insights into pathogenesis of CCA. Therefore, chromosome aberration might be used as a prognostic factor and therapeutic planning of this cancer. This aim of this study is to examine the correlation between an increase of chromosome 7 (C7) and/or 17 (C17) copy number variants (CNVs) with clinicopathological data and the overall survival time (OS) of CCA patients using fluorescence in situ hybridization (FISH) assays. C7 and C17 CNVs were examined using FISH form 157 formalin-fixed paraffin-embedded (FFPE) tissues of CCA patients from Khon Kaen, Thailand between 2011 and 2015. OS was visualized using Kaplan-Meier plot. Univariate and multivariate analyses were used to determine the ability of the clinicopathological parameters to predict OS. C17 > trisomy (odd ratio, 6.944, P < 0.001), C7/17 trisomy (odd ratio; 4.488, P = 0.019), and C7/17 > trisomy (odd ratio; 6.723, P < 0.001) were independently predictive factors for lymph node metastasis. Interestingly, an increase of C7, C17, and C7/17 CNVs in both trisomy and > trisomy was independently correlated with short median OS. An increased of C7 and/or 17 have a potential as a poor prognostic marker in CCA patients.

The development of the ADO-SQ model to predict 1-year mortality in patients with COPD

BACKGROUND

Goals of end-of-life care must be adapted to the needs of patients with chronic obstructive pulmonary disease (COPD) who are in the last phase of life. However, identification of those patients is limited by moderate performances of existing prognostic models and by limited validation of the often-recommended surprise question.

AIM

To develop a clinical prediction model to predict 1-year mortality in patients with COPD.

DESIGN

Prospective study using logistic regression to develop a model in two steps: (1) external validation of the ADO, BODEX, or CODEX models (A = age; B = body mass index; C = comorbidity; D = dyspnea; EX = exacerbations; O = airflow obstruction); (2) updating of best performing model and extending it with the surprise question. Discriminative performance of the new model was assessed using internal-external validation and measured with area under the curve (AUC). A nomogram and web application were developed.

SETTINGS/PARTICIPANTS

Patients with COPD from five hospitals (September-November 2017).

RESULTS

Of the 358 included patients (median age 69.5 years, 50% male), 63 (17%) died within a year. The ADO index (AUC 0.73) had the best discriminative ability compared to the BODEX (AUC 0.71) or CODEX (AUC 0.68), and was extended with the surprise question. The resulting ADO-surprise question (SQ) model had an AUC of 0.79.

CONCLUSION

The ADO-SQ model offers improved discriminative performance for predicting 1-year mortality compared to the surprise question, ADO, BODEX, or CODEX. A user-friendly nomogram and web application (https://dnieboer.shinyapps.io/copd) were developed. Further external validation of the ADO-SQ in patient groups is needed.

Performance of clinician prediction of survival in oncology outpatients with advanced cancer

Background

We aimed to investigate the performance of clinician prediction of survival (CPS) and the association between CPS and the prognostic confidence of clinicians in ambulatory medical oncology outpatients.

Methods

Eight medical oncologists estimated the expected survival of their patients in a prospective cohort study. They were asked to provide a temporal type of CPS in weeks, together with their level of confidence for each prediction (0–100%). We analyzed the accuracy of CPS, the association between CPS and the prognostic confidence, and the characteristics of patients showing inaccurate CPS.

Results

A total of 200 advanced cancer patients were enrolled and the median overall survival was 7.6 months. CPS was accurate in 67 (33.5%) patients, underestimated in 87 (43.5%), and overestimated in 46 (23.0%). The overall accuracy of CPS for 12-week, 24-week, 36-week, and 48-week survival was 83.0%, 72.0%, 75.5%, and 74.0%, respectively. The specificity was highest for 12-week survival (90.2%) and the sensitivity was highest for 48-week survival (96.1%). The sensitivity of 12-week CPS was 51.4% and the area under the receiver operating characteristic (AUROC) curve was 0.79 (95% confidence interval [CI] 0.71–0.87). The prognostic confidence of clinicians was not significantly associated with the accuracy of prediction (P = 0.359). Patients with overestimated survival had significantly poorer global health status and physical/role/emotional functioning in the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30). Additionally, they showed significantly higher levels of fatigue, nausea/vomiting, pain, dyspnea, and loss of appetite.

Conclusion

The overall accuracy of CPS in predicting 12-week to 48-week survival was high in medical oncology outpatients. However the sensitivity of 12-week CPS was low and prognostic confidence was not associated with the accuracy of CPS. Patients with overestimated CPS showed poorer quality of life and higher symptom burden.

Enhancing Palliative Care for Patients With Advanced Heart Failure Through Simple Prognostication Tools: A Comparison of the Surprise Question, the Number of Previous Heart Failure Hospitalizations, and the Seattle Heart Failure Model for Predicting 1-Year Survival

Background

Score-based survival prediction in patients with advanced heart failure (HF) is complicated. Easy-to-use prognostication tools could inform clinical decision-making and palliative care delivery.

Objective

To compare the prognostic utility of the Seattle HF model (SHFM), the surprise question (SQ), and the number of HF hospitalizations (NoH) within the last 12 months for predicting 1-year survival in patients with advanced HF.

Methods

We retrospectively analyzed data from a cluster-randomized controlled trial of advanced HF patients, predominantly with reduced ejection fraction. Primary outcome was the prognostic discrimination of SHFM, SQ (“Would you be surprised if this patient were to die within 1 year?”) answered by HF cardiologists, and NoH, assessed by receiver operating characteristic (ROC) curve analysis. Optimal cut-offs were calculated using Youden’s index (SHFM: <86% predicted 1-year survival; NoH ≥ 2).

Results

Of 535 subjects, 82 (15.3%) had died after 1-year of follow-up. SHFM, SQ, and NoH yielded a similar area under the ROC curve [SHFM: 0.65 (0.60–0.71 95% CI); SQ: 0.58 (0.54–0.63 95% CI); NoH: 0.56 (0.50–0.62 95% CI)] and similar sensitivity [SHFM: 0.76 (0.65–0.84 95% CI); SQ: 0.84 (0.74–0.91 95% CI); NoH: 0.56 (0.45–0.67 95% CI)]. As compared to SHFM, SQ had lower specificity [SQ: 0.33 (0.28–0.37 95% CI) vs. SHFM: 0.55 (0.50–0.60 95% CI)] while NoH had similar specificity [0.56 (0.51–0.61 95% CI)]. SQ combined with NoH showed significantly higher specificity [0.68 (0.64–0.73 95% CI)].

Conclusion

SQ and NoH yielded comparable utility to SHFM for 1-year survival prediction among advanced HF patients, are easy-to-use and could inform bedside decision-making.

Palliative Care in Children With Advanced Heart Disease in a Tertiary Care Environment: A Mini Review

Palliative care for children continues to evolve. More recently, this has also been true in the field of pediatric cardiology, particularly for children with advanced heart disease. In these children, similarly to children with cancer, treatment successes are offset by the risks of long-term morbidities, including premature death. This mini review aims to provide an overview of current knowledge on children suffering from advanced heart disease, their medical care during various phases of illness (including the palliative and end-of-life phase), symptom burden, experiences of parents, prognostic understanding of parents and physicians, and current status of the involvement of pediatric palliative care. In conclusion, the suffering of these children at the end of their young lives is pronounced and many parents feel prepared neither for medical problems nor for the child’s death. An effective and mutually trusting partnership between pediatric cardiology and pediatric palliative care would appear to be a prerequisite for the timely involvement of palliative care in further supporting these children and their families.

Prognostic disclosure in oncology - current communication models: a scoping review

BACKGROUND

Prognostic disclosure is essential to informed decision making in oncology, yet many oncologists are unsure how to successfully facilitate this discussion. This scoping review determines what prognostic communication models exist, compares and contrasts these models, and explores the supporting evidence.

METHOD

A protocol was created for this study using the Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols extension for Scoping Reviews. Comprehensive literature searches of electronic databases MEDLINE, EMBASE, PsycINFO and Cochrane CENTRAL were executed to identify relevant publications between 1971 and 2020.

RESULTS

In total, 1532 articles were identified, of which 78 met inclusion criteria and contained 5 communication models. Three of these have been validated in randomised controlled trials (the Serious Illness Conversation Guide, the Four Habits Model and the ADAPT acronym) and have demonstrated improved objective communication measures and patient reported outcomes. All three models emphasise the importance of exploring patients' illness understanding and treatment preferences, communicating prognosis and responding to emotion.

CONCLUSION

Communicating prognostic estimates is a core competency skill in advanced cancer care. This scoping review highlights available communication models and identifies areas in need of further assessment. Such areas include how to maintain learnt communication skills for lifelong practice, how to assess patient and caregiver understanding during and after these conversations, and how to best scale these protocols at the institutional and national levels.

Patient, Caregiver, and Oncologist Predictions of Quality of Life in Advanced Cancer: Accuracy and Associations with End-of-Life Care and Caregiver Bereavement

OBJECTIVE

Informed treatment decision-making necessitates accurate prognostication,including predictions about quality of life. We examined whether oncologists, patients with advanced cancer, and caregivers accurately predict patients' future quality of life and whether these predictions are prospectively associated with end-of-life care and bereavement.

METHODS

We secondary analyses of clinical trial data. Patients with advanced cancer (n=156), caregivers (n=156), and oncologists (n=38) predicted patient quality of life 3 months into the future. Patients subsequently rated their quality of life 3 months later. Medical record data documented chemotherapy and emergency department (ED)/inpatient visits in the 30 days before death (n=79 decedents). Caregivers self-reported on depression, anxiety, grief, purpose, 21 and regret 7-months post-mortem. In mixed-effects models, patient, caregiver, and oncologist quality-of-life predictions at study entry were used to predict end-of-life care and caregiver outcomes, controlling for patients' quality of life at 3-month follow-up, demographic and clinical characteristics, and nesting within oncologists.

RESULTS

Caregivers (P<.0001) and oncologists (P=.001) predicted lower quality of life than what patients actually experienced. Among decedents, 24.0% received chemotherapy and 54.5% had an ED/inpatient visit. When caregivers' predictions were more negative, patients were less likely to receive chemotherapy (P=.028) or have an ED/inpatient visit (P=.033), and caregivers reported worse depression (P=.002), anxiety (P=.019), and grief (P=.028) and less purpose in life (P<.001) 7-months post-mortem.

CONCLUSION

When caregivers have more negative expectations about patients' quality of life, patients receive less intensive end-of-life care, and caregivers report worse bereavement This article is protected by copyright. All rights reserved.

“That little bit of time”: Transition-to-Hospice Perspectives from Hospice Staff and Bereaved Family

Background and Objectives

Many patients lack understanding of hospice services and their preparation for the transition to hospice at home may be insufficient. This study explored how hospice admissions staff and caregivers of hospice patients perceive the hospice admission process and the transition to hospice at home.

Research Design and Methods

We conducted in-depth, semistructured interviews with 2 subgroups: hospice admissions staff (n = 15) and bereaved caregivers of former hospice patients (n = 20). We performed a 3-coder descriptive content analysis.

Results

There were 4 overall themes: (a) issues relating to the referring/prehospice provider, (b) issues relating to hospital discharge/care transition home, (c) issues relating to the first touch of hospice, and (d) the impact of coronavirus disease 2019 (COVID-19) on hospice admissions. Patients are often referred to hospice without clear prognostic understanding, at times placing staff in the uncomfortable position of breaking difficult news. Stigma may make patients and families fearful of enrolling in hospice, and misconceptions about hospice are common. Caregivers emphasize the need for increased attention to their emotional needs. Staff revealed the emotional challenges they experience conducting admissions. Both staff and caregivers indicate that the transition to hospice is often emotionally and logistically burdensome, especially when discharging home from the hospital. Both subgroups report insufficient caregiver preparation for taking care of a dying patient at home, particularly regarding medication management. COVID-19 created challenges yet prompted innovative changes to hospice admission processes.

Discussion and Implications

Findings demonstrate a need to improve the hospice admissions process, better supporting terminally ill patients and their families.

Top Ten Tips Palliative Care Clinicians Should Know About Prognostication in Children

Pediatric palliative care (PPC) is different from palliative care (PC) for adults. However, conceptualizing PPC remains cumbersome due to the high heterogeneity of often rare diseases, the high diversity of disease trajectories, and the particular difficulty to predict the future of an individual, severely ill child. This article aims to provide an overview and critical reflection of different aspects of prognostication in children with PC needs. This includes different diseases from neurology to oncology, from the unborn baby to the young adult, new approaches in treatment, advance care planning, and, most important, communication with the affected child as well as parents.

Comparing the performance of the palliative prognostic (PaP) score with clinical predictions of survival: A systematic review

BACKGROUND

In patients with advanced cancer, prognosis is usually determined using clinicians' predictions of survival (CPS). The palliative prognostic (PaP) score is a prognostic algorithm that was developed to predict survival in patients with advanced cancer. The score categorises patients into three risk groups in accordance with their probability of surviving for 30 days. The relative accuracy of PaP and CPS is unclear.

DESIGN

This was a systematic review of MEDLINE, Embase, AMED, CINAHL Plus and the Cochrane Database of Systematic Reviews and Trials from inception up to June 2021. The inclusion criteria were studies in adults with advanced cancer reporting data on performance of both PaP and CPS. Data were extracted on accuracy of prognoses and where available on discrimination (area under the receiver operating characteristic curve or C-index) and/or diagnostic performance (sensitivity, specificity).

RESULTS

Eleven studies were included. One study reported a direct comparison between PaP risk groups and equivalent risk groups defined by CPS and found that PaP was as accurate as CPS. Five studies reported discrimination of PaP as a continuous total score (rather than using the previously validated risk categories) and reported C-statistics that ranged from 0.64 (95% confidence interval [CI] 0.54, 0.74) up to 0.90 (95% CI 0.87, 0.92). Other studies compared PaP against CPS using non-equivalent metrics (e.g. comparing probability estimates against length of survival estimates).

CONCLUSIONS

PaP risk categories and CPS are equally able to discriminate between patients with different survival probabilities. Total PaP scores show good discrimination between patients in accordance with their length of survival. The role of PaP in clinical practice still needs to be defined.

TRIAL REGISTRATION

PROSPERO (CRD42021241074, 5th March 2021).

Artificial Intelligence in Brain Tumour Surgery-An Emerging Paradigm

Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced.

Improving the Care of Patients With Serious Illness: What Are the Palliative Care Education Needs of Internal Medicine Residents?

BACKGROUND

Hospitalized patients with serious illness have significant symptom burden and face complex medical decisions that often require goals of care discussions. Given the shortage of specialty palliative care providers, there is a pressing need to improve the palliative care skills of internal medicine (IM) residents, who have a central role in the care of seriously ill patients hospitalized at academic medical centers.

METHODS

We conducted an anonymous survey of IM residents at a large, urban, academic medical center to identify which aspects of palliative care trainees find most important and their knowledge gaps in palliative care. The survey measured trainees' self-assessed degree of importance and knowledge of core palliative care skills and evaluated frequency of completing advance care planning documentation.

RESULTS

Overall, 51 (23%) IM residents completed the survey. The majority of trainees considered multiple palliative care skills to be "very important/important": symptom management, prognostication, introducing the palliative care approach, discussing code status, and breaking serious news. Across these same skills, trainees reported variable levels of knowledge. In our sample, trainees reported completing healthcare proxy forms and Medical Orders for Life-Sustaining Treatment infrequently.

CONCLUSIONS

IM trainees rated core palliative care skills as important to their practice. Yet, they reported knowledge gaps across multiple core palliative care skills that should be addressed given their role as frontline providers for patients with serious illness.

An Exploratory Qualitative Study of Computer Screening to Support Decision-Making about Use of Palliative Care Registers in Primary Care: GP Think Aloud and Patient and Carer Interviews

Objectives:

This study aimed to understand factors that influence general practitioner (GP) use of automated computer screening to identify patients for the palliative care register (PCR) and the experiences of palliative care and this emerging technology from patients’ and carers’ perspectives.

Methods:

A computer screening program electronically searches primary care records in routine clinical practice to identify patients with advanced illness who are not already on a PCR. Five GPs were asked to “think aloud” about adding patients identified by computer screening to the PCR. Key informant interviews with 6 patients on the PCR and 4 carers about their experiences of palliative care while on the PCR and their views of this technology. Data were analyzed thematically.

Results and Conclusions:

Using computer screening, 29% additional patients were added by GPs to the PCR. GP decision-making for the PCR was informed by clinical factors such as: if being treated with curative intent; having stable or unstable disease; end-stage disease, frailty; the likelihood of dying within the next 12 months; and psychosocial factors such as, age, personality, patient preference and social support. Six (60%) patients/carers did not know that they/their relative was on the PCR. From a patient/carer perspective, having a non-curative illness was not in and of itself sufficient reason for being on the PCR; other factors such as, unstable disease and avoiding pain and suffering were equally if not more, important. Patients and carers considered that computer screening should support but not replace, GP decision-making about the PCR. Computer screening merits ongoing development as a tool to aid clinical decision-making around entry to a PCR, but should not be used as a sole criterion. Care need, irrespective of diagnosis, disease trajectory or prognosis, should determine care.

The role of EORTC QLQ-C15-PAL scores and inflammatory biomarkers in predicting survival in terminally ill patients with cancer

Background

The clinical use of patient-reported outcomes as compared to inflammatory biomarkers for predicting cancer survival remains a challenge in palliative care settings. We evaluated the role of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 15 Palliative scores (EORTC QLQ-C15-PAL) and the inflammatory biomarkers C-reactive protein (CRP), albumin (Alb), and neutrophil-lymphocyte ratio (NLR) for survival prediction in patients with advanced cancer.

Methods

This was an observational study in terminally ill patients with cancer hospitalized in a palliative care unit between June 2018 and December 2019. Patients’ data collected at the time of hospitalization were analyzed. Cox regression was performed to examine significant factors influencing survival. A receiver operating characteristic (ROC) analysis was performed to estimate cut-off values for predicting survival within 3 weeks, and a log-rank test was performed to compare survival curves between groups divided by the cut-off values.

Results

Totally, 130 patients participated in the study. Cox regression suggested that the QLQ-C15-PAL dyspnea and fatigue scores and levels of CRP, Alb, and NLR were significantly associated with survival time, and cut-off values were 66.67, 66.67, 3.0 mg/dL, 2.5 g/dL, and 8.2, respectively. The areas under ROC curves of these variables were 0.6–0.7. There were statistically significant differences in the survival curves between groups categorized using each of these cut-off values (p < .05 for all cases).

Conclusion

Our findings suggest that the assessment of not only objective indicators for the systemic inflammatory response but also patient-reported outcomes using EORTC QLQ-C15-PAL is beneficial for the prediction of short-term survival in terminally ill patients with cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08049-3.

Observational study of agreement between attending and trainee physicians on the surprise question: "Would you be surprised if this patient died in the next 12 months?"

BACKGROUND

Optimal end-of-life care requires identifying patients that are near the end of life. The extent to which attending physicians and trainee physicians agree on the prognoses of their patients is unknown. We investigated agreement between attending and trainee physician on the surprise question: "Would you be surprised if this patient died in the next 12 months?", a question intended to assess mortality risk and unmet palliative care needs.

METHODS

This was a multicentre prospective cohort study of general internal medicine patients at 7 tertiary academic hospitals in Ontario, Canada. General internal medicine attending and senior trainee physician dyads were asked the surprise question for each of the patients for whom they were responsible. Surprise question response agreement was quantified by Cohen's kappa using Bayesian multilevel modeling to account for clustering by physician dyad. Mortality was recorded at 12 months.

RESULTS

Surprise question responses encompassed 546 patients from 30 attending-trainee physician dyads on academic general internal medicine teams at 7 tertiary academic hospitals in Ontario, Canada. Patients had median age 75 years (IQR 60-85), 260 (48%) were female, and 138 (25%) were dependent for some or all activities of daily living. Trainee and attending physician responses agreed in 406 (75%) patients with adjusted Cohen's kappa of 0.54 (95% credible interval 0.41 to 0.66). Vital status was confirmed for 417 (76%) patients of whom 160 (38% of 417) had died. Using a response of "No" to predict 12-month mortality had positive likelihood ratios of 1.84 (95% CrI 1.55 to 2.22, trainee physicians) and 1.51 (95% CrI 1.30 to 1.72, attending physicians), and negative likelihood ratios of 0.31 (95% CrI 0.17 to 0.48, trainee physicians) and 0.25 (95% CrI 0.10 to 0.46, attending physicians).

CONCLUSION

Trainee and attending physician responses to the surprise question agreed in 54% of cases after correcting for chance agreement. Physicians had similar discriminative accuracy; both groups had better accuracy predicting which patients would survive as opposed to which patients would die. Different opinions of a patient's prognosis may contribute to confusion for patients and missed opportunities for engagement with palliative care services.

Physicians' views on the usefulness and feasibility of identifying and disclosing patients' last phase of life: a focus group study

OBJECTIVES

Accurate assessment that a patient is in the last phase of life is a prerequisite for timely initiation of palliative care in patients with a life-limiting disease, such as advanced cancer or advanced organ failure. Several palliative care quality standards recommend the surprise question (SQ) to identify those patients. Little is known about physicians' views on identifying and disclosing the last phase of life of patients with different illness trajectories.

METHODS

Data from two focus groups were analysed using thematic analysis with a phenomenological approach.

RESULTS

Fifteen medical specialists and general practitioners participated. Participants thought prediction of patients' last phase of life, i.e. expected death within 1 year, is important. They seemed to find that prediction is more difficult in patients with advanced organ failure compared with cancer. The SQ was considered a useful prognostic tool; its use is facilitated by its simplicity but hampered by its subjective character. The medical specialist was considered mainly responsible for prognosticating and gradually disclosing the last phase. Participants' reluctance to such disclosure was related to uncertainty around prognostication, concerns about depriving patients of hope, affecting the physician-patient relationship, or a lack of time or availability of palliative care services.

CONCLUSIONS

Physicians consider the assessment of patients' last phase of life important and support use of the SQ in patients with different illness trajectories. However, barriers in disclosing expected death are prognostic uncertainty, possible deprivation of hope, physician-patient relationship, and lack of time or palliative care services. Future studies should examine patients' preferences for those discussions.

"I Had a Lot More Planned": The Existential Dimensions of Prognosis Communication with Adults with Advanced Cancer

Background: Communication about prognosis is a key ingredient of effective palliative care. When patients with advanced cancer develop increased prognostic understanding, there is potential for existential distress to occur. However, the existential dimensions of prognosis communication are underexplored. Objective: To describe the existential dimensions of prognosis communication in naturally-occurring palliative care conversations. Methods: This study was an explanatory sequential mixed methods design. We analyzed a random subset of patients from the Palliative Care Communication Research Initiative (PCCRI) parent study (n = 34, contributing to 45 palliative care conversations). Data were based on audio-recorded and transcribed inpatient palliative care conversations between adults with advanced cancer, their families, and palliative care clinicians. We stratified the study sample by levels of prognosis communication, and qualitatively examined patterns of existential communication, comparing the intensity, frequency, and content, within and across levels. Results: Existential communication was more common, and of stronger intensity, within conversations with higher levels of prognosis communication. Conversations with more prognosis communication appeared to exhibit a shift toward the existential and away from the more physical nature of the serious illness experience. Conclusion: Existential and prognosis communication are intimately linked within palliative care conversations. Results highlight the multiplicity and mutuality of concerns that arise when contemplating mortality, drawing attention to areas of palliative care communication that warrant future research.

A 3-month Journey: The Case of a 29-year Old Woman With Metastatic Renal Cell Carcinoma

Early goal-oriented conversations at the end of life can help alleviate suffering and anxiety for surgical patients with cancer and their loved ones. This is especially important in the young adult population in whom there are limited patient-reported perspectives about their experiences at the end of life. We report the illustrative case of a 29-year-old woman who underwent palliative surgery for widely metastatic renal cell carcinoma. Her postoperative course was complicated by a proximal anastomotic stricture, bilateral pulmonary emboli, and delayed gastric emptying, ultimately leading to a functional decline in health and quality of life. Several wide-ranging palliative care needs arose during our patient's surgery, discharge, and subsequent readmissions. In addition to our patient's case, we discuss 3 fundamental surgical palliative care principles, and broad considerations for end-of-life management of young adults with advanced malignancies.

End-of-life decision making in the context of chronic life-limiting disease: a concept analysis and conceptual model

BACKGROUND

Conceptual ambiguities prevent advancements in end-of-life decision making in clinical practice and research.

PURPOSE

To clarify the components of and stakeholders (patients, caregivers, healthcare providers) involved in end-of-life decision making in the context of chronic life-limiting disease and develop a conceptual model.

METHOD

Walker and Avant's approach to concept analysis.

FINDINGS

End-of-life decision making is a process, not a discrete event, that begins with preparation, including decision maker designation and iterative stakeholder communication throughout the chronic illness (antecedents). These processes inform end-of-life decisions during terminal illness, involving: 1) serial choices 2) weighed in terms of potential outcomes 3) through patient and caregiver collaboration (attributes). Components impact patients' death, caregivers' bereavement, and healthcare systems' outcomes (consequences).

DISCUSSION

Findings provide a foundation for improved inquiry into and measurement of the end-of-life decision making process, accounting for the dose, content, and quality the antecedent and attribute factors that collectively contribute to outcomes.

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.

One last round of chemo? Insights from conversations between oncologists and lung cancer patients about prognosis and treatment decisions

One more chemo or one too many? The increasing use of expensive cancer treatments close to the patient's death is often explained by oncologists' failure to communicate to patients how close to dying they are, implying that patients are often both ill-prepared and over-treated when they die. This article aims at interrogating the politically charged task of prognosticating. Drawing on an ethnographic study of conversations between oncologists and patients with metastatic lung cancer in a Danish oncology clinic, I show that oncologists utilize, rather than avoid, prognostication in their negotiations with patients about treatment withdrawal. The study informs the emerging sociology of prognosis in three ways: First, prognostication is not only about foreseeing and foretelling, but also about shaping the patient's process of dying. Second, oncologists prognosticate differently depending on the level of certainty about the patient's trajectory. To unfold these differences, the article provides a terminology that distinguishes between four 'modes of prognostication', namely hinting, informing, calibrating and organizing. Third, prognosticating can unfold over time through multiple consultations, emphasizing the relevance of adopting methodologies enabling the study of prognosticating over time.

Prognostication in Serious Illness

Prognostication is a vital aspect of decision making because it provides patients and families with information to establish realistic and achievable goals of care, is used in determining eligibility for certain benefits, and helps in targeting interventions to those likely to benefit. Prognostication consists of 3 components: clinicians use their clinical judgment or other tools to estimate the probability of an individual developing a particular outcome over a specific period of time; this prognostic estimate is communicated in accordance with the patient's information preferences; the prognostic estimate is interpreted by the patient or surrogate and used in clinical decision making.

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

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

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

Online training improves medical students' ability to recognise when a person is dying: The ORaClES randomised controlled trial

BACKGROUND

Recognising dying is a key clinical skill for doctors, yet there is little training.

AIM

To assess the effectiveness of an online training resource designed to enhance medical students' ability to recognise dying.

DESIGN

Online multicentre double-blind randomised controlled trial (NCT03360812). The training resource for the intervention group was developed from a group of expert palliative care doctors' weightings of various signs/symptoms to recognise dying. The control group received no training.

SETTING/PARTICIPANTS

Participants were senior UK medical students. They reviewed 92 patient summaries and provided a probability of death within 72 hours (0% certain survival - 100% certain death) pre, post, and 2 weeks after the training. Primary outcome: (1) Mean Absolute Difference (MAD) score between participants' and the experts' scores, immediately post intervention. Secondary outcomes: (2) weight attributed to each factor, (3) learning effect and (4) level of expertise (Cochran-Weiss-Shanteau (CWS)).

RESULTS

Out of 168 participants, 135 completed the trial (80%); 66 received the intervention (49%). After using the training resource, the intervention group had better agreement with the experts in their survival estimates (δMAD = -3.43, 95% CI -0.11 to -0.34, p = <0.001) and weighting of clinical factors. There was no learning effect of the MAD scores at the 2-week time point (δMAD = 1.50, 95% CI -0.87 to 3.86, p = 0.21). At the 2-week time point, the intervention group was statistically more expert in their decision-making versus controls (intervention CWS = 146.04 (SD 140.21), control CWS = 110.75 (SD 104.05); p = 0.01).

CONCLUSION

The online training resource proved effective in altering the decision-making of medical students to agree more with expert decision-making.

The utility of a shortened palliative care screening tool to predict death within 12 months - a prospective observational study in two south African hospitals with a high HIV burden

Background

Timely identification of people who are at risk of dying is an important first component of end-of-life care. Clinicians often fail to identify such patients, thus trigger tools have been developed to assist in this process. We aimed to evaluate the performance of a identification tool (based on the Gold Standards Framework Prognostic Indicator Guidance) to predict death at 12 months in a population of hospitalised patients in South Africa.

Methods

Patients admitted to the acute medical services in two public hospitals in Cape Town, South Africa were enrolled in a prospective observational study. Demographic data were collected from patients and patient notes. Patients were assessed within two days of admission by two trained clinicians who were not the primary care givers, using the identification tool. Outcome mortality data were obtained from patient folders, the hospital electronic patient management system and the Western Cape Provincial death registry which links a unique patient identification number with national death certificate records and system wide electronic records.

Results

822 patients (median age of 52 years), admitted with a variety of medical conditions were assessed during their admission. 22% of the cohort were HIV-infected. 218 patients were identified using the screening tool as being in the last year of their lives. Mortality in this group was 56% at 12 months, compared with 7% for those not meeting any criteria. The specific indicator component of the tool performed best in predicting death in both HIV-infected and HIV-uninfected patients, with a sensitivity of 74% (68–81%), specificity of 85% (83–88%), a positive predictive value of 56% (49–63%) and a negative predictive value of 93% (91–95%). The hazard ratio of 12-month mortality for those identified vs not was 11.52 (7.87–16.9, p < 0.001).

Conclusions

The identification tool is suitable for use in hospitals in low-middle income country setting that have both a high communicable and non-communicable disease burden amongst young patients, the majority under age 60.

Prognostication in palliative care

An accurate prognosis about how long a terminally ill patient has left to live, when disclosed sensitively in open discussions, can facilitate patient-centred care and shared decision making. In addition, several guidelines, policies and funding streams rely, to some extent, on a clinician estimated prognosis. However, clinician predictions alone have been shown to be unreliable and over-optimistic. The factors underlying clinicians' prognostic decisions (particularly at the very end of life) are beginning to be elucidated. As an alternative to clinicians' subjective estimates, a number of prognostic algorithms and scores have been developed and validated, but only a few have consistently shown superiority to clinician predictions. Therefore, an element of uncertainty remains and this needs to be acknowledged when having conversations with patients and their families. Guidelines are available to advise clinicians about how to prepare for, participate in and record prognostic conversations.

A Tripartite Model of Community Attitudes to Palliative Care

Background:

Despite a growth in palliative care services, access and referral patterns are inconsistent and only a minority of people who would benefit from such care receive it. Use of palliative care is also affected by community attitudes toward palliative care. As such, determining community attitudes toward palliative care is crucial. We also need to determine what predicts attitudes in order to provide appropriate information and education.

Objectives:

The 2 research questions were: (1) What are community attitudes toward palliative care? and (2) what are the determinants of community attitudes toward palliative care?

Design:

A tripartite model of attitudes was used, which articulates attitudes as comprising knowledge and experience, emotions, and beliefs. A cross-sectional descriptive survey was used.

Participants:

A community sample of 180 participants completed the survey.

Results:

The average attitude and belief responses were very positive, the average emotions responses were somewhat positive. The sample had good knowledge of palliative care. Lowest knowledge scores were reported for the items: “Euthanasia is not part of palliative care,” “Palliative care does not prolong or shorten life,” and “Specialist palliative care is only available in hospitals.” After controlling place of birth and age, it was found that beliefs, emotions, and knowledge each accounted for a significant proportion of unique variance in attitude toward palliative care. Each variable had a positive relationship with attitude.

Conclusion:

Beliefs, emotions, and knowledge all need to be incorporated into palliative care community education programs.

Racial/ethnic differences in prognosis communication during initial inpatient palliative care consultations among people with advanced cancer

OBJECTIVE

We examined whether conversations involving Black or Latino patients with advanced cancer differ in the presence or characteristics of prognosis communication.

METHODS

We audio-recorded initial consultations between 54 palliative care clinicians and 231 hospitalized people with advanced cancer. We coded for the presence and characteristics of prognosis communication. We examined whether the presence or characteristics of prognosis communication differed by patients' self-reported race/ethnicity.

RESULTS

In 231 consultations, 75.7% contained prognosis communication. Prognosis communication was less than half as likely to occur during conversations with Black or Latino patients (N = 48) compared to others. Among consultations in which prognosis was addressed, those involving Black or Latino patients were more than 8 times less likely to contain optimistically cued prognoses compared to others.

CONCLUSION

Prognosis communication occurred less frequently for Black and Latino patients and included fewer optimistic cues than conversations with other patients. More work is needed to better understand these observed patterns of prognosis communication that vary by race and ethnicity.

PRACTICE IMPLICATIONS

Growing evidence supports prognosis communication being important for end-of-life decision-making and disproportionately rare among non-White populations. Therefore, our findings identify a potentially salient target for clinical interventions that are focused on ameliorating disparities in end-of-life care.

The contagion of optimism: The relationship between patient optimism and palliative care clinician overestimation of survival among hospitalized patients with advanced cancer

OBJECTIVE

Clinicians frequently overestimate survival time among seriously ill patients, and this can result in medical treatment at end of life that does not reflect the patient's preferences. Little is known, however, about the sources of clinicians' optimistic bias in survival estimation. Related work in social networks and experimental psychology demonstrates that psychological states-such as optimism-can transfer from one person to another.

METHODS

We directly observed and audio recorded 189 initial inpatient palliative care consultations among hospitalized patients with advanced cancer. Patients self-reported their level of trait optimism and expectations for survival prognosis prior to the palliative care consultation, and the palliative care clinicians rated their expectations for the patient's survival time following the initial conversation with the patient. We followed patient mortality for 6 months.

RESULTS

Patient optimism was associated with clinician overestimation of their survival in a dose-response relationship. Clinicians were approximately three times as likely to overestimate the survival of patients endorsing both high trait optimism and optimistic ratings of their survival time compared with neither (OR: 2.95; 95% CI: 1.24-7.02). This association was not attenuated by adjustment for age, gender, race, ethnicity, education, income, cancer type, functional status, quality of life, or white blood cell count (OR_adj : 3.45; 95% CI: 1.24-9.66).

CONCLUSION

Patients' optimism may have some influence over their clinicians' prognostic judgments.