This issue of Medical Clinics, guest edited by Dr. Eric Widera, is devoted to Palliative Care. Articles in this important issue include: Hospice and palliative care: an overview; Goals of care conversations in palliative care: A practical guide; The art and science of prognostication in palliative care; Recognizing and managing polypharmacy in advanced illness; Pain management in those with serious illness; Management of grief, depression, and suicidal thoughts in those with serious illness; Management of respiratory symptoms in those with serious illness; Management of gastrointestinal symptoms inadvanced illness; Management of urgent medical conditions at the end of life; Delirium at the end of life; Options of last resort: palliative sedation, Physician aid in dying and voluntary cessation of eating and drinking; Cannabis for symptom management; and Self-care of physicians caring for patients with serious illness.
Context: Clinicians often worry that patients' recognition of the terminal nature of their illness may impair psychological well-being.
Objectives: To determine if such recognition was associated with decrements to psychological well-being that persisted over time.
Methods: About 87 patients with advanced cancer, with an oncologist-expected life expectancy of less than six months, were assessed before and after an oncology visit to discuss cancer restaging scan results and again at follow-up (median time between assessments, approximately six weeks). Prognostic understanding (PU) was assessed at previsit and postvisit, and a change score was computed. Psychological well-being was assessed at pre, post, and follow-up, and two change scores were computed (post minus pre; follow-up minus post).
Results: Changes toward more accurate PU was associated with a corresponding initial decline in psychological well-being (r = -0.33; P < 0.01) but thereafter was associated with subsequent improvements (r = 0.40; P < 0.001). This pattern remained controlling for potential confounds. Patients showed different patterns of psychological well-being change (F = 3.07, P = 0.05; F = 6.54, P < 0.01): among patients with improved PU accuracy, well-being initially decreased but subsequently recovered; by contrast, among patients with stable PU accuracy, well-being remained relatively unchanged, and among patients with decrements in PU accuracy, well-being initially improved but subsequently declined.
Conclusion: Improved PU may be associated with initial decrements in psychological well-being, followed by patients rebounding to baseline levels. Concerns about lasting psychological harm may not need to be a deterrent to having prognostic discussions with patients.
Context: Clinicians deciding whether to refer a patient or family to specialty palliative care report facing high levels of uncertainty. Most research on medical uncertainty has focused on prognostic uncertainty. As part of a pediatric palliative referral intervention for oncology teams we explored how uncertainty might influence palliative care referrals.
Objectives: To describe distinct meanings of the term “uncertainty” that emerged during the qualitative evaluation of the development and implementation of an intervention to help oncologists overcome barriers to palliative care referrals.
Methods: We conducted a phenomenological qualitative analysis of “uncertainty” as experienced and described by interdisciplinary pediatric oncology team members in discussions, group activities and semistructured interviews regarding the introduction of palliative care.
Results: We found that clinicians caring for patients with advanced cancer confront seven broad categories of uncertainty: prognostic, informational, individual, communication, relational, collegial, and inter-institutional. Each of these kinds of uncertainty can contribute to delays in referring patients to palliative care.
Conclusion: Various types of uncertainty arise in the care of pediatric patients with advanced cancer. To manage these forms of uncertainty, providers need to develop strategies and techniques to handle professionally challenging situations, communicate bad news, manage difficult interactions with families and colleagues, and collaborate with other organizations.
On March 28, 2020, the Office of Civil Rights at the Department of Health and Human Services (HHS) opened investigations into recently released critical care crisis triage protocols. Disability rights advocates are urging Congress to prohibit crisis triage based on “anticipated or demonstrated resource-intensity needs, the relative survival probabilities of patients deemed likely to benefit from medical treatment, and assessments of pre- or post-treatment quality of life.”
Acute-on-chronic liver failure (ACLF) occurs in about 25% of hospitalized patients with decompensated cirrhosis (DC) and is associated with high, short-term mortality . The European Association for the Study of the Liver (EASL) - Chronic Liver Failure (CLIF) EASL-CLIF Acute-on-Chronic Liver Failure in Cirrhosis (CANONIC) study showed that 28-day mortality could be predicted after the 3rd day in the intensive care unit (ICU) applying the CLIF-sequential organ failure assessment (SOFA) score.
OBJECTIVES: to establish the accuracy of community nurses' predictions of mortality among older people with multiple long-term conditions, to compare these with a mortality rating index and to assess the incremental value of nurses' predictions to the prognostic tool.
DESIGN: a prospective cohort study using questionnaires to gather clinical information about patients case managed by community nurses. Nurses estimated likelihood of mortality for each patient on a 5-point rating scale. The dataset was randomly split into derivation and validation cohorts. Cox proportional hazard models were used to estimate risk equations for the Revised Minimum Dataset Mortality Risk Index (MMRI-R) and nurses' predictions of mortality individually and combined. Measures of discrimination and calibration were calculated and compared within the validation cohort.
SETTING: two NHS Trusts in England providing case-management services by nurses for frail older people with multiple long-term conditions.
PARTICIPANTS: 867 patients on the caseload of 35 case-management nurses. 433 and 434 patients were assigned to the derivation and validation cohorts, respectively. Patients were followed up for 12 months.
RESULTS: 249 patients died (28.72%). In the validation cohort, MMRI-R demonstrated good discrimination (Harrell's c-index 0.71) and nurses' predictions similar discrimination (Harrell's c-index 0.70). There was no evidence of superiority in performance of either method individually (P = 0.83) but the MMRI-R and nurses' predictions together were superior to nurses' predictions alone (P = 0.01).
CONCLUSIONS: patient mortality is associated with higher MMRI-R scores and nurses' predictions of 12-month mortality. The MMRI-R enhanced nurses' predictions and may improve nurses' confidence in initiating anticipatory care interventions.
CONTEXT: Racial and ethnic disparities in end-of-life care are well documented among adults with advanced cancer.
OBJECTIVES: To examine the extent to which communication and care differ by race and ethnicity among children with advanced cancer.
METHODS: We conducted a prospective cohort study at 9 pediatric cancer centers enrolling 95 parents (42% racial/ethnic minorities) of children with poor prognosis cancer (relapsed/refractory high risk neuroblastoma). Parents were surveyed about whether prognosis was discussed; likelihood of cure; intent of current treatment; and primary goal of care. Medical records were used to identify higher intensity medical care since the most recent recurrence. Logistic regression evaluated differences between white non-Hispanic and minority (black, Hispanic, Asian/other race) parents.
RESULTS: 26% of parents recognized the child's low likelihood of cure. Minority parents were less likely to recognize the poor prognosis (OR .19, 95%CI .06-.63, P=.006) and the fact that current treatment was unlikely to offer cure (OR .07, 95%CI .02-.27, P<.0001). Children of minority parents were more likely to experience higher intensity medical care (OR 3.01, 95%CI 1.29-7.02, P=.01). After adjustment for understanding of prognosis, race/ethnicity was no longer associated with higher intensity medical care (aOR 2.14, 95%CI .84-5.46, P=.11), although power to detect an association was limited.
CONCLUSION: Parental understanding of prognosis is limited across racial and ethnic groups; racial and ethnic minorities are disproportionately affected. Perhaps as a result, minority children experience higher rates of high intensity medical care. Work to improve prognostic understanding should include focused work to meet needs of minority populations.
BACKGROUND: Emotional preparedness for death (hereafter called death preparedness) and prognostic awareness (PA), a distinct but related concept, each contributes to patients' practical, psychological, and interpersonal preparation for death. However, the distinction between these two concepts has never been investigated.
OBJECTIVE: To evaluate the distinction between death preparedness and accurate PA by examining their agreement over cancer patients' last year and the similarity of their predictors.
METHODS: For this secondary analysis of a longitudinal study of death preparedness for 277 cancer patients, agreement between death preparedness and accurate PA was evaluated by percentages and kappa coefficients, and predictors of the two outcomes were evaluated by multivariate logistic regression models with the generalized estimating equation.
RESULTS: Levels of agreement between reported death preparedness and accurate PA increased slightly (42.44%-52.85%) from 181-365 to 1-30 days before death, with kappa values from -0.190 (-0.319, -0.061) to -0.006 (-0.106, 0.093), indicating poor agreement. Participants who were male, older, reported financial sufficiency, had fewer distressing symptoms, and perceived higher levels of social support were more likely to report death preparedness. Participants who were female, had greater than high-school educational attainment, and endured higher levels of functional dependence were more likely to report accurate PA.
CONCLUSION: The distinction between death preparedness and accurate PA was supported by their poor agreement, lack of reciprocal associations, and two different sets of predictors. Healthcare professionals should not only cultivate cancer patients' accurate PA, but also facilitate emotional preparation for death to achieve a good death and improve end-of-life-care quality.
BACKGROUND: While prognostic information is considered important for treatment decision-making, physicians struggle to communicate prognosis to advanced cancer patients. This systematic review aimed to offer up-to-date, evidence-based guidance on prognostic communication in palliative oncology.
METHODS: PubMed and PsycInfo were searched until September 2019 for literature on the association between prognostic disclosure (strategies) and patient outcomes in palliative cancer care, and its moderators. Methodological quality was reported.
RESULTS: Eighteen studies were included. Concerning prognostic disclosure, results revealed a positive association with patients' prognostic awareness. Findings showed no or positive associations between prognostic disclosure and the physician-patient relationship or the discussion of care preferences. Evidence for an association with the documentation of care preferences or physical outcomes was lacking. Findings on the emotional consequences of prognostic disclosure were multifaceted. Concerning disclosure strategies, affective communication seemingly reduced patients' physiological arousal and improved perceived physician's support. Affective and explicit communication showed no or beneficial effects on patients' psychological well-being and satisfaction. Communicating multiple survival scenarios improved prognostic understanding. Physicians displaying expertise, positivity and collaboration fostered hope. Evidence on demographic, clinical and personality factors moderating the effect of prognostic communication was weak.
CONCLUSION: If preferred by patients, physicians could disclose prognosis using sensible strategies. The combination of explicit and affective communication, multiple survival scenarios and expert, positive, collaborative behaviour likely benefits most patients. Still, more evidence is needed, and tailoring communication to individual patients is warranted.
IMPLICATIONS: Future research should examine the effect of prognostic communication on psychological well-being over time and treatment decision-making, and focus on individualising care.
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.
OPINION STATEMENT: Palliative care provides an extra layer of support to patients and families facing a serious illness. To date, several studies support the use of early, integrated palliative care for patients with cancer, based upon documented improvements in quality of life, symptoms, mood, satisfaction, utilization, and even overall survival. Despite this, patients with cancer continue to have unmet palliative care needs, and palliative care services are often engaged late in their care, if at all. Amid this under-utilization, questions remain about the optimal timing and nature of palliative care integration. To answer this question, we briefly review the evidence based for palliative care in oncology, and discuss three approaches to optimizing the timing of palliative care integration: (1) prognosis-based, (2) needs-based, and (3) trigger-based models. Prognosis-based models most closely mirror the approach of randomized trials to date, but are overly dependent on prognostication, and may miss patients with unmet needs who do not meet standard definitions of poor-prognosis disease. Needs-based models may better capture patients in a personalized manner, based on actual needs, but require sophisticated screening systems to be integrated into routine care processes, along with clinician buy-in. This may lead to excessive referrals, which strain the already limited palliative care workforce. As such, a blended, trigger-based approach may be best, allowing one to utilize certain disease-based and prognosis-based triggers for referral, plus screening of unmet needs, to identify those patients most likely to benefit from integrated palliative care when they need it most.
Accurate prognostication is challenging in the setting of SARS-CoV-2, the virus responsible for COVID-19, due to rapidly changing data, studies that are not generalizable and lack of morbidity and functional outcomes in survivors. To provide meaningful guidance to patients, existing mortality data must be considered and appropriately applied. While most people infected with SARS-CoV-2 will recover, mortality increases with age and co-morbidity in those who develop severe illness.
Objectives: Research suggests that clinicians are not very accurate at prognosticating in palliative care. The ‘horizon effect’ suggests that accuracy ought to be better when the survival of patients is shorter. The aim of this study was to determine the accuracy of specialist palliative care clinicians at identifying which patients are likely to die within 72 hours.
Design In a secondary data analysis of a prospective observational study, specialist palliative care doctors and nurses (in a hospice and a hospital palliative care team) provided survival predictions (yes/no/uncertain) about which patients would die within 72 hours.
Results: Survival predictions were obtained for 49 patients. A prediction from a nurse was obtained for 37/49 patients. A prediction from a doctor was obtained for 46/49 patients. In total, 23 (47%)/49 patients actually died within 72 hours of assessment. Nurses accurately predicted the outcome in 27 (73%)/37 cases. Doctors accurately predicted the outcome in 30 (65%)/46 cases. When comparing predictions given on the same patients (27 [55%]/49), nurses were slightly better at recognising imminent death than doctors (positive predictive value (the proportion of patients who died when the clinician predicted death)=79% vs 60%, respectively). The difference in c-statistics (nurses 0.82 vs doctors 0.63) was not significant (p=0.13).
Conclusion: Even when patients are in the terminal phase and close to death, clinicians are not very good at predicting how much longer they will survive. Further research is warranted to improve prognostication in this population.
We developed a predictive score system for 30-day mortality after palliative radiotherapy by using predictors from routine electronic medical record. Patients with metastatic cancer receiving first course palliative radiotherapy from 1 July, 2007 to 31 December, 2017 were identified. 30-day mortality odds ratios and probabilities of the death predictive score were obtained using multivariable logistic regression model. Overall, 5,795 patients participated. Median follow-up was 39.6 months (range, 24.5–69.3) for all surviving patients. 5,290 patients died over a median 110 days, of whom 995 (17.2%) died within 30 days of radiotherapy commencement. The most important mortality predictors were primary lung cancer (odds ratio: 1.73, 95% confidence interval: 1.47–2.04) and log peripheral blood neutrophil lymphocyte ratio (odds ratio: 1.71, 95% confidence interval: 1.52–1.92). The developed predictive scoring system had 10 predictor variables and 20 points. The cross-validated area under curve was 0.81 (95% confidence interval: 0.79–0.82). The calibration suggested a reasonably good fit for the model (likelihood-ratio statistic: 2.81, P = 0.094), providing an accurate prediction for almost all 30-day mortality probabilities. The predictive scoring system accurately predicted 30-day mortality among patients with stage IV cancer. Oncologists may use this to tailor palliative therapy for patients.
Background: When patients are likely to die in the coming hours or days, families often want prognostic information. Prognostic uncertainty and a lack of end-of-life communication training make these conversations challenging.
Aim: The objective of this study is to understand how clinicians and the relatives/friends of patients at the very end of life manage uncertainty and reference time in prognostic conversations.
Design: Conversation analysis of audio-recorded conversations between clinicians and the relatives/friends of hospice inpatients.
Setting/participants: Experienced palliative care clinicians and relatives/friends of imminently dying hospice inpatients. Twenty-three recorded conversations involved prognostic talk and were included in the analysis.
Results: Requests for prognostic information were initiated by families in the majority of conversations. Clinicians responded using categorical time references such as ‘days’, allowing the provision of prognostic estimates without giving a precise time. Explicit terms such as ‘dying’ were rare during prognostic discussions. Instead, references to time were understood as relating to prognosis. Relatives displayed their awareness of prognostic uncertainty when requesting prognostic information, providing clinicians with ‘permission’ to be uncertain. In response, clinicians often stated their uncertainty explicitly, but presented evidence for their prognostic estimates, based on changes to the patient’s function previously discussed with the family.
Conclusion: Prognostic uncertainty was managed collaboratively by clinicians and families. Clinicians were able to provide prognostic estimates while being honest about the related uncertainty, in part because relatives displayed their awareness of uncertainty within their requests. The conversation analytic method identified contributions of both clinicians and families, and identified strategies based on real interactions, which could inform communication training.
Objective: To develop a proposal for a 2-year mortality prognostic approach for patients with advanced chronic conditions based on the palliative care need (PCN) items of the
NECesidades PALiativas (NECPAL) CCOMS-ICO V.3.1 2017 tool.
Methods: A phase 1 study using three components based on the NECPAL items: (1) a rapid review of systematic reviews (SRs) on prognostic factors of mortality in patients with advanced chronic diseases and PCNs; (2) a clinician and statistician experts' consensus based on the Delphi technique on the selection of mortality prognostic factors; and (3) a panel meeting to discuss the findings of components (1) and (2).
Results: Twenty SRs were included in a rapid review, and 50% were considered of moderate quality. Despite methodological issues, nutritional and functional decline, severe and refractory dyspnoea, multimorbidity, use of resources and specific disease indicators were found to be potentially prognostic variables for mortality across four clinical groups and end-of-life (EoL) trajectories: cancer, dementia and neurologic diseases, chronic organ failure and frailty. Experts’ consensus added ‘needs’ identified by health professionals. However, clinicians were less able to discriminate which NECPAL items were more reliable for a ‘general’ model. A retrospective cohort study was designed to evaluate this proposal in phase 2.
Conclusions: We identified several parameters with prognostic value and linked them to the tool’s utility to timely identify PCNs of patients with advanced chronic conditions in all settings of care. Initial results show this is a clinical and feasible tool, that will help with clinical pragmatic decision-making and to define services.
Context: Universal screening to identify vulnerable patients who may receive limited benefits from life-sustaining treatments can facilitate palliative care in dialysis populations.
Objectives: We aimed to develop prediction models for 1-year mortality in peritoneal dialysis patients.
Methods: This prospective cohort study included 401 adult Taiwanese prevalent peritoneal dialysis patients (average age 56.2 ± 14 years). In addition to obtaining clinical characteristics and laboratory data, the primary care nurses evaluated the “surprise question” and “palliative care screening tool” for each patient in March 2015. Multivariate logistic regression models were conducted to predict the primary outcome of 1-year all-cause mortality.
Results: There were 34 (8.5%) patients who died during the first year of follow-up. Patients allocated to the “not surprised” group according to the surprise question and those who received a score = 4 on the palliative care screening tool had increased odds of death [odds ratio 24.68 (95% CI 10.66 - 57.13) and 12.18 (95% CI 5.66 - 26.21), respectively]. We also developed a clinical risk model for 1-year mortality that included sex, dialysis vintage, coronary artery disease, malignancy, normalized protein nitrogen appearance, white blood cell count, and serum albumin and sodium levels. Integrating the surprise question, palliative care screening tool, and clinical risk model exhibited good discrimination with an area under the receiver operating characteristic curve of 0.95. Kaplan-Meier analysis showed worse survival in high risk patients predicted by the integrated model (log-rank P<.001).
Conclusion: screening with the use of the integrated measurement can identify high-risk peritoneal dialysis patients. This approach may facilitate palliative care interventions for at-risk the subpopulations.
Background: General practitioners’ (GPs) play a central role in facilitating end-of-life discussions with older patients nearing the end-of-life. However, prognostic uncertainty of time to death is one important barrier to initiation of these discussions.
Objective: To explore GPs’ perceptions of the feasibility and acceptability of a risk prediction checklist to identify older patients in their last 12 months of life and describe perceived barriers and facilitators for implementing end-of-life planning.
Methods: Qualitative, semi-structured interviews were conducted with 15 GPs practising in metropolitan locations in New South Wales and Queensland between May and June 2019. Data were analysed thematically.
Results: Eight themes emerged: accessibility and implementation of the checklist, uncertainty around checklist’s accuracy and usefulness, time of the checklist, checklist as a potential prompt for end-of-life conversations, end-of-life conversations not an easy topic, end-of-life conversation requires time and effort, uncertainty in identifying end-of-life patients and limited community literacy on end-of-life. Most participants welcomed a risk prediction checklist in routine practice if assured of its accuracy in identifying which patients were nearing end-of-life.
Conclusions: Most participating GPs saw the value in risk assessment and end-of-life planning. Many emphasized the need for appropriate support, tools and funding for prognostic screening and end-of-life planning for this to become routine in general practice. Well validated risk prediction tools are needed to increase clinician confidence in identifying risk of death to support end-of-life care planning.
OBJECTIVE: The "surprise question" ("Would you be surprised if this patient died in the next year?") has been shown to be predictive of 12-month mortality in multiple populations, but has not been studied in gynecologic oncology (GO) patients. We sought to evaluate the prognostic performance of the surprise question in GO patients among physician and non-physician providers.
METHODS: GO providers at two tertiary care centers were asked the surprise question about a cohort of their patients undergoing chemotherapy or radiation. Demographic and clinical information was chart abstracted. Mortality data were collected at one year; relative risk of death at one year based on response to the surprise question was then calculated.
RESULTS: 32 providers (12 MDs, 7 APPs, 13 RNs) provided 942 surprise question assessments for 358 patients. Fifty-seven % had ovarian cancer and 54% had recurrent disease. Eighty-three (24%) patients died within a year. Patients whose physician answered "No" to the surprise question had a 43% one-year mortality (compared to 10% for "Yes"). Overall RR of 12-month mortality for "No" was 3.76 (95% CI 2.75-5.48); this association remained significant in all provider types. Among statistically significant predictors of 12-month mortality (including recurrent disease and >2 prior lines of chemotherapy), the surprise question had the highest RR.
CONCLUSIONS: The surprise question is a simple, one question tool that effectively identifies GO patients increased risk of 12-month mortality. The surprise question could be used to identify patients for early referral to palliative care and initiation advance care planning.
Prediction of short-term mortality in elderly patients with heart failure (HF) would be useful for clinicians when discussing HF management or palliative care.
A prospective multicenter cohort study was conducted between July 2014 and July 2018. A total of 504 consecutive elderly patients (age = 75 years) with HF (mean age 85 years, 50% women) were enrolled. We used a multiple logistic regression analysis with stepwise variable selection to select predictive variables and to determine weighted point scores. After analysis, the following variables predicted short-term mortality and comprised the risk score: previous HF admission (3 points), New York Heart Association III or IV (2 points), body mass index < 17.7 kg/m2 (4 points), serum albumin < 3.5 g/dL (9 points), and left ventricular ejection fraction < 50% (2 points). The c-statistic was 0.820. We compared mortality in low-risk (0-6 points, n = 188), intermediate-risk (7-13 points, n = 241), and high-risk (14-20 points, n = 75) groups. A total of 43 (8.5%) patients died within 6 months after discharge. Mortality was significantly higher in groups with higher scores (low-risk group, 0.5%; intermediate-risk group, 9.1%; high-risk group, 26.7%; P < 0.001).
We developed a predictive model for 6-month mortality in elderly patients with HF. This risk score could be useful when discussing advanced HF therapies, palliative care, or hospice referral with patients.