Background: Timely palliative care in frail older persons remains challenging. Scales to identify older patients at risk of functional decline already exist. However, factors to predict short term mortality in older hospitalized patients are scarce.
Methods: In this prospective study, we recruited patients of 75 years and older at the department of cardiology and geriatrics. The usual gait speed measurement closest to discharge was chosen. We used the risk of dying within 1 year as parameter for starting palliative care. ROC curves were used to determine the best cut-off value of usual gait speed to predict one-year mortality. Time to event analyses were assessed by COX regression.
Results: On the acute geriatric ward (n = 60), patients were older and more frail (assessed by Katz and iADL) in comparison to patients on the cardiology ward (n = 82); one-year mortality was respectively 27 and 15% (p = 0.069). AUC on the acute geriatric ward was 0.748 (p = 0.006). The best cut-off value was 0.42 m/s with a sensitivity and specificity of 0.857 and 0.643. Slow walkers died earlier than faster walkers (HR 7.456, p = 0.011), after correction for age and sex. On the cardiology ward, AUC was 0.560 (p = 0.563); no significant association was found between usual gait speed and survival time.
Conclusions: Usual gait speed may be a valuable prognostic factor to identify patients at risk for one-year mortality on the acute geriatric ward but not on the cardiology ward.
Background: The prognosis of patients with incurable head and neck cancer (HNC) is a relevant topic. The mean survival of these patients is 5 months but may vary from weeks to more than 3 years. Discussing the prognosis early in the disease trajectory enables patients to make well-considered end-of-life choices, and contributes to a better quality of life and death. However, physicians often are reluctant to discuss prognosis, partly because of the concern to be inaccurate. This study investigated the accuracy of physicians’ clinical prediction of survival of palliative HNC patients.
Methods: This study was part of a prospective cohort study in a tertiary cancer center. Patients with incurable HNC diagnosed between 2008 and 2011 (n = 191), and their treating physician were included. Analyses were conducted between July 2018 and February 2019. Patients’ survival was clinically predicted by their physician =3 weeks after disclosure of the palliative diagnosis. The clinical prediction of survival in weeks (CPS) was based on physicians’ clinical assessment of the patient during the outpatient visits. More than 25% difference between the actual survival (AS) and the CPS was regarded as a prediction error. In addition, when the difference between the AS and CPS was 2 weeks or less, this was always considered as correct.
Results: In 59% (n = 112) of cases survival was overestimated. These patients lived shorter than predicted by their physician (median AS 6 weeks, median CPS 20 weeks). In 18% (n = 35) of the cases survival was correctly predicted. The remaining 23% was underestimated (median AS 35 weeks, median CPS 20 weeks). Besides the differences in AS and CPS, no other significant differences were found between the three groups. There was worse accuracy when predicting survival closer to death: out of the 66 patients who survived 6 weeks or shorter, survival was correctly predicted in only eight (12%).
Conclusion: Physicians tend to overestimate the survival of palliative HNC patients. This optimism can result in suboptimal use of palliative and end-of-life care. The future development of a prognostic model that provides more accurate estimates, could help physicians with personalized prognostic counseling.
Background: Evaluating the need for palliative care and predicting its mortality play important roles in the emergency department.
Aim: We developed a screening model for predicting 1-year mortality.
Design: A retrospective cohort study was conducted to identify risk factors associated with 1-year mortality. Our risk scores based on these significant risk factors were then developed. Its predictive validity performance was evaluated using area under receiving operating characteristic analysis and leave-one-out cross-validation.
Setting and participants: Patients aged 15 years or older were enrolled from June 2015 to May 2016 in the emergency department.
Results: We identified five independent risk factors, each of which was assigned a number of points proportional to its estimated regression coefficient: age (0.05 points per year), qSOFA >= 2 (1), Cancer (4), Eastern Cooperative Oncology Group Performance Status score >= 2 (2), and Do-Not-Resuscitate status (3). The sensitivity, specificity, positive predictive value, and negative predictive value of our screening tool given the cutoff larger than 3 points were 0.99 (0.98–0.99), 0.31 (0.29–0.32), 0.26 (0.24–0.27), and 0.99 (0.98–1.00), respectively. Those with screening scores larger than 9 points corresponding to 64.0% (60.0–67.9%) of 1-year mortality were prioritized for consultation and communication. The area under the receiving operating characteristic curves for the point system was 0.84 (0.83–0.85) for the cross-validation model.
Conclusions: A-qCPR risk scores provide a good screening tool for assessing patient prognosis. Routine screening for end-of-life using this tool plays an important role in early and efficient physician-patient communications regarding hospice and palliative needs in the emergency department.
OBJECTIVE: To examine transitions out of prognostic talk in interactions between clinicians and the relatives and friends of imminently dying hospice patients.
METHODS: Conversation analysis of 20 conversations between specialist palliative care clinicians and the families of imminently dying patients in a hospice.
RESULTS: Following the provision and acknowledgement of a prognostic estimate, clinicians were able to transition gradually towards making assurances about actions that could be taken to ensure patient comfort. When families raised concerns or questions, this transition sequence was extended. Clinicians addressed these questions or concerns and then pivoted to action-oriented talk, most often relating to patient comfort.
CONCLUSION: In conversations at the end of life, families and clinicians used practices to transition from the uncertainty of prognosis to more certain, controllable topics including comfort care.
PRACTICE IMPLICATIONS: In a context in which there is a great deal of uncertainty, transitioning towards talk on comfort care can emphasise action and the continued care of the patient and their family.
Context: The unmet needs of patients with advanced disease are indicative of the patient centredness of healthcare. By tracking unmet needs in clinical practice, palliative interventions are aligned with patient priorities, and clinicians receive support in intervention delivery decisions for patients with overlapping, complex needs.
Objective: Identify tools used in everyday clinical practice for the purpose of identifying and addressing unmet healthcare needs for patients with advanced disease.
Methods: We conducted PubMed and Cumulative Index of Nursing and Allied Health Literature searches to include studies published between 1 January 2008 and 21 April 2020. Three concepts were used in constructing a search statement: (1) patient need, (2) validated instrument and (3) clinical practice. 2313 citations were reviewed according to predefined eligibility, exclusion and inclusion criteria. Data were collected from 17 tools in order to understand how instruments assess unmet need, who is involved in tool completion, the psychometric validation conducted, the tool’s relationship to delivering defined palliative interventions, and the number of palliative care domains covered.
Results: The majority of the 17 tools assessed unmet healthcare needs and had been validated. However, most did not link directly to clinical intervention, nor did they facilitate interaction between clinicians and patients to ensure a patient-reported view of unmet needs. Half of the tools reviewed covered =3 dimensions of palliative care. Of the 17 tools evaluated, 4 were compared in depth, but all were determined to be insufficient for the specific clinical applications sought in this research.
Conclusion: A new, validated tool is needed to track unmet healthcare needs and guide interventions for patients with advanced disease.
BACKGROUND: Myelophthisis (MPT) has been associated with a dreadful prognosis. Patients' access to palliative care (PC) and factors influencing its clinical outcomes are poorly described. Our aim was to analyze the impact of patient- and disease-specific characteristics on survival of patients with MPT and describe their use of PC in a resource-limited setting.
METHODS: Retrospective study including patients with solid tumor MPT, diagnosed between 1996 and 2018.
RESULTS: Seventy patients (median 58 years) were included. 58% were synchronously diagnosed with MPT at time of primary tumor diagnosis. Most common oncologic diagnoses were prostate (25.7%), gastrointestinal (20%), and breast (18.6%) neoplasms. Median overall survival (OS) was 1.9 months. Primaries other than prostate, breast, and lung (HR 1.37, 95% CI 1.15 - 1.8; p = 0.02) and transfusion requirements (HR 2.8, 95% CI 1.01 - 7.9; p = 0.04) were independently associated with decreased OS. Administration of multiple systemic therapeutic interventions (HR 0.15, 95% CI 0.06 - 0.39; p = 0.01) was the sole factor improving OS. Assessment by PC was pursued in 51.4% of patients. The median number of consults per patient was two, with no difference in assessment rate or consult number across different primaries (P = 0.96). Four cases of palliative sedation were reported, all performed by the primary care team.
CONCLUSION: MPT is highly heterogeneous and risk stratification to optimize the use of therapeutic interventions in unison with palliative interventions is needed to maximize efforts toward improving patient quality of life. There is an alarming need of PC services in the multidisciplinary management of patients within developing regions.
Because of their longstanding relationships with patients, family physicians often are in the best position to identify signs of serious illness progression, provide support and guidance to patients and caregivers, and tailor care plans to individual needs and preferences at the end of life. Significant signs of illness progression include worsening of one or more conditions, decline in function, and increase in the number of emergency department visits or hospitalizations. Prognostication refers to estimation of the remaining life expectancy. Several tools are available to inform such estimates. Prognostication should include discussion of the expected illness progression to help patients and family members prepare, plan, and cope. Advance care planning, ideally started before or early in the course of illness, should include identification of patient surrogate decision-makers as well as a discussion of patient values, priorities, and care preferences. Planning should continue and evolve to inform care plans that match patient and family member priorities at each stage of illness. Family physicians should be familiar with resources available in their communities to support care plans, including palliative care subspecialists, home- and facility-based palliative care teams, and hospice physicians.
OBJECTIVES: To evaluate a machine learning model designed to predict mortality for Medicare beneficiaries aged >65 years treated for hip fracture in Inpatient Rehabilitation Facilities (IRFs).
DESIGN: Retrospective design/cohort analysis of Centers for Medicare & Medicaid Services Inpatient Rehabilitation Facility-Patient Assessment Instrument data.
SETTING AND PARTICIPANTS: A total of 17,140 persons admitted to Medicare-certified IRFs in 2015 following hospitalization for hip fracture.
MEASURES: Patient characteristics include sociodemographic (age, gender, race, and social support) and clinical factors (functional status at admission, chronic conditions) and IRF length of stay. Outcomes were 30-day and 1-year all-cause mortality. We trained and evaluated 2 classification models, logistic regression and a multilayer perceptron (MLP), to predict the probability of 30-day and 1-year mortality and evaluated the calibration, discrimination, and precision of the models.
RESULTS: For 30-day mortality, MLP performed well [acc = 0.74, area under the receiver operating characteristic curve (AUROC) = 0.76, avg prec = 0.10, slope = 1.14] as did logistic regression (acc = 0.78, AUROC = 0.76, avg prec = 0.09, slope = 1.20). For 1-year mortality, the performances were similar for both MLP (acc = 0.68, AUROC = 0.75, avg prec = 0.32, slope = 0.96) and logistic regression (acc = 0.68, AUROC = 0.75, avg prec = 0.32, slope = 0.95).
CONCLUSION AND IMPLICATIONS: A scoring system based on logistic regression may be more feasible to run in current electronic medical records. But MLP models may reduce cognitive burden and increase ability to calibrate to local data, yielding clinical specificity in mortality prediction so that palliative care resources may be allocated more effectively.
Background: This study was to investigate the prognostic factors of patients with advanced gastric cancer and described a sample model to better differentiate the patients who could better benefit from palliative chemotherapy.
Patients and methods: In this retrospective study, 112 gastric cancer patients at stage IV following first-line chemotherapy were enrolled from July 2013 to September 2019. The clinical factors including age, sex, ECOG, pathologic types, metastatic sites, blood indexes, response of first-line chemotherapy, and survival were collected. The treatment responses were evaluated using the response evaluation criteria in solid tumors (RECIST). The survival curves were drawn by the Kaplan-Meier method, and the independent prognostic factors of overall survival (OS) were analyzed by Cox proportional hazards regression model.
Results: In this study, the median overall survival (mOS) of gastric cancer patients was 10.5 months, the disease remission rate (PR) was 21.4%, and the disease control rate (DCR) was 86.6%. Multivariate analysis identified 5 independent prognostic factors: peritoneal metastasis [P = 0.002; hazard risk (HR), 2.394; 95% CI 1.394-4.113], hemoglobin <90g/L [P = 0.001; hazard risk (HR), 2.674; 95% CI 1.536-4.655], LDH =225 U/L [P = 0.033; hazard risk (HR), 1.818; 95% CI 1.409-3.150], and 3 times higher level of CEA [P = 0.006; hazard risk (HR), 2.123; 95% CI 1.238-3.640] along with CA199 [P = 0.005; hazard risk (HR), 2.544; 95% CI 1.332-4.856] than upper limit of normal. Based on the obtained data, a prognostic index was constructed, dividing the patients into three risk groups: low (n = 67), intermediate (n = 35), and high-risk group (n = 10). The mOS for low, intermediate, and high-risk groups was 13.9 months (95% CI 10.7-17.1), 8.1 months (95% CI 5.7-10.4), and 3.9 months (95% CI 2.6-5.3), respectively, whereas the 1-year survival rate was 56.4%, 20.0%, and 0.0%, respectively (P < 0.001).
Conclusion: This model should facilitate the prediction of treatment outcomes and then individualized treatment of advanced gastric cancer patients.
Background: Prognostic uncertainty delays discussions and leads to unnecessary treatments for older patients who are dying. The aim of this study was to investigate the feasibility of using routinely collected data from MedicineInsight, a large Australian general practice database, to flag indicators of near end-of-life (nEOL) in patients aged =75 years and evaluate their association with death over 12 months.
Methods: A retrospective chart review was used to assess the feasibility of identifying these indicators in the data (160,897 patients from 464 practices across Australia). Conditional logistic regression was used to assess the independent contribution of nEOL indicators in patients aged 75–84 and =85 years using a case-control design matching by practice.
Results: The strongest indicators for nEOL status were advanced malignancy, residential aged care, nutritional vulnerability, anaemia, cognitive impairment and heart failure. Other indicators included hospital attendance, pneumonia, decubitus ulcer, chronic obstructive pulmonary disease, antipsychotic prescription, male sex and stroke.
Discussion: Consideration of routinely collected patient data may suggest nEOL status and trigger advance care planning discussions.
BACKGROUND: Patients with end-stage liver disease (ESLD) have limited treatment options and have a deteriorated quality of life with an uncertain prognosis. Early identification of ESLD patients with a poor prognosis is valuable, especially for palliative care. However, it is difficult to predict ESLD patients that require either acute care or palliative care.
OBJECTIVE: We sought to create a machine-learning monitoring system that can predict mortality or classify ESLD patients. Several machine-learning models with visualized graphs, decision trees, ensemble learning, and clustering were assessed.
METHODS: A retrospective cohort study was conducted using electronic medical records of patients from Wan Fang Hospital and Taipei Medical University Hospital. A total of 1214 patients from Wan Fang Hospital were used to establish a dataset for training and 689 patients from Taipei Medical University Hospital were used as a validation set.
RESULTS: The overall mortality rate of patients in the training set and validation set was 28.3% (257/907) and 22.6% (145/643), respectively. In traditional clinical scoring models, prothrombin time-international normalized ratio, which was significant in the Cox regression (P<.001, hazard ratio 1.288), had a prominent influence on predicting mortality, and the area under the receiver operating characteristic (ROC) curve reached approximately 0.75. In supervised machine-learning models, the concordance statistic of ROC curves reached 0.852 for the random forest model and reached 0.833 for the adaptive boosting model. Blood urea nitrogen, bilirubin, and sodium were regarded as critical factors for predicting mortality. Creatinine, hemoglobin, and albumin were also significant mortality predictors. In unsupervised learning models, hierarchical clustering analysis could accurately group acute death patients and palliative care patients into different clusters from patients in the survival group.
CONCLUSIONS: Medical artificial intelligence has become a cutting-edge tool in clinical medicine, as it has been found to have predictive ability in several diseases. The machine-learning monitoring system developed in this study involves multifaceted analyses, which include various aspects for evaluation and diagnosis. This strength makes the clinical results more objective and reliable. Moreover, the visualized interface in this system offers more intelligible outcomes. Therefore, this machine-learning monitoring system provides a comprehensive approach for assessing patient condition, and may help to classify acute death patients and palliative care patients. Upon further validation and improvement, the system may be used to help physicians in the management of ESLD patients.
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.
BACKGROUND: Long-term survival and functional outcomes should influence admission decisions to intensive care, especially for patients with advanced disease.
AIM: To determine whether physicians' predictions of long-term prognosis influenced admission decisions for patients with and without advanced disease.
DESIGN: A prospective study was conducted. Physicians estimated patient survival with intensive care and with care on the ward, and the probability of 4 long-term outcomes: leaving hospital alive, survival at 6 months, recovery of functional status, and recovery of cognitive status. Patient mortality at 28 days was recorded. We built multivariate logistic regression models using admission to the intensive care unit (ICU) as the dependent variable.
SETTING/PARTICIPANTS: ICU consultations for medical inpatients at a Swiss tertiary care hospital were included.
RESULTS: Of 201 evaluated patients, 105 (52.2%) had an advanced disease and 140 (69.7%) were admitted to the ICU. The probability of admission was strongly associated with the expected short-term survival benefit for patients with or without advanced disease. In contrast, the predicted likelihood that the patient would leave the hospital alive, would be alive 6 months later, would recover functional status, and would recover initial cognitive capacity was not associated with the decision to admit a patient to the ICU. Even for patients with advanced disease, none of these estimated outcomes influenced the admission decision.
CONCLUSIONS: ICU admissions of patients with advanced disease were determined by short-term survival benefit, and not by long-term prognosis. Advance care planning and developing decision-aid tools for triage could help limit potentially inappropriate admissions to intensive care.
Aim: We validated the NUE rule, using three criteria (Non-shockable initial rhythm, Unwitnessed arrest, Eighty years or older) to predict futile resuscitation of patients with out-of-hospital cardiac arrest (OHCA).
Methods: We performed a retrospective cohort analysis of all recorded OHCA in Marion County, Indiana, from January 1, 2014 to December 31, 2019. We described patient, arrest, and emergency medical services (EMS) response characteristics, and assessed the performance of the NUE rule in identifying patients unlikely to survive to hospital discharge.
Results: From 2014 to 2019, EMS responded to 4370 patients who sustained OHCA. We excluded 329 (7.5%) patients with incomplete data. Median patient age was 62 years (IQR 49 - 73), 1599 (39.6%) patients were female, and 1728 (42.8%) arrests were witnessed. The NUE rule identified 290 (7.2%) arrests, of whom none survived to hospital discharge.
Conclusion: In external validation, the NUE rule (Non-shockable initial rhythm, Unwitnessed arrest, Eighty years or older) correctly identified 7.2% of OHCA patients unlikely to survive to hospital discharge. The NUE rule could be used in EMS protocols and policies to identify OHCA patients very unlikely to benefit from aggressive resuscitation.
End-of-life (EOL) decision making in the intensive care unit (ICU) is challenging for both families and clinicians. This decision-making process is ideally framed around a shared understanding of a patient’s values and goals, all taken in the context of their critical illness and prognosis. However, clinicians commonly face uncertainty regarding prognosis and may have difficulty offering families an accurate assessment of the likely outcomes of treatment decisions. Adding to the complexity of these scenarios, clinicians, patients and families are each susceptible to unconscious but influential cognitive biases when making decisions under stress. Given these challenges, and a rapidly growing interest in data science to inform care in the ICU, investigators have explored the use of prediction models (eg, machine learning or ML algorithms) to assist with prognostication. Prediction models describe an outcome distribution among individuals with a particular set of characteristics, such as risk of acute kidney injury among individuals with particular laboratory values and clinical characteristics in a population. However, they do not compare how that outcome distribution would change were different treatment decisions made in that population—this requires causal effect estimation, rather than prediction modelling. Herein, we explain why prediction modelling alone is not sufficient to inform many ICU treatment decisions, including EOL decision making, and describe why causal effect estimation is necessary.
This article presents the case of a mother of young children who has terminal stage IV cancer with whom providers had not discussed goals of care and prognostication. Communications about prognostication and goals of care are commonly initiated by physicians. Adolescents and young and middle-age adults with complex chronic or terminal illness often are not provided with timely, clear, complete information or palliative care support. Early palliative care for chronically ill patients facilitates discussions of prognostication and goals of care, in addition to providing symptom management. Such discussions do not diminish hope but rather allow patients to adjust hope to attain an optimal quality of life. Nurses can become active, confident advocates for patients with terminal illness of any age, and they are well positioned to assess patients and engage in goals of care and end-of-life conversations. It is especially important that palliative care nurses promote and maintain these early and comprehensive discussions during the COVID-19 pandemic because this population is at a high risk of complications from the coronavirus.
Background: For residential aged care facility (RACF) residents with dementia, lack of prognostic guidance presents a significant challenge for end of life care planning. In an attempt to address this issue, models have been developed to assess mortality risk for people with advanced dementia, predominantly using long-term care minimum data set (MDS) information from the USA. A limitation of these models is that the information contained within the MDS used for model development was not collected for the purpose of identifying prognostic factors. The models developed using MDS data have had relatively modest ability to discriminate mortality risk and are difficult to apply outside the MDS setting. This study will aim to develop a model to estimate 6- and 12-month mortality risk for people with dementia from prognostic indicators recorded during usual clinical care provided in RACFs in Australia.
Methods: A secondary analysis will be conducted for a cohort of people with dementia from RACFs participating in a cluster-randomized trial of a palliative care education intervention (IMPETUS-D). Ten prognostic indicator variables were identified based on a literature review of clinical features associated with increased mortality for people with dementia living in RACFs. Variables will be extracted from RACF files at baseline and mortality measured at 6 and 12 months after baseline data collection. A multivariable logistic regression model will be developed for 6- and 12-month mortality outcome measures using backwards elimination with a fractional polynomial approach for continuous variables. Internal validation will be undertaken using bootstrapping methods. Discrimination of the model for 6- and 12-month mortality will be presented as receiver operating curves with c statistics. Calibration curves will be presented comparing observed and predicted event rates for each decile of risk as well as flexible calibration curves derived using loess-based functions.
Discussion: The model developed in this study aims to improve clinical assessment of mortality risk for people with dementia living in RACFs in Australia. Further external validation in different populations will be required before the model could be developed into a tool to assist with clinical decision-making in the future.
CONTEXT: Previous studies suggest that clinicians' prediction of survival (CPS) may have reduce the accuracy of objective indicators for prognostication in palliative care.
OBJECTIVES: We aimed to examine the accuracy of CPS alone, compared to the original Palliative Prognostic Score (PaP), and five clinical/laboratory variables of the PaP in far advanced cancer patients.
METHODS: We compared the discriminative accuracy of three prediction models [the PaP-CPS (the score of the categorical CPS of PaP), PaP without CPS (sum of the scores of only the objective variables of PaP), and PaP total score] across 3 SETTINGS: inpatient palliative care consultation team, palliative care unit, and home palliative care. We computed the area under receiver operating characteristic curve (AUROC) for 30-day survival and concordance index (C-index) to compare the discriminative accuracy of these three models.
RESULTS: We included a total of 1,534 subjects with median survival of 34.0 days. The AUROC and C-index in the three settings were 0.816-0.896 and 0.732-0.799 for the PaP total score, 0.808-0.884 and 0.713-0.782 for the PaP-CPS, and 0.726-0.815 and 0.672-0.728 for the PaP without CPS, respectively. The PaP total score and PaP-CPS showed similar AUROCs and C-indices across the three settings. The PaP total score had significantly higher AUROCs and C-indices than the PaP without CPS across the three settings.
CONCLUSION: Overall, the PaP total score, PaP-CPS, and PaP without CPS showed good discriminative performances. However, the PaP total score and PaP-CPS were significantly more accurate than the PaP without CPS.
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.
What components of the physical examination (PE) are valuable when providing comfort-based care for an imminently dying patients? While patient factors must be individualized, this Fast Fact assimilates the sparse published evidence along with anecdotal experience to offer clinical pearls on how to tailor the PE.