De quoi et où meurent les Françaises et Français ? Quelle est l’offre sanitaire globale mais aussi plus spécifiquement de soins palliatifs aujourd’hui en France ? Quel est le profil des patients pris en charge dans les unités de soins palliatifs ? Quelle est la part des personnes âgées de 75 ans et plus dans les statistiques de mortalité ? Quelles sont leurs particularités ? Observe-t-on des différences géographiques concernant toutes ces données ?
Cette deuxième édition de l'Atlas national a vocation à répondre à ces multiples questions pour aider le lecteur à appréhender les enjeux et les réalités de l’accompagnement de la fin de vie et de la place des soins palliatifs en France aujourd’hui. Il rassemble des données démographiques, sanitaires qui sont analysées le plus finement possible pour mettre en lumière les spécificités départementales en termes d’offre sanitaire mais aussi de besoins des patients dans leurs trajectoires de fin de vie.
OBJECTIVES: To develop and validate a model for prediction of near-term in-hospital mortality among patients with COVID-19 by application of a machine learning (ML) algorithm on time-series inpatient data from electronic health records.
METHODS: A cohort comprised of 567 patients with COVID-19 at a large acute care healthcare system between 10 February 2020 and 7 April 2020 observed until either death or discharge. Random forest (RF) model was developed on randomly drawn 70% of the cohort (training set) and its performance was evaluated on the rest of 30% (the test set). The outcome variable was in-hospital mortality within 20-84 hours from the time of prediction. Input features included patients' vital signs, laboratory data and ECG results.
RESULTS: Patients had a median age of 60.2 years (IQR 26.2 years); 54.1% were men. In-hospital mortality rate was 17.0% and overall median time to death was 6.5 days (range 1.3-23.0 days). In the test set, the RF classifier yielded a sensitivity of 87.8% (95% CI: 78.2% to 94.3%), specificity of 60.6% (95% CI: 55.2% to 65.8%), accuracy of 65.5% (95% CI: 60.7% to 70.0%), area under the receiver operating characteristic curve of 85.5% (95% CI: 80.8% to 90.2%) and area under the precision recall curve of 64.4% (95% CI: 53.5% to 75.3%).
CONCLUSIONS: Our ML-based approach can be used to analyse electronic health record data and reliably predict near-term mortality prediction. Using such a model in hospitals could help improve care, thereby better aligning clinical decisions with prognosis in critically ill patients with COVID-19.
Objectives: Patients with terminal illness are at high risk of developing delirium, in particular, those with multiple predisposing and precipitating risk factors. Delirium in palliative care is largely under-researched, and few studies have systematically assessed key aspects of delirium in elderly, palliative-care patients.
Methods: In this prospective, observational cohort study at a tertiary care center, 229 delirious palliative-care patients stratified by age: <65 (N = 105) and =65 years (N = 124), were analyzed with logistic regression models to identify associations with respect to predisposing and precipitating factors.
Results: In 88% of the patients, the underlying diagnosis was cancer. Mortality rate and median time to death did not differ significantly between the two age groups. No inter-group differences were detected with respect to gender, care requirements, length of hospital stay, or medical costs. In patients =65 years, exclusively predisposing factors were relevant for delirium, including hearing impairment [odds ratio (OR) 3.64; confidence interval (CI) 1.90–6.99; P < 0.001], hypertonia (OR 3.57; CI 1.84–6.92; P < 0.001), and chronic kidney disease (OR 4.84; CI 1.19–19.72; P = 0.028). In contrast, in patients <65 years, only precipitating factors were relevant for delirium, including cerebral edema (OR 0.02; CI 0.01–0.43; P = 0.012).
Significance of results: The results of this study demonstrate that death in delirious palliative-care patients occurs irrespective of age. The multifactorial nature and adverse outcomes of delirium across all age in these patients require clinical recognition. Potentially reversible factors should be detected early to prevent or mitigate delirium and its poor survival outcomes.
BACKGROUND: The general in-hospital mortality and interrelationship with delirium are vastly understudied. Therefore, this study aimed to assess the rates of in-hospital mortality and terminal delirium.
METHOD: In this prospective cohort study of 28,860 patients from 37 services including 718 in-hospital deaths, mortality rates and prevalence of terminal delirium were determined with simple logistic regressions and their respective odds ratios (ORs).
RESULTS: Although overall in-hospital mortality was low (2.5%), substantial variance between services became apparent: Across intensive care services the rate was 10.8% with a 5.8-fold increased risk, across medical services rates were 4.4% and 2.4-fold, whereas at the opposite end, across surgical services rates were 0.7% and 87% reduction, respectively. The highest in-hospital mortality rate occurred on the palliative care services (27.3%, OR 19.45). The general prevalence of terminal delirium was 90.7% and ranged from 83.2% to 100%. Only across intensive care services (98.1%, OR 7.48), specifically medical intensive care (98.1%, OR 7.48) and regular medical services (95.8%, OR 4.12) rates of terminal delirium were increased. In contrast, across medical services (86.4%, OR 0.32) and in particular oncology (73.9%, OR 0.25), pulmonology (72%, OR 0.31) and cardiology (63.2%, OR 0.4) rates were decreased. For the remaining services, rates of terminal delirium were the same.
SIGNIFICANCE OF RESULTS: Although in-hospital mortality was low, the interrelationship with delirium was vast: most patients were delirious at the end of life. The implications of terminal delirium merit further studies.
BACKGROUND: Automated systems that use machine learning to estimate a patient's risk of death are being developed to influence care. There remains sparse transparent reporting of model generalizability in different subpopulations especially for implemented systems.
METHODS: A prognostic study included adult admissions at a multi-site, academic medical center between 2015 and 2017. A predictive model for all-cause mortality (including initiation of hospice care) within 60,days of admission was developed. Model generalizability is assessed in temporal validation in the context of potential demographic bias. A subsequent prospective cohort study was conducted at the same sites between October 2018 and June 2019. Model performance during prospective validation was quantified with areas under the receiver operating characteristic and precision recall curves stratified by site. Prospective results include timeliness, positive predictive value, and the number of actionable predictions.
RESULTS: Three years of development data included 128,941 inpatient admissions (94,733 unique patients) across sites where patients are mostly white (61%) and female (60%) and 4.2% led to death within 60 days. A random forest model incorporating 9614 predictors produced areas under the receiver operating characteristic and precision recall curves of 87.2 (95% CI, 86.1-88.2) and 28.0 (95% CI, 25.0-31.0) in temporal validation. Performance marginally diverges within sites as the patient mix shifts from development to validation (patients of one site increases from 10 to 38%). Applied prospectively for nine months, 41,728 predictions were generated in real-time (median [IQR], 1.3 [0.9, 32] minutes). An operating criterion of 75% positive predictive value identified 104 predictions at very high risk (0.25%) where 65% (50 from 77 well-timed predictions) led to death within 60 days.
CONCLUSION: Temporal validation demonstrates good model discrimination for 60-day mortality. Slight performance variations are observed across demographic subpopulations. The model was implemented prospectively and successfully produced meaningful estimates of risk within minutes of admission.
Context: Hospice facilities are increasingly preferred as a location of death, but little is known about the characteristics of patients who die in these facilities in the U.S.
Objectives: We sought to examine the trends and factors associated with death in a hospice facility.
Methods: Retrospective cross-sectional study using mortality data for years 2003–2017 for deaths attributed to natural causes in the U.S.
Results: The proportion of natural deaths occurring in hospice facilities increased from 0.2% in 2003 to 8.3% in 2017, resulting in nearly 1.7 million deaths during this time frame. Females had increased odds of hospice facility deaths (odds ratio [OR] = 1.04; 95% CI = 1.04, 1.05). Nonwhite race was associated with lower odds of hospice facility death (black [OR = 0.915; 95% CI = 0.890, 0.940]; Native American [OR = 0.559; 95% CI = 0.515, 0.607]; and Asian [OR = 0.655; 95% CI = 0.601, 0.713]). Being married was associated with hospice facility death (OR = 1.06; 95% CI = 1.04, 1.07). Older age was associated with increased odds of hospice facility death (85 and older [OR = 1.40; 95% CI = 1.39, 1.41]). Having at least some college education was associated with increased odds of hospice facility death (OR = 1.13; 95% CI = 1.11, 1.15). Decedents from cardiovascular disease had the lowest odds of hospice facility death (OR = 0.278; 95% CI = 0.274, 0.282).
Conclusion: Hospice facility deaths increased among all patient groups; however, striking differences exist by age, sex, race, marital status, education level, cause of death, and geography. Factors underlying these disparities should be examined.
Background: To investigate the use of do-not-resuscitate (DNR) orders in patients hospitalized with community-acquired pneumonia (CAP) and the association with mortality.
Methods: We assembled a cohort of 1317 adults hospitalized with radiographically confirmed CAP in three Danish hospitals. Patients were grouped into no DNR order, early DNR order (=48 h after admission), and late DNR order (> 48 h after admission). We tested for associations between a DNR order and mortality using a cox proportional hazard model adjusted for patient and disease related factors.
Results: Among 1317 patients 177 (13%) patients received a DNR order: 107 (8%) early and 70 (5%) late, during admission. Patients with a DNR order were older (82 years vs. 70 years, p < 0.001), more frequently nursing home residents (41% vs. 6%, p < 0.001) and had more comorbidities (one or more comorbidities: 73% vs. 59%, p < 0.001). The 30-day mortality was 62% and 4% in patients with and without a DNR order, respectively. DNR orders were associated with increased risk of 30-day mortality after adjustment for age, nursing home residency and comorbidities. The association was modified by the CURB-65 score Hazard ratio (HR) 39.3 (95% CI 13.9–110.6), HR 24.0 (95% CI 11.9–48,3) and HR 9.4 (95% CI: 4.7–18.6) for CURB-65 score 0–1, 2 and 3–5, respectively.
Conclusion: In this representative Danish cohort, 13% of patients hospitalized with CAP received a DNR order. DNR orders were associated with higher mortality after adjustment for clinical risk factors. Thus, we encourage researcher to take DNR orders into account as potential confounder when reporting CAP associated mortality.
Background: COVID-19 has directly and indirectly caused high mortality worldwide.
Aim: To explore patterns of mortality during the COVID-19 pandemic and implications for palliative care, service planning and research.
Design: Descriptive analysis and population-based modelling of routine data.
Participants and setting: All deaths registered in England and Wales between 7 March and 15 May 2020. We described the following mortality categories by age, gender and place of death: (1) baseline deaths (deaths that would typically occur in a given period); (2) COVID-19 deaths and (3) additional deaths not directly attributed to COVID-19. We estimated the proportion of people who died from COVID-19 who might have been in their last year of life in the absence of the pandemic using simple modelling with explicit assumptions.
Results: During the first 10 weeks of the pandemic, there were 101,614 baseline deaths, 41,105 COVID-19 deaths and 14,520 additional deaths. Deaths in care homes increased by 220%, while home and hospital deaths increased by 77% and 90%, respectively. Hospice deaths fell by 20%. Additional deaths were among older people (86% aged >= 75 years), and most occurred in care homes (56%) and at home (43%). We estimate that 22% (13%–31%) of COVID-19 deaths occurred among people who might have been in their last year of life in the absence of the pandemic.
Conclusion: The COVID-19 pandemic has led to a surge in palliative care needs. Health and social care systems must ensure availability of palliative care to support people with severe COVID-19, particularly in care homes.
BACKGROUND: The mortality rate of pulmonary tuberculosis (TB) patients with respiratory failure remains high. This study aimed to identify factors contributing to death in these patients, and develop a mortality prediction model for pulmonary TB patients with respiratory failure.
METHODS: A retrospective study of patients admitted consecutively to the medical ICU of Beijing Chest Hospital, (Beijing, China), Chaoyang Fourth Hospital (Liaoning, China) and Hebi Third People's Hospital (Henan, China) from May 2018 to May 2019 was conducted. 153 patients with pulmonary TB accompanied by respiratory failure were enrolled. A multivariate analysis was performed to identify risk factors for death. A predictive fatality score was determined. The utility of the score for predicting death was evaluated using receiver operating characteristic (ROC) curve analysis.
RESULTS: The patients' median age was 57.82±19.42 years (17.0-87.0 years) and there were 106 males (69.28%). The mortality rate was 39.22% (60 of 153). Independent predictive factors of mortality included the PaO2 (hazard ratio 0.928, 95% CI: 0.882 - 0.976, P=0.004), Albumin (hazard ratio 0.881, 95% CI: 0.792- 0.980, P=0.019), Apache II score (hazard ratio 1.120, 95% CI: 1.017-1.234, P=0.022) and C-reactive protein (hazard ratio 1.012, 95% CI: 1.004-1.019, P=0.003). Establishing a logistic model of the death risk grade of pulmonary TB with respiratory failure was Y=1.710 - 0.068*PaO2-0.163* albumin + 0.215* Apache II +0.012* C-reactive protein. The value was Y=-0.494. If the Y value was greater than or equal to -0.494, the patients belonged to the deceased group, and if less than -0.494 the patients belonged to the survival group. AUC=0.818, The sensitivity was 83.3%; specificity was 73.1%.
CONCLUSIONS: Pulmonary TB patients with respiratory failure have a high mortality rate and poor prognosis, particularly those with high Apache II scores, high C-reactive protein levels, low PaO2 admission to ICU and low albumin level. The prediction model will help assess the risk of death in patients with TB and respiratory failure.
BACKGROUND AND PURPOSE: Do-not-resuscitate (DNR) orders in the first 24 hours after intracerebral hemorrhage have been associated with an increased risk of early death. This relationship is less certain for ischemic stroke. We assessed the relation between treatment restrictions and mortality in patients with ischemic stroke and in patients with intracerebral hemorrhage. We focused on the timing of treatment restrictions after admission and the type of treatment restriction (DNR order versus more restrictive care).
METHODS: We retrospectively assessed demographic and clinical data, timing and type of treatment restrictions, and vital status at 3 months for 622 consecutive stroke patients primarily admitted to a Dutch university hospital. We used a Cox regression model, with adjustment for age, sex, comorbidities, and stroke type and severity.
RESULTS: Treatment restrictions were installed in 226 (36%) patients, more frequently after intracerebral hemorrhage (51%) than after ischemic stroke (32%). In 187 patients (83%), these were installed in the first 24 hours. Treatment restrictions installed within the first 24 hours after hospital admission and those installed later were independently associated with death at 90 days (adjusted hazard ratios, 5.41 [95% CI, 3.17-9.22] and 5.36 [95% CI, 2.20-13.05], respectively). Statistically significant associations were also found in patients with ischemic stroke and in patients with just an early DNR order. In those who died, the median time between a DNR order and death was 520 hours (interquartile range, 53-737).
CONCLUSIONS: The strong relation between treatment restrictions (including DNR orders) and death and the long median time between a DNR order and death suggest that this relation may, in part, be causal, possibly due to an overall lack of aggressive care.
Background: Withdrawal from renal replacement therapy is common in patients with end-stage kidney disease (ESKD), but end-of-life service planning is challenging without population-specific data. We aimed to describe mortality after treatment withdrawal in Australian and New Zealand ESKD patients and evaluate death-certified causes of death.
Methods: We performed a retrospective cohort study on incident patients with ESKD in Australia, 1980–2013, and New Zealand, 1988–2012, from the Australian and New Zealand Dialysis and Transplant registry. We estimated mortality rates (by age, sex, calendar year and country) and summarized withdrawal-related deaths within 12 months of treatment modality change. Certified causes of death were ascertained from data linkage with the Australian National Death Index and New Zealand Mortality Collection database.
Results: Of 60 823 patients with ESKD, there were 8111 treatment withdrawal deaths and 26 207 other deaths over 381 874 person-years. Withdrawal-related mortality rates were higher in females and older age groups. Rates increased between 1995 and 2013, from 1142 (95% confidence interval 1064–1226) to 2706/100 000 person-years (95% confidence interval 2498–2932), with the greatest increase in 1995–2006. A third of withdrawal deaths occurred within 12 months of treatment modality change. The national death registers reported kidney failure as the underlying cause of death in 20% of withdrawal cases, with other causes including diabetes (21%) and hypertensive disease (7%). Kidney disease was not mentioned for 18% of withdrawal patients.
Conclusions: Treatment withdrawal represents 24% of ESKD deaths and has more than doubled in rate since 1988. Population data may supplement, but not replace, clinical data for end-of-life kidney-related service planning.
Background: Difficulties in prognostication are common deterrents to palliative care among dementia patients. This study aimed to evaluate the effectiveness of palliative care in reducing the extent of utilization of medical services and the potential risk factors of mortality among dementia patients receiving palliative care.
Methods: We surveyed dementia patients involved in a palliative care program at a long-term care facility in Taipei, Taiwan. We enrolled 57 patients with advanced dementia (clinical dementia rating = 5 or functional assessment staging test stage 7b). We then compared the extent of their utilization of medical services before and after the provision of palliative care. Based on multivariable logistic regression, we identified potential risk factors before and after the provision of palliative care associated with 6-month mortality.
Results: The utilization of medical services was significantly lower among dementia patients after the provision of palliative care than before, including visits to medical departments (p < 0.001), medications prescribed (p < 0.001), frequency of hospitalization (p < 0.001), and visits to the emergency room (p < 0.001). Moreover, patients dying within 6 months after the palliative care program had a slightly but not significantly higher number of admissions before receiving hospice care (p = 0.058) on univariate analysis. However, no significant differences were observed in multivariate analysis.
Conclusions: The provision of palliative care to dementia patients reduces the extent of utilization of medical services. However, further studies with larger patient cohorts are required to stratify the potential risk factors of mortality in this patient group.
Background: Health care practitioners have developed complex algorithms to numerically calculate surgical risk. We examined the association between the initiation of a new do-not-resuscitate (DNR) status during hospitalization and postoperative outcomes, including mortality. We hypothesized that new DNR status would be associated with similar complication rates, even though mortality rates may be higher.
Methods: A retrospective cohort study using the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Geriatric Surgery Research File. Two cohorts were defined by the presence of a new DNR status during the hospitalization that was not present on hospital admission. Multivariable logistic regression was used to control for differences between the DNR and non-DNR cohorts. The primary outcome was 30-day mortality. Secondary outcomes included rates of postoperative complications, including returning to the operating room, reintubation, failure to wean from ventilation, surgical site infections, dehiscence, pneumonia, acute kidney injury, renal failure, stroke, cardiac arrest, acute myocardial infarction, transfusion requirements, sepsis, urinary tract infections, venous thromboembolisms, total number of complications for each patient, and hospital length of stay.
Results: In our geriatric population with a newly established DNR status, the mortality rate was 39.29%, significantly greater than the non-DNR population after multivariable regression. Secondary outcomes also occurred at an increased rate in the DNR cohort including surgical site infections (8.29% vs 4.04%), pneumonia (18% vs 2.26%), renal insufficiency (2.43% vs 0.35%), acute renal failure (5% vs 0.19%), stroke (3% vs 0.36%), acute myocardial infarction (6.29% vs 0.95%), and cardiac arrest (5.86% vs 0.51%).
Conclusions: The initiation of a new DNR status during hospitalization is associated with a significantly higher burden of both morbidity and mortality. This contrasts with prior studies that did not show an increased rate of adverse outcomes and suggests that a new DNR status in postoperative patients may reflect a consequence of adverse postoperative events. The informed consent process in older patients at risk for adverse outcomes after surgery should include discussions regarding goals of care and acceptable risk.
BACKGROUND: As health-care institutions mobilize resources to address the coronavirus disease 2019 (COVID-19) pandemic, palliative care may potentially be underutilized. It is important to assess the use of palliative care in response to the COVID-19 pandemic.
METHODS: This is a retrospective single-center study of patients with COVID-19 diagnosed via reverse transcriptase-polymerase chain reaction assay admitted between March 1, 2020, and April 24, 2020. An analysis of the utilization of palliative care in accordance with patient comorbidities and other characteristics was performed while considering clinical outcomes. Chi-square test was used to determine associations between categorical variables while t-tests were used to compare continuous variables.
RESULTS: The overall mortality rate was 21.5% (n = 52), and in 48% (n = 25) of these patients, palliative care was not involved. Fifty-nine percent (n = 24) of those who had palliative consults eventually elected for comfort measures and transitioned to hospice care. Among those classified as having severe COVID-19, only 40% (n = 31) had palliative care involvement. Of these patients with severe COVID-19, 68% (n = 52) died. Patients who got palliative care consults were of older age, had higher rates of intubation, a need for vasopressors, and were dead.
CONCLUSION: There was a low utilization rate of palliative care in patients with COVID-19. Conscious utilization of palliative care is needed at the time of COVID-19.
Background: Since a national lockdown was introduced across the UK in March, 2020, in response to the COVID-19 pandemic, cancer screening has been suspended, routine diagnostic work deferred, and only urgent symptomatic cases prioritised for diagnostic intervention. In this study, we estimated the impact of delays in diagnosis on cancer survival outcomes in four major tumour types.
Methods: In this national population-based modelling study, we used linked English National Health Service (NHS) cancer registration and hospital administrative datasets for patients aged 15–84 years, diagnosed with breast, colorectal, and oesophageal cancer between Jan 1, 2010, and Dec 31, 2010, with follow-up data until Dec 31, 2014, and diagnosed with lung cancer between Jan 1, 2012, and Dec 31, 2012, with follow-up data until Dec 31, 2015. We use a routes-to-diagnosis framework to estimate the impact of diagnostic delays over a 12-month period from the commencement of physical distancing measures, on March 16, 2020, up to 1, 3, and 5 years after diagnosis. To model the subsequent impact of diagnostic delays on survival, we reallocated patients who were on screening and routine referral pathways to urgent and emergency pathways that are associated with more advanced stage of disease at diagnosis. We considered three reallocation scenarios representing the best to worst case scenarios and reflect actual changes in the diagnostic pathway being seen in the NHS, as of March 16, 2020, and estimated the impact on net survival at 1, 3, and 5 years after diagnosis to calculate the additional deaths that can be attributed to cancer, and the total years of life lost (YLLs) compared with pre-pandemic data.
Findings: We collected data for 32 583 patients with breast cancer, 24 975 with colorectal cancer, 6744 with oesophageal cancer, and 29 305 with lung cancer. Across the three different scenarios, compared with pre-pandemic figures, we estimate a 7·9–9·6% increase in the number of deaths due to breast cancer up to year 5 after diagnosis, corresponding to between 281 (95% CI 266–295) and 344 (329–358) additional deaths. For colorectal cancer, we estimate 1445 (1392–1591) to 1563 (1534–1592) additional deaths, a 15·3–16·6% increase; for lung cancer, 1235 (1220–1254) to 1372 (1343–1401) additional deaths, a 4·8–5·3% increase; and for oesophageal cancer, 330 (324–335) to 342 (336–348) additional deaths, 5·8–6·0% increase up to 5 years after diagnosis. For these four tumour types, these data correspond with 3291–3621 additional deaths across the scenarios within 5 years. The total additional YLLs across these cancers is estimated to be 59 204–63 229 years.
Interpretation: Substantial increases in the number of avoidable cancer deaths in England are to be expected as a result of diagnostic delays due to the COVID-19 pandemic in the UK. Urgent policy interventions are necessary, particularly the need to manage the backlog within routine diagnostic services to mitigate the expected impact of the COVID-19 pandemic on patients with cancer.
BACKGROUND: The COVID-19 pandemic has placed unprecedented strain on health-care systems. Frailty is being used in clinical decision making for patients with COVID-19, yet the prevalence and effect of frailty in people with COVID-19 is not known. In the COVID-19 in Older PEople (COPE) study we aimed to establish the prevalence of frailty in patients with COVID-19 who were admitted to hospital and investigate its association with mortality and duration of hospital stay.
METHODS: This was an observational cohort study conducted at ten hospitals in the UK and one in Italy. All adults ((=18 years) admitted to participating hospitals with COVID-19 were included. Patients with incomplete hospital records were excluded. The study analysed routinely generated hospital data for patients with COVID-19. Frailty was assessed by specialist COVID-19 teams using the clinical frailty scale (CFS) and patients were grouped according to their score (1-2=fit; 3-4=vulnerable, but not frail; 5-6=initial signs of frailty but with some degree of independence; and 7-9=severe or very severe frailty). The primary outcome was in-hospital mortality (time from hospital admission to mortality and day-7 mortality).
FINDINGS: Between Feb 27, and April 28, 2020, we enrolled 1564 patients with COVID-19. The median age was 74 years (IQR 61-83); 903 (57·7%) were men and 661 (42·3%) were women; 425 (27·2%) had died at data cutoff (April 28, 2020). 772 (49·4%) were classed as frail (CFS 5-8) and 27 (1·7%) were classed as terminally ill (CFS 9). Compared with CFS 1-2, the adjusted hazard ratios for time from hospital admission to death were 1·55 (95% CI 1·00-2·41) for CFS 3-4, 1·83 (1·15-2·91) for CFS 5-6, and 2·39 (1·50-3·81) for CFS 7-9, and adjusted odds ratios for day-7 mortality were 1·22 (95% CI 0·63-2·38) for CFS 3-4, 1·62 (0·81-3·26) for CFS 5-6, and 3·12 (1·56-6·24) for CFS 7-9.
INTERPRETATION: In a large population of patients admitted to hospital with COVID-19, disease outcomes were better predicted by frailty than either age or comorbidity. Our results support the use of CFS to inform decision making about medical care in adult patients admitted to hospital with COVID-19.
Introduction: For patients with brain metastases, palliative radiation therapy (RT) has long been a standard of care for improving quality of life and optimizing intracranial disease control. The duration of time between completion of palliative RT and patient death has rarely been evaluated.
Methods: A compilation of two prospective institutional databases encompassing April 2015 through December 2018 was used to identify patients who received palliative intracranial radiation therapy. A multivariate logistic regression model characterized patients adjusting for age, sex, admission status (inpatient versus outpatient), Karnofsky Performance Status (KPS), and radiation therapy indication.
Results: 136 consecutive patients received intracranial palliative radiation therapy. Patients with baseline KPS <70 (OR = 2.2; 95%CI = 1.6–3.1; p < 0.0001) were significantly more likely to die within 30 days of treatment. Intracranial palliative radiation therapy was most commonly delivered to provide local control (66% of patients) or alleviate neurologic symptoms (32% of patients), and was most commonly delivered via whole brain radiation therapy in 10 fractions to 30 Gy (38% of patients). Of the 42 patients who died within 30 days of RT, 31 (74%) received at least 10 fractions.
Conclusions: Our findings indicate that baseline KPS <70 is independently predictive of death within 30 days of palliative intracranial RT, and that a large majority of patients who died within 30 days received at least 10 fractions. These results indicate that for poor performance status patients requiring palliative intracranial radiation, hypofractionated RT courses should be strongly considered.
BACKGROUND: Coronavirus disease 2019 (COVID-19) has a substantial mortality risk with increased rates in the elderly. We hypothesized that age is not sufficient, and that frailty measured by preadmission Palliative Performance Scale would be a predictor of outcomes. Improved ability to identify high-risk patients will improve clinicians' ability to provide appropriate palliative care, including engaging in shared decision-making about life-sustaining therapies.
AIM: To evaluate whether preadmission Palliative Performance Scale predicts mortality in hospitalized patients with COVID-19.
DESIGN: Retrospective observational cohort study of patients admitted with COVID-19. Palliative Performance Scale was calculated from the chart. Using logistic regression, Palliative Performance Scale was assessed as a predictor of mortality controlling for demographics, comorbidities, palliative care measures and socioeconomic status.
SETTING/PARTICIPANTS: Patients older than 18 years of age admitted with COVID-19 to a single urban public hospital in New Jersey, USA.
RESULTS: Of 443 admitted patients, we determined the Palliative Performance Scale score for 374. Overall mortality was 31% and 81% in intubated patients. In all, 36% (134) of patients had a low Palliative Performance Scale score. Compared with patients with a high score, patients with a low score were more likely to die, have do not intubate orders and be discharged to a facility. Palliative Performance Scale independently predicts mortality (odds ratio 2.89; 95% confidence interval 1.42-5.85).
CONCLUSIONS: Preadmission Palliative Performance Scale independently predicts mortality in patients hospitalized with COVID-19. Improved predictors of mortality can help clinicians caring for patients with COVID-19 to discuss prognosis and provide appropriate palliative care including decisions about life-sustaining therapy.
Cause of death is an important outcome in end-of-life (EOL) research. However, difficulties in assigning cause of death have been well documented. We compared causes of death in national death registrations with those reported in EOL interviews. Data were from The Irish Longitudinal Study on Ageing (TILDA), a nationally representative sample of community-dwelling adults aged 50 years and older. The kappa agreement statistic was estimated to assess the level of agreement between two methods: cause of death reported in EOL interviews and those recorded in official death registrations. There was moderate agreement between underlying cause of death recorded on death certificates and those reported in EOL interviews. Discrepancies in reporting in EOL interviews were systematic with better agreement found among younger decedents and where the EOL informant was the decedents' partner/spouse. We have shown that EOL interviews may have limited utility if the main goal is to understand the predictors and antecedents of different causes of death.
Background: Population-based data are presented on the nature of dying in intellectual disability services.
Methods: A retrospective survey was conducted over 18 months with a sample of UK-based intellectual disability service providers that supported over 12,000. Core data were obtained for 222 deaths within this population. For 158 (71%) deaths, respondents returned a supplemented and modified version of VOICES-SF.
Results: The observed death was 12.2 deaths per 1,000 people supported per year, but just over a third deaths had been deaths anticipated by care staff. Mortality patterns, place of usual care and availability of external support exerted considerable influence over outcomes at the end of life.
Conclusion: Death is not a common event in intellectual disability services. A major disadvantage experienced by people with intellectual disabilities was that their deaths were relatively unanticipated. People with intellectual disabilities living in supported living settings, even when their dying was anticipated, experienced poorer outcomes.