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.
Objectives: To identify the types of factors included in research examining mortality in patients with dementia, and to stratify the identified factors by care settings.
Design: We systematically searched PubMed, Embase, PsycINFO and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases, and identified grey literature from the Networked Digital Library of Theses and Dissertations, Open Grey and Grey Literature Report. Two authors independently screened for eligibility of studies. Independent reviewers extracted relevant study information. We conducted a narrative synthesis of the data.
Results: We identified 8254 articles, of which 94 met the inclusion criteria. More than half (n=53) were published between 2009 and 2018 with half from Europe. Studies were conducted across hospices/nursing homes (n=25), hospital (n=23), outpatient clinics (n=21), mixed settings (n=15) and in the community (n=10). Nearly 60% adopted a prospective cohort study design with 87% performing multivariable analysis. Overall, 239 variables were identified and classified into six themes—individual factors, health status, functional ability, cognition and mental health, treatments and health system factors. Although a general set of factors were common across all studies, when stratified by care settings, variations were seen in the specific variables included.
Conclusion: Identifying prognostic variables relevant to the dementia population in each setting is key to facilitate appropriate care plans and to ensure timely access to palliative care options. Future research should also focus on ensuring the replicability of prognostic models and to generate a better understanding of the direct and interacting influence of the identified factors on mortality.
ntroduction: Patients dying a short time after receiving palliative radiation are unlikely to have received benefit and may experience harm. To monitor the potential for avoidable harm, 30-day mortality following palliative radiation has been recommended for use as a quality indicator and the Royal College of Radiologist have recommended a rate of lower than 20%. At the Canterbury Regional Cancer and Haematology Service in Christchurch, New Zealand (CRCHS), we investigated 30-day mortality and evaluated the prognostic value of the TEACHH model in our population.
Methods: Palliative treatments from two, two-year periods (2012/2013 and 2016/2017) were retrospectively reviewed. We analysed 30-day mortality and several influencing variables. Patients were divided into three groups using the TEACHH model (type of cancer, performance status, age, prior palliative chemotherapy, prior hospitalizations and hepatic metastases).
Results: There were 1744 patients; 30-day mortality was 10% and was higher in patients with lung cancer (17% vs. 8% in non–lung cancer patients, P < 0.0001), patients having less than five fractions (13% vs. 9%, P : 0.0199) and patients in TEACHH group B/C (21% in C, 11% in B and 2% in group A, P < 0.0001). The majority of treatments (84%) used five fractions or less.
Conclusions: The mortality rate is within the suggested quality indicator, and the decreasing mortality with increasing fractionation demonstrates suitable selection of patients for longer treatment regimens. The TEACHH model can be used to increase precision in estimating prognosis, identifying patients who should not receive treatment and conversely identifying those for whom a prolonged fractionation schedule may be appropriate.
Objectives: To develop a mortality-predictive model for correct identification of patients with non-cancer multiple chronic conditions who would benefit from palliative care, recognise predictive indicators of death and provide with tools for individual risk score calculation.
Design: Retrospective observational study with multivariate logistic regression models.
Participants: All patients with high-risk multiple chronic conditions incorporated into an integrated care strategy that fulfil two conditions: (1) they belong to the top 5% of the programme’s risk pyramid according to the adjusted morbidity groups stratification tool and (2) they suffer simultaneously at least three selected chronic non-cancer pathologies (n=591).
Main outcome measure: 1 year mortality since patient inclusion in the programme.
Results: Among study participants, 201 (34%) died within the 1 year follow-up. Variables found to be independently associated to 1 year mortality were the Barthel Scale (p<0.001), creatinine value (p=0.032), existence of pressure ulcers (p=0.029) and patient global status (p<0.001). The area under the curve (AUC) for our model was 0.751, which was validated using bootstrapping (AUC=0.751) and k-fold cross-validation (10 folds; AUC=0.744). The Hosmer-Lemeshow test (p=0.761) showed good calibration.
Conclusions This study develops and validates a mortality prediction model that will guide transitions of care to non-cancer palliative care services. The model determines prognostic indicators of death and provides tools for the estimation of individual death risk scores for each patient. We present a nomogram, a graphical risk calculation instrument, that favours a practical and easy use of the model within clinical practices.
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.
Background: factors associated with place of death inform policies with respect to allocating end-of-life care resources and tailoring supportive measures.
Objective: To determine factors associated with non-hospital deaths among cancer patients.
Design: Retrospective cohort study of cancer decedents, examining factors associated with non-hospital deaths using multinomial logistic regression with hospital deaths as the reference category.
Setting/subjects: Cancer patients (n = 15254) in Singapore who died during the study period from January 1, 2012 till December 31, 2105 at home, acute hospital, long-term care (LTC) or hospice were included.
Results: Increasing age (categories =65 years: RRR 1.25–2.61), female (RRR 1.40; 95% CI 1.28–1.52), Malays (RRR 1.67; 95% CI 1.47–1.89), Brain malignancy (RRR 1.92; 95% CI 1.15–3.23), metastatic disease (RRR 1.33–2.01) and home palliative care (RRR 2.11; 95% CI 1.95–2.29) were associated with higher risk of home deaths. Patients with low socioeconomic status were more likely to have hospice or LTC deaths: those living in smaller housing types had higher risk of dying in hospice (1–4 rooms apartment: RRR 1.13–3.17) or LTC (1–5 rooms apartment: RRR 1.36–4.11); and those with Medifund usage had higher risk of dying in LTC (RRR 1.74; 95% CI 1.36–2.21). Patients with haematological malignancies had increased risk of dying in hospital (categories of haematological subtypes: RRR 0.06–0.87).
Conclusions: We found key sociodemographic and clinical factors associated with non-hospital deaths in cancer patients. More can be done to enable patients to die in the community and with dignity rather than in a hospital.
Coronavirus-19 disease (COVID-19) has quickly spread to cause a global pandemic, and produces a spectrum of disease from mild respiratory illness to severe acute respiratory distress syndrome. Current estimates indicate that 15% of patients with COVID-19 will develop severe disease, and 5 to 10% will require intensive care-level support. In certain scenarios, escalation of life-sustaining therapies (defined as intubation, mechanical ventilation,vasopressor support, and/or hemodialysis) will either not be within the patient’s goals of care, or will unfortunately be unsuccessful. Overall mortality risk from COVID-19is estimated to be between 3 and 5%.
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: Heart failure (HF) impacts 6.2 million American adults. With no cure, therapies aim to prevent progression and manage symptoms. Inclusion of palliative care (PC) helps improve symptoms and quality of life. Heart failure guidelines recommend the inclusion of PC in HF therapy, but referrals are often delayed.
Objective: Introduce PC to patients with HF and examine the impact on PC consults, readmission, mortality, and intensive care unit (ICU) transfers.
Methods: Patients (n = 60) admitted with HF to an academic hospital were asked to view a PC educational module. A number of PC consults, re-admissions, mortality, and transfers to the ICU were compared among participants and those who declined.
Results: Nine patients in the intervention group (n = 30) requested a PC consult ( P = .042) versus 2 in the usual care group (n = 30; P = .302). There was no statistically significant difference in readmissions, mortality, or ICU transfers between groups.
Conclusions: Palliative care education increases the likelihood of PC utilization but in this short-term project was not found to statistically impact mortality, re-admissions, or transfers to higher levels of care.
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: Age and massive transfusion are predictors of mortality after trauma. We hypothesized that increasing age and high-volume transfusion would result in progressively elevated mortality rates and that a transfusion “ceiling” would define futility.
Methods: The Trauma Quality Improvement Program (TQIP) database was queried for 2013-2016 records and our level I trauma registry was reviewed from 2013 to 2018. Demographic, mortality, and blood transfusion data were collected. Patients were grouped by decade of life and by packed red blood cell (pRBC) transfusion requirement (zero units, 1-3 units, or =4 units) within 4 h of admission.
Results: TQIP analysis demonstrated an in-hospital mortality risk that increased linearly with age, to an odds ratio of 10.1 in =80 y old ( P < 0.01). Mortality rates were significantly higher in older adults ( P < 0.01) and those with more pRBCs transfused. In massively transfused patients, the transfusion “ceiling” was dependent on age. Owing to the lack granularity in the TQIP database, 230 patients from our institution who received =4 units of pRBCs within 4 h of admission were reviewed. On arrival, younger patients had significantly higher heart rates and more severe derangements in lactate levels, base deficits, and pH compared with older patients. There were no differences among age groups in injury severity score, systolic blood pressure, or mortality.
Conclusions: In massively transfused patients, mortality increased with age. However, a significant proportion of older adults were successfully resuscitated. Therefore, age alone should not be considered a contraindication to high-volume transfusion. Traditional physiologic and laboratory criteria indicative of hemorrhagic shock may have reduced reliability with increasing age, and thus providers must have a heightened suspicion for hemorrhage in the elderly. Early transfusion requirements can be combined with age to establish prognosis to define futility to help counsel families regarding mortality after traumatic injury.
Background: Despite mounting evidence that specialty palliative care (PC) improves patients' symptoms, quality of life, and goal concordant care, these services are likely underutilized.
Objective: To determine the rate of missed and delayed opportunities for specialty PC in patients with peri-hospital death.
Design: A retrospective, cross-sectional analysis, using electronic medical records of a state-wide healthcare system in Colorado, was performed. Included were adults who died during admission or within seven days of discharge from January 2015 to October 2018 at an academic medical center and had prior encounters within the affiliated state-wide healthcare system in the last year of life. Excluded were patients with sudden or obstetrics-related deaths. Referral orders from the electronic medical record identified specialty PC consultation. Data from the Colorado Department of Public Health and Environment linked with the medical record determined time from first PC consultation to death.
Results: The sample included 2088 decedent patients, with most deaths (81%) occurring in the hospital. Only 33% of patients had PC consultation, which was higher for patients with cancer (42%) than for those without cancer (26%). Of patients with specialty PC consultation, the median time from first referral to death was eight days (interquartile range: 3.25–25 days).
Conclusions: Patients with peri-hospital death have low rates of specialty PC consultation, which, when present, often occurs close to death. This suggests there is a high rate of missed opportunities for specialty PC in this population.
Objective:To investigate trends and associated factors of utilization of hospital palliative care among patients with systemic lupus erythematosus (SLE) and analyze its impact on length of hospital stay, hospital charges, and in-hospital mortality.
Methods: Using the 2005-2014 National Inpatient Sample in the United States, the compound annual growth rate was used to investigate the temporal trend of utilization of hospital palliative care. Multivariate multilevel logistic regression analyses were performed to analyze the association with patient-related factors, hospital factors, length of stay, in-hospital mortality, and hospital charges.
Results: The overall proportion of utilization of hospital palliative care for the patient with SLE was 0.6% over 10 years. It increased approximately 12-fold from 0.1% (2005) to 1.17% (2014). Hospital palliative care services were offered more frequently to older patients, patients with high severity illnesses, and in urban teaching hospitals or large size hospitals. Patients younger than 40 years, the lowest household income group, or Medicare beneficiaries less likely received palliative care during hospitalization. Hospital palliative care services were associated with increased length of stay (? = 1.407, P < .0001) and in-hospital mortality (odds ratio, 48.18; 95% confidence interval, 41.59-55.82), and reduced hospital charge (? = ?0.075, P = .009).
Conclusion: Hospital palliative care service for patients with SLE gradually increased during the past decade in US hospitals. However, this showed disparities in access and was associated with longer hospital length of stay and higher in-hospital mortality. Nevertheless, hospital palliative care services yielded a cost-saving effect.
BACKGROUND: Little is known about the characteristics of patients needing palliative care consultation in the ED. This study aimed to investigate the impacts of initiating screening in acute critically ill patients needing palliative care on mortality, healthcare resources, and end-of-life care (EOL) in the intensive care unit in ED (EICU).
METHODS: We conducted an analysis study in Taipei Veterans General Hospital. From February 1 to July 31, 2018, acute critically ill patients in EICU were recruited. The primary outcomes were inhospital mortality and EOL care. The secondary outcomes included clinical characteristics and healthcare utilization.
RESULTS: A total of 796 patients were screened, with 396 eligible and 400 non-eligible patients needing palliative care consultations. The mean age was 74.8 ± 17.1 years, and 62.6% of the patients were male. According to logistic regression analysis, clinical predictors, including age (adjusted odds ratio [AOR], 1.028; 95% confidence interval [CI], 1.015-1.042), respiratory distress and/or respiratory failure (AOR, 2.670; 95% CI, 1.829-3.897), the Acute Physiology and Chronic Health Evaluation II score (AOR, 1.036; 95% CI, 1.009-1.064), Charlson Comorbidity Index score (AOR, 1.212; 95% CI, 1.125-1.306), and Glasgow Coma Scale (AOR, 0.843; 95% CI, 0.802-0.885), were statistically more significant in eligible patients than in non-eligible patients. The inhospital mortality rate was significantly higher in eligible patients than that in non-eligible patients (40.7% vs. 11.5%, p < 0.01). Eligible patients have a higher ratio in both vasopressor and narcotic use and withdrawal of endotracheal tube than non-eligible patients (p < 0.05).
CONCLUSION: Our study results demonstrated that initiating palliative consultation for acute critically ill patients in ED had an impact on the utilization of healthcare resources and quality of EOL care. Further assessments of the viewpoints of ED patients and their family regarding palliative care consultations and hospice care are required.
EXECUTIVE SUMMARY: Usage of hospice services for patients facing life-limiting illness has steadily increased. In these services, hospitals discharge patients to various hospice settings, including the inpatient model, where a patient may remain in the discharging hospital to receive hospice services. In this discharge practice, the patient is considered a hospital survivor and subsequent hospice death. The purpose of the study was to determine if the decline of in-hospital mortality for six common high-volume admission diagnoses could be attributed in part to an increase in discharges to a hospice setting for end-of-life care. In this retrospective study using the National Inpatient Sample database from 2007 to 2011, we identified patients = 18 years for six acute and chronic diagnoses: heart failure, chronic obstructive pulmonary disease, acute myocardial infarction, acute myocardial infarction with cardiogenic shock, septic shock, and lung neoplasm (cancer). We categorized patients according to their hospital discharge disposition as hospice or in-hospital mortality. A total of 10,458,728 patients met our criteria, of which 2.72% were discharged to hospice and 6.38% died. Compared to patients who died in the hospital, hospice patients were older, had a shorter length of stay, and experienced more comorbidities. Hospice use was more common in Medicare patients, in nonteaching hospitals, and in the South. White individuals were more likely to be discharged to hospice compared to nonwhites. Among the six selected diagnoses over the 5-year period, hospice use rose as observed mortality decreased. Our findings suggest that variability among hospitals in hospice use will affect benchmarked hospital mortality comparisons and could inappropriately reward or penalize hospitals in their public reporting.
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.
Background. Modern intensive care methods led to an increased survival of critically ill patients over the last decades. But an unreflected application of modern intensive care measures might lead to prolonged treatment for incurable diseases, and an inadaequate or too aggressive therapy can prolong the dying process of patients. In this study, we analysed end-of-life decisions regarding withholding and withdrawal of intensive care measures in a German intensive care unit (ICU) of a communal tertiary hospital.
Methods. Patient datasets of all adult patients dying in an ICU or an intermediate care unit (IMC) in a tertiary communal hospital (Klinikum Hanau, Germany) between 01.01.2011 and 31.12.2012 were analysed for withholding and withdrawal of intensive care measures.
Results. During the two-year period, 1317 adult patients died in Klinikum Hanau. Of these, 489 (37%) died either in an ICU/IMC unit. The majority of those deceased patients (n = 427, 87%) was 60 years or older. In 306 (62%) of 489 patients, at least one life-sustaining measure was withheld or withdrawn. In 297 (61%) of 489 patients dying in ICU/IMC, any type of therapy was withheld, and in 139 patients (28%), any type of therapy was withdrawn. Mostly, cardiopulmonary resuscitation (n = 222), invasive (n = 121) and noninvasive (n = 40) ventilation followed by renal replacement therapy (n = 71) and catecholamine therapy (n = 66) were withheld. More invasive measures as ventilation or renal replacement therapy were withdrawn in 18 and 22 patients only. After withholding/withdrawal of therapy, most patients died within two days. More than 20% of patients dying in ICU/IMC did not have an analgesic medication.
Conclusions. About one-third of patients dying in the hospital died in ICU/IMC. At least one life-sustaining therapy was limited/withdrawn in more than 60% of those patients. Withholding of a therapy was more common than active therapy withdrawal. Ventilation and renal replacement therapy were withdrawn in less than 5% of patients, respectively.
OBJECTIVES: To describe the clinical management of palliative sedation and the characteristics of sedated patients in 11 Catalan hospital emergency departments.
MATERIAL AND METHODS: Prospective descriptive study of a cohort of patients given palliative sedation between April and July 2018. We registered patient demographic and disease data, the Charlson comorbidity index (CCI), patient's point of origin before emergency department arrival, times related to emergency care, and medications used.
RESULTS: We included 323 patients (48.9% men) with a mean (SD) age of 84 (12) years. The CCIs were significantly higher in patients attended in level-I hospitals. Palliative sedation was the first option in 27% and was initiated within 18 (28) hours of arrival on average, an interval that was significantly shorter in level-II hospitals. Most patients (74.2%) died in the emergency department.
CONCLUSION: Patients treated with palliative sedation in hospital emergency departments are older and have serious concomitant conditions. Most patients are first treated with intention to cure. Time until the start of palliative sedation differs significantly according to hospital level.