OBJECTIVE: To describe the characteristics and outcomes of patients undergoing mechanical ventilation withdrawal and to compare them to mechanically ventilated patients with limitations (withhold or withdrawal) of life-sustaining therapies but who did not undergo mechanical ventilation withdrawal.
METHODS: This was a retrospective cohort study from January 2014 to December 2018 of mechanically ventilated patients with any organ support limitation admitted to a single intensive care unit. We compared patients who underwent mechanical ventilation withdrawal and those who did not regarding intensive care unit and hospital mortality and length of stay in both an unadjusted analysis and a propensity score matched subsample. We also analyzed the time from mechanical ventilation withdrawal to death.
RESULTS: Out of 282 patients with life-sustaining therapy limitations, 31 (11%) underwent mechanical ventilation withdrawal. There was no baseline difference between groups. Intensive care unit and hospital mortality rates were 71% versus 57% and 93% versus 80%, respectively, among patients who underwent mechanical ventilation withdrawal and those who did not. The median intensive care unit length of stay was 7 versus 8 days (p = 0.6), and the hospital length of stay was 9 versus 15 days (p = 0.015). Hospital mortality was not significantly different (25/31; 81% versus 29/31; 93%; p = 0.26) after matching. The median time from mechanical ventilation withdrawal until death was 2 days [0 - 5], and 10/31 (32%) patients died within 24 hours after mechanical ventilation withdrawal.
CONCLUSION: In this Brazilian report, mechanical ventilation withdrawal represented 11% of all patients with treatment limitations and was not associated with increased hospital mortality after propensity score matching on relevant covariates.
BACKGROUND: Advanced cancer patients are part of a group likely to be more susceptible to COVID-19.
AIMS: To describe the profile of advanced cancer inpatients to an exclusive Palliative Care Unit (PCU) with the diagnosis of COVID-19, and to evaluate the factors associated with death in these cases.
DESIGN: Retrospective cohort study with data from advanced cancer inpatients to an exclusive PCU, from March to July 2020, with severe acute respiratory syndrome. Diagnostic of COVID-19 and death were the dependent variables. Logistic regression analyses were performed, with the odds ratio (OR) and 95% confidence interval (CI).
RESULTS: One hundred fifty-five patients were selected. The mean age was 60.9 (±13.4) years old and the most prevalent tumor type was breast (30.3%). Eighty-three (53.5%) patients had a diagnostic confirmation of COVID-19. Having diabetes mellitus (OR: 2.2; 95% CI: 1.1-6.6) and having received chemotherapy in less than 30 days before admission (OR: 3.8; 95% CI: 1.2-12.2) were associated factors to diagnosis of COVID-19. Among those infected, 81.9% died and, patients with Karnofsky Performance Status (KPS) < 30% (OR: 14.8; 95% CI 2.7-21.6) and C-reactive protein (CRP) >21.6mg/L (OR: 9.3; 95% CI 1.1-27.8), had a greater chance of achieving this outcome.
CONCLUSION: Advanced cancer patients who underwent chemotherapy in less than 30 days before admission and who had diabetes mellitus were more likely to develop Coronavirus 2019 disease. Among the confirmed cases, those hospitalized with worse KPS and bigger CRP were more likely to die.
OBJECTIVES: Palliative care has been demonstrated to have positive effects for patients, families, health care providers, and health systems. Early identification of patients who are likely to benefit from palliative care would increase opportunities to provide these services to those most in need. This study predicted all-cause mortality of patients as a surrogate for patients who could benefit from palliative care.
STUDY DESIGN: Claims and electronic health record (EHR) data for 59,639 patients from a large integrated health care system were utilized.
METHODS: A deep learning algorithm-a long short-term memory (LSTM) model-was compared with other machine learning models: deep neural networks, random forest, and logistic regression. We conducted prediction analyses using combined claims data and EHR data, only claims data, and only EHR data, respectively. In each case, the data were randomly split into training (80%), validation (10%), and testing (10%) data sets. The models with different hyperparameters were trained using the training data, and the model with the best performance on the validation data was selected as the final model. The testing data were used to provide an unbiased performance evaluation of the final model.
RESULTS: In all modeling scenarios, LSTM models outperformed the other 3 models, and using combined claims and EHR data yielded the best performance.
CONCLUSIONS: LSTM models can effectively predict mortality by using a combination of EHR data and administrative claims data. The model could be used as a promising clinical tool to aid clinicians in early identification of appropriate patients for palliative care consultations.
Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to maximize the quality of life (QoL) for the last stage of life. They are currently based on clinical evaluation of the risk of 1-year mortality. The main aim of this work is to develop and validate machine-learning-based models to predict the exitus of a patient within the next year using data gathered at hospital admission. Five machine-learning techniques were applied using a retrospective dataset. The evaluation was performed with five metrics computed by a resampling strategy: Accuracy, the area under the ROC curve, Specificity, Sensitivity, and the Balanced Error Rate. All models reported an AUC ROC from 0.857 to 0.91. Specifically, Gradient Boosting Classifier was the best model, producing an AUC ROC of 0.91, a sensitivity of 0.858, a specificity of 0.808, and a BER of 0.1687. Information from standard procedures at hospital admission combined with machine learning techniques produced models with competitive discriminative power. Our models reach the best results reported in the state of the art. These results demonstrate that they can be used as an accurate data-driven palliative care criteria inclusion.
BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) often receive burdensome care at end-of-life (EOL) and infrequently complete advance care planning (ACP). The surprise question (SQ) is a prognostic tool that may facilitate ACP.
OBJECTIVE: To assess how well the SQ predicts mortality and prompts ACP for COPD patients.
DESIGN: Retrospective cohort study.
SUBJECTS: Patients admitted to the hospital for an acute exacerbation of COPD between July 2015 and September 2018.
MAIN MEASURES: Emergency department (ED) and inpatient clinicians answered, "Would you be surprised if this patient died in the next 30 days (ED)/one year (inpatient)?" The primary outcome measure was the accuracy of the SQ in predicting 30-day and 1-year mortality. The secondary outcome was the correlation between SQ and ACP (palliative care consultation, documented goals-of-care conversation, change in code status, or completion of ACP document).
KEY RESULTS: The 30-day SQ had a high specificity but low sensitivity for predicting 30-day mortality: sensitivity 12%, specificity 95%, PPV 11%, and NPV 96%. The 1-year SQ demonstrated better accuracy for predicting 1-year mortality: sensitivity 47%, specificity 75%, PPV 35%, and NPV 83%. After multivariable adjustment for age, sex, and prior 6-month admissions, 1-year SQ+ responses were associated with greater odds of 1-year mortality (OR 2.38, 95% CI 1.39-4.08) versus SQ-. One-year SQ+ patients were more likely to have a goals-of-care conversation (25% vs. 11%, p < 0.01) and complete an advance directive or POLST (46% vs. 23%, p < 0.01). After multivariable adjustment, SQ+ responses to the 1-year SQ were associated with greater odds of ACP receipt (OR 2.67, 95% CI 1.64-4.36).
CONCLUSIONS: The 1-year surprise question may be an effective component of prognostication and advance care planning for COPD patients in the inpatient setting.
Purpose: Performance status (PS) is assessed during cancer treatment to determine clinical trial eligibility, appropriateness for treatment, and need for supportive care. There is rising interest for patients to report this information directly. We determined whether clinician- and patient-reported PS were equally associated with mortality and service utilization in patients with cancer.
Methods: A secondary analysis was conducted using data from an radiotherapy plus chemotherapy in which 441 patients with advanced cancer and clinicians reported PS using the Eastern Cooperative Oncology Group scale. Simple kappa statistics measured agreement between clinician-reported performance status (cPS) and patient-reported performance status (pPS). Associations of cPS and pPS with emergency department (ED) and hospital visits and overall survival were evaluated via Cox regression, competing risk regression, and Fisher's exact tests.
Results: cPS and pPS correlated weakly (kappa = 0.27). Both pPS and cPS were associated with survival, ED visits, and hospitalizations, but only cPS remained associated after adjustment (survival: HR, 1.75; P < .0001). The first available cPS predicted mortality more strongly than the first available pPS (HR for death, comparing PS = 1 v 0: 2.05 for cPS and 1.41 for pPS). When pPS questionnaires were repeated over time and averaged, associations with outcomes were stronger as measured by AIC model fit. Both pPS and cPS were associated with EQ-5D subcomponents (eg, 75%-77% with no usual activity deficits for PS 0, v 42%-51% for PS = 1).
Conclusion: Both clinician-reported PS and patient-reported PS provide useful information and can be considered for clinical trials and routine care.
Objective: To assess trends in place of death for children with a life-limiting condition and the factors associated with death at home or hospice rather than hospital.
Design: Observational cohort study using linked routinely collected data.
Patients: Children aged 0-25 years who died between 2003 and 2017.
Main outcome measures: Place of death: hospital, hospice, home. Multivariable multinomial logistic regression models.
Results: 39 349 children died: 73% occurred in hospital, 6% in hospice and 16% at home. In the multivariable models compared with dying in a hospital: neonates were less likely, and those aged 1-10 years more likely, than those aged 28 days to <1 year to die in hospice. Children from all ethnic minority groups were significantly less likely to die in hospice, as were those in the most deprived group (RR 0.8, 95% CI 0.7 to 0.9). Those who died from 2008 were more likely than those who died earlier to die in a hospice.Children with cancer (RR 4.4, 95% CI 3.8 to 5.1), neurological (RR 2.0, 95% CI 1.7 to 2.3) or metabolic (RR 3.7, 95% CI 3.0 to 4.6) diagnoses were more likely than those with a congenital diagnosis to die in a hospice.Similar patterns were seen for clinical/demographic factors associated with home versus hospital deaths.
Conclusions: Most children with a life-limiting condition continue to die in the hospital setting. Further research on preferences for place of death is needed especially in children with conditions other than cancer. Paediatric palliative care services should be funded adequately to enable equal access across all settings, diagnostic groups and geographical regions.
BACKGROUND: Over 90 million Americans suffer from advanced illness (AI) and spend their last days of life in critical care units receiving costly, unwanted, aggressive medical care.
OBJECTIVE: Evaluate the impact of a specialized care model in medical/surgical units for hospitalized geriatric patients and patients with complex care requirements where designated AI beds align care with patient's wishes/goals, minimize aggressive interventions, and influence efficient resource utilization.
DESIGN: US based multi-facility retrospective, longitudinal descriptive study of screened positive AI patients in AI Beds (N = 1,237) from 3 facilities from 2015 to 2017.
RESULTS: Patient outcomes included 60% referrals to AI beds from ICU, a decrease of 39-49% in average ICU LOS, a 23% reduction of AI bed patient expirations, 9.0% referrals to hospice, and projected cost savings of $4,361.66/patient, US dollars.
CONCLUSION: Allocating AI beds to deliver care to AI patients resulted in a decreased cost of care by reducing overall hospital LOS, mortality, and efficient use of both critical care and hospital resources.
Objectives :Hospitals are the most common place of death in Australia. Bereavement care is recognised by national standards as being central to providing high-quality care at the end of life, and has significant health implications on morbidity, mortality and health service usage. Despite this, bereavement care is not routinely or systematically provided in most Australian hospitals. This study aimed to develop a comprehensive, evidence-based model of bereavement care specific to the needs of an acute Australian adult tertiary hospital.
Methods:This study used a multiple-methods design, which included a scoping literature review, a survey of current institutional bereavement practices, interviews with bereaved family members and staff focus groups and the development of a model of bereavement care for the acute hospital service through advisory group and expert consensus.
Results:Staff and bereaved family members strongly supported a systematic approach to bereavement, perceiving the need for greater support, training, coordination and follow-up. In all, 10 core elements were developed to support a structured model of bereavement care provision and follow-up for the acute hospital organisation.ConclusionsThis evidence-generated model of care promotes the provision of quality and systematic bereavement care in the acute hospital setting.What is known about the topic?Acute hospitals are the most common place to die in Australia, yet there is a lack of understanding of how bereavement care is or should be provided in these environments. The bereavement period is associated with increased use of health services and worse morbidity and mortality, and thus has significant implications for public health. The provision of bereavement care in acute hospitals is often sporadic, often involving untrained staff who may not provide evidence-based care.What does this paper add?This paper describes the development of a comprehensive, evidence-based model of bereavement care specific to the needs of an Australian acute hospital.What are the implications for practitioners?Developing a consistent approach to bereavement for the acute care sector has the potential to support staff, minimise conflict at the end of life, facilitate recognition of those suffering from difficult bereavement and proactively engage services for these people. It is hoped that such a model of care can find relevance across acute hospitals in Australia, to improve the quality and consistency of bereavement care.
OBJECTIVE: To determine the prevalence of life support limitation (LSL) in patients who died after at least 24h of a pediatric intensive care unit (PICU) stay, parent participation and to describe how this type of care is delivered.
METHODS: Retrospective cohort study in a tertiary PICU at a university hospital in Brazil. All patients aged 1 month to 18 years who died were eligible for inclusion. The exclusion criteria were those brain death and death within 24h of admission.
RESULTS: 53 patients were included in the study. The prevalence of a LSL report was 45.3%. Out of 24 patients with a report of LSL on their medical records only 1 did not have a do-not-resuscitate order. Half of the patients with a report of LSL had life support withdrawn. The length of their PICU stay, age, presence of parents at the time of death, and severity on admission, calculated by the Pediatric Index of Mortality 2, were higher in patients with a report of LSL. Compared with other historical cohorts, there was a clear increase in the prevalence of LSL and, most importantly, a change in how limitations are carried out, with a high prevalence of parental participation and an increase in withdrawal of life support.
CONCLUSIONS: LSLs were associated with older and more severely ill patients, with a high prevalence of family participation in this process. The historical comparison showed an increase in LSL and in the withdrawal of life support.
CONTEXT: Palliative care triggers have been used in the ICU setting, usually in high income countries, to identify patients who may benefit from palliative care consults. The utility and benefits of palliative care triggers in the ICU has not been previously studied in sub-Saharan Africa.
OBJECTIVES: Our objectives were to determine the prevalence of ICU admissions who met at least one palliative care trigger and whether a palliative care consult influenced the length of ICU stay and time to change of goals order.
METHODS: We conducted a prospective observational cohort study within our ICU at The Aga Khan University Hospital, Nairobi between December 2019 and August 2020. Data including initiation of a palliative care consult, length of ICU stay, mortality and time to change of goals order was collected.
RESULTS: During our study period, 72 of 159 (45.9%) patients met at least one palliative care trigger point. Of the patients who met the palliative care triggers, only 29.2% received a palliative care consult. Patients who received palliative care consults had higher rates of change of goals orders signed (52.3%) versus those who did not (P=0.009). There was no statistically significant difference between the consult and non-consult groups in regards to length of ICU stay, time to change of goals order and mortality.
CONCLUSION: A trigger-based model, geared to the needs of the specific ICU, may be one way of improving integration of palliative care into the ICU, especially in sub-Saharan Africa.
BACKGROUND: Anesthesiologists caring for patients with do-not-resuscitate (DNR) orders may have ethical concerns because of their resuscitative wishes and may have clinical concerns because of their known increased risk of morbidity/mortality. Patient heterogeneity and/or emphasis on mortality outcomes make previous studies among patients with DNR orders difficult to interpret. We sought to explore factors associated with morbidity and mortality among patients with DNR orders, which were stratified by surgical subgroups.
METHODS: Exploratory retrospective cohort study in adult patients undergoing prespecified colorectal, vascular, and orthopedic surgeries was performed using the American College of Surgeons National Surgical Quality Improvement Program Participant Use File data from 2010 to 2013. Among patients with preoperative DNR orders (ie, active DNR order written in the patient's chart before surgery), factors associated with 30-day mortality, increased length of stay, and inpatient death were determined via penalized regression. Unadjusted and adjusted estimates for selected variables are presented.
RESULTS: After selection as above, 211,420 patients underwent prespecified procedures, and of those, 2755 (1.3%) had pre-existing DNR orders and met above selection to address morbidity/mortality aims. By specialty, of these patients with a preoperative DNR, 1149 underwent colorectal, 870 vascular, and 736 orthopedic surgery. Across groups, 36.2% were male and had a mean age 79.9 years (range 21-90). The 30-day mortality was 15.4%-27.2% and median length of stay was 6-12 days. Death at discharge was 7.0%, 13.1%, and 23.0% in orthopedics, vascular, and colorectal patients with a DNR, respectively. The strongest factors associated with increased odds of 30-day mortality were preoperative septic shock in colorectal patients, preoperative ascites in vascular patients, and any requirement of mechanical ventilation at admission in orthopedic patients.
CONCLUSIONS: In patients with DNR orders undergoing common surgical procedures, the association of characteristics with morbidity and mortality varies in both direction and magnitude. The DNR order itself should not be the defining measure of risk.
CONTEXT: In spring 2020, New York experienced as surge of patients hospitalized with severe acute respiratory syndrome coronavirus 2 (COVID-19) disease, as part of a global pandemic. There is limited data on populations of COVID-19 infected patients seen by palliative care services.
OBJECTIVE: To describe a palliative care population at one New York hospital system during the initial pandemic surge.
METHODS: This repeated cross sectional, observational study collected data on palliative care patients in a large health system seen during the COVID-19 outbreak and compared it to pre-COVID data.
RESULTS: Palliative service volume surged from 678 (4% of total admissions) pre-COVID-19 to 1,071 (10% of total admissions) during the COVID-19 outbreak. During the outbreak, 695 (64.9%) of palliative patients tested positive for the virus. Compared with a pre-outbreak group, this COVID-19 positive group had higher rates of male (60.7% vs 48.6%, p < 0.01) and Latino (21.3% vs 13.3%; p < 0.01) patients and less white patients (21.3% vs 13.3%; p < 0.01). Our patient's with COVID-19 also had greater prevalence of obesity and diabetes and lower rates of end-stage organ disease and cancers. The COVID-19 positive group had a higher rate of intensive care unit admissions (58.9% vs 33.9%; p < 0.01) and in-hospital mortality rate (57.4% vs 13.1%; p < 0.01) compared to the pre-outbreak group. There was increased odds of mortality in palliative care patients who were COVID-19 positive (OR = 3.21; 95% CI = 2.43 - 4.24) and those admitted to the ICU (OR = 1.45; 95% CI = 1.11 - 1.9).
CONCLUSION: During the initial surge of the COVID-19 pandemic in New York, palliative care services experienced a large surge of patients who tended to be healthier at baseline and more acutely ill at time of admission than pre-COVID palliative patients.
Context: Few studies have described the characteristics and palliative care needs in hospitalized patients with coronavirus disease 2019 (COVID-19).
Objectives: Describing characteristics, consultation demands, patients' needs, and outcomes of hospitalized patients with COVID-19 who received a palliative care evaluation.
Methods: Retrospective chart review of patients (aged 18+ years) with COVID-19 admitted to an academic quaternary center and seen by the geriatrics and palliative medicine team from March 1st to May 11th, 2020. Socio-demographics, operational metrics, severity of illness, goals of care-advanced care planning documentation, and outcomes were analyzed.
Results: Three hundred seventy-six (17.6%) out of 2138 COVID-19 admissions were seen by the consultation team. Compared with prepandemic situation (September 1st, 2019, to February 29th, 2020), overall new consults (205 vs. 371, P < 0.001) significantly increased, particularly in the intensive care unit (ICU; 9.5% vs. 36.9%, P < 0.001). For the COVID-19 population, median age was 78 years (interquartile range, 70-87; range, 36-102); 56% were male. LACE score, D-dimer, and C-reactive protein suggested severe disease and increased risk of mortality. Seventy-five percent of consults were for goals of care-advanced care planning, and 9.6% for symptoms. During the index admission, 7.1% had documented advanced directives, and 69.7% became do not resuscitate. Of all deaths, 55.5% were in the ICU, and 87.2% were aged =65 years. Underserved minority patients had a disproportionate mortality. Overall consultation mortality (38.3% vs. 70.4%, P < 0.001) and ICU mortality (55.2% vs. 78.1%, P < 0.001) significantly increased compared with those before COVID-19.
Conclusion: During this pandemic, understanding inpatient specialized palliative care needs and the vulnerable populations driving these causes may encourage health-care agencies and local, state, and federal governments to support the dedicated palliative care workforce.
Objectives: To investigate the rate and patterns of accumulation of frailty manifestations in relationship to all-cause mortality and whether there is a point in the progression of frailty beyond which the process becomes irreversible and death becomes imminent (a.k.a. point of no return).
Design: Longitudinal observational study.
Setting: Community or a non-nursing home residential care setting.
Participants: Two thousand five hundred and fifty seven robust older adults identified at baseline in 2011 with follow-up for all cause mortality between 2011 and 2018.
Measurements: Frailty was measured by the physical frailty phenotype. Cox models were used to study the relationships of the number of frailty criteria (0–5) at each point in time and its accumulation patterns with all cause mortality. Markov state-transition models were used to study annual transitions between health states (i.e., frailty, recovery, and death) after becoming frail among those with frailty onset (n = 373).
Results: There was a nonlinear association between greater number of frailty criteria and increasing risk of mortality, with a notable risk acceleration after having accumulated all five criteria (hazard ratio (HR) = 32.6 vs none, 95% confidence interval (CI) = 15.7–67.5). In addition, the risk of one year mortality tripled, and the likelihood of recovery (i.e., reverting to be robust or pre-frail) halved among those with five frailty criteria compared to those with three or four criteria. A 50% increase in mortality risk was also associated with frailty onset without (vs with) a prior history of pre-frailty (HR = 1.51, 95% CI = 1.20–1.90).
Conclusion: Both the number and rate of accumulation of frailty criteria were associated with mortality risk. Although there was insufficient evidence to declare a point of no return, having all five frailty criteria signals the beginning of a transition toward a point of no return. Ongoing monitoring of frailty progression could aid clinical and personal decision-making regarding timing of intervention and eventual transition from curative to palliative care.
Background: Admissions due to emergency general surgery (EGS) are on the rise, and patients who undergo emergency surgery are at increased risk of mortality. We hypothesized that utilization of palliative care and discharge to hospice in the EGS population have increased over time and that this is associated with a decrease in inpatient mortality.
Methods: Using the 2002-2011 nationwide inpatient sample and American Association for the Surgery of Trauma-defined EGS diagnosis codes, we identified patients =18 years old with an EGS admission. Demographics, hospitalization characteristics, mortality, use of palliative care services, and discharge to hospice were queried. All Patient Refined-Diagnosis Related Group risk of mortality was used to categorize those with an extreme likelihood of dying (ELD). Multivariable logistic regression was used to investigate the association between palliative care consult and discharge to hospice.
Results: Of the included patients, .3% received palliative care and .2% were discharged to hospice. Over time, rates of palliative care and hospice discharge increased while inpatient mortality decreased. In the 4% of patients with ELD, 3% received palliative care, 5% were transitioned to hospice care, and 22% suffered inpatient mortality. Controlling for patient characteristics, utilization of palliative care services was associated with increased odds of discharge to hospice compared to inpatient mortality (OR = 1.78 all patients and OR = 2.04 for ELD).
Conclusions: Despite the known increased risks associated with emergency surgical diagnoses, palliative care services remain infrequently utilized in the EGS population. This may be an opportunity for lessening suffering, improving patient-concordant care and outcomes, and reducing nonbeneficial and unwanted care.
Purpose: Despite advancements in cancer therapeutics, mortality and morbidity due to anti-cancer treatments still occur but are not frequently reported. We aimed to report the 30-day mortality and morbidity of all curative and palliative anti-cancer treatments.
Patients and Methods: Adults with solid and hematological malignancies from two large cancer centers in Saudi Arabia, irrespective of the cancer stage and treatment type, were included in this retrospective observational study.
Results: Between December 1, 2019 and February 29, 2020, 1694 patients from King Abdullah Medical City in Makkah and King Fahad Medical City in Riyadh were included in the study. Among them, 77.5% were younger than 65 years of age; 72.8% were female; the prevalence of obesity, diabetes, and hypertension was 35%, 34%, and 28%, respectively; and 66.5% of patients had breast and gastrointestinal cancers. Fifty-nine (3.5%) patients died within 30 days of receiving anti-cancer treatment. Of them, 9 (0.3%) were treated with curative intent, and 50 (3%) were treated with palliative intent.
Conclusion: Our results emphasize the need to address preventable metabolic changes and implement innovative, predictive, preventive, and personalized medicine (PPPM) approaches focusing on patient profiles. Reporting the 30-day outcomes of all anti-cancer treatments will also allow the identification of factors underlying mortality and morbidity and lead to an improvement in oncological outcomes via innovative programs designed to improve clinical decision-making.
Background: Prognostic assessment is essential to plan the care of patients with advanced cancer in palliative care.
Objective: Thus, this study aims to assess the predictors of death in inpatients with advanced cancer in palliative care.
Methods: This is a clinical, observational cohort study with patients aged >20 years, of both genders, evaluated within 48 hours of the first hospitalization. The independent variables were tumor location, nutritional risk [through the Patient-Generated Subjective Global Assessment (PG-SGA) short form], laboratory tests [C-reactive protein and albumin] and Karnofsky Performance Status (KPS). Logistic regression analyses were performed.
Results: Eighty-two patients were evaluated, whose mean age was 61.8 (± 13.2) years. Forty-nine (59.8%) patients died during hospitalization, among which the majority had KPS of 30-40% (p-value = 0.043), higher means of the total score of the PG-SGA (p-value = 0.050) and lower serum albumin concentrations (p-value = 0.011). According to the multivariate model, tumor location in the gastrointestinal (GI) tract (OR: 1.73; 95% CI: 1.57 -1.94), 30-40% KPS (OR: 1.29; 95% CI: 1.07 -1.63) and albumin concentrations <3.5 g/dL (OR: 4.65; 95% CI: 1.22-17.7) were independent factors associated with an increased chance of death from hospitalization.
Conclusion: Presenting an advanced tumor with localization in the GI tract, KPS =40% and serum albumin concentration <3.5 g/dL at admission were predictors of death in inpatients under palliative care.
Background: Dying at home is the most frequent preference of patients with advanced chronic conditions, their caregivers, and the general population. However, most deaths continue to occur in hospitals. The objective of this study was to analyse the socioeconomic inequalities in the place of death in urban areas of Mediterranean cities during the period 2010–2015, and to assess if such inequalities are related to palliative or non-palliative conditions.
Methods: This is a cross-sectional study of the population aged 15 years or over. The response variable was the place of death (home, hospital, residential care). The explanatory variables were: sex, age, marital status, country of birth, basic cause of death coded according to the International Classification of Diseases, 10th revision, and the deprivation level for each census tract based on a deprivation index calculated using 5 socioeconomic indicators. Multinomial logistic regression models were adjusted in order to analyse the association between the place of death and the explanatory variables.
Results: We analysed a total of 60,748 deaths, 58.5% occurred in hospitals, 32.4% at home, and 9.1% in residential care. Death in hospital was 80% more frequent than at home while death in a nursing home was more than 70% lower than at home. All the variables considered were significantly associated with the place of death, except country of birth, which was not significantly associated with death in residential care. In hospital, the deprivation level of the census tract presented a significant association (p < 0.05) so that the probability of death in hospital vs. home increased as the deprivation level increased. The deprivation level was also significantly associated with death in residential care, but there was no clear trend, showing a more complex association pattern. No significant interaction for deprivation level with cause of death (palliative, not palliative) was detected.
Conclusions: The probability of dying in hospital, as compared to dying at home, increases as the socioeconomic deprivation of the urban area of residence rises, both for palliative and non-palliative causes. Further qualitative research is required to explore the needs and preferences of low-income families who have a terminally-ill family member and, in particular, their attitudes towards home-based and hospital-based death.
Objective: Difficulties with prognostication prevent more patients with advanced dementia from receiving timely palliative support. The aim of this study is to develop and validate a prognostic model for 6-month and 1-year mortality in home-dwelling patients with advanced dementia.
Design: Prospective cohort study.
Setting and Participants: The data set of 555 home-dwelling patients with dementia at Functional Assessment Staging Test stage 7 was split into derivation (n = 275) and validation (n = 280) cohorts.
Methods: Cox proportional hazards regression modeled survival in the derivation cohort using prognostic variables identified in univariate analysis. The model was validated internally and using 10-fold cross-validation. Area under the receiver operating characteristic curve measured the accuracy of the final model.
Results: Four hundred nineteen (75.5%) patients died with a median follow-up of 47 days [interquartile range (IQR) 161]. Prognostic variables in the multivariate model included serum albumin level, dementia etiology, number of homecare admission criteria fulfilled, presence of moderate to severe chronic kidney disease, peripheral vascular disease, quality of life in late-stage dementia scores, housing type, and the Australian National Sub-Acute and Non-Acute Patient palliative care phase.
The model was refined into a parsimonious 6-variable model [Palliative Support DEMentia Model (PalS-DEM)] consisting of age, dementia etiology, Functional Assessment Staging Test stage, Charlson Comorbidity Index scores, Australian National Sub-Acute and Non-Acute Patient palliative care phase, and 30-day readmission frequency for the prediction of 1-year mortality. The area under the receiver operating characteristic curve was 0.65 (95% confidence interval 0.59–0.70). Risk scores categorized patients into 3 prognostic groups, with a median survival of 175 days (IQR 365), 104 days (IQR 246), and 19 days (IQR 88) for the low-risk (0 1 points), moderate-risk (2 4), and high-risk (=5) groups, respectively.
Conclusions and Implications: The PalS-DEM identifies patients at high risk of death in the next 1 year. The model produced consistent survival results across the derivation, validation, and cross-validation cohorts and will help healthcare providers identify patients with advanced dementia earlier for palliative care.