Background: Timely palliative care in frail older persons remains challenging. Scales to identify older patients at risk of functional decline already exist. However, factors to predict short term mortality in older hospitalized patients are scarce.
Methods: In this prospective study, we recruited patients of 75 years and older at the department of cardiology and geriatrics. The usual gait speed measurement closest to discharge was chosen. We used the risk of dying within 1 year as parameter for starting palliative care. ROC curves were used to determine the best cut-off value of usual gait speed to predict one-year mortality. Time to event analyses were assessed by COX regression.
Results: On the acute geriatric ward (n = 60), patients were older and more frail (assessed by Katz and iADL) in comparison to patients on the cardiology ward (n = 82); one-year mortality was respectively 27 and 15% (p = 0.069). AUC on the acute geriatric ward was 0.748 (p = 0.006). The best cut-off value was 0.42 m/s with a sensitivity and specificity of 0.857 and 0.643. Slow walkers died earlier than faster walkers (HR 7.456, p = 0.011), after correction for age and sex. On the cardiology ward, AUC was 0.560 (p = 0.563); no significant association was found between usual gait speed and survival time.
Conclusions: Usual gait speed may be a valuable prognostic factor to identify patients at risk for one-year mortality on the acute geriatric ward but not on the cardiology ward.
Background: Evaluating the need for palliative care and predicting its mortality play important roles in the emergency department.
Aim: We developed a screening model for predicting 1-year mortality.
Design: A retrospective cohort study was conducted to identify risk factors associated with 1-year mortality. Our risk scores based on these significant risk factors were then developed. Its predictive validity performance was evaluated using area under receiving operating characteristic analysis and leave-one-out cross-validation.
Setting and participants: Patients aged 15 years or older were enrolled from June 2015 to May 2016 in the emergency department.
Results: We identified five independent risk factors, each of which was assigned a number of points proportional to its estimated regression coefficient: age (0.05 points per year), qSOFA >= 2 (1), Cancer (4), Eastern Cooperative Oncology Group Performance Status score >= 2 (2), and Do-Not-Resuscitate status (3). The sensitivity, specificity, positive predictive value, and negative predictive value of our screening tool given the cutoff larger than 3 points were 0.99 (0.98–0.99), 0.31 (0.29–0.32), 0.26 (0.24–0.27), and 0.99 (0.98–1.00), respectively. Those with screening scores larger than 9 points corresponding to 64.0% (60.0–67.9%) of 1-year mortality were prioritized for consultation and communication. The area under the receiving operating characteristic curves for the point system was 0.84 (0.83–0.85) for the cross-validation model.
Conclusions: A-qCPR risk scores provide a good screening tool for assessing patient prognosis. Routine screening for end-of-life using this tool plays an important role in early and efficient physician-patient communications regarding hospice and palliative needs in the emergency department.
OBJECTIVES: To evaluate a machine learning model designed to predict mortality for Medicare beneficiaries aged >65 years treated for hip fracture in Inpatient Rehabilitation Facilities (IRFs).
DESIGN: Retrospective design/cohort analysis of Centers for Medicare & Medicaid Services Inpatient Rehabilitation Facility-Patient Assessment Instrument data.
SETTING AND PARTICIPANTS: A total of 17,140 persons admitted to Medicare-certified IRFs in 2015 following hospitalization for hip fracture.
MEASURES: Patient characteristics include sociodemographic (age, gender, race, and social support) and clinical factors (functional status at admission, chronic conditions) and IRF length of stay. Outcomes were 30-day and 1-year all-cause mortality. We trained and evaluated 2 classification models, logistic regression and a multilayer perceptron (MLP), to predict the probability of 30-day and 1-year mortality and evaluated the calibration, discrimination, and precision of the models.
RESULTS: For 30-day mortality, MLP performed well [acc = 0.74, area under the receiver operating characteristic curve (AUROC) = 0.76, avg prec = 0.10, slope = 1.14] as did logistic regression (acc = 0.78, AUROC = 0.76, avg prec = 0.09, slope = 1.20). For 1-year mortality, the performances were similar for both MLP (acc = 0.68, AUROC = 0.75, avg prec = 0.32, slope = 0.96) and logistic regression (acc = 0.68, AUROC = 0.75, avg prec = 0.32, slope = 0.95).
CONCLUSION AND IMPLICATIONS: A scoring system based on logistic regression may be more feasible to run in current electronic medical records. But MLP models may reduce cognitive burden and increase ability to calibrate to local data, yielding clinical specificity in mortality prediction so that palliative care resources may be allocated more effectively.
BACKGROUND: Patients with end-stage liver disease (ESLD) have limited treatment options and have a deteriorated quality of life with an uncertain prognosis. Early identification of ESLD patients with a poor prognosis is valuable, especially for palliative care. However, it is difficult to predict ESLD patients that require either acute care or palliative care.
OBJECTIVE: We sought to create a machine-learning monitoring system that can predict mortality or classify ESLD patients. Several machine-learning models with visualized graphs, decision trees, ensemble learning, and clustering were assessed.
METHODS: A retrospective cohort study was conducted using electronic medical records of patients from Wan Fang Hospital and Taipei Medical University Hospital. A total of 1214 patients from Wan Fang Hospital were used to establish a dataset for training and 689 patients from Taipei Medical University Hospital were used as a validation set.
RESULTS: The overall mortality rate of patients in the training set and validation set was 28.3% (257/907) and 22.6% (145/643), respectively. In traditional clinical scoring models, prothrombin time-international normalized ratio, which was significant in the Cox regression (P<.001, hazard ratio 1.288), had a prominent influence on predicting mortality, and the area under the receiver operating characteristic (ROC) curve reached approximately 0.75. In supervised machine-learning models, the concordance statistic of ROC curves reached 0.852 for the random forest model and reached 0.833 for the adaptive boosting model. Blood urea nitrogen, bilirubin, and sodium were regarded as critical factors for predicting mortality. Creatinine, hemoglobin, and albumin were also significant mortality predictors. In unsupervised learning models, hierarchical clustering analysis could accurately group acute death patients and palliative care patients into different clusters from patients in the survival group.
CONCLUSIONS: Medical artificial intelligence has become a cutting-edge tool in clinical medicine, as it has been found to have predictive ability in several diseases. The machine-learning monitoring system developed in this study involves multifaceted analyses, which include various aspects for evaluation and diagnosis. This strength makes the clinical results more objective and reliable. Moreover, the visualized interface in this system offers more intelligible outcomes. Therefore, this machine-learning monitoring system provides a comprehensive approach for assessing patient condition, and may help to classify acute death patients and palliative care patients. Upon further validation and improvement, the system may be used to help physicians in the management of ESLD patients.
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.
End-of-life (EOL) decision making in the intensive care unit (ICU) is challenging for both families and clinicians. This decision-making process is ideally framed around a shared understanding of a patient’s values and goals, all taken in the context of their critical illness and prognosis. However, clinicians commonly face uncertainty regarding prognosis and may have difficulty offering families an accurate assessment of the likely outcomes of treatment decisions. Adding to the complexity of these scenarios, clinicians, patients and families are each susceptible to unconscious but influential cognitive biases when making decisions under stress. Given these challenges, and a rapidly growing interest in data science to inform care in the ICU, investigators have explored the use of prediction models (eg, machine learning or ML algorithms) to assist with prognostication. Prediction models describe an outcome distribution among individuals with a particular set of characteristics, such as risk of acute kidney injury among individuals with particular laboratory values and clinical characteristics in a population. However, they do not compare how that outcome distribution would change were different treatment decisions made in that population—this requires causal effect estimation, rather than prediction modelling. Herein, we explain why prediction modelling alone is not sufficient to inform many ICU treatment decisions, including EOL decision making, and describe why causal effect estimation is necessary.
Background: For residential aged care facility (RACF) residents with dementia, lack of prognostic guidance presents a significant challenge for end of life care planning. In an attempt to address this issue, models have been developed to assess mortality risk for people with advanced dementia, predominantly using long-term care minimum data set (MDS) information from the USA. A limitation of these models is that the information contained within the MDS used for model development was not collected for the purpose of identifying prognostic factors. The models developed using MDS data have had relatively modest ability to discriminate mortality risk and are difficult to apply outside the MDS setting. This study will aim to develop a model to estimate 6- and 12-month mortality risk for people with dementia from prognostic indicators recorded during usual clinical care provided in RACFs in Australia.
Methods: A secondary analysis will be conducted for a cohort of people with dementia from RACFs participating in a cluster-randomized trial of a palliative care education intervention (IMPETUS-D). Ten prognostic indicator variables were identified based on a literature review of clinical features associated with increased mortality for people with dementia living in RACFs. Variables will be extracted from RACF files at baseline and mortality measured at 6 and 12 months after baseline data collection. A multivariable logistic regression model will be developed for 6- and 12-month mortality outcome measures using backwards elimination with a fractional polynomial approach for continuous variables. Internal validation will be undertaken using bootstrapping methods. Discrimination of the model for 6- and 12-month mortality will be presented as receiver operating curves with c statistics. Calibration curves will be presented comparing observed and predicted event rates for each decile of risk as well as flexible calibration curves derived using loess-based functions.
Discussion: The model developed in this study aims to improve clinical assessment of mortality risk for people with dementia living in RACFs in Australia. Further external validation in different populations will be required before the model could be developed into a tool to assist with clinical decision-making in the future.
Purpose: Identifying mortality risk factors in people living in nursing homes could help healthcare professionals to individualize or develop specific plans for predicting future care demands and plan end-of-life care in this population. This study aims to identify mortality risk factors in elderly nursing home (NH) residents, based on variables adapted to this environment, routinely collected and easily accessible to their healthcare professionals.
Methods: A prospective, longitudinal, observational study of NH residents aged 65 years and older was carried out collecting sociodemographic, functional and cognitive status, nutritional variables, comorbidities, and other health variables. These variables were analyzed as mortality risk factors by Cox proportional hazard models.
Results: A total of 531 residents (75.3% female; average age 86.7 years (SD: 6.6)) were included: 25.6% had total dependence, 53.4% had moderate to severe cognitive impairment, 84.5% were malnourished or at risk of malnutrition, and 79.9% were polymedicated. Risk of mortality (hazard ratio, HR) increased in totally dependent residents (HR = 1.52; p = 0.02) and in those with moderate or severe cognitive impairment ((HR = 1.59; p = 0.031) and (HR = 1.93; p = 0.002), respectively). Male gender (HR = 1.88; p < 0.001), age =80 years (HR = 1.73; p = 0.034), hypertension (HR = 1.53; p = 0.012), atrial fibrillation/arrhythmia (HR = 1.43; p = 0.048), and previous record of pneumonia (HR = 1.65; p = 0.029) were also found to be mortality drivers.
Conclusion: Age and male gender (due to the higher prevalence of associated comorbidity in these two variables), certain comorbidities (hypertension, atrial fibrillation/arrhythmia, and pneumonia), higher functional and cognitive impairment, and frequency of medical emergency service care increased the risk of mortality in our study. Given their importance and their easy identification by healthcare professionals in nursing homes, these clinical variables should be used for planning care in institutionalized older adults.
CONTEXT: Cancer prognosis data often comes from clinical trials which exclude patients with acute illness.
OBJECTIVES: For patients with Stage IV cancer and acute illness hospitalization, to 1) describe predictors of 60-day mortality, and 2) compare documented decision-making for survivors and decedents.
METHODS: Investigators studied a consecutive prospective cohort of patients with Stage IV cancer and acute illness hospitalization. Structured health record and obituary reviews provided data on 60-day mortality (outcome), demographics, health status, and treatment; logistic regression models identified mortality predictors.
RESULTS: 492 patients with Stage IV cancer and acute illness hospitalization had median age of 60.2 (51% female, 38% minority race/ethnicity); 156 (32%) died within 60 days and median survival for decedents was 28 days. Nutritional insufficiency (OR 1.83), serum albumin (OR 2.15 per 1.0 g/dL) and hospital days (OR 1.04) were associated with mortality; age, gender, race, cancer and acute illness type were not predictive. On admission 79% of patients had orders indicating Full Code. During 60-day follow-up 42% of patients discussed goals of care. Documented goals of care discussions were more common for decedents than survivors (70% vs 28%, p<0.001), as were orders for DNR / DNI (68% vs 24%, p<0.001), stopping cancer-directed therapy (29% vs 10%, p<0.001), specialty Palliative Care (79% vs 44%, p<0.001) and Hospice (68% vs 14%, p<0.001).
CONCLUSION: Acute illness hospitalization is a sentinel event in Stage IV cancer. Short-term mortality is high; nutritional decline increases risk. For patients with Stage IV cancer, acute illness hospitalization should trigger goals of care discussions.
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.