Background: A systematic understanding of socio-economic inequalities in end-of-life (EOL) suffering among advanced cancer patients is required to inform efforts to reduce these inequalities as part of Universal Health Coverage goals.
Aims: To assess inequalities in multiple domains of EOL suffering among advanced cancer patients – physical, functional, psychological, social, and spiritual –, using two socio-economic status (SES) indicators, education and perceived economic status of the household.
Methods: We used cross-sectional data from surveys of stage IV cancer patients (n = 1378) from seven hospitals across five countries (China, Sri Lanka, India, Vietnam and Myanmar). We conducted separate multivariable linear regression models for each EOL suffering domain. We also tested interactions between the two SES indicators and between each SES indicator and patient age.
Results: Patients living in low economic status households /with fewer years of education reported greater suffering in several domains. We also found significant interaction effects between economic status of the household and years of education for all EOL suffering outcomes. Age significantly moderated the association between economic status of the household and social suffering and between years of education and psychological, social, and spiritual suffering (p < 0.05 for all).
Conclusion: Results highlight that SES inequalities in EOL suffering vary depending on the suffering domain, the SES indicator assessed, and by patient age. Greater palliative care resources for patients with low SES may help reduce these inequalities.
Context/Objective: Essential indicators of high-quality end-of-life care in intensive care units (ICUs) have been established but examined inconsistently and predominantly with small samples, mostly from Western countries. Our study goal was to comprehensively measure end-of-life-care quality delivered in ICUs using chart-derived process-based quality measures for a large cohort of critically ill Taiwanese patients.
Methods: For this observational study, patients with APACHE II score =20 or goal of palliative care and with ICU stay exceeding three days ( N = 326) were consecutively recruited and followed until death.
Results: Documentation of process-based indicators for Taiwanese patients dying in ICUs was variable (8.9%–96.3%), but high for physician communication of the patient's poor prognosis to his/her family members (93.0%), providing specialty palliative-care consultations (73.3%), a do-not-resuscitate order in place at death (96.3%), death without cardiopulmonary resuscitation (93.5%), and family presence at patient death (76.1%). Documentation was infrequent for social-worker involvement (8.9%) and interdisciplinary family meetings to discuss goals of care (22.4%). Patients predominantly (79.8%) continued life-sustaining treatments (LSTs) until death and died with full life support, with 88.3% and 58.9% of patients dying with mechanical ventilation support and vasopressors, respectively.
Conclusions: Taiwanese patients dying in ICUs heavily used LSTs until death despite high prevalences of documented prognostic communication, providing specialty palliative-care consultations, having a do-not-resuscitate order in place, and death without cardiopulmonary resuscitation. Family meetings should be actively promoted to facilitate appropriate end-of-life-care decisions to avoid unnecessary suffering from potentially inappropriate LSTs during the last days of life.
Background: Palliative care is highly relevant for patients with heart failure (HF), and there is a need for quantitative information on quality of care. Accordingly, this study aimed to develop a set of quality indicators (QIs) for palliative care of HF patients, and to conduct a practical pilot measurement of the proposed QIs in clinical practice.
Methods and Results: We used a modified Delphi technique, a consensus method that involves a comprehensive literature review, face-to-face multidisciplinary panel meeting, and anonymous rating in 2 rounds. A 15-member multidisciplinary expert panel individually rated each potential indicator on a scale of 1 (lowest) to 9 (highest) for appropriateness. All indicators receiving a median score =7 without significant disagreement were included in the final set of QIs. Through the consensus-building process, 35 QIs were proposed for palliative care in HF patients. Practical measurement in HF patients (n=131) from 3 teaching hospitals revealed that all of the proposed QIs could be obtained retrospectively from medical records, and the following QIs had low performance (<10%): “Intervention by multidisciplinary team”, “Opioid therapy for patients with refractory dyspnea”, and “Screening for psychological symptoms”.
Conclusions: The first set of QIs for palliative care of HF patients was developed and could clarify quantitative information and might improve the quality of care.
The diagnosis of brain death (BD) is legally and medically accepted. Recently, several high-profile cases have led to discussions regarding the integrity of current criteria, and many physiologic problems have been identified to support the necessity for their reevaluation. These include a global variability of the criteria, the suggestion of a clinical “hierarchy,” and the resultant approximation of BD. Further ambiguity has been exposed through case reports of reversible BD, and an inconsistent understanding from physicians who are viewed as experts in this domain. Meeting BD criteria clearly does not equate to a physiologic “death” of the brain, and a greater community perspective should be considered as the dialogue moves forward.
PURPOSE: To examine quality indicators of end-of-life (EOL) care among privately insured people with cancer in Brazil.
METHODS: We evaluated medical records linked to health insurance databank to study consecutive patients who died of cancer. We collected information about demographics, cancer type, and quality indicators of EOL care including emergency department (ED) visits, intensive care unit (ICU) admissions, chemotherapy use, medical imaging utilization, blood transfusions, home care support, days of inpatient care, and hospital deaths.
RESULTS: We included 865 patients in the study. In the last 30 days of life, 62% visited the ED, 33% were admitted to the ICU, 24% received blood transfusions, and 51% underwent medical imaging. Only 1% had home care support in the last 60 days of life, and 29% used chemotherapy in the last 14 days of life. Patients had an average of 8 days of inpatient care and 52% died in the hospital. Patients with advanced cancer who used chemotherapy were more likely to visit the ED (78% vs 59%; P < .001), undergo medical imaging (67% vs 51%; P < .001), and die in the hospital (73% vs 50%; P = .03) than patients who did not use chemotherapy. In the multivariate analysis, chemotherapy use near death and advanced cancer were associated with ED visits and ICU admissions, respectively (odds ratio >1).
CONCLUSION: Our study suggests that privately insured people with cancer receive poor quality EOL care in Brazil. Further research is needed to assess the impact of improvements in palliative care provision in this population.
Background: Delivery of health services in the province of Ontario is organized into 14 Local Health Integration Networks (LHINs), and further into 76 LHIN subregions, making these a natural unit of comparing the regional differences in palliative care receipt among decedents who were identified as having palliative care needs.
Objective: To assess the presence and magnitude of the remaining regional variation in palliative care receipt in Ontario after accounting for demographic and socioeconomic differences between the LHIN subregions, and therefore to assess whether the standardized proportion of palliative care receipt as a performance indicator can capture potential performance-related issues.
Design: A retrospective cohort study based on Ontario administrative data sources.
Setting/Subjects: Ontario residents who died between April 1, 2015 and March 31, 2016 and were identified as having palliative care needs.
Measurements: Date of death, diagnostic codes used for determining palliative care needs, and services receipt in last year of life were identified from multiple administrative databases. Demographic and socioeconomic information were derived from linking decedents' postal codes to Statistics Canada Census data and Ontario Marginalization Index.
Results: Statistically significant variation ranging from 63% to 75% in palliative care receipt exists between Ontario subregions even after accounting for demographic and socioeconomic differences, including age, sex, rurality, income quintile, and the four dimensions of the Ontario Marginalization Index.
Conclusions: Annual directly standardized proportion of palliative care receipt can be used as a performance indicator to detect regional differences in service receipt while adjusting for regional differences in the characteristics of the decedent populations. The factors to be adjusted for can be chosen based on the comparison of interest.
BACKGROUND: Timely documentation of care preferences is an endorsed quality indicator for seriously ill patients admitted to intensive care units. Clinicians document their conversations about these preferences as unstructured free text in clinical notes from electronic health records.
AIM: To apply deep learning algorithms for automated identification of serious illness conversations documented in physician notes during intensive care unit admissions.
DESIGN: Using a retrospective dataset of physician notes, clinicians annotated all text documenting patient care preferences (goals of care or code status limitations), communication with family, and full code status. Clinician-coded text was used to train algorithms to identify documentation and to validate algorithms. The validated algorithms were deployed to assess the percentage of intensive care unit admissions of patients aged >=75 that had care preferences documented within the first 48 h.
SETTING/PARTICIPANTS: Patients admitted to one of five intensive care units.
RESULTS: Algorithm performance was calculated by comparing machine-identified documentation to clinician-coded documentation. For detecting care preference documentation at the note level, the algorithm had F1-score of 0.92 (95% confidence interval, 0.89 to 0.95), sensitivity of 93.5% (95% confidence interval, 90.0% to 98.0%), and specificity of 91.0% (95% confidence interval, 86.4% to 95.3%). Applied to 1350 admissions of patients aged >=75, we found that 64.7% of patient intensive care unit admissions had care preferences documented within the first 48 h.
CONCLUSION: Deep learning algorithms identified patient care preference documentation with sensitivity and specificity approaching that of clinicians and computed in a tiny fraction of time. Future research should determine the generalizability of these methods in multiple healthcare systems.