Observational Evidence of For-Profit Delivery and Inferior Nursing Home Care: When Is There Enough Evidence for Policy Change?

This research was financially supported by the Social Sciences and Humanities Research Council

Derivation and validation of an algorithm to predict transitions from community to residential long-term care among persons with dementia—A retrospective cohort study

Objectives: To develop and validate a model to predict time-to-LTC admissions among individuals with dementia. Design: Population-based retrospective cohort study using health administrative data. Setting and participants: Community-dwelling older adults (65+) in Ontario living with dementia and assessed with the Resident Assessment Instrument for Home Care (RAI-HC) between April 1, 2010 and March 31, 2017. Methods: Individuals in the derivation cohort (n = 95,813; assessed before March 31, 2015) were followed for up to 360 days after the index RAI-HC assessment for admission into LTC. We used a multivariable Fine Gray sub-distribution hazard model to predict the cumulative incidence of LTC entry while accounting for all-cause mortality as a competing risk. The model was validated in 34,038 older adults with dementia with an index RAI-HC assessment between April 1, 2015 and March 31, 2017. Results: Within one year of a RAI-HC assessment, 35,513 (37.1%) individuals in the derivation cohort and 10,735 (31.5%) in the validation cohort entered LTC. Our algorithm was well-calibrated (Emax = 0.119, ICIavg = 0.057) and achieved a c-statistic of 0.707 (95% confidence interval: 0.703–0.712) in the validation cohort. Conclusions and implications: We developed an algorithm to predict time to LTC entry among individuals living with dementia. This tool can inform care planning for individuals with dementia and their family caregivers

Do-not-attempt-cardiopulmonary-resuscitation decisions : an evidence synthesis

Background: Cardiac arrest is the final common step in the dying process. In the right context, resuscitation can reverse the dying process, yet success rates are low. However, cardiopulmonary resuscitation (CPR) is a highly invasive medical treatment, which, if applied in the wrong setting, can deprive the patient of dignified death. Do-not-attempt-cardiopulmonary-resuscitation (DNACPR) decisions provide a mechanism to withhold CPR. Recent scientific and lay press reports suggest that the implementation of DNACPR decisions in NHS practice is problematic. Aims and objectives: This project sought to identify reasons why conflict and complaints arise, identify inconsistencies in NHS trusts’ implementation of national guidelines, understand health professionals’ experience in relation to DNACPR, its process and ethical challenges, and explore the literature for evidence to improve DNACPR policy and practice. Methods: A systematic review synthesised evidence of processes, barriers and facilitators related to DNACPR decision-making and implementation. Reports from NHS trusts, the National Reporting and Learning System, the Parliamentary and Health Service Ombudsman, the Office of the Chief Coroner, trust resuscitation policies and telephone calls to a patient information line were reviewed. Multiple focus groups explored service-provider perspectives on DNACPR decisions. A stakeholder group discussed the research findings and identified priorities for future research. Results: The literature review found evidence that structured discussions at admission to hospital or following deterioration improved patient involvement and decision-making. Linking DNACPR to overall treatment plans improved clarity about goals of care, aided communication and reduced harms. Standardised documentation improved the frequency and quality of recording decisions. Approximately 1500 DNACPR incidents are reported annually. One-third of these report harms, including some instances of death. Problems with communication and variation in trusts’ implementation of national guidelines were common. Members of the public were concerned that their wishes with regard to resuscitation would not be respected. Clinicians felt that DNACPR decisions should be considered within the overall care of individual patients. Some clinicians avoid raising discussions about CPR for fear of conflict or complaint. A key theme across all focus groups, and reinforced by the literature review, was the negative impact on overall patient care of having a DNACPR decision and the conflation of ‘do not resuscitate’ with ‘do not provide active treatment’. Limitations: The variable quality of some data sources allows potential overstatement or understatement of findings. However, data source triangulation identified common issues. Conclusion: There is evidence of variation and suboptimal practice in relation to DNACPR decisions across health-care settings. There were deficiencies in considering, discussing and implementing the decision, as well as unintended consequences of DNACPR decisions being made on other aspects of patient care. Future work: Recommendations supported by the stakeholder group are standardising NHS policies and forms, ensuring cross-boundary recognition of DNACPR decisions, integrating decisions with overall treatment plans and developing tools and training strategies to support clinician and patient decision-making, including improving communication. Study registration: This study is registered as PROSPERO CRD42012002669. Funding: The National Institute for Health Research Health Services and Delivery Research programme

Using routinely collected data to develop and evaluate a clinical tool for early identification of palliative care needs in long-term care: The RESPECT Project.

Objectives Prognostication tools reporting personalized mortality risk and survival can improve advance care planning and discussions about end-of-life care. We developed, validated, and implemented a mortality risk algorithm for older adults with diverse care needs in long-term care (LTC) homes, called the Risk Evaluation for Support: Predictions for Elder-Life in the Community Tool for LTC (RESPECT-LTC). Approach RESPECT-LTC was developed using routinely-collected health information on residents in LTC homes in Ontario, Canada. Model development used a cohort of LTC residents aged 50 years or older with at least 1 Resident Assessment Instrument—Minimum Data Set (RAI-MDS) record between January 2010 and December 2016. The primary outcome was mortality 6 months after a RAI-MDS assessment. We used proportional hazards regression with robust standard errors to account for clustering by the individual. We validated this algorithm, temporally, in a cohort of LTC residents who were assessed between January and December 2017. We constructed 37 risk bins based on incremental increases in estimated median survival of ~3 weeks among residents at high risk of death and 3 months among residents with lower mortality risk. We implemented and are evaluating the use of RESPECT-LTC for early identification of palliative care needs in LTC homes across Ontario. Results Development and validation cohorts included 2,228,176 and 328,204 RAI-MDS assessments, respectively. Mean predicted 6-month mortality risk ranged from 1.38% (95% CI 0.63%-1.61%) in the lowest to 91.97% (95% CI 81.47%-99.9%) in the highest risk group. Estimated median survival spanned from 42 days (15 to 128 d at the 25th and 75th percentiles) in the highest risk group to over 8 years (2,066 to 3,428 d) in the lowest risk group. The algorithm had a c-statistic of 0.730 (95% CI 0.726–0.736) in our validation cohort. Conclusion RESPECT-LTC makes use of routinely-collected information to improve the identification of palliative and end-of-life care needs in LTC. Ongoing evaluation will assess its impact on referrals to palliative care, hospitalization at the end of life, and location of death

Using Large Date to Present Uncertainty for Risk Prediction in the Era of Precision Medicine: The RESPECT Algorithm for Predicted Death at End-of-Life

Introduction In Ontario, only 52% of people received palliative care in their last year of life, with only 20\% of those receiving it at home, which can improve the dying experience. Existing algorithms identifying people at end-of-life can potentially improve access to palliative care but are difficult for patients to understand. Objectives and Approach To predict and communicate risk of death for community dwelling older adults using a pre-specified and published approach (Trial registration NCT02779309). All assessments from community-dwelling Ontarians (N = 488,636) who received at least one home care assessment from the residential assessment instrument – home care (RAI HC) from 2007 to 2013 (N=1,331,273) were included. The algorithm used a two-step approach by rank ordering participants into 61 groups based on six-month probability of death (from Cox-proportional hazard models) and generated Kaplan-Meier five-year survival curves for each group. Median Survival time is reported with uncertainties expressed with 25th to 75th percentiles. Results The median predicted probability of death within six-months was 0.1095 (0.1093-0.1097, 95% CI). Risk varied among the 61 groups from 0.0158 (0.0158-0.0159) to 0.9820 (0.9810-0.9830). Median observed survival time varied from 27 days (10 to 81 days, 25th and 75th percentile) in the highest risk group to 10 years (3655 days (2111 to >3655 days)) in the lowest risk group. Discrimination and calibration were satisfactory between the derivation (2007-2012 assessments) and validation (2013 assessments) cohorts, with a C statistics of 0.77 and discrimination plot intercept 0.094, slope 0.914. The Kaplan-Meier five-year survival curves for each of the 61 groups will be visually represented in six different ways displaying the risk and uncertainty, and can be altered to yield information of interest specific to each patient/caregiver. Conclusion/Implications RESPECT is adaptive and personalized, with instantaneous feedback as the user provides a response to each question. We will present RESPECT’s development and implementation processes and set up an interactive presentation of the calculator, demonstrating RESPECT’s ability to deliver patient-comprehensible end-of-life prognoses with uncertainty to patients and their caregivers

A Data Science Approach to Predictive Analytic Research and Knowledge Translation

Introduction Current approaches to the development and application of predictive studies is inefficient and difficult to reproduce. Thousands of predictive health algorithms have been developed; however, less than 2\% have been assessed outside their original setting and even fewer have been applied and evaluated in practice. Objectives and Approach Objective: To develop a standardized workflow for algorithm development, dissemination and implementation. Existing predictive analytics workflow and open standards were adapted and expanded for health research and health care settings. The approach was designed to work within multidisciplinary teams and to improve research transparency, reproducibility, quality, efficiency and application. Key components include standardized algorithm description files, documentation and code libraries. All libraries and programming packages, which were created for/with open-source software, can be used for a wide range of statistical and machine learning models. Publicly-available repositories contain the algorithms, validation data, R code and other supporting infrastructure. Results Algorithm development involves variable pre-specification and documentation of model variables, followed by creation of data preprocessing code to generate model variables from the study dataset. Preprocessing uses algorithm specification documentation and a function library, building upon and integrating with existing algorithms when possible to preventing code duplication. Models are output as a Predictive Modelling Markup Language (PMML) file, a portable industry standard for describing and scoring predictive models. A separate scoring "engine" is used to implement PMML-described algorithms in a range of settings, including algorithm validation at other research institutions. Algorithm applications currently include the Project Big Life (www.projectbiglife.ca) online calculators, population, health services and public health planning uses and an algorithm visualization tool. An API permits use of the calculator engine by other organizations. Conclusion/Implications Barriers to the implementation of predictive analytics in real-world settings—such as within electronic medical records or decision aid applications—can be mitigated with well described algorithms that are easy to replicate and implement, especially as access to big health data increases and algorithms become increasingly complex

Care trajectory in homes care users across mortality-risk profiles: an observational study.

Objectives RESPECT is a prognostic tool, developed using linked population-based data, to predict 6-month mortality in community-dwelling older adults. RESPECT is implemented and openly accessible as a web-based tool on ProjectBigLife.ca, where over 700,000 calculations have been performed to date. Our objective was to describe healthcare utilization patterns among home care (HC) users across mortality risk profiles generated from RESPECT to inform care planning for older persons who have varying mortality risks and levels of care needs as they decline. Approach We conducted a retrospective cohort study examining healthcare use among HC users in Ontario, Canada, who received at least one interRAI HC assessment between April 2018 and September 2019.  Using linked health administrative data at the individual level, we examined the use of acute care (hospitalizations and emergency department (ED) visits), long-term care (LTC), and palliative home care within 6-months of each assessment and prognostication using RESPECT. Mortality risk profiles from RESPECT were created based on the median survival. Results The cohort comprised 247,377 community-dwelling older adults; 14.3% died within 6-months of an assessment. Among decedents, half (51.51%) of HC users with a predicted median survival of less than 3-months received at least one palliative care home visit; 39.17%, 34.82% and 13.84% visited the ED, were hospitalized, or were admitted to LTC, respectively. The proportion of assessments that received at least one palliative HC visit declined to 43.11% and 30.28% of assessments with a median survival between 3- and 6-months and those between 6-months and 12-months, respectively.  The proportion of assessments with an acute care use increases with increasing median survival. Conclusion A considerable proportion of people at the end-of-life do not receive any palliative home care and continued to be institutionalized. This may be indication that the reduced life expectancies and palliative care needs of many older adults are not being recognized, thus demonstrating the value of prognostic models like RESPECT to inform care planning  for individuals in their final years of life

Protocol: Health, social care and technological interventions to improve functional ability of older adults: Evidence and gap map

This is the final version. Available on open access from Wiley via the DOI in this frecordThis is a protocol for a Campbell Evidence and Gap Map. The objectives are to identify and assess the available evidence on health, social care and technological interventions to improve functional ability among older adults

A Multi-Stage Process to Develop Quality Indicators for Community-Based Palliative Care Using interRAI Data

Background: Individuals receiving palliative care (PC) are generally thought to prefer to receive care and die in their homes, yet little research has assessed the quality of home- and community-based PC. This project developed a set of valid and reliable quality indicators (QIs) that can be generated using data that are already gathered with interRAI assessments-an internationally validated set of tools commonly used in North America for home care clients. The QIs can serve as decision-support measures to assist providers and decision makers in delivering optimal care to individuals and their families. Methods: The development efforts took part in multiple stages, between 2017-2021, including a workshop with clinicians and decision-makers working in PC, qualitative interviews with individuals receiving PC, families and decision makers and a modified Delphi panel, based on the RAND/ULCA appropriateness method. Results: Based on the workshop results, and qualitative interviews, a set of 27 candidate QIs were defined. They capture issues such as caregiver burden, pain, breathlessness, falls, constipation, nausea/vomiting and loneliness. These QIs were further evaluated by clinicians/decision makers working in PC, through the modified Delphi panel, and five were removed from further consideration, resulting in 22 QIs. Conclusions: Through in-depth and multiple-stakeholder consultations we developed a set of QIs generated with data already collected with interRAI assessments. These indicators provide a feasible basis for quality benchmarking and improvement systems for care providers aiming to optimize PC to individuals and their families