Suchergebnisse

ObjectivesPrognostication 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).
ApproachRESPECT-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.
ResultsDevelopment 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.
ConclusionRESPECT-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.

IntroductionCurrent 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 ApproachObjective: 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.
ResultsAlgorithm 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/ImplicationsBarriers 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.

IntroductionIn 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 ApproachTo 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.
ResultsThe 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/ImplicationsRESPECT 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.

ObjectivesRESPECT 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.
ApproachWe 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.
ResultsThe 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.
ConclusionA 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.

Context: The precarious work arrangements experienced by many long-term care workers have led to the creation of a "shared" workforce across residential, home, and community aging care sectors. This shared workforce was identified as a contributor to the spread of COVID-19 early in the pandemic. Objective: This analysis sought to review policy measures targeting the long-term care workforce across seven high income jurisdictions during the first year of the COVID-19 pandemic. The focus was on financial supports introduced to recognize long-term care workers for the increased risks they faced, including both (1) health risks posed by direct care provision during the pandemic and (2) economic risks associated with restrictions to multi-site work. Method: Environmental scan of publicly available policy documents and government news releases published between March 1, 2020 and March 31, 2021, across seven high income jurisdictions. Findings: While there was limited use of financial measures in the United States to compensate long-term care workers for the increased health risks they faced, these measures were widely used across Canada, as well as in Wales, Scotland, and Australia. Moreover, there was a corresponding use of financial measures to protect workers from income loss in parts of Canada, Australia and the UK. Limitations: Our analysis did not include additional policy measures such as sick pay or recruitment incentives. We also relied primarily on publicly available policy documentation. In some cases, documents had been archived or revised, making it difficult to ascertain and clarify original information and amendments. Implications: While these financial measures are temporary, they brought to light long-standing issues related to the supply of and support for workers providing care to older adults in long-term care homes.

ObjectivesThe objective of this study was to describe the receipt of potentially non-beneficial interventions in the last 100 days of life of cancer patients and to examine variations in these interventions according to patient characteristics and cancer site.
ApproachWe conducted a population-based retrospective cohort study of all adults age 18+ who died of cancer in Ontario, Canada between January 1, 2013 and December 31, 2017 using linked administrative health data held at ICES. Potentially non-beneficial interventions were captured via hospital discharge records and included chemotherapy, major surgery, intensive care unit admission, cardiopulmonary resuscitation, defibrillation, dialysis, percutaneous coronary intervention, mechanical ventilation, feeding tube placement, blood transfusion and bronchoscopy. We used bivariate analyses and multivariable Poisson regression to examine associations between the receipt of interventions and decedent age, sex, rurality, area-level income, and cancer site.
ResultsAmong the 125,755 decedents, the most common intervention was blood transfusion (18.1%) and major surgery (12.8%); 23.8% received no interventions, while 14% of decedents received 3+ interventions. Lower intervention rates were observed in older patients (adjusted rate ratio (RR) 0.46, 95% confidence interval (CI) 0.44-0.49 for age 95+ vs. 19-44), females (RR 0.93, 95% CI 0.92-0.94), and individuals living in higher income areas (RR 0.96, 95% CI 0.95-0.98 for highest vs. lowest income quintile). Higher intervention rates were observed in rural patients (RR 1.13, 95% CI 1.11-1.14). Patients with pancreatic cancer had the highest intervention rate (RR 1.13, 95% CI 1.10-1.16), while breast cancer patients had the lowest intervention rate (RR 0.86, 95% CI 0.84-0.89) compared to colorectal cancer patients.
ConclusionPotentially non-beneficial interventions were common in the last 100 days of life of patients with cancer. Variations in interventions across patient demographics and cancer site may reflect differences in healthcare access, end-of-life care preferences, patients' prognostic awareness, and disease factors.

ObjectiveAccurate estimates of survival guide decision-making for patients and oncologists. Advances in the capacity to measure complex tumour biology and patient factors allow for concurrent consideration of clinical, pathological, molecular, and biological markers for prognostication. Clinical prediction tools are a mechanism to combine and personalize these increasingly large amounts of complex information for prognostication.
ApproachWe describe the process of linking routinely collected health data, cancer registry, and pathology report data in two provinces to develop (Ontario, Canada) and validate (Manitoba, Canada) a clinical prediction tool in esophageal cancer. We compared the performance of a base model restricted to patient and disease characteristics available prior to surgical resection (e.g., age, sex, histology, comorbidities), and a more complex model including pathology specimen details (e.g., tumour stage). Cox proportional hazards models were fit to predict death at three years following resection. Internal and external validity was assessed using overall calibration and optimism corrected c-statistics. Equity was assessed through calibration in predefined patient subgroups.
Results2124 patients who underwent surgical resection for esophageal cancer between May 1, 2004 and June 30, 2016 for whom a pathology record was available were included in the study cohort. Median age was 66, with 80% males and 85% adenocarcinomas. Survival data were available until March 31, 2020. The model with pathology data had superior discrimination and calibration (calibration slope of 1.02 and intercept -0.01, and optimism-corrected c-statistic 0.77), compared to the base model (calibration slope of 0.95, intercept 0.02, and c-statistic 0.60). External validation is ongoing.
ConclusionOur study demonstrates that prediction models for cancer prognosis built solely on data from health administrative databases may be unreliable. The addition of high-quality pathology report data from electronic medical records or population-based cancer registries is necessary for accurate estimation. Our work provides a framework for combining administrative and clinical data which could be applied to the development of other clinical prediction models.

ObjectivesOur preliminary work revealed significant variations in the prescribing of end-of-life symptom management medications in nursing homes prior to the onset of the COVID-19 pandemic. In this study, we sought to explore whether the prescribing of end-of-life medications in nursing homes changed with the onset of the pandemic.
ApproachThis was a retrospective cohort study of nursing home residents age 65+ who died in Ontario, Canada, divided into two time periods based on death date: pre-COVID-19 (January 1st, 2017 – March 17th, 2020) and during COVID-19 (March 18th, 2020 – March 31st, 2021). Using routinely collected health administrative data and our evidence-based end-of-life medications list, we linked resident data to prescription claims to identify whether residents were prescribed these medications in the last 14 days of life. We grouped homes into quintiles according to the proportion of decedents who received ≥1 prescription and examined changes in prescribing before and during COVID-19.
ResultsNursing homes in the lowest prescribing quintile prescribed, on average, 11.5% fewer end-of-life symptom management medications during COVID-19 compared to pre-pandemic. Conversely, homes in the highest quintile prescribed an average of 13.7% more medications during COVID-19. Nursing homes in the lowest quintile had more COVID-19-positive residents (33% of residents) compared to homes in the highest quintile (9% of residents). Additionally, nursing homes in the lowest prescribing quintile spent more time with active COVID-19 outbreaks compared to homes in the highest quintile (mean 72.7 days versus 24.1 days, respectively, standardized difference 0.819).
ConclusionThe COVID-19 pandemic has disproportionately impacted nursing homes across Canada. Our findings suggest that nursing homes with low rates of prescribing of end-of-life medications prior to the pandemic had even lower prescribing rates during the pandemic. These homes were also harder hit by COVID-19 infections and outbreaks.

ObjectivesPrescribing of symptom management medications may reflect the quality of end-of-life care provided to nursing home residents who are nearing death. The objective of this study was to examine variations in the prescribing of end-of-life symptom management medications in nursing home residents in the last 14 days of life.
ApproachThis was a retrospective cohort study of nursing home residents age 65+ who died in Ontario, Canada between January 2017 and February 2020. Through expert consultations, we compiled a list of medications used to manage common end-of-life symptoms. Using routinely collected health administrative data held at ICES, we linked resident data to prescription claims to identify whether residents were prescribed these medications in the last 14 days of life. We grouped nursing homes into quintiles according to the proportion of decedents in a home who received ≥1 prescription and examined variations in resident and facility characteristics across quintiles.
ResultsThere were 55,029 deaths across 626 nursing homes. Overall, 64.8% of residents received at least one end-of-life symptom management medication. The proportion of dying residents who received ≥1 end-of-life medication ranged from 37.6% in quintile 1, 59.8% in quintile 2, 69.1% in quintile 3, 74.8% in quintile 4, and 82.9% in quintile 5. Opioids were the most commonly prescribed medications, with an average of 62.2% of residents receiving a prescription (35.9% to 81.2% across the quintiles). Nursing home residents that resided in homes in the lowest prescribing quintile were older and more likely to be Allophones (first language not English or French). Low prescribing homes were also larger, with a higher number of beds, and were more likely to be in rural areas.
ConclusionThe observed variations in the prescribing of medications to manage end-of-life symptoms in nursing home residents raises concerns that some residents may have received inadequate end-of-life symptom management. Prescription data may provide an opportunity to rapidly evaluate the quality of end-of-life care in nursing homes at a population level.