Monday, July 12, 2021

The Algorithms of Death: RESPECT: An Algorithm for Predicting Time to Death for 'Frail Older Adults'


Pix Credit HERE


Predictive analytics has become an indispensable tool of markets, and of the state. As the role of both shifts from structural to managerial responsibilities--from creating and maintaining frameworks within which individuals in collectives can order their lives and make choices to  transforming frameworks into  systems of nudging to induce individuals to make the "right" choices--the need for data based analytics becomes more acute.  These analytics firsts help those whose business it is to ensure appropriate conduct to understand the collective behaviors of the target population.  Second, on the basis of that understanding, it is then possible to predict behavior or outcomes given  functional relationships among the factors that are critical to the model.  Eventually, a sophisticated enough analytics (what some might also call algorithms, though the algorithm is better understood as the calculation of judgment derived from the application of an analytics either to describe or predict) can be used to control behavior by changing the intensity or direction of factors (coefficients of values) that produce behavior movement in particular directions. 

However, one cannot get to the third stage (the ultimate objective of planners and those seeking to mold society toward ideal types) without a sound basis in predictive analytics.  It is to that end that modelers are now focusing with greater intensity on predicting all sorts of behaviors, conditions, events, and effects that touch on regulatory, policy, or market roles for societal or governmental collectives or institutions. Not that there is some sort of sinister object at work here.  The move from qualitative to quantitative and comprehensive management of populations has been a long time coming and has emerged in strong alignment with cultural, political, and societal changes that have appeared to increase (as technology makes possible) an appetite  for the scientific management of life in organized society--even (or especially in live within the liberal democratic  collectives of states).  At some point, there is no doubt, our sociologists (if permitted--for by then it may be a taboo to study these things) will be able to explain this draft and then rush toward  administrative managerialism within both public and private sector components of liberal democratic societies. Foe the moment it may be enough to knwo that it appears now to be an irresistible force--one augmented by the promise of technology and the reassurance of certainty in collectives making decisions that affect the lives of individuals in ways that are said to align with collective societal (or political) desire.

 It is in that context that one might usefully encounter an important manifestation of that trend in the so-called RESPECT project that has recently garnered the voyeuristic attention of the press (though hardly its understanding of other it or its context). .

The algorithm used in this calculator is called RESPECT. RESPECT is short for Risk Evaluation for Support: Predictions for Elder life in the Community Tool.

RESPECT was developed on data from 491,277 older people in Ontario, Canada, who used home care between 2007 and 2013. The dataset contains detailed health information from the standardized Resident Assessment Instrument for Home Care (RAI-HC), which case managers and nurses use to assess the needs of home care users. The calculator includes a wide range of questions that reflect different trajectories in physical health and cognition, including age, sex, cognitive impairment (memory decline), diseases (e.g. diabetes, cardiovascular disease, dementia, cancer), sociodemographic factors (marital status, level of education), health status (e.g. difficulties with activities of daily living), symptoms of reduced physiologic reserve (e.g. weight loss), use life-sustaining therapies (e.g., dialysis), and health care use (e.g., number of hospitalizations and emergency department visits).

The algorithm is calibrated to 1.3 million assessments and 112,823 deaths. RESPECT accurately predicts a wide range in life expectancy; we ranked and classified our cohort based on their six-month risk of death, which spanned from 1.5% to 98%. This translates to an average survival of 4 weeks (interquartile range [IQR] of 11 to 84 days) for the frailest people in our cohort to 8.1 years among people with the few physical limitations and chronic health conditions (IQR of 5.9 years to 9.4 years).

RESPECT is incorporated into the Project Big Life scoring engine. The scoring engine generates calculations on any combination of responses to the algorithm questions. The main output of the engine is the actual health experience of Ontarians who completed the 1.3 million assessments. These health experiences can be translated into a wide range of patient-oriented measures. For more information on how we developed the RESPECT–End of Life algorithm, click here. (RESPECT website HERE))

RESPECT was developed by a team of researchers Amy T. Hsu, Douglas G. Manuel, Sarah Spruin, Carol Bennett, Monica Taljaard, Sarah Beach, Yulric Sequeira, Robert Talarico, Mathieu Chalifoux, Daniel Kobewka, Andrew P. Costa, Susan E. Bronskill and Peter Tanuseputro, and recently published CMAJ 193(26): Predicting death in home care users: derivation and validation of the Risk Evaluation for Support: Predictions for Elder-Life in the Community Tool (RESPECT) (ARTICLE PDF HERE)

Pix Credit HERE
New online calculator can help predict death and end-of-life care needs for older adults

Predicting death in home care users: derivation and validation of the Risk Evaluation for Support: Predictions for Elder-Life in the Community Tool (RESPECT) CMAJ 193(26):

INTERPRETATION: The RESPECT mortality risk prediction tool that makes use of readily available information can improve the identification of palliative and end-of-life care needs in a diverse older adult population receiving home care.

Most people in high-income countries die of causes with progressive, predictable trajectories of decline.14 Since 2000, the 3 leading causes of death in Canada — accounting for 55% of all deaths — have been cancer, heart disease and stroke.1 Other leading causes of death, such as dementia and chronic lower respiratory diseases, also share signs and symptoms of senescence that are common across chronic diseases, including deterioration of physical and cognitive function, as well as an increased need for assistance.

Despite the predictable nature of most deaths, many Canadian residents who are at the end of life do not receive adequate home-based supports.5 In Ontario — the largest province in Canada with more than 14 million residents and the setting of this study — only 40% of decedents receive formal home care, and less than 20% receive a physician home visit in their last year of life.6,7 Even among those who had received palliative and end-of-life care, the start of service was often too close to death and failed to have a positive impact on the quality of life in those last months.8 The lack of available and accurate prognostic information is a key challenge. There are few existing tools that can be used to inform palliative care planning for the general population of older adults who live in the community and in people without cancer.9 Other barriers to accurate prognostic estimates include clinicians’ reluctance or lack of time and existing prognostication tools’ reliance on complex or specialized inputs, such as laboratory data and previous health care use. As a result, many older and frail adults do not receive timely palliative care and do not have an advance care plan.6,1013

Our primary objective was to develop and validate a model for predicting mortality risk among the general population of community-dwelling adults with and without cancer that spans an actionable period for end-of-life planning (5 yr to imminent death). The variables included in our prognostication model — the Risk Evaluation for Support: Predictions for Elder-life in the Community Tool (RESPECT) — were prespecified to include exposures that could be self-reported by patients and their caregivers, including family members.


We derived and validated RESPECT using population-based home care data housed at ICES in Ontario. The reporting of our approach and findings adhere to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.14 A detailed protocol prespecifying our approach was published elsewhere.15

Study population

The study population included people who were 50 years of age or older, eligible for government-funded long-term home care in the community and had received at least 1 assessment using the Resident Assessment Instrument for Home Care (RAI-HC) between Jan. 1, 2007, and Dec. 31, 2013. The derivation cohort included those who used home care between Jan. 1, 2007, and Dec. 31, 2012, and the temporal validation cohort received care and were assessed between Jan. 1 and Dec. 31, 2013. The RAI-HC is a comprehensive, multidimensional instrument for clinical assessment that contains nearly 400 data elements that capture the home care client’s sociodemographic profile, cognitive and functional capacities, chronic diseases and comorbidities, and signs of health instability, as well as recent use of health care. It is primarily used for care planning within the home care setting. However, it also contains quality indicators and outcome measures that could be used to evaluate the impact of services provided. In Ontario, home care services, such as nursing, personal support and rehabilitative or restorative therapy, are publicly funded and provided to people to improve or manage their health based on assessed need. The RAI-HC is used by home care coordinators to evaluate the level of care need in those who are expected to require at least 60 days of uninterrupted service (also known as “long-stay clients”). Evaluations using the RAI-HC are performed at initial consideration for home care and completed at least once every 6 months for those receiving home care over an extended period, or when substantial changes in the client’s situation have been observed.

The primary outcome of our prediction model was death within 6 months of a RAI-HC assessment. Death was ascertained from the Registered Persons Database, a registry of health card numbers that have been issued under the Ontario Health Insurance Plan to all eligible residents of Ontario.

The selection of predictors included in our model was informed by clinical experience and our review of existing mortality prediction models for a population of older, community-dwelling adults. We also considered variables included in existing frailty indices.9 We considered risk factors related to physical functioning (e.g., difficulties with activities of daily living [ADL], inability to independently carry out instrumental activities of daily living [IADL] and reduced mobility), cognitive impairment (e.g., memory decline and psychosis), sociodemographic factors (e.g., level of education) and biological diseases (e.g., diabetes, heart disease and cancer). We also included self-reported measures of recent use of health care (i.e., number of hospital admissions or visits to the emergency department in the last 90 d), the prescription and receipt of life-sustaining therapies (e.g., dialysis and ventilation), and symptoms of reduced health and physiologic reserve (e.g., weight loss, edema and vomiting). The latter variables were selected to capture the acute symptomatology of people in the terminal period of life. We also incorporated cohort characteristics (e.g., year of the RAI-HC assessment, and the type of and reason for assessment) that may account for remaining heterogeneity in the estimated risks. No stepwise variable selection procedure was used.




Frequently Asked Questions

What is RESPECT?

RESPECT is short for Risk Evaluation for Support: Predictions for Elder-life in the Community Tool. It was developed for frail older people who might need supports and care in their homes. Technically, it is an algorithm that calculates a frail person’s survival—that is, how long they will live. Using the responses to 17 questions about their health and ability to care for themselves, the tool provides an estimate of a person’s survival based on information gathered on people who have similar characteristics.

Who will use RESPECT?

RESPECT was designed for frail older adults living in the community who are uncertain about their survival and the family members and caregivers who support them. RESPECT can also be used by formal care providers—such as physicians, home care staff or palliative care teams—to understand their patient’s decline.

How will RESPECT be used?

As a person’s health declines, they may need more supports and care in their home. RESPECT calculates a person’s survival and provides information that can help them understand what type of care and services they may need. A patient can use this information to discuss their care needs with their caregivers and healthcare providers. Similarly, formal care providers can use this tool to discuss, with their patient, what can be expected as the patient approaches the end of life and plan for the supports that their patient may need.

How was RESPECT created?

RESPECT was developed and validated using home care data from Ontario. We used data from 1.3 million Ontario home care assessments that were followed to 80,000 deaths between 2007 and 2014. The dataset contains detailed health information from the standardized Resident Assessment Instrument for Home Care, which case managers or care coordinators commonly use to assess the needs of frail individuals who might need home care.

Can RESPECT be used by those not receiving home care?

RESPECT was designed for frail older adults living in the community, which includes those living in assisted living and retirement home communities, who might need support at home. People receiving care in other settings—such as those in long-term care or nursing homes—may have different underlying risks that contribute to their survival and RESPECT will not be able to accurately estimate the mortality risk for these populations.

Who developed RESPECT?

RESPECT was created by the Project Big Life Team, which includes patients and caregivers to older adults who need care. The Project Big Life Team is led by researchers at the Ottawa Hospital, the Bruyère Research Institute, the University of Ottawa and ICES. ICES, formerly known as the Institute for Clinical Evaluative Sciences, is a not-for-profit research institute encompassing a community of research, data and clinical experts, and a secure and accessible array of Ontario's health-related data.

Can RESPECT be integrated with electronic medical records?

Through funding from the Canadian Institutes of Health Research, the Project Big Life Team will be collaborating with various healthcare providers and organizations to improve the integration of this information into existing electronic medical records. At this time, results generated from the online calculator can be printed and shared with a patient’s formal care provider and those in their circle of care.

Who funded this research?

This work was funded by the Canadian Institutes of Health Research and supported by ICES. ICES receives core funding from the Ontario Ministry of Health and Long-Term Care.

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