mHOMR: the acceptability of an automated mortality prediction model for timely identification of patients for palliative care

Which medicare advantage enrollees are at highest one-year mortality risk?

BACKGROUND

While a number of tools exist to predict mortality among older adults, less research has described the characteristics of Medicare Advantage (MA) enrollees at higher risk for 1 year mortality.

OBJECTIVES

To describe the characteristics of MA enrollees at higher mortality risk using patient survey data.

RESEARCH DESIGN

Retrospective cohort.

SUBJECTS

MA enrollees completing the 2019 MA Consumer Assessment of Healthcare Providers and Systems (CAHPS) Survey.

MEASURES

Linked demographic, health, and mortality data from a sample of MA enrollees were used to predict 1-year mortality risk and describe enrollee characteristics across levels of predicted mortality risk.

RESULTS

The mortality model had a 0.80 c-statistic. Mortality risks were skewed: 6 % of enrollees had a ≥ 10 % 1-year mortality risk, while 45 % of enrollees had 1 % to < 5 % 1-year mortality risk. Among the high-risk (≥10 %) group, 47 % were age 85+ versus 12 % among those with mortality risk <5 %. 79 % were in fair or poor self-rated health versus 29 % among those with mortality risk of <5 %. 71 % reported needing urgent care in the prior 6 months versus 40 % among those with a mortality risk of 1 to<5 %.

CONCLUSIONS

Relatively few older adults enrolled in MA are at high 1-year mortality risk. Nonetheless, MA enrollees over age 85, in fair or poor health, or with recent urgent care needs are far more likely to be in a high mortality risk group.

With advancement in health technology comes great responsibility - Ethical and safety considerations for using digital health technology: A narrative review

The accelerated adoption of digital health technologies in the last decades has raised important ethical and safety concerns. Despite the potency and usefulness of digital health technologies, addressing safety, and ethical considerations needs to take greater prominence. This review paper focuses on ethical and safety facets, including health technology-related risks, users' safety and well-being risks, security and privacy concerns, and risks to transparency and diminished accountability associated with the utilization of digital health technologies. In order to maximize the potential of health technology benefits, awareness of safety risks, and ethical concerns should be increased, and the use of appropriate strategies and measures should be considered.

Study protocol for the development and validation of a clinical prediction tool to estimate the risk of 1-year mortality among hospitalized patients with dementia

BACKGROUND

Patients with dementia and their caregivers could benefit from advance care planning though may not be having these discussions in a timely manner or at all. A prognostic tool could serve as a prompt to healthcare providers to initiate advance care planning among patients and their caregivers, which could increase the receipt of care that is concordant with their goals. Existing prognostic tools have limitations. We seek to develop and validate a clinical prediction tool to estimate the risk of 1-year mortality among hospitalized patients with dementia.

METHODS

The derivation cohort will include approximately 235,000 patients with dementia, who were admitted to hospital in Ontario from April 1st, 2009, to December 31st, 2017. Predictor variables will be fully prespecified based on a literature review of etiological studies and existing prognostic tools, and on subject-matter expertise; they will be categorized as follows: sociodemographic factors, comorbidities, previous interventions, functional status, nutritional status, admission information, previous health care utilization. Data-driven selection of predictors will be avoided. Continuous predictors will be modelled as restricted cubic splines. The outcome variable will be mortality within 1 year of admission, which will be modelled as a binary variable, such that a logistic regression model will be estimated. Predictor and outcome variables will be derived from linked population-level healthcare administrative databases. The validation cohort will comprise about 63,000 dementia patients, who were admitted to hospital in Ontario from January 1st, 2018, to March 31st, 2019. Model performance, measured by predictive accuracy, discrimination, and calibration, will be assessed using internal (temporal) validation. Calibration will be evaluated in the total validation cohort and in subgroups of importance to clinicians and policymakers. The final model will be based on the full cohort.

DISCUSSION

We seek to develop and validate a clinical prediction tool to estimate the risk of 1-year mortality among hospitalized patients with dementia. The model would be integrated into the electronic medical records of hospitals to automatically output 1-year mortality risk upon hospitalization. The tool could serve as a trigger for advance care planning and inform access to specialist palliative care services with prognosis-based eligibility criteria. Before implementation, the tool will require external validation and study of its potential impact on clinical decision-making and patient outcomes.

TRIAL REGISTRATION

NCT05371782.

Default Palliative Care Consultation for Seriously Ill Hospitalized Patients: A Pragmatic Cluster Randomized Trial

Importance

Increasing inpatient palliative care delivery is prioritized, but large-scale, experimental evidence of its effectiveness is lacking.

Objective

To determine whether ordering palliative care consultation by default for seriously ill hospitalized patients without requiring greater palliative care staffing increased consultations and improved outcomes.

Design, Setting, and Participants

A pragmatic, stepped-wedge, cluster randomized trial was conducted among patients 65 years or older with advanced chronic obstructive pulmonary disease, dementia, or kidney failure admitted from March 21, 2016, through November 14, 2018, to 11 US hospitals. Outcome data collection ended on January 31, 2019.

Intervention

Ordering palliative care consultation by default for eligible patients, while allowing clinicians to opt-out, was compared with usual care, in which clinicians could choose to order palliative care.

Main Outcomes and Measures

The primary outcome was hospital length of stay, with deaths coded as the longest length of stay, and secondary end points included palliative care consult rate, discharge to hospice, do-not-resuscitate orders, and in-hospital mortality.

Results

Of 34 239 patients enrolled, 24 065 had lengths of stay of at least 72 hours and were included in the primary analytic sample (10 313 in the default order group and 13 752 in the usual care group; 13 338 [55.4%] women; mean age, 77.9 years). A higher percentage of patients in the default order group received palliative care consultation than in the standard care group (43.9% vs 16.6%; adjusted odds ratio [aOR], 5.17 [95% CI, 4.59-5.81]) and received consultation earlier (mean [SD] of 3.4 [2.6] days after admission vs 4.6 [4.8] days; P < .001). Length of stay did not differ between the default order and usual care groups (percent difference in median length of stay, -0.53% [95% CI, -3.51% to 2.53%]). Patients in the default order group had higher rates of do-not-resuscitate orders at discharge (aOR, 1.40 [95% CI, 1.21-1.63]) and discharge to hospice (aOR, 1.30 [95% CI, 1.07-1.57]) than the usual care group, and similar in-hospital mortality (4.7% vs 4.2%; aOR, 0.86 [95% CI, 0.68-1.08]).

Conclusions and Relevance

Default palliative care consult orders did not reduce length of stay for older, hospitalized patients with advanced chronic illnesses, but did improve the rate and timing of consultation and some end-of-life care processes.

Trial Registration

ClinicalTrials.gov Identifier: NCT02505035.

Study of Patient and Physician Attitudes Toward Automated Prognostic Models for Patients With Metastatic Cancer

PURPOSE

For patients with cancer and their doctors, prognosis is important for choosing treatments and supportive care. Oncologists' life expectancy estimates are often inaccurate, and many patients are not aware of their general prognosis. Machine learning (ML) survival models could be useful in the clinic, but there are potential concerns involving accuracy, provider training, and patient involvement. We conducted a qualitative study to learn about patient and oncologist views on potentially using a ML model for patient care.

METHODS

Patients with metastatic cancer (n = 15) and their family members (n = 5), radiation oncologists (n = 5), and medical oncologists (n = 5) were recruited from a single academic health system. Participants were shown an anonymized report from a validated ML survival model for another patient, which included a predicted survival curve and a list of variables influencing predicted survival. Semistructured interviews were conducted using a script.

RESULTS

Every physician and patient who completed their interview said that they would want the option for the model to be used in their practice or care. Physicians stated that they would use an AI prognosis model for patient triage and increasing patient understanding, but had concerns about accuracy and explainability. Patients generally said that they would trust model results completely if presented by their physician but wanted to know if the model was being used in their care. Some reacted negatively to being shown a median survival prediction.

CONCLUSION

Patients and physicians were supportive of use of the model in the clinic, but had various concerns, which should be addressed as predictive models are increasingly deployed in practice.

Usability and Acceptability of a Palliative Care Mobile Intervention for Older Adults With Heart Failure and Caregivers: Observational Study

BACKGROUND

Heart failure is a leading cause of death among older adults. Digital health can increase access to and awareness of palliative care for patients with advanced heart failure and their caregivers. However, few palliative care digital interventions target heart failure or patients' caregivers, family, and friends, termed here as the social convoy. To address this need, the Social Convoy Palliative Care (Convoy-Pal) mobile intervention was developed to deliver self-management tools and palliative care resources to older adults with advanced heart failure and their social convoys.

OBJECTIVE

The goal of the research was to test the acceptability and usability of Convoy-Pal among older adults with advanced heart failure and their social convoys.

METHODS

Convoy-Pal includes tablet-based and smartwatch tools facilitating self-management and access to palliative care resources. Older adults and social convoy caregivers completed an acceptability and usability interview via Zoom, including open-ended questions and the Mobile Application Rating Scale: User Version (uMARS). Descriptive analysis was conducted to summarize the results of open-ended feedback and self-reported acceptability and usability.

RESULTS

A total of 26 participants (16 older adults and 10 social convoy caregivers) participated in the interview. Overall, the feedback from users was good (uMARS mean 3.96/5 [SD 0.81]). Both older adults and social convoy caregivers scored information provided by Convoy-Pal the highest (mean 4.22 [SD 0.75] and mean 4.21 [SD 0.64], respectively). Aesthetics, functionality, and engagement were also perceived as acceptable (mean >3.5). Open-ended feedback resulted in 5 themes including improvements to goal setting, monitoring tools, daily check-in call feature, portal and mobile app, and convoy assessment.

CONCLUSIONS

Convoy-Pal was perceived as acceptable with good usability among older adults with heart failure and their social convoy caregivers. With good acceptability, Convoy-Pal may ultimately lead to increased access to palliative care resources and facilitate self-management among older adults with heart failure and their social convoy caregivers.

Dying with Parkinson's Disease: Healthcare Utilization and Costs in the Last Year of Life

BACKGROUND

The end-of-life period is associated with disproportionately higher health care utilization and cost at the population level but there is little data in Parkinson's disease (PD).

OBJECTIVE

The goals of this study were to 1) compare health care use and associated cost in the last year of life between decedents with and without PD, and 2) identify factors associated with palliative care consultation and death in hospital.

METHODS

Using linked administrative datasets held at ICES, we conducted a retrospective, population-based cohort study of all Ontario, Canada decedents from 2015 to 2017. We examined demographic data, rate of utilization across healthcare sectors, and cost of health care services in the last year of life.

RESULTS

We identified 291,276 decedents of whom 12,440 (4.3%) had a diagnosis of PD. Compared to decedents without PD, decedents with PD were more likely to be admitted to long-term care (52% vs. 23%, p < 0.001) and received more home care (69.0 vs. 41.8 days, p < 0.001). Receipt of palliative homecare or physician palliative home consultation were associated with lower odds of dying in hospital (OR: 0.24, 95% CI: 0.19- 0.30, and OR: 0.38, 95% CI: 0.33- 0.43, respectively). Mean cost of care in the last year of life was greater for decedents with PD ($68,391 vs. $59,244, p < 0.001).

CONCLUSION

Compared to individuals without PD, individuals with PD have higher rates of long-term care, home care and higher health care costs in the last year of life. Palliative care is associated with a lower rate of hospital death.

An Exploratory Qualitative Study of Computer Screening to Support Decision-Making about Use of Palliative Care Registers in Primary Care: GP Think Aloud and Patient and Carer Interviews

Objectives:

This study aimed to understand factors that influence general practitioner (GP) use of automated computer screening to identify patients for the palliative care register (PCR) and the experiences of palliative care and this emerging technology from patients’ and carers’ perspectives.

Methods:

A computer screening program electronically searches primary care records in routine clinical practice to identify patients with advanced illness who are not already on a PCR. Five GPs were asked to “think aloud” about adding patients identified by computer screening to the PCR. Key informant interviews with 6 patients on the PCR and 4 carers about their experiences of palliative care while on the PCR and their views of this technology. Data were analyzed thematically.

Results and Conclusions:

Using computer screening, 29% additional patients were added by GPs to the PCR. GP decision-making for the PCR was informed by clinical factors such as: if being treated with curative intent; having stable or unstable disease; end-stage disease, frailty; the likelihood of dying within the next 12 months; and psychosocial factors such as, age, personality, patient preference and social support. Six (60%) patients/carers did not know that they/their relative was on the PCR. From a patient/carer perspective, having a non-curative illness was not in and of itself sufficient reason for being on the PCR; other factors such as, unstable disease and avoiding pain and suffering were equally if not more, important. Patients and carers considered that computer screening should support but not replace, GP decision-making about the PCR. Computer screening merits ongoing development as a tool to aid clinical decision-making around entry to a PCR, but should not be used as a sole criterion. Care need, irrespective of diagnosis, disease trajectory or prognosis, should determine care.

mHOMR: a prospective observational study of an automated mortality prediction model to identify patients with unmet palliative needs

OBJECTIVE

Identification of patients with shortened life expectancy is a major obstacle to delivering palliative/end-of-life care. We previously developed the modified Hospitalised-patient One-year Mortality Risk (mHOMR) model for the automated identification of patients with an elevated 1-year mortality risk. Our goal was to investigate whether patients identified by mHOMR at high risk for mortality in the next year also have unmet palliative needs.

METHOD

We conducted a prospective observational study at two quaternary healthcare facilities in Toronto, Canada, with patients admitted to general internal medicine service and identified by mHOMR to have an expected 1-year mortality risk of 10% or more. We measured patients' unmet palliative needs-a severe uncontrolled symptom on the Edmonton Symptom Assessment Scale or readiness to engage in advance care planning (ACP) based on Sudore's ACP Engagement Survey.

RESULTS

Of 518 patients identified by mHOMR, 403 (78%) patients consented to participate; 87% of those had either a severe uncontrolled symptom or readiness to engage in ACP, and 44% had both. Patients represented frailty (38%), cancer (28%) and organ failure (28%) trajectories were admitted for a median of 6 days, and 94% survived to discharge.

CONCLUSIONS

A large majority of hospitalised patients identified by mHOMR have unmet palliative needs, regardless of disease, and are identified early enough in their disease course that they may benefit from a palliative approach to their care. Adoption of such a model could improve the timely introduction of a palliative approach for patients, especially those with non-cancer illness.