Power and perils of prediction in palliative care

Palliative Care Exposure Relative to Predicted Risk of Six-Month Mortality in Hospitalized Adults

CONTEXT

The optimal strategy for implementing mortality-predicting algorithms to facilitate clinical care, prognostic discussions, and palliative care interventions remains unknown.

OBJECTIVES

To develop and validate a real-time predictive model for 180 day mortality using routinely available clinical and laboratory admission data and determine if palliative care exposure varies with predicted mortality risk.

METHODS

Adult admissions between October 1, 2013 and October.1, 2017 were included for the model derivation. A separate cohort was collected between January 1, 2018 and July 31, 2020 for validation. Patients were followed for 180 days from discharge, and logistic regression with selected variables was used to estimate patients' risk for mortality.

RESULTS

In the model derivation cohort, 7963 events of 180 day mortality (4.5% event rate) were observed. Median age was 53.0 (IQR 24.0-66.0) with 92,734 females (52.5%). Variables with strongest association with 180 day mortality included: Braden Score (OR 0.83; 95% CI 0.82-0.84); admission Do Not Resuscitate orders (OR 2.61; 95% CI 2.43-2.79); admission service and admission status. The model yielded excellent discriminatory ability in both the derivation (c-statistic 0.873; 95% CI 0.870-0.877; Brier score 0.04) and validation cohorts (c-statistic 0.844; 95% CI 0.840-0.847; Brier score 0.072). Inpatient palliative care consultations increased from 3% of minimal-risk encounters to 41% of high-risk encounters (P < 0.01).

CONCLUSION

We developed and temporally validated a predictive mortality model for adults from a large retrospective cohort, which helps quantify the potential need for palliative care referrals based on risk strata. Machine learning algorithms for mortality require clinical interpretation, and additional studies are needed to design patient-centered and risk-specific interventions.

Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs

Palliative care (PC) has demonstrated benefits for life-limiting illnesses. Bad survival prognosis and patients' decline are working criteria to guide PC decision-making for older patients. Still, there is not a clear consensus on when to initiate early PC. This work aims to propose machine learning approaches to predict frailty and mortality in older patients in supporting PC decision-making. Predictive models based on Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) were implemented for binary 1-year mortality classification, survival estimation and 1-year frailty classification. Besides, we tested the similarity between mortality and frailty distributions. The 1-year mortality classifier achieved an Area Under the Curve Receiver Operating Characteristic (AUC ROC) of 0.87 [0.86, 0.87], whereas the mortality regression model achieved an mean absolute error (MAE) of 333.13 [323.10, 342.49] days. Moreover, the 1-year frailty classifier obtained an AUC ROC of 0.89 [0.88, 0.90]. Mortality and frailty criteria were weakly correlated and had different distributions, which can be interpreted as these assessment measurements are complementary for PC decision-making. This study provides new models that can be part of decision-making systems for PC services in older patients after their external validation.

Expected clinical utility of automatable prediction models for improving palliative and end-of-life care outcomes: Toward routine decision analysis before implementation

Objective

The study sought to evaluate the expected clinical utility of automatable prediction models for increasing goals-of-care discussions (GOCDs) among hospitalized patients at the end of life (EOL).

Materials and Methods

We built a decision model from the perspective of clinicians who aim to increase GOCDs at the EOL using an automated alert system. The alternative strategies were 4 prediction models—3 random forest models and the Modified Hospital One-year Mortality Risk model—to generate alerts for patients at a high risk of 1-year mortality. They were trained on admissions from 2011 to 2016 (70 788 patients) and tested with admissions from 2017-2018 (16 490 patients). GOCDs occurring in usual care were measured with code status orders. We calculated the expected risk difference (beneficial outcomes with alerts minus beneficial outcomes without alerts among those at the EOL), the number needed to benefit (number of alerts needed to increase benefit over usual care by 1 outcome), and the net benefit (benefit minus cost) of each strategy.

Results

Models had a C-statistic between 0.79 and 0.86. A code status order occurred during 2599 of 3773 (69%) hospitalizations at the EOL. At a risk threshold corresponding to an alert prevalence of 10%, the expected risk difference ranged from 5.4% to 10.7% and the number needed to benefit ranged from 5.4 to 10.9 alerts. Using revealed preferences, only 2 models improved net benefit over usual care. A random forest model with diagnostic predictors had the highest expected value, including in sensitivity analyses.

Discussion

Prediction models with acceptable predictive validity differed meaningfully in their ability to improve over usual decision making.

Conclusions

An evaluation of clinical utility, such as by using decision curve analysis, is recommended after validating a prediction model because metrics of model predictiveness, such as the C-statistic, are not informative of clinical value.

AI-based clinical decision-making systems in palliative medicine: ethical challenges

BACKGROUND

Improving palliative care (PC) is demanding due to the increase in people with PC needs over the next few years. An early identification of PC needs is fundamental in the care approach: it provides effective patient-centred care and could improve outcomes such as patient quality of life, reduction of the overall length of hospitalisation, survival rate prolongation, the satisfaction of both the patients and caregivers and cost-effectiveness.

METHODS

We reviewed literature with the objective of identifying and discussing the most important ethical challenges related to the implementation of AI-based data processing services in PC and advance care planning.

RESULTS

AI-based mortality predictions can signal the need for patients to obtain access to personalised communication or palliative care consultation, but they should not be used as a unique parameter to activate early PC and initiate an ACP. A number of factors must be included in the ethical decision-making process related to initiation of ACP conversations, among which are autonomy and quality of life, the risk of worsening healthcare status, the commitment by caregivers, the patients' psychosocial and spiritual distress and their wishes to initiate EOL discussions CONCLUSIONS: Despite the integration of artificial intelligence (AI)-based services into routine healthcare practice could have a positive effect of promoting early activation of ACP by means of a timely identification of PC needs, from an ethical point of view, the provision of these automated techniques raises a number of critical issues that deserve further exploration.

Smart homes for the older population: particularly important during the COVID-19 outbreak

Osteoporosis, one of the leading causes of disability in older adults, significantly reduces the quality of life and leads to loss of independence. Dynamic development of “smart” solutions based on artificial intelligence more and more commonly applied in older people’s houses may be an answer to the above issues.

The aim of this study is to present selected “smart home” solutions for the diagnosis and prevention of falls in the older population through a literature review. The conducted meta-analysis based on a review of the scientific literature available in English and Polish in the Medline/PubMed, Embase, Scopus, and GBL databases was undertaken from 01.01.2015 to 01.10.2020 with the string search method using key words.

According to the authors of this study, the development of new technology based on artificial intelligence allows older people to live independently, which contributes to a higher level of life satisfaction and quality.

Conversing with High-Risk Patients to Determine Serious Illness Goals and Values in the Time of Covid-19

During the Covid-19 pandemic’s first surge in Boston, Brigham Health sought to ensure that patients’ health care proxies and serious illness wishes were known to care teams. The authors engaged a diverse set of team members in outreach regarding serious illness conversations. Patients enrolled in the Integrated Care Management Program (iCMP) were contacted by their own nurse care coordinator for a serious illness conversation, discussing patients’ goals and values in the context of underlying illness and the threat of Covid-19. Simultaneously, nurses, medical students, and social care team members reached out to non-iCMP primary care patients identified as being at high risk of morbidity or mortality from Covid-19 and engaged these patients in conversations regarding health care proxy documentation and social determinants of health needs. The authors’ experience demonstrates that such a population health approach can facilitate timely and well-accepted outreach regarding serious illness to patients with varied needs and profiles.