Is a dementia diagnosis predictive for palliative care? ‐ A machine learning approach
Elena Rakusa, Constantin Reinke, Gabriele Doblhammer, Matthias Schmid, Thomas Welchowski- Psychiatry and Mental health
- Cellular and Molecular Neuroscience
- Geriatrics and Gerontology
- Neurology (clinical)
- Developmental Neuroscience
- Health Policy
- Epidemiology
Abstract
Background
Palliative care (PC) aims to provide dignified and self‐determined life for dying individuals. While the integration of PC for tumor diseases in health care is established in case of progressive course and unfavorable prognosis, PC is surprisingly not mentioned in the German S3‐guideline for dementia. PC is mainly used by older people, with proximity to death rather than age being the determining factor. However, more people are dying at older ages. Moreover, as the elderly population increases, the number of dementia patients also increases. The aim of this paper is to examine the utilization of PC at the end of life and to explore whether a dementia diagnosis is among the top factors that predict PC utilization.
Method
We developed predictive models using discrete survival conditional inference trees (ctrees) and discrete survival conditional inference forests (cforests). We used randomly drawn claims data from a German health insurer from 2014 aged 50+ (N = 250.000), and followed them quarterly until 2019. We analyzed the last eight quarters of life of 13,628 individuals who had died in the observation period and used outpatient (OPC) and inpatient (IPC) PC as outcomes. We included medical history, care measures, medications, comorbidities and patient status (diagnosis of dementia or cancer, combinations of both diagnoses, and diagnosis‐free patients) as possible predictors. Dementia was defined by ICD‐10 codes F00‐F03,F05.1,G23.1,G30,G31.0 and G31.82 and cancer by C00‐C26,C30‐C41,C45‐C58,C60‐C97 and D00‐D09. For evaluation we used concordance‐index (C‐Index) and calibration plots. We identified the most important predictors by using a permutation variable importance approach.
Result
The models for OPC and IPC had a discriminatory power of more than 0.7 (C‐Index) on the test data. The calibration plots showed well calibrated ctrees, but the cforests require further tuning (Figure 1). Among the top 20 predictors of OPC were proximity to death as well as cancer and dementia diagnoses, and their combinations. Neither cancer nor dementia diagnoses were important predictors of IPC.
Conclusion
We found that a dementia diagnosis has comparably predictive power for OPC as a cancer diagnosis. This issue should be addressed by including appropriate measures in dementia care guidelines.