Dataset Information

Machine learning models of healthcare expenditures predicting mortality: A cohort study of spousal bereaved Danish individuals.

ABSTRACT:

Background

The ability to accurately predict survival in older adults is crucial as it guides clinical decision making. The added value of using health care usage for predicting mortality remains unexplored. The aim of this study was to investigate if temporal patterns of healthcare expenditures, can improve the predictive performance for mortality, in spousal bereaved older adults, next to other widely used sociodemographic variables.

Methods

This is a population-based cohort study of 48,944 Danish citizens 65 years of age and older suffering bereavement within 2013-2016. Individuals were followed from date of spousal loss until death from all causes or 31st of December 2016, whichever came first. Healthcare expenditures were available on weekly basis for each person during the follow-up and used as predictors for mortality risk in Extreme Gradient Boosting models. The extent to which medical spending trajectories improved mortality predictions compared to models with sociodemographics, was assessed with respect to discrimination (AUC), overall prediction error (Brier score), calibration, and clinical benefit (decision curve analysis).

Results

The AUC of age and sex for mortality the year after spousal loss was 70.8% [95% CI 68.8, 72.8]. The addition of sociodemographic variables led to an increase of AUC ranging from 0.9% to 3.1% but did not significantly reduce the overall prediction error. The AUC of the model combining the variables above plus medical spending usage was 80.8% [79.3, 82.4] also exhibiting smaller Brier score and better calibration. Overall, patterns of healthcare expenditures improved mortality predictions the most, also exhibiting the highest clinical benefit among the rest of the models.

Conclusion

Temporal patterns of medical spending have the potential to significantly improve our assessment on who is at high risk of dying after suffering spousal loss. The proposed methodology can assist in a more efficient risk profiling and prognosis of bereaved individuals.

SUBMITTER: Katsiferis A

PROVIDER: S-EPMC10406307 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Machine learning models of healthcare expenditures predicting mortality: A cohort study of spousal bereaved Danish individuals.

Katsiferis Alexandros A Bhatt Samir S Mortensen Laust Hvas LH Mishra Swapnil S Jensen Majken Karoline MK Westendorp Rudi G J RGJ

PloS one 20230807 8

<h4>Background</h4>The ability to accurately predict survival in older adults is crucial as it guides clinical decision making. The added value of using health care usage for predicting mortality remains unexplored. The aim of this study was to investigate if temporal patterns of healthcare expenditures, can improve the predictive performance for mortality, in spousal bereaved older adults, next to other widely used sociodemographic variables.<h4>Methods</h4>This is a population-based cohort stu ...[more]

PMID: 37549164

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Dataset Information

Machine learning models of healthcare expenditures predicting mortality: A cohort study of spousal bereaved Danish individuals.

Background

Methods

Results

Conclusion

Publications

Machine learning models of healthcare expenditures predicting mortality: A cohort study of spousal bereaved Danish individuals.

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